Ultrasound imaging speckle filtering system and method

Abstract

Systems and methods for filtering speckle from ultrasound images of an ultrasound imaging system. The method can include receiving an ultrasound image of a plurality of ultrasound images, determining a zone of the ultrasound image, applying a mask to the zone of the ultrasound image, wherein the mask is applied to the zone a first number of iterations, determining a noise level in the ultrasound image, determining that additional noise suppression is needed based on the noise level, applying the mask to the zone of the ultrasound image, wherein the mask is applied to the zone a second number of iterations, and displaying the ultrasound image on a display.

Description

Priority and related applications

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 571760, filed March 29, 2024, entitled "ULTRASOUND IMAGING DEBRIS FILTER SYSTEM AND METHODS". Any and all foreign or domestic priority claims identified in the Application Data Sheet filed with this application, pursuant to 37 CFR § 1.57, are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of tissue imaging using, for example, ultrasound. Several embodiments of the invention relate to systems and methods for reducing noise (e.g., noise caused by debris) that may appear in ultrasound images from an ultrasound imaging system. Background Technology

[0003] Conventional techniques for removing debris from ultrasound images may include physically removing debris from the ultrasound imaging device. Summary of the Invention

[0004] There is a need to improve the resolution of ultrasound imaging to rapidly, efficiently, and accurately image tissue for aesthetic and / or cosmetic treatments of the skin and / or subcutaneous tissue. In several embodiments, the ultrasound system is configured to image tissues (e.g., epidermis, dermis and / or subdermis, fascia, muscle, fat, and other tissues) to visualize them. In various embodiments, the ultrasound system is configured to image tissues to visualize them, in order to determine the appropriate depth for relevant cosmetic or medical treatments, such as to avoid certain tissues (e.g., nerves, bones, etc.).

[0005] In several embodiments, this disclosure relates to an ultrasound imaging system. In some embodiments, the system is configured for ultrasound therapy. In some embodiments, the system includes ultrasound imaging and therapy. An ultrasound imaging system may include various components, such as an imaging element and a display, to visualize tissue regions and / or anatomical structures of the body. Furthermore, a coupling medium, such as a gel or fluid (e.g., water, saline), may be used to allow sound waves from the imaging element to enter the tissue region undisturbed. Debris, bubbles, and / or other materials between the imaging element and the tissue region can interfere with imaging and ultimately affect the image captured by the ultrasound imaging system. This can result in speckles or artifacts on the ultrasound image. Alternatively, or additionally, noisy images may be due to the degradation and aging of the imaging element over time.

[0006] Several embodiments of this disclosure relate to systems and methods for digital noise suppression in ultrasound imaging systems. For example, digital filtering algorithms can be used to suppress fragmented noise that may appear in ultrasound images. In one embodiment, the ultrasound image can be algorithmically divided into discrete zones through which a noise suppression filter can pass a noise suppression filter. In one embodiment, the systems and methods described herein can determine whether additional iterations or passes of the fragmentation filtering algorithm can be used to further suppress noise.

[0007] In several embodiments, systems and methods for ultrasound imaging of tissue are adapted and / or configured to use one or more digital filtering algorithms to help suppress debris noise in the ultrasound images. The ultrasound transducer for imaging may have an offset gap between the imaging transducer and a portion of the housing of the ultrasound probe (e.g., at a window, such as a PEEK window), whereby this portion of the housing is positioned via acoustic coupling to contact the tissue (e.g., the skin surface) to image one or more focal zones beneath the skin surface. In some embodiments, the ultrasound transducer for imaging, with the offset gap between the imaging transducer and a portion of the housing, uses two or more (e.g., 2, 3, 4, 5, 6, or more) focal zones that may produce debris artifacts due to acoustic ultrasound energy bouncing between the imaging transducer and (i) the acoustic window and / or (ii) the imaged area. These debris artifacts can obscure the clarity of the image. In the various embodiments described herein, systems and methods reduce and / or eliminate such artifacts and improve ultrasound image quality.

[0008] In various embodiments, ultrasound imaging is used to visualize tissue regions and / or anatomical structures. In one embodiment, ultrasound imaging is used to confirm adequate acoustic coupling with the tissue region to improve the imaging correlation between the movement of the ultrasound imaging transducer in first and second directions during image formation. In various embodiments, ultrasound imaging is used in conjunction with cosmetic or medical treatments to visualize, plan, and / or monitor the cosmetic or medical treatment. Ultrasound imaging may be used in conjunction with the application of energy to tissue. Ultrasound imaging may be used in conjunction with the application of ultrasound therapy to tissue. In several embodiments, ultrasound imaging is used in conjunction with the application of dermal fillers to tissue. Ultrasound imaging and / or therapy may be used in conjunction with the application of drugs or compounds to tissue. Ultrasound imaging may be used in conjunction with the application of botulinum toxin to tissue. In various embodiments, the ultrasound system may be configured to focus ultrasound to generate localized mechanical motion within tissues and cells, with the aim of generating localized heating for tissue coagulation or mechanical cell membrane rupture for non-invasive aesthetic purposes. In various embodiments, the ultrasound system may be configured to lift the forehead (e.g., eyebrows) and / or lift sagging tissue, such as subchinarm (below the chin) and neck tissue. In various embodiments, the ultrasound system is configured to improve the lines and wrinkles of the neck and shoulders. The ultrasound system may be configured to reduce fat. In various embodiments, the ultrasound system is configured to reduce the appearance of cellulite. In several embodiments disclosed herein, the non-invasive ultrasound system is suitable for achieving one or more of the following beneficial aesthetic and / or cosmetic improvements: face lifting, forehead lifting, chin lifting, eye treatments (e.g., cheekbone bags, treatment of infraorbital laxity), wrinkle reduction, fat reduction (e.g., treatment of fat and / or cellulite), cellulite (which may be referred to as female fat metabolism disorder) treatment (e.g., dimple or non-dimpled female fat metabolism disorder), neck and chest improvement (e.g., upper chest), buttock lifting (e.g., buttock tightening), skin tightening (e.g., treating laxity to result in facial or body tightening, such as the face, neck, chest, arms, thighs, abdomen, buttocks, etc.), scar reduction, burn treatment, tattoo removal, vein removal, vein reduction, sweat gland treatment, hyperhidrosis treatment, sunspot removal, acne treatment, and pimple reduction.

[0009] Several embodiments are particularly advantageous because they include one, several, or all of the following advantages: (i) higher imaging resolution, (ii) removal of blur artifacts and debris from the imaging, (iii) clearer imaging using a moving imaging transducer, (iv) more efficient imaging, and / or (v) improved imaging to aid in associated treatments or therapies.

[0010] In several embodiments, a method for filtering fragments from an ultrasound image includes receiving a frame of an ultrasound image from an ultrasound imaging system; determining that the frame satisfies a contact threshold; applying a first mask to a band of the frame, wherein the band corresponds to a portion of the frame within a first depth range; determining a difference between the frame and a frame to which the first mask is applied within the band; applying an edge detection filter to the band of the frame; determining an average intensity of the band, wherein the average intensity corresponds to an estimated noise level within the band; applying a second mask to the band based on the estimated noise level in the band to generate a noise suppression band for the frame; and displaying the frame on a display of the ultrasound imaging system.

[0011] In one embodiment, the contact threshold corresponds to the intensity distribution of the frame. The first and second masks can be noise suppression masks. The method may include normalizing the estimated noise level in the band. In one embodiment, applying an edge detection filter results in a gradient being output. The method may include determining the need for additional noise suppression based on the estimated noise level and applying the second mask for additional iterations to the band.

[0012] In one embodiment, the application of the first or second mask includes determining, for each pixel of the band of the frame, whether an input pixel value is equal to the result of a horizontal maximum filter; in response to the determination that the input pixel value is equal to the result of the horizontal maximum filter, determining an output pixel value based on a weighted median filter for the pixels; and in response to the determination that the input pixel value is not equal to the result of the horizontal maximum filter, determining an output pixel value based on the average of the horizontal maximum filter and the horizontal minimum filter.

[0013] In several embodiments, an ultrasound imaging system configured for digitally filtering fragments from ultrasound images includes: an ultrasound probe including an ultrasound imaging transducer adapted for imaging a tissue region; a display coupled to the ultrasound probe; and a processor coupled to the ultrasound probe and configured to: receive a frame of an ultrasound image from the ultrasound imaging system; determine that the frame satisfies a contact threshold; apply a first mask to the frame; determine an absolute difference between the frame and a frame to which the first mask is applied; apply an edge detection filter to the frame; determine an average intensity of the frame, wherein the average intensity corresponds to an estimated noise level within the frame; apply a second mask to the frame based on the estimated noise level to generate a noise-suppressed frame; and display the frame on the display of the ultrasound imaging system.

[0014] In one embodiment, the contact threshold corresponds to the intensity distribution of the frame. The first and second masks can be noise suppression masks. The processor can also normalize the estimated noise level in the frame. In one embodiment, applying an edge detection filter results in a gradient being output. The processor can also be configured to determine, based on the estimated noise level, that additional noise suppression is needed; and to apply additional iterations of the second mask to the frame. The application of the first or second mask can also enable the processor to: for each pixel of the frame, determine whether the input pixel value is equal to the result of the horizontal maximum filter; in response to the determination that the input pixel value is equal to the result of the horizontal maximum filter, determine the output pixel value based on a weighted median filter for the pixels; and in response to the determination that the input pixel value is not equal to the result of the horizontal maximum filter, determine the output pixel value based on the average of the horizontal maximum filter and the horizontal minimum filter.

[0015] In several embodiments, an ultrasound imaging system configured for digitally filtering fragments from ultrasound images includes: an ultrasound probe including an ultrasound imaging transducer adapted for imaging a tissue region; a display coupled to the ultrasound probe; and a processor coupled to the ultrasound probe and configured to: apply a first filter to the ultrasound image; determine an absolute difference between the ultrasound image and an ultrasound image to which the first filter has been applied; apply an edge detection filter to the ultrasound image; determine the intensity of the ultrasound image, wherein the intensity corresponds to an estimated noise level of the ultrasound image; apply a second filter to the frame based on the estimated noise level to produce a noise-suppressed ultrasound image; and display the noise-suppressed ultrasound image on the display of the ultrasound imaging system.

[0016] The first and second filters can be noise suppression filters. In one embodiment, the processor is also configured to normalize the estimated noise level. The application of the edge detection filter can result in a gradient being output. The processor can also be configured to determine the need for additional noise suppression based on the estimated noise level and apply additional iterations of the second filter to the frame. In one embodiment, the application of the first or second filter further enables the processor to: determine, for each pixel of the frame, whether the input pixel value is equal to the result of the horizontal maximum filter; in response to the determination that the input pixel value is equal to the result of the horizontal maximum filter, determine the output pixel value based on a weighted median filter for the pixel; and in response to the determination that the source pixel value is not equal to the result of the horizontal maximum filter, determine the output pixel value based on the average of the horizontal maximum filter and the horizontal minimum filter. In some embodiments, the system includes various features that exist as a single feature (as opposed to multiple features). Multiple features or components are provided in alternative embodiments. In various embodiments, the system includes one, two, three or more embodiments of any feature or component disclosed herein, or is substantially composed of or consisting of one, two, three or more embodiments of any feature or component disclosed herein. In some embodiments, features or components are not included and may be negatively not claimed from a particular claim, such that the system does not have such features or components. In some embodiments, the method is performed without omitting a step. In some embodiments, the system does not include a component. Furthermore, the applicable domain will become apparent from the description provided herein. It should be understood that the descriptions and specific examples herein are for illustrative purposes only and are not intended to limit the scope of the embodiments disclosed herein. Attached Figure Description

[0017] Various features will now be described with reference to the following accompanying drawings. Throughout the drawings, reference numerals may be repeatedly used to indicate correspondences between referenced elements. The drawings are provided to illustrate the examples described herein and are not intended to limit the scope of this disclosure. The various embodiments will be more fully understood through the detailed description and the accompanying drawings. In several embodiments, features in one figure are applicable to other figures.

[0018] Figure 1 This is a schematic diagram of an ultrasound system according to various embodiments.

[0019] Figure 2 This is a schematic diagram of an ultrasound system according to various embodiments.

[0020] Figure 3 This is a schematic diagram of an ultrasound system according to various embodiments.

[0021] Figure 4 An example block diagram of an image processing system within an ultrasound imaging system according to various embodiments of the present disclosure is shown.

[0022] Figure 5 An example block diagram of components of an image processing system for processing ultrasound images captured by an ultrasound imaging system, according to various embodiments of the present disclosure, is shown.

[0023] Figures 6A-6C The following are illustrated sample ultrasound images that can be processed by an image thresholding system according to various embodiments of the present disclosure.

[0024] Figures 7A-7E The illustration shows sample ultrasound images that can be processed by a noise suppression system and a noise estimation system according to various embodiments of the present disclosure.

[0025] Figure 8 This is an example flowchart of the fragment detection and filtering logic used in an image processing system according to various embodiments of the present disclosure.

[0026] Figure 9 This is a table showing example filter aperture sizes according to various embodiments of the present disclosure.

[0027] Figure 10 This is a table illustrating example filters and corresponding depth ranges and iterations according to various embodiments of this disclosure.

[0028] Figure 11 These are example schemes of filter sizes and iterations used by image processing systems according to various embodiments of the present disclosure at corresponding zones in ultrasound images.

[0029] Figure 12 These are example schemes of filter sizes and iterations used by image processing systems according to various embodiments of the present disclosure in corresponding zones of ultrasound images containing weak fragment noise.

[0030] Figure 13A -B is an example flowchart of a routine for digitally filtering noise fragments from an ultrasound image from an ultrasound imaging system according to various embodiments of this disclosure. Detailed Implementation

[0031] The following description illustrates examples of embodiments of systems and methods related to ultrasound imaging and is not intended to limit the invention or its teachings, applications, or uses. It should be understood that in all the figures, corresponding reference numerals denote the same or corresponding parts and features. The description of specific examples indicated in the various embodiments is for illustrative purposes only and is not intended to limit the scope of the invention disclosed herein. Furthermore, references to multiple embodiments having the described features do not imply exclusion of other embodiments having additional features or other embodiments comprising different combinations of the described features. Moreover, a feature in one embodiment (e.g., in one figure) may be combined with the descriptions (and figures) of other embodiments.

[0032] Noise, speckle noise, or artifact noise (collectively referred to as noise) can be a characteristic of ultrasound imaging that often reduces the image resolution or contrast of ultrasound images. Ultimately, this reduction in resolution or contrast can decrease the diagnostic value of one or more ultrasound images. In some cases, noise may be caused by the accumulation of debris and other obstructions on or around the ultrasound imaging element (“debris noise”). In other cases, debris noise may be caused by the accumulation of debris and other obstructions between the ultrasound imaging element and the target area to be imaged or treated. For example, air bubbles, dirt, and other materials within the coupling medium can prevent the ultrasound imaging element from capturing a clean ultrasound image. In the image, debris noise may be characterized by high-intensity curves and lines pointing vertically and obliquely. In several embodiments, the projection of these lines onto the X-axis is typically significantly smaller than their projection onto the Y-axis. Therefore, the horizontal cross-section of these high-intensity lines typically consists of no more than a few pixels.

[0033] In several embodiments, median filtering can be used to address noise problems in ultrasound and other types of imaging. This is a filter where each output pixel is calculated as the median of the input pixels within the aperture. Median filtering can suppress impulse noise, which manifests in an image as bright and dark pixels that can appear randomly throughout the spatial distribution. The result of this filtering includes smoothing and shifting object boundaries, which can lead to a reduction in the number of small objects appearing in the image. To suppress high-intensity debris noise lines using median filtering, it is advantageous to use an aperture filter size that is appropriate for the width of the high-intensity noise debris line (e.g., its projection on the X-axis). The debris noise filtering system described herein can be configured to suppress debris noise caused by the accumulation of debris and other obstructions around the ultrasound imaging element. An indicator of the presence of suspected debris in an image line includes the output value of a filter with a horizontal maximum value equal to the pixel value of the input image. In this case, the result of the filtering will be the average between a minimum filter and a median filter with the same aperture. Using a minimum filter can accelerate the convergence of the filtering process. If no debris is detected, the result of noise suppression includes the average between the pixels of the original image and the result of applying a weighted median filter. This allows for optical image filtering and can reduce the differences between pixels processed in different branches.

[0034] Furthermore, the system described herein can be configured to perform an iterative filtering process. As described herein, in one embodiment, the upper portion of an ultrasound image contains very little debris. Therefore, the top portion (5-10% depth from the top) is typically not filtered. The middle portion of an ultrasound image typically contains weak debris with a small cross-section along the X-axis. Therefore, the system can filter the middle portion with a filter of 3 pixels in aperture size. As depth increases, debris noise typically becomes stronger and denser. Therefore, according to various embodiments, the bottom portion of an ultrasound image can be filtered by several passes of a 3x3 filter (“F3”) and several passes of a 5x5 filter (“F5”).

[0035] Furthermore, the system described herein involves noise level assessment and filter parameter setting. To assess noise and set filter parameters, the system uses a filter designed to suppress fragmented noise. In some embodiments, the system performs a noise level assessment every N frames. This frame may first be input into an image thresholding system. This ensures that the noise assessment is based on a realistic ultrasound image. This allows the system to avoid situations where the ultrasound imaging system's sensor is pointing into the air or has poor contact with the patient's body or poor acoustic coupling. The image thresholding processing system analyzes the intensity distribution of the ultrasound image across width and depth, symmetry across width, and other characteristics. If sufficient contact is determined in the image, further image processing can be performed.

[0036] Figure 1-3 An example ultrasound imaging system 20 configured to suppress fragment noise from ultrasound images according to various embodiments of the present disclosure is shown.

[0037] In some embodiments, the ultrasound imaging system 20 includes a hand wand (e.g., a handheld device) 100, modules (e.g., transducer modules, boxes, probes) 200, and a console 300. In several embodiments, the console 300 includes a controller 305. In some embodiments, the console 300 includes a communication system, such as Wi-Fi, Bluetooth, a modem, etc., for communicating with other parties, manufacturers, suppliers, service providers, the Internet, and / or the cloud. In various embodiments, the console 300 includes a metal housing (e.g., magnesium, aluminum, titanium, and alloys). In some embodiments, a trolley 301 provides mobility and / or location for the system 20 and may include wheels, a surface for writing or placing components, and / or compartments 302 (e.g., drawers, containers, shelves, etc.) for, for example, storing or organizing components. In some embodiments, the trolley has a power source, such as a power connection to a battery, and / or one or more wires connecting power, communications (e.g., the Internet, Ethernet, etc.) to the system 20. In some embodiments, the system 20 includes a trolley 301. In some embodiments, the system 20 does not include a trolley 301.

[0038] Handpiece 100 can be connected to controller 305 via interface 130, which can be a wired (e.g., cable) or wireless (e.g., Bluetooth, Wi-Fi, etc.) interface. Interface 130 can be connected to handpiece 100 via mechanical and / or electrical connector 145. The distal end of interface 130 can be connected to a controller connector on circuitry 345 (not shown). In one embodiment, interface 130 can transmit controllable power from controller 305 to handpiece 100. In one embodiment, system 20 has one or more imaging channels (e.g., 1, 2, 4, 6, 8, 10 channels) for ultra-high definition (HD) visualization of subcutaneous structures to improve imaging. In one embodiment, system 20 has one or more treatment channels (e.g., 1, 2, 4, 6, 8, 10 channels) and a precision linear drive motor that doubles treatment accuracy while increasing speed (e.g., by 25%, 40%, 50%, 60%, 75%, 100%, or more).

[0039] In various embodiments, controller 305 may be adapted and / or configured to operate in conjunction with the functionality of handpiece 100 and module 200, as well as the overall ultrasound system 20. Controller 305 may include connectivity to one or more interactive graphic displays 310, which may include touchscreen monitors and graphical user interfaces (GUIs) enabling users to interact with the ultrasound system 20. In one embodiment, a second, smaller, more portable display allows users to more easily locate and view the treatment screen. In one embodiment, the second display allows system users to view the treatment screen (e.g., on a wall, on a mobile device, on a large screen, or on a remote screen). In one embodiment, graphic display 310 includes a touchscreen interface 315 (not shown). In various embodiments, display 310 sets and displays operating conditions, including device activation status, treatment parameters, system messages and prompts, and ultrasound images.

[0040] In various embodiments, controller 305 may be adapted and / or configured to include, for example, a microprocessor and input / output devices having software, systems and devices for controlling electronic and / or mechanical scanning and / or multiplexing and / or multiplexing of transducer modules, systems for power delivery, systems for monitoring, systems for sensing the spatial position of probes and / or transducers and / or multiplexing of transducer modules, and / or systems for processing user input and recording treatment results, etc. In some embodiments, controller 305 may include a processor (not shown) configured to execute instructions stored in memory to suppress noise in ultrasound images. For example, the processor may be configured to execute the processes of image processing system 401 to perform noise suppression on ultrasound images captured by ultrasound imaging system 20.

[0041] In various embodiments, controller 305 may include a system processor and various analog and / or digital control logic, such as one or more of a microcontroller, microprocessor, field-programmable gate array, computer board, and related components, including firmware and control software, which may be able to interface with user controls and interface circuitry, as well as input / output circuitry and the system, for communication, display, interface, storage, archiving, and other useful functions. The system software running on the system process may be adapted and / or configured to control all initialization, timing, level setting, monitoring, safety monitoring, and all other ultrasound system functions for achieving user-defined therapeutic goals. Furthermore, controller 305 may include various input / output modules, such as switches, buttons, etc., which may also be suitably adapted and / or configured to control the operation of ultrasound system 20.

[0042] In one embodiment, handpiece 100 includes one or more finger-activated controllers or switches, such as 150 and 160. In various embodiments, one or more thermal therapy controllers 160 (e.g., switches, buttons) activate and / or deactivate treatment. In various embodiments, one or more imaging controllers 150 (e.g., switches, buttons) activate and / or deactivate imaging. In one embodiment, handpiece 100 may include a removable module 200. In other embodiments, module 200 may be non-removable. In various embodiments, module 200 may be mechanically coupled to handpiece 100 using latches or connectors 140. In various embodiments, one or more interface guides 235 may be used to assist in coupling module 200 to handpiece 100. Module 200 may include one or more ultrasound imaging transducers 270. In some embodiments, ultrasound imaging transducers 270 include one or more ultrasound elements. Module 200 may include one or more ultrasound imaging transducers 270 and / or one or more ultrasound therapy transducers 280. In some embodiments, ultrasound therapy transducers 280 include one or more ultrasound elements. Module 200 may include one or more ultrasonic elements. In one embodiment, module 200 includes a bubble trap to reduce air bubbles in the acoustic medium. Handpiece 100 may include or be connected to an imaging-only module, a treatment-only module, an imaging and treatment module, etc. In various embodiments, ultrasonic transducers 270 and / or 280 may be movable within module 200 in one or more directions 290. In some embodiments, transducers 270 and / or 280 are connected to motion mechanism 400. In some embodiments, transducers 270 and / or 280 are not connected to motion mechanism 400. In various embodiments, motion mechanism may include one or more bearings, shaft 400 (e.g., rod, screw, lead screw), optionally include position sensing device 402 (e.g., encoder for measuring transducer position), motor 403 (e.g., stepper motor) to help ensure accurate and repeatable movement of the transducer within module 200. In various embodiments, components in motion mechanism may be in module 200 and / or handpiece 100. In various embodiments, module 200 may include transducers 270 and / or 280 that can emit energy through acoustically transparent member 230. In one embodiment, module 200 has an offset distance 210 between transducers 270 and / or 280 and acoustically transparent member 230. In one embodiment, module 200 has an offset distance 211 between imaging transducer 270 and imaging region from bottom. In one embodiment, console 300 includes a control module that can be coupled to handpiece 100 via interface 130, and graphical user interface 310 may be adapted and / or configured for controlling module 200. In one embodiment, console 300 may provide power to handpiece 100. In one embodiment, handpiece 100 may include a power source.In one embodiment, switch 150 may be adapted and / or configured to control tissue imaging functions, and switch 160 may be adapted and / or configured to control tissue treatment functions. In various embodiments, through controlled operation of the control system of the console 300 of the treatment transducer 280, module 200 provides delivery of emitted energy 50 with appropriate depth of focus, distribution, timing, and energy level to achieve the desired therapeutic effect on the thermal coagulation zone 550.

[0043] In one embodiment, module 200 may be coupled to handpiece 100. Module 200 may emit and receive energy, such as ultrasonic energy. Module 200 may be electronically coupled to handpiece 100, and this coupling may include an interface for communication with controller 305. In one embodiment, interface guide 235 may be adapted and / or configured to provide electronic communication between module 200 and handpiece 100. Module 200 may include various probe and / or transducer configurations. For example, module 200 may be adapted and / or configured for combined dual-mode imaging / therapeutic transducers, coupled or housed in the same housing imaging / therapeutic transducers, separate therapeutic and imaging probes, etc. In one embodiment, when module 200 is inserted into or connected to handpiece 100, controller 305 automatically detects it and updates interactive graphic display 310.

[0044] Figure 4 Example block diagrams of an image processing system 401 according to various embodiments of the present disclosure are shown. In several embodiments, the image processing system 401 may be configured to process ultrasound images captured by the ultrasound imaging system 20. Although Figure 1-3 Although not shown, the image processing system 401 can be implemented in any of the aforementioned components, such as the handheld device 100, transducer module 200, console 300, or through any other related component or connected interface. In various embodiments, the image processing system 401 can be a single computing device, or it can include multiple different computing devices, such as computer servers, logically or physically grouped together to operate as a system. In some embodiments, the features and services provided by the image processing system 401 can be implemented as web services that can be consumed over a communication network. In other embodiments, the image processing system 401 is provided by one or more virtual machines implemented in a managed computing environment. This managed computing environment can include one or more computing resources that can be rapidly configured and released, including computing, networking, and / or storage devices. The managed computing environment can also be referred to as a cloud computing environment.

[0045] In one embodiment, the image processing system 401 may include a processing unit 403, an image data storage 404, a filter data storage 406, an image thresholding system 408, a noise estimation system 410, and a noise suppression system 412.

[0046] In one embodiment, processing unit 403 may include a processor (or processors) that executes program instructions or modules stored in memory or other non-transitory computer-readable storage media or devices (e.g., solid-state storage devices, disk drives, etc.). For example, processing unit 403 may be configured to perform any process, step, or task of image processing system 401, such as processing ultrasound images. For example, in some embodiments, processing unit 403 may be configured to perform any process or task of noise estimation system 410 and noise suppression system 412. In some examples, processing unit 403 may be communicatively coupled to other components of image processing system 401, such as image data storage 404, filter data storage 406, etc. Processing unit 403 may be configured to access image data stored in image data storage 404 for processing. Furthermore, processing unit 403 may be configured to access filters, masks, or other algorithms stored in filter data storage 406 to process images accessed from image data storage 404.

[0047] like Figure 4 As shown, the image processing system 401 may include an image data storage 404. In some embodiments, the image data storage 404 may be configured to store ultrasound images from the ultrasound imaging system 20. In some embodiments, the module 200 may be configured to visualize tissue regions and / or anatomical structures. Furthermore, the module 200 may capture ultrasound images of visualized tissue regions and / or anatomical structures. In some embodiments, the module 200 of the ultrasound imaging system 20 may capture multiple ultrasound images, such as in the form of 2D, 3D ultrasound images, videos, recordings, etc. In one embodiment, each ultrasound image in a recording or video may be a frame in an ultrasound recording or video. In some examples, the image processing system 401 may be configured to access frames of ultrasound images from the image data storage 404 for further processing. For example, the image processing system 401 may be configured to suppress fragmented noise appearing on the ultrasound images. The image data storage 404 may include random access memory (RAM), cache, and / or other dynamic storage devices for storing information associated with the ultrasound imaging system 20.

[0048] Image processing system 401 may include filter data storage 406. In some embodiments, filter data storage 406 may be configured to store algorithms, filters, functions, masks, etc., which can be accessed by processing unit 403 of image processing system 401 to process ultrasound frames. In some examples, filter data storage 406 may store noise suppression filters. In this example, noise suppression filters may include masks of any relevant pixel size. In some embodiments, image processing system 401 may be configured to access masks stored in filter data storage 406 to apply them to frames accessed from image data storage 404, thereby suppressing noise (e.g., debris noise) from the ultrasound image. In some embodiments, masks may be used to convolve ultrasound images. Furthermore, filter data storage 406 may include any noise suppression filters, denoising filters, smoothing filters, low-pass filters, Sobel filters, etc., which can be used to suppress noise caused by debris in ultrasound images.

[0049] In some embodiments, the image thresholding system 408 may be configured to determine whether an ultrasound image is suitable for further image processing. In the context of ultrasound imaging, some images may be unsuitable for processing due to insufficient contact with the patient's skin, hardware malfunction, etc. The image thresholding system 408 may be configured to determine whether an image meets a threshold for further processing, such as noise suppression and / or noise estimation.

[0050] Noise estimation system 410 can be configured to estimate the noise level present in ultrasound images of ultrasound imaging system 20. After image thresholding system 408 determines that the image meets a threshold for further processing, noise estimation system 410 can process the image. In some embodiments, noise estimation system 410 can access ultrasound images, such as ultrasound frames, stored in image data storage 404. Furthermore, noise estimation system 410 can estimate the noise level present in ultrasound images accessed from image data storage 404. For example, noise estimation system 410 can run one or more filters or functions on the ultrasound images. Additionally, noise estimation system 410 can calculate the difference in contrast or other imaging values ​​between the original ultrasound image and the filtered ultrasound image. Noise estimation system 410 can apply thresholding and / or other gradient filters to determine the intensity of the ultrasound image. Note that noise estimation system 410 can perform various processes to estimate the noise level of the ultrasound image.

[0051] The noise suppression system 412 can be configured to suppress noise in ultrasound images of the ultrasound imaging system 20. The noise suppression system 412 can suppress fragmented noise in the ultrasound images based on an estimated noise level determined by a noise estimation system. In some embodiments, the noise suppression system 412 can be configured to suppress fragmented noise appearing on the ultrasound images. In some embodiments, the noise suppression system 412 can be configured to access ultrasound images stored in image data storage 404. Furthermore, the noise suppression system 412 can be configured to access one or more masks, such as noise suppression masks, stored in filter data storage 406. In some embodiments, the noise suppression system 412 can be configured to apply the mask to ultrasound frames. In some embodiments, the image processing system 401 can apply the mask to ultrasound frames or iterate multiple times. For example, the mask can consist of pixel masks (e.g., 3x3 pixel masks, 5x5 pixel masks), which can be passed through ultrasound images 1, 2, 3, 5, 7, 10, 15, 20…N times.

[0052] Figure 5 An example block diagram of the components of an image processing system 401 for processing ultrasound images captured by an ultrasound imaging system 20 is shown. Note that the processes performed by the image processing system 401 can occur during an active ultrasound imaging session.

[0053] Image processing system 401 can receive and / or access frames 500 corresponding to ultrasound images from ultrasound imaging system 20. As described herein, ultrasound imaging system 20 can capture multiple ultrasound images, such as in the form of 2D, 3D ultrasound images, video, recordings, etc. In one embodiment, each ultrasound image in a recording or video can be a frame in an ultrasound recording or video. Therefore, image processing system 401 can be configured to access frames of ultrasound images from image data storage 404 for further processing. For example, image processing system 401 can receive or access frames 500 from multiple frames of ultrasound images.

[0054] It should be noted that the noise level assessment and filter parameter setting processes described herein can occur periodically during an active ultrasound imaging session. For example, for each Nth frame received by the ultrasound imaging system 20, the image processing system 401 can perform the various processes described herein.

[0055] In some embodiments, the image thresholding system 408 may be configured to determine whether a frame 500 of an ultrasound image (e.g., the Nth frame) is suitable for further image processing. In the context of ultrasound imaging, some images may be unsuitable for processing due to insufficient contact with the patient's skin, hardware malfunctions, etc. For example, the image thresholding system 408 may determine whether there is sufficient contact between the module 200 and the patient's skin. The image thresholding system 408 may be configured to determine whether a frame of the ultrasound image meets a threshold for further processing, such as noise estimation and / or noise suppression. Note that the described processes and steps may be performed by the processing unit 403 of the image processing system 401.

[0056] To assess whether frame 500 is suitable for further noise estimation and suppression, image thresholding system 408 can determine the intensity distribution across various regions of the frame. In some embodiments, image thresholding system 408 can determine the absolute intensity of frame 500. In some embodiments, if the absolute intensity is higher than a threshold, frame 500 is considered suitable for further processing by noise estimation system 412 and / or noise suppression system 410. If the absolute intensity is equal to or lower than the threshold, image thresholding system 408 can perform additional processing on the ultrasound image.

[0057] In some embodiments, the image thresholding system 408 may be configured to determine the intensity distribution over regions of frame 500. For example, the image thresholding system 408 may divide, segment, or otherwise separate frame 500 into individual regions. Furthermore, the image thresholding system 408 may compare the average intensity between each region when determining the intensity distribution over regions of frame 500.

[0058] Figures 6A-6C Various portions of a sample frame 500 that can be processed by an image thresholding system 408 according to various embodiments of the present disclosure are shown.

[0059] For example, such as Figure 6A As shown, the image thresholding system 408 can divide frame 500 into a top row 602, a middle row 604, and a bottom row 606. In some embodiments, frame 500 can be divided into more or fewer rows. Furthermore, the image thresholding system 408 can determine the intensity values, such as average intensity, of the top row 602, middle row 604, and bottom row 606 respectively. Figure 5As shown in Figure A, the calculated intensity values ​​of the top row 602, middle row 604, and bottom row 606 can be represented by arrays mean_Y_D[0], mean_Y_D[1], and mean_YD[2], respectively. In some embodiments, the image thresholding system 408 can compare the intensity values ​​of rows (or other portions) to determine the intensity distribution of frame 500. For example, the image thresholding system 408 can determine whether the intensity value of the top row 602 is greater than that of the middle row 604 and / or the bottom row 606. This can inform the image thresholding system 408 whether frame 500 is a suitable image for further processing.

[0060] In one example, such as Figure 6B As shown, the image thresholding system 408 can divide frame 500 into a left column 608, a middle column 610, and a right column 612. Furthermore, the image thresholding system 408 can determine the intensity values, such as average intensity, of the left column 608, the middle column 610, and the right column 612 respectively. Figure 6B As shown, the calculated intensity values ​​of the left column 608, the middle column 610, and the right column 612 can be represented by arrays mean_X_D[0], mean_X_D[1], and mean_X_D[2], respectively. In some embodiments, the image thresholding system 412 can compare the intensity values ​​of rows (or other portions) to determine the intensity distribution of frame 500. For example, the image thresholding system 408 can determine whether the intensity values ​​of the left column 608 and the right column 612 are greater than those of the middle column 610. This can inform the image thresholding system 408 whether frame 500 is a suitable image for further processing.

[0061] In one example, such as Figure 6C As shown, the image thresholding system 408 can divide frame 500 into a left half 614 and a right half 616. Furthermore, the image thresholding system 408 can determine the intensity values, such as average intensity, of the left half 614 and the right half 616 respectively. Figure 5 As shown in Figure C, the calculated intensity values ​​of the left half 514 and the right half 616 can be represented by arrays X_sym[0] and X_sym[1], respectively. In some embodiments, the image thresholding system 408 can compare the intensity values ​​of rows (or other portions) to determine the intensity distribution of frame 500. For example, the image thresholding system 408 can determine whether the intensity values ​​of the left half 614 and the right half 616 are approximately equal. This can inform the image thresholding system 408 whether frame 500 is suitable for further image processing.

[0062] If the image thresholding system 408 determines that frame 500 is not suitable for further processing, for example through further processing by the noise estimation system 410, then that frame can be ignored. Furthermore, as... Figure 5As shown, the counter can be incremented if the frame is determined to be inappropriate.

[0063] If the image thresholding system 408 determines that frame 500 is suitable for further processing, the frame can be transmitted to the noise estimation system 410.

[0064] The noise estimation system 410 can receive a frame 500 that has been determined to be suitable (e.g., good contact between module 200 and patient skin) from the image thresholding system 408. The noise estimation system 410 can estimate the noise level in frame 500 before the noise suppression system 412 suppresses noise. Alternatively or additionally, the noise estimation system 410 can estimate the noise level in frame 500 after the noise estimation system 410 has suppressed noise. It should be noted that the processes described with respect to the noise estimation system 410 and the noise suppression system 412 can be iterative. For example, the noise estimation system 410 can determine an initial noise level in frame 500, and the noise suppression system 412 can suppress the noise. Frame 500 can be fed back to the noise estimation system 410 to estimate the remaining noise level, thereby determining whether additional iterations of the noise suppression mask(s)(s)(s) are needed.

[0065] like Figure 5 As shown, the noise estimation system 410 includes various filters or functions to process frame 500 to estimate the noise level. For example, the estimation process performed by the noise estimation system 410 may include a standard filtering function 502, a difference function 504, an edge detection function 506, and a summation function 508. In some embodiments, more or fewer functions or filters may be applied to frame 500 as processing steps within the noise estimation system 410.

[0066] The noise estimation system 410 may apply a standard filtering function 502 to frame 500. The standard filtering function 502 may include any filter with parameters unadjusted or tuned to frame 500 (e.g., initial settings). For example, the standard filtering function 502 may include a 3x3 pixel mask or a 5x5 pixel mask for suppressing low-noise bands and / or small speckles in the ultrasound image. The 3x3 pixel mask may be any noise suppression filter mask or a standard mask, such as a smoothing or blurring mask, which may be used to reduce noise or artifacts in frame 500. In some embodiments, the standard filtering function 502 is a linear smoothing filter, where each pixel of frame 500 is replaced by the average of its neighboring pixels. In this case, the size of the neighboring pixels may be defined by the mask size. In some embodiments, the standard filtering function 502 includes a non-linear smoothing filter that replaces each pixel in frame 500 with the median of its neighboring pixels. For example, in some embodiments, the center value of the mask may be assigned an average between local maxima and local minima within the standard filtering function 502. Furthermore, the noise estimation system 410 can apply the standard filter function 502 or multiple filters to frame 500 multiple times. In response to applying the standard filter function 502 to frame 500, the noise estimation system 410 can determine the filtered frame 500.

[0067] In some embodiments, the image processing system 104, such as through the noise estimation system 410, may segment, assign, or otherwise determine bands of frame 500 to apply various functions or filters. Different levels of noise may exist in frame 500. To minimize information loss, different filters or masks may be applied to frame 500 in segmented regions based on the detected amount of noise. For example, regions in frame 500 with smaller noise speckle sizes may require smaller masks to smooth out or reduce noise. In this case, a larger mask in that region may be unnecessary and overprocess the ultrasound image. In some cases, regions with larger noise speckle sizes may require larger masks to smooth out or reduce noise. For example, in this case, a small mask may not adequately reduce noise. Therefore, the noise estimation system 410 may apply different standard filtering functions 502 to different bands of frame 500. Furthermore, after applying the standard filtering function 502, the noise estimation system 410 may apply a difference function 504 to frame 500. In some embodiments, the noise estimation system 412 may be configured to determine the difference between frame 500 and the filtered frame 500. Noise estimation system 410 can determine the pixel-wise difference between frame 500 and the filtered frame 500. Noise estimation system 412 can be configured to determine the pixel-wise difference by subtracting the filtered frame from the original frame 500. The pixel-wise difference can represent the amount of noise filtered out by applying a standard filtering function 502 to frame 500. In some examples, noise estimation system 412 can determine the difference between frame 500 and the filtered frame 500' within a band. The difference determined by difference function 504 can be the absolute difference between the filtered frame and frame 500. In response to applying difference function 504 to the filtered frame 500, noise estimation system 410 can determine the difference frame 500.

[0068] After the noise estimation system 410 determines the difference frame 500, the noise estimation system 510 may apply an edge detection function 506. Edge detection by the edge detection function 506 can highlight significant changes in image processing and can be used to detect any discontinuities in brightness or contrast. In this case, the edge detection function 506 can indicate whether additional processing and / or noise suppression is needed. For example, the noise estimation system 412 may apply any edge detection filter, such as a vertical filter (e.g., a Sobel filter), a horizontal filter, etc. In some examples, the filter may have an aperture size of 3x3 pixels. In some embodiments, applying the edge detection filter to the pixel-wise difference results in the noise estimation system 412 outputting a gradient frame 500.

[0069] In response to the determination of the gradient frame 500 based on the application of the edge detection function 506, the noise estimation system 410 may apply a summation function 508. The summation function 508 may sum the results of the edge detection function 506, such as a vertical Sobel filtering operation. In some embodiments, the noise estimation system 410 may be configured to determine the average intensity of the gradient frame 500. In some embodiments, the average intensity of the frame 500 may represent the level of noise estimation. In addition to summing the results of the edge detection function 506, the noise estimation system may also normalize the average intensity (although in Figure 5 the normalization decision box is shown within the noise suppression system 412). For example, the noise estimation system 410 may normalize the average intensity ("E") of the gradient frame 500 to a value between 0 and 1. In this example, an average normalized intensity close to 0 indicates the presence of low noise, while an average normalized intensity close to 1 indicates the presence of high noise.

[0070] In response to the estimated noise level determined by the noise estimation system 410, the noise suppression system 412 may suppress the spurious noise from the frame 500 (and / or the gradient frame 500 as the output of the noise estimation system 510). In some embodiments, the amount of noise estimated by the noise estimation system 410 may be used to determine the type of mask / filter to be applied to the frame 500.

[0071] As described herein, the frame 500 may be divided into different zones. Thus, the noise estimation system 410 and the noise suppression system 412 may determine the noise level of each zone and apply different filters to different zones based on that determination.

[0072] In one example, the noise estimation system 410 may determine a value of the normalized average intensity that is less than 0.001. Thus, the noise suppression system 412 may determine that there is little or no noise. In this case, the noise suppression system 412 may determine that the frame 500 or the zone of the frame 500 does not require a filter.

[0073] In another example, the noise estimation system 410 may determine that the normalized average intensity is between 0.001 and 0.005 (e.g., 0.001 < E ≤ 0.005). Thus, the noise suppression system 412 may determine that there is a low level of noise and that a first filter should be applied to the mask. The first filter includes any noise suppression filter as described herein. In some cases, the noise suppression system 412 determines that multiple iterations of the first filter are required for the frame 500. Based on the estimated noise level of each zone, the noise suppression system 412 may determine additional iterations of the first filter based on the zone.

[0074] In another example, the noise estimation system 410 may determine that the normalized average intensity is between 0.005 and 0.1 (e.g., 0.005 < E ≤ 0.1). Thus, the noise suppression system 412 may determine that there is a moderate amount of noise and that a second filter should be applied to the mask. The second filter includes any noise suppression filter as described herein but is configured to suppress a greater amount of noise than the first filter. In some cases, the noise suppression system 412 determines that multiple iterations of the second filter are required for frame 500. Based on the noise level estimated for each zone, the noise suppression system 412 may determine additional iterations of the second filter based on the zone.

[0075] In another example, the noise estimation system 410 may determine that the normalized average intensity is between 0.1 and 0.3 (e.g., 0.1 < E ≤ 0.3). Thus, the noise suppression system 412 may determine that there is a large amount of noise and that a third filter should be applied to the mask. The third filter includes any noise suppression filter as described herein but is configured to suppress a greater amount of noise than the first and second filters. In some cases, the noise suppression system 412 determines that multiple iterations of the third filter are required for frame 500. Based on the noise level estimated for each zone, the noise suppression system 412 may determine additional iterations of the third filter based on the zone.

[0076] In another example, the noise estimation system 410 may determine the additional noise level present in frame 500 and, thus, an additional filter (not shown in Figure 5 ) may be applied to frame 500. For example, the noise estimation system 410 may determine that the normalized average intensity is greater than 0.3 (e.g., E > 0.3). Thus, the noise suppression system 412 may determine that there is a very large amount of noise and that additional iterations of the additional filter are required for frame 500.

[0077] Figures 7A-7E A sample ultrasound image 700 is shown that may be processed by a noise estimation system 410 and a noise suppression system 412 as described in Figure 5 . As described herein, debris, bubbles, and / or other materials between the imaging element and the tissue region interfere with imaging and ultimately affect the images captured by an ultrasound imaging system. As Figures 7A-7EAs shown, debris may appear on the ultrasound image 700 in the form of spots or artifacts. In some embodiments, the noise estimation system 412 may estimate the noise level in the original ultrasound image 700 before the noise suppression system 410 suppresses noise. In some embodiments, the noise estimation system 412 may estimate the noise level in the ultrasound image 700 after the noise suppression system 410 has suppressed noise. In one embodiment, the noise estimation system 412 may estimate the remaining noise level to determine whether additional iterations of the noise suppression mask(s) are needed. In some embodiments, the image processing system 401 (e.g., the noise suppression system 412 and / or the noise estimation system 410) may segment, assign, or otherwise determine zones of the ultrasound image 700. Figure 7A and 7B As shown, ultrasound image 700 may contain varying levels of noise. To minimize information loss, different filters or masks can be applied to the segmented regions of ultrasound image 700 based on the detected noise level. For example, regions in ultrasound image 700 with smaller noise speckle sizes may require smaller masks to smooth out or reduce the noise. In this case, a larger mask in that region may be unnecessary and would overprocess the ultrasound image. In some cases, regions with larger noise speckle sizes may require larger masks to smooth out or reduce the noise. For example, in such cases, a small mask may not be sufficient to reduce the noise.

[0078] like Figure 7A As shown, ultrasound image 700 can be divided or assigned into three zones, including a first zone 702, a second zone 704, and a third zone 706. In some examples, ultrasound image 700 may contain more or fewer than three zones. In some embodiments, a zone may include any portion of a region of ultrasound image 700 and can be of any shape or size. For example, a zone of ultrasound image 700 may include any shape, such as a rectangle, square, circle, ellipse, triangle, or other shape. In some embodiments, a zone may correspond to the entire region of ultrasound image 700. In some embodiments, image processing system 401 may determine zones on ultrasound image 700 based on the amount of noise.

[0079] like Figure 7A As shown, the first zone 702 may be located at the top of the ultrasound image 700. In some embodiments, the image processing system 401 may determine the first zone based on the amount of noise. For example, as Figure 7AAs shown, the top of the ultrasound image 700 does not contain much noise, possibly due to its proximity to module 200 and / or tissue surface. Therefore, filtering of the first band 702 may not be necessary. Consequently, the noise estimation system 410 can omit filtering the ultrasound image 700 using the standard filtering function 502. Furthermore, the noise suppression system 412 can omit filtering in the first band 702. Figure 7B The filtering result of the standard filter function 502 is shown.

[0080] In some embodiments, the second zone 704 may correspond to a region of the ultrasound image 700 that does not overlap with the first zone 702. In some embodiments, the second zone 704 may be located below the first zone 702 and may represent a region of the ultrasound image 700 at a depth greater than that of the first zone 702. Figure 7A As shown, the second zone 704 may have a low noise level and a small noise spot size. In some embodiments, the second zone 704 may correspond to a region of the ultrasound image 700 with a low noise level and / or a small spot size. In some embodiments, the noise estimation system 410 and / or the noise suppression system 412 may apply a mask or filter, such as a standard filter function 502, to the second zone 704 of the ultrasound image 700. For example, the mask may include a noise suppression or smoothing function. Furthermore, the mask may be of any relevant size, such as a 3x3 pixel or 5x5 pixel size. In some embodiments, the noise estimation system 410 and / or the noise suppression system 412 may apply the mask to the second zone 704 for multiple iterations. The number of iterations may be predetermined or pre-configured. For example, for a 3x3 pixel mask, the number of iterations may be five. In some embodiments, the number of iterations may correspond to the noise level determined in the ultrasound image 700 and / or the second zone 704.

[0081] In some embodiments, the third zone 706 may correspond to a region of the ultrasound image 600 that does not overlap with the first zone 702 or the second zone 704. In some examples, the third zone 706 may be located below the second zone 704 and may represent a region of the ultrasound image 700 at a depth greater than that of the first zone 702 or the second zone 704. Figure 7AAs shown, the third zone 706 may have a high noise level and a large noise spot size. In some embodiments, the third zone 706 may correspond to a region of the ultrasound image 700 with a high noise level and / or a large noise spot size. In some embodiments, the noise estimation system 410 and / or the noise suppression system 412 may apply a mask or filter (such as a standard filter function 502, etc.) to the third zone 706 of the ultrasound image 700. For example, the mask may include a noise suppression or smoothing function. Furthermore, the mask may be of any relevant size, such as a 3x3 or 5x5 pixel size. It should be noted that the mask size of the third zone may be larger or smaller than the masks used in different zones of the ultrasound image 700 to accommodate the relative spot size in that zone. In some embodiments, the noise suppression system 410 may apply the mask to the third zone 706 multiple times. The number of iterations may be predetermined or pre-configured. For example, for a 5x5 pixel mask, the number of iterations may be seven. In some embodiments, the number of iterations may correspond to the noise level determined in the ultrasound image 700 and / or the third zone 706.

[0082] In some embodiments, the image processing system 401 may determine additional zones on the ultrasound image 700. For example, the image processing system 401 may determine sub-zones within zones 702, 704, 706, or additional zones at deeper depths, etc.

[0083] Figure 7A An example source ultrasound image 700 that can be received by the image thresholding system 408 is shown. The image thresholding system 408 can be based on a reference... Figure 5 The described process is used to process the received ultrasound image 700. The image thresholding system 408 can determine whether the ultrasound image 700 meets the threshold for further processing. In response to applying a standard filtering function 502 to the ultrasound image 700, the noise estimation system 410 can determine the filtered ultrasound image 708. Figure 7B As shown in the reference Figure 5 The ultrasound image 708 described is processed by the noise estimation system 410 using a standard filtering function 502. In this example, as shown, the ultrasound image 700 can be generated after five iterations of a standard filtering function consisting of a 3x3 pixel mask in the second band 704 and seven iterations of a 5x5 pixel mask in the third band 706. It should be noted that due to the iterations of the 5x5 pixel mask in the third band 706, the speckles (e.g., the speckles in the third band 708) are reduced.

[0084] Figure 7C The filtered ultrasound image 708 processed by the noise estimation system 410 is shown, in which the following is applied: Figure 5The difference function 504 is described herein. As described herein, the noise estimation system 410 can be configured to determine the difference between the original ultrasound image 700 and the filtered ultrasound image 708. Figure 7C The pixel-wise difference between the original ultrasound image 700 and the filtered ultrasound image 708 is shown. In some embodiments, the noise estimation system 410 may be configured to determine the pixel-wise difference by subtracting the filtered ultrasound image 708 from the original ultrasound image 700. In some embodiments, the pixel-wise difference may represent the amount of noise filtered out by applying one or more masks to the original ultrasound image 700. In some examples, the noise estimation system 410 may determine the difference between the original ultrasound image 700 and the filtered ultrasound image 708 within a band. For example, the noise estimation system 412 may determine the pixel-wise difference by subtracting the filtered ultrasound image 708 in the third band 706 from the original ultrasound image 700 in the third band 706. Figure 7D A gradient ultrasound image 712 is shown obtained by applying an edge detection function to a differential ultrasound image 710. In some embodiments, the noise estimation system 410 may be configured to apply an edge detection function 506 to the pixel-wise difference. Edge detection can highlight significant changes in image processing and can be used to detect any discontinuities in brightness or contrast. In this case, edge detection can indicate whether additional processing and / or noise suppression is required. For example, the noise estimation system 410 may apply any edge detection filter, such as a vertical filter (e.g., a Sobel filter), a horizontal filter, etc. In some examples, the filter may have an aperture size of 3x3 pixels. In some embodiments, applying an edge detection filter to the pixel-wise difference results in the noise estimation system 410 outputting a gradient ultrasound image 710. Figure 7D The gradient ultrasound image 712 is shown as the output obtained by applying the edge detection function 506.

[0085] Figure 7E A summed ultrasound image 714 is shown, obtained by applying a summation function 508 to the gradient ultrasound image 712. In some embodiments, the noise estimation system 412 may be configured to determine the average intensity of the gradient ultrasound image 712. Figure 7EAs shown, the average intensity of the summed ultrasound image 714 can be displayed. In some embodiments, the average intensity of the summed ultrasound image 714 can represent the level of noise estimation. In some embodiments, the noise estimation system 412 can perform thresholding on the summed ultrasound image 714. For example, the noise estimation system 412 can disregard small or minute variations in the intensity of the summed ultrasound image 714, such as variations that may be unrelated to a particular noise. Furthermore, the noise estimation system 412 can normalize the average intensity. For example, the noise estimation system 412 can normalize the average intensity (“E”) of the summed ultrasound image 714 to a value between 0 and 1. In this example, an average intensity close to 0 indicates the presence of low noise, while an average intensity close to 1 indicates the presence of high noise. Figure 8 An example flowchart of the fragment detection and filtering logic used by the image processing system 401 is shown. Figure 8 The process described herein can be applied by the image processing system 401 (e.g., noise estimation system 410, noise suppression system 412) with respect to filters or masks.

[0086] As described herein, image processing system 401 may apply one or more filters or one or more masks, such as noise suppression masks, to an input ultrasound image or frames of an ultrasound image. In some examples, standard filtering function 502 may include a noise suppression mask as described herein. Furthermore, masks accessed by noise suppression system 412 may include noise suppression masks as described herein. A noise suppression mask may be a smoothing or blurring mask, which can be used to reduce noise or artifacts in an ultrasound image. Alternatively or additionally, a noise suppression mask may be a linear smoothing filter, where each pixel is replaced by the average of neighboring pixels. In this case, the size of the neighboring pixels may be defined by the mask size. In some embodiments, the noise suppression filter includes a nonlinear smoothing filter that replaces each pixel in the image with the median of neighboring pixels, depending on the filter size. For example, in some embodiments, the center value of the mask may be assigned an average between local maxima and local minima within the mask. In some embodiments, the ultrasound image may be convolved with a noise suppression filter. The size of the noise suppression mask can be used to suppress or filter noise or speckles of different sizes. For example, a 3x3 pixel mask may be used to suppress low-noise bands and / or small speckles in an ultrasound image. In some embodiments, a 5x5 pixel mask can be used to suppress high-noise bands and / or large spots in ultrasound images.

[0087] As described below, flowchart 800 can depict filtering an ultrasound image using a 3x3 or 5x5 filter to suppress debris noise. For this purpose, a portion of the filter used to suppress brightness outliers can be adapted to the cross-sectional size of the brightness outliers along the x-axis. Specifically, the flowchart can represent an algorithm executed by image processing system 401 based on a comparison of the source pixel values ​​(“SRC”) and the results of the horizontal maximum value filter (“F_MAX_H”).

[0088] In step 802, the image processing system 401 receives an input frame of an ultrasound image. Each pixel of the input frame can be processed by the image processing system 401 according to flowchart 800. In step 804, the image processing system 401 can determine whether the SRC is equal to the result of the applied F_MAX_H for the first pixel of the input frame. As described herein, debris noise can be characterized by high-intensity curves and lines pointing vertically and obliquely. The projection of these lines onto the X-axis is typically significantly smaller than their projection onto the Y-axis. Therefore, the horizontal cross-section of these high-intensity lines typically consists of no more than a few pixels. Therefore, in 804, when the output value of the horizontal maximum filter is equal to the SRC, the image processing system 401 can determine the presence of suspected debris.

[0089] If F_MAX_H equals SRC, then the image processing system 401 can proceed to step 806 in flowchart 800. In step 806, the image processing system 401 can determine the target pixel value ("DST") based on the average of the horizontal minimum filter ("F_MIN_H") and the median filter ("F_Median_H").

[0090] If F_MAX_H is not equal to SRC, then image processing system 401 can proceed to step 808 in flowchart 800. In step 808, image processing system 401 can determine DST based on the average of SRC and the median filter (“F_Median_H”). In block 808, the process performs small filtering on the portions of the image without noise fragments.

[0091] Steps 804-808 can be performed iteratively for each pixel of the input frame. In some cases, when each pixel of the input frame has been processed by the image processing system 104 according to flowchart 800, the image processing system 140 can output the output frame 810.

[0092] Figure 9Table 900 shows example filter aperture sizes. Table 900 includes various types of filters / masks used by image processing system 104, including horizontal maximum filters, horizontal minimum filters, horizontal median filters, and vertical median filters. As shown in the columns, each filter can have the same size. An F3 filter name can correspond to a 3x1 filter of 3x3 pixels. An F5 filter name can correspond to a 5x1 filter of 5x5 pixels.

[0093] Figure 10 This is a table showing example filters and their corresponding depth ranges and iterations. Depending on the type of filter (e.g., soft, medium, hard), the image processing system 401 can apply F3 or F5 filters at various depths and iterations as shown in the figure.

[0094] Figure 11 This is an example scheme of filter size and iteration used by the image processing system in the corresponding zone of the ultrasound image.

[0095] Figure 12 This is an example scheme of filter size and iteration used by an image processing system in a corresponding zone of an ultrasound image containing weak fragment noise.

[0096] Figure 13A This is an example flowchart of routine 1300A for digitally filtering noise fragments from ultrasound images of ultrasound imaging system 20. Routine 1300A can be executed by processing unit 403 of image processing system 401 and various components of image processing system 401.

[0097] In block 1302, image processing system 401 receives frame 500 of an ultrasound image. The ultrasound image may originate from ultrasound imaging system 20. As described herein, ultrasound imaging system 20 may capture multiple ultrasound images, such as in the form of 2D, 3D ultrasound images, video, recordings, etc. In one embodiment, each ultrasound image in a recording or video may be a frame of an ultrasound recording or video. Therefore, image processing system 401 may be configured to access frames of ultrasound images from image data storage 404 for further processing. For example, image processing system 401 may receive or access frame 500 of a plurality of frames of ultrasound images.

[0098] In box 1304, the image thresholding system 408 can determine that the frame meets a contact threshold. To determine whether the frame meets the contact threshold, the image thresholding system 408 can determine whether frame 500 of the ultrasound image (e.g., the Nth frame) is suitable for further image processing. In the context of ultrasound imaging, some images may be unsuitable for processing due to insufficient contact with the patient's skin, hardware malfunctions, etc. For example, the image thresholding system 408 can determine whether there is sufficient contact between module 200 and the patient's skin. The image thresholding system 408 can be configured to determine whether a frame of the ultrasound image meets a contact threshold for further processing, such as noise estimation and / or noise suppression.

[0099] A contact threshold can correspond to the intensity distribution of a frame. To assess whether frame 500 is suitable for further noise estimation and noise suppression, image thresholding system 408 can determine the intensity distribution across individual regions of the frame. In some embodiments, image thresholding system 408 can determine the absolute intensity of frame 500. In some embodiments, if the absolute intensity is higher than a threshold, frame 500 is considered suitable for further processing by noise estimation system 412 and / or noise suppression system 410. If the absolute intensity is equal to or lower than the threshold, image thresholding system 408 can perform additional processing on the ultrasound image. In some embodiments, image thresholding system 408 can be configured to determine the intensity distribution across regions of frame 500. For example, image thresholding system 408 can divide, segment, or otherwise separate frame 500 into individual regions. Furthermore, image thresholding system 408 can compare the average intensity between each region when determining the intensity distribution across regions of frame 500.

[0100] In frame 1306A, image processing system 104 filters frame 500 using standard settings. As used herein, standard settings may include, for example... Figure 10 The corresponding column shows the standard filter value.

[0101] In box 1308A, image processing system 104 determines or calculates the difference between the fully filtered frame 500 and the original frame 500.

[0102] In block 1310A, image processing system 104 can calculate the vertical portion of the Sobel filter based on the differences in frame 500 obtained in block 1308.

[0103] In block 1312A, image processing system 104 determines the average intensity of the vertical portion (e.g., the edge) of the Sobel filter obtained in block 1310.

[0104] In box 1314A, select the filtering settings for the current frame 500 and the next N-1 frames. Figure 5 The threshold used to select filter settings is shown in the figure. Figure 10 The diagram shows the filter bands (e.g., represented as a fraction of image depth) and the number of iterations for each filter type.

[0105] In frame 1316A, image processing system 104 filters the frame and the next N-1 frames using the determined filter settings and displays them on the display of the ultrasound imaging system.

[0106] Figure 13B This is an example flowchart of routine 1300B for digitally filtering noise fragments from ultrasound images of ultrasound imaging system 20. Routine 1300 can be executed by processing unit 403 of image processing system 401 and various components of image processing system 401.

[0107] In block 1302B, image processing system 401 receives frame 500 of an ultrasound image. The ultrasound image may originate from ultrasound imaging system 20. As described herein, ultrasound imaging system 20 may capture multiple ultrasound images, such as in the form of 2D, 3D ultrasound images, video, recordings, etc. In one embodiment, each ultrasound image in a recording or video may be a frame of an ultrasound recording or video. Therefore, image processing system 401 may be configured to access frames of ultrasound images from image data storage 404 for further processing. For example, image processing system 401 may receive or access frame 500 of a plurality of frames of ultrasound images.

[0108] In box 1304B, the image thresholding system 408 can determine that the frame meets a contact threshold. To determine whether the frame meets the contact threshold, the image thresholding system 408 can determine whether frame 500 of the ultrasound image (e.g., the Nth frame) is suitable for further image processing. In the context of ultrasound imaging, some images may be unsuitable for processing due to insufficient contact with the patient's skin, hardware malfunctions, etc. For example, the image thresholding system 408 can determine whether there is sufficient contact between module 200 and the patient's skin. The image thresholding system 408 can be configured to determine whether frames of the ultrasound image meet a contact threshold for further processing, such as noise estimation and / or noise suppression.

[0109] A contact threshold can correspond to the intensity distribution of a frame. To assess whether frame 500 is suitable for further noise estimation and suppression, image thresholding system 408 can determine the intensity distribution across individual regions of the frame. In some embodiments, image thresholding system 408 can determine the absolute intensity of frame 500. In some embodiments, if the absolute intensity is higher than a threshold, frame 500 is considered suitable for further processing by noise estimation system 412 and / or noise suppression system 410. If the absolute intensity is equal to or lower than the threshold, image thresholding system 408 can perform additional processing on the ultrasound image. In some embodiments, image thresholding system 408 can be configured to determine the intensity distribution across regions of frame 500. For example, image thresholding system 408 can divide, segment, or otherwise separate frame 500 into individual regions. Furthermore, image thresholding system 408 can compare the average intensity between each region when determining the intensity distribution across regions of frame 500.

[0110] At block 1306B, image processing system 104 applies a first mask to a band of the frame. In some embodiments, image processing system 104, for example via noise estimation system 410, may segment, assign, or otherwise determine bands of frame 500 to apply various functions or filters. Furthermore, at block 1306, image processing system 104 (e.g., via noise estimation system 410) may apply a first mask to the band. The first mask includes a standard filtering function 502 and may include any filter with parameters unadjusted or tuned to frame 500 (e.g., initial settings). For example, the first mask may include a 3x3 pixel mask or a 5x5 pixel mask for suppressing low-noise bands and / or small speckles in the ultrasound image. The 3x3 pixel mask may be any noise suppression filter mask or a standard mask, such as a smoothing or blurring mask, which can be used to reduce noise or artifacts in frame 500. In some embodiments, the first mask is a linear smoothing filter, where each pixel of frame 500 is replaced by an average of neighboring pixels. In this case, the size of the neighboring pixels may be defined by the mask size. In some embodiments, the first mask includes a nonlinear smoothing filter that replaces each pixel in frame 500 with the median of neighboring pixels. For example, in some embodiments, the center value of the mask can be assigned the average of local maxima and local minima within a standard filtering function 502. Furthermore, the noise estimation system 410 can apply the first mask or multiple masks to frame 500 multiple times.

[0111] In block 1308B, image processing system 104 determines the difference within the band. In some embodiments, image processing system 104 determines the difference between the frame and a frame to which a first mask is applied. To determine the difference, noise estimation system 410 may apply a difference function 504 to frame 500. In some embodiments, noise estimation system 412 may be configured to determine the difference between frame 500 and filtered frame 500. Noise estimation system 410 may determine the pixel-wise difference between frame 500 and filtered frame 500. Noise estimation system 412 may be configured to determine the pixel-wise difference by subtracting the filtered frame from the original frame 500. The pixel-wise difference may represent the amount of noise filtered out by applying a standard filtering function 502 to frame 500. In some examples, noise estimation system 412 may determine the difference between frame 500 and filtered frame within the band. The difference determined by difference function 504 may be the absolute difference between the filtered frame and frame 500. In response to applying difference function 504 to filtered frame 500, noise estimation system 410 may determine a difference frame.

[0112] In box 1310B, image processing system 104 applies edge detection filters to the bands of a frame. For example, noise estimation system 410 may apply edge detection function 506. Edge detection by edge detection function 506 can highlight significant changes in image processing and can be used to detect any discontinuities in brightness or contrast. In this case, edge detection function 506 can indicate whether additional processing and / or noise suppression is needed. For example, noise estimation system 412 may apply any edge detection filter, such as a vertical filter (e.g., a Sobel filter), a horizontal filter, etc. In some examples, the filter may have an aperture size of 3x3 pixels. In some embodiments, applying an edge detection filter to pixel-wise differences results in noise estimation system 412 outputting a gradient frame 500.

[0113] In box 1312B, image processing system 104 determines the average intensity of the band. The average intensity may correspond to the estimated noise level within the band. Noise estimation system 410 may apply a summation function 508. Summation function 508 may sum the results of edge detection function 506, such as a vertical Sobel filtering operation. In some embodiments, noise estimation system 410 may be configured to determine the average intensity of gradient frame 500. In some embodiments, the average intensity of frame 500 may represent the level of noise estimation. In addition to summing the results of edge detection function 506, the noise estimation system may also normalize the average intensity (although in...). Figure 5In this context, the normalized decision box is shown within the noise suppression system 412. For example, the noise estimation system 410 can normalize the average intensity of the gradient frame 500 to a value between 0 and 1. In this example, an average normalized intensity close to 0 indicates the presence of low noise, while an average normalized intensity close to 1 indicates the presence of high noise.

[0114] In block 1314B, image processing system 104 applies a second mask to the band based on the estimated noise level in the band to produce a noise-suppressed band for the frame. The second mask includes a standard filtering function 502 and may include any filter with parameters unadjusted or tuned to frame 500 (e.g., initial settings). For example, the second mask may include a 3x3 pixel mask or a 5x5 pixel mask for suppressing low-noise bands and / or small speckles in the ultrasound image. The 3x3 pixel mask may be any noise-suppressing filter mask or a standard mask, such as a smoothing or blurring mask, which can be used to reduce noise or artifacts in frame 500. In some embodiments, the second mask is a linear smoothing filter, where each pixel of frame 500 is replaced by the average of its neighboring pixels. In this case, the size of the neighboring pixels may be defined by the mask size. In some embodiments, the second mask includes a non-linear smoothing filter that replaces each pixel in frame 500 with the median of its neighboring pixels. For example, in some embodiments, the center value of the mask may be assigned an average between local maxima and local minima within the standard filtering function 502. Furthermore, the noise estimation system 410 can apply the second mask or multiple masks to frame 500 multiple times.

[0115] In some embodiments, the application of the first or second mask includes determining whether the input pixel value is equal to the result of the horizontal maximum filter for each pixel of the band of the frame, determining the output pixel value based on a weighted median filter for the pixel in response to the determination that the input pixel value is equal to the result of the horizontal maximum filter, and determining the output pixel value based on the average of the horizontal maximum filter and the horizontal minimum filter in response to the determination that the input pixel value is not equal to the result of the horizontal maximum filter.

[0116] In frame 1316B, the image processing system 104 displays the frame on the display of the ultrasound imaging system.

[0117] It should be understood that not all objectives or advantages may be achieved according to any particular embodiment described herein. Therefore, for example, those skilled in the art will recognize that certain embodiments may be configured to operate in a manner that achieves or optimizes one or more advantages taught herein, without necessarily achieving other objectives or advantages taught or suggested herein.

[0118] All processes described herein can be embodied in and fully automated through software code modules, including one or more dedicated computer-executable instructions executed by a computing system. The computing system may include one or more computers or processors. Code modules may be stored on any type of non-transitory computer-readable medium or other computer storage device. Some or all of these methods may be embodied in dedicated computer hardware.

[0119] Many other variations besides those described herein will be apparent from this disclosure. For example, according to embodiments, certain actions, events, or functions of any algorithm described herein may be performed in a different order, may be added, combined, or omitted entirely (e.g., not all described actions or events are necessary for the implementation of the algorithm). Furthermore, in some embodiments, individual actions or events may be performed concurrently, for example, through multithreaded processing, interrupt handling, or multiple processors or processor cores, or on other parallel architectures, rather than sequentially. Additionally, different tasks or processes may be performed by different machines and / or computing systems that can work together.

[0120] The various illustrative logic blocks and modules described in conjunction with the embodiments disclosed herein can be implemented or executed by machines, such as processing units or processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. The processor may be a microprocessor, but alternatively, it may be a controller, microcontroller, or state machine, a combination thereof, etc. The processor may include circuitry configured to process computer-executable instructions. In another embodiment, the processor includes an FPGA or other programmable device that performs logic operations without processing computer-executable instructions. The processor may also be implemented as a combination of multiple electronic devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. Although this document is primarily described with respect to digital technologies, the processor may also primarily include analog components. The computing environment may include any type of computer system, including but not limited to microprocessor-based computer systems, mainframe computers, digital signal processors, portable electronic devices, device controllers, or computing engines within devices, etc.

[0121] Unless otherwise specified, conditional language such as “can,” “may,” “may,” or “may,” unless specifically indicated otherwise, is understood in the context of certain features, elements, and / or steps typically included in some embodiments but not in others. Therefore, such conditional language generally does not imply that one or more embodiments require those features, elements, and / or steps in any way, nor does it imply that one or more embodiments must include logic for determining, with or without user input or prompting, whether to include or perform those features, elements, or steps in any particular embodiment.

[0122] Unless otherwise explicitly stated, negative language such as the phrase “at least one of X, Y, or Z” should be understood in conjunction with the context in which items, terms, etc., can be X, Y, Z, or any combination thereof (e.g., X, Y, and / or Z). Therefore, such disjunctive language is generally not intended, nor should it imply, that certain embodiments require at least one of X, Y, or Z to be present.

[0123] Any process description, element, or block in the flowcharts described herein and / or illustrated in the accompanying drawings should be understood to potentially represent modules, segments, or code portions, including one or more executable instructions for implementing a particular logical function or element within a process. Alternative embodiments are included within the scope of the embodiments described herein, wherein elements or functions may be removed, performed in the order shown or discussed, including substantially simultaneously or in reverse order, depending on the functionality involved, as understood by those skilled in the art.

[0124] Unless otherwise explicitly stated, articles such as “a” or “an” should generally be interpreted as including one or more described items. Therefore, phrases such as “device configured as…” are intended to include one or more described devices. Such one or more described devices can also be collectively configured to perform the descriptions. For example, “processors configured to perform records A, B, and C” could include a first processor configured to perform record A, which works in conjunction with a second processor configured to perform records B and C.

Claims

1. A method for filtering fragments from an ultrasound image, comprising: Receive frames of ultrasound images from the ultrasound imaging system; The frame is determined to meet the contact threshold; A first mask is applied to a band of the frame, wherein the band corresponds to a portion of the frame within a first depth range; Within the defined zone, determine the difference between the frame and the frame to which the first mask was applied; An edge detection filter is applied to the band of the frame; Determine the average intensity of the zone, wherein the average intensity corresponds to the estimated noise level within the zone; Based on the estimated noise level in the band, a second mask is applied to the band to generate a noise-suppressed band for the frame; and The frame is displayed on the monitor of the ultrasound imaging system.

2. The method according to claim 1, wherein, The contact threshold corresponds to the intensity distribution of the frame.

3. The method according to claim 1, wherein, The first mask and the second mask are noise suppression masks.

4. The method according to any one of claims 1-3, further comprising normalizing the estimated noise level in the said band.

5. The method according to any one of claims 1-3, wherein, Applying the edge detection filter results in the gradient being output.

6. The method according to any one of claims 1-3, further comprising: The estimated noise level indicates that additional noise suppression is required; and Additional iterations of the second mask are applied to the zone.

7. The method according to any one of claims 1-3, wherein, The applications of the first mask or the second mask include: For each pixel in the band of the frame, determine whether the input pixel value is equal to the result of the horizontal maximum value filter; In response to the determination that the input pixel value equals the result of the horizontal maximum value filter, the output pixel value is determined based on a weighted median filter for each pixel; and In response to the determination that the input pixel value is not equal to the result of the horizontal maximum value filter, the output pixel value is determined based on the average of the horizontal maximum value filter and the horizontal minimum value filter.

8. An ultrasound imaging system configured for digitally filtering fragments from ultrasound images, comprising: An ultrasound probe, the ultrasound probe including an ultrasound imaging transducer suitable for imaging a tissue region; A display, the display being connected to the ultrasound probe; and A processor, which is connected to the ultrasound probe and configured to: Receive frames of ultrasound images from the ultrasound imaging system; The frame is determined to meet the contact threshold; Apply the first mask to the frame. Determine the absolute difference between the frame and the frame to which the first mask was applied; An edge detection filter is applied to the frame; Determine the average intensity of the frame, wherein the average intensity corresponds to the estimated noise level within the frame; Based on the estimated noise level, a second mask is applied to the frame to generate a noise-suppressed frame; and The frame is displayed on the monitor of the ultrasound imaging system.

9. The ultrasound imaging system according to claim 8, wherein, The contact threshold corresponds to the intensity distribution of the frame.

10. The ultrasound imaging system according to claim 8, wherein, The first mask and the second mask are noise suppression masks.

11. The ultrasound imaging system according to claim 8, wherein, The processor is also used to normalize the estimated noise level in the frame.

12. The ultrasound imaging system according to any one of claims 8-11, wherein, Applying the edge detection filter results in the gradient being output.

13. The ultrasound imaging system according to any one of claims 8-11, wherein, The processor is also used for: Based on the estimated noise level, it is determined that additional noise suppression is needed; and Additional iterations of the second mask are applied to the frame.

14. The ultrasound imaging system according to any one of claims 8-11, wherein, The application of the first mask or the second mask also enables the processor to: For each pixel in the frame, determine whether the input pixel value is equal to the result of the horizontal maximum value filter; In response to the determination that the input pixel value is equal to the result of the horizontal maximum value filter, the output pixel value is determined based on a weighted median filter for the pixel; and In response to the determination that the input pixel value is not equal to the result of the horizontal maximum value filter, the output pixel value is determined based on the average of the horizontal maximum value filter and the horizontal minimum value filter.

15. An ultrasound imaging system configured for digitally filtering fragments from ultrasound images, comprising: An ultrasound probe, the ultrasound probe including an ultrasound imaging transducer suitable for imaging a tissue region; A display, the display being connected to the ultrasound probe; and A processor, which is connected to the ultrasound probe and configured to: The first filter is applied to the ultrasound image. Determine the absolute difference between the ultrasound image and the ultrasound image with the first filter applied; An edge detection filter is applied to the ultrasound image; Determine the intensity of the ultrasound image, wherein the intensity corresponds to an estimated noise level of the ultrasound image; Based on the estimated noise level, a second filter is applied to the frame to produce a noise-suppressed ultrasound image; and The noise-suppressed ultrasound image is displayed on the monitor of the ultrasound imaging system.

16. The ultrasound imaging system according to claim 15, wherein, The first filter and the second filter are noise suppression filters.

17. The ultrasound imaging system according to claim 15, wherein, The processor is also used to normalize the estimated noise level.

18. The ultrasound imaging system according to claim 15, wherein, The application of the edge detection filter results in the gradient being output.

19. The ultrasound imaging system according to any one of claims 15-18, wherein, The processor is also used for: Based on the estimated noise level, it is determined that additional noise suppression is needed; and Additional iterations of the second filter are applied to the frame.

20. The ultrasound imaging system according to any one of claims 15-18, wherein, The application of the first filter or the second filter also enables the processor to: For each pixel in the frame, determine whether the input pixel value is equal to the result of the horizontal maximum value filter; In response to the determination that the input pixel value is equal to the result of the horizontal maximum value filter, the output pixel value is determined based on a weighted median filter for the pixel; and In response to the determination that the source pixel value is not equal to the result of the horizontal maximum value filter, the output pixel value is determined based on the average of the horizontal maximum value filter and the horizontal minimum value filter.

Images