An image subtraction method, system, and storage medium

Abstract

The application provides an image subtraction method, system and storage medium. The method can include at least one operation. A contrast image and a plurality of mask images can be obtained. At least two mask images of the plurality of mask images can be combined based on the contrast image to obtain a combined mask image. The method disclosed in the application can combine at least two mask images according to the contrast image to reduce the influence of motion artifacts on the subtraction image.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical image, in particular to an image subtraction method, system and storage medium. BACKGROUND

[0002] In the subtraction technique, an image is acquired in a region of interest of a patient before the patient is injected with a contrast agent, and a certain frame of the image is used as a mask. Then, the patient is injected with a contrast agent or is made to take a contrast agent (e.g., iodine), and a contrast image is acquired by continuing to scan. The contrast image is subtracted from the mask, and theoretically, an image containing only the part containing the contrast agent (e.g., blood vessels) can be obtained. However, there is a time difference between the acquisition of the contrast image and the mask, and the human body will move due to spontaneous shaking of the body and involuntary movement of the body during this period. For example, abdominal breathing and heartbeats will produce a large amount of motion artifacts, thereby affecting the subtraction effect. Currently, the influence of motion artifacts can be reduced by performing pixel displacement on the mask image. However, the effect of simply using pixel displacement to reduce the influence of motion artifacts is limited, especially for complex parts such as the heart. Therefore, it is desirable to provide a more effective subtraction technique to reduce the influence of motion artifacts on the subtraction image. SUMMARY

[0003] One aspect of the present application provides an image subtraction method. The method can include at least one operation. A contrast image and a plurality of mask images can be acquired. At least two mask images of the plurality of mask images can be acquired based on the contrast image. Low-frequency components and high-frequency components of the at least two mask images can be acquired. The low-frequency components of at least one mask image of the at least two mask images and the high-frequency components of each mask image of the at least two mask images can be combined to obtain a combined mask image.

[0004] Another aspect of the present application provides an image subtraction system. The system can include at least one processor and at least one memory for storing computer instructions, and the at least one processor is configured to execute at least part of the computer instructions to implement the operations of the image subtraction method as described above.

[0005] Still another aspect of the present application provides a computer-readable storage medium storing computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes the operations of the image subtraction method as described above.

[0006] Additional features will be set forth in the description that follows, and in part will be apparent from the description and the accompanying drawings, or can be learned by practice of the presented embodiments of the application. Features of the present application can be realized and obtained by means of the instruments and combinations described in or through practice of the methods described in the following detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0007] The present application can be further described in reference to the following examples. The examples are not meant to limit the exemplary embodiments described herein, in which like numerals refer to like elements throughout the several views of the drawings, and in which:

[0008] Figure 1 is a schematic diagram of an application scenario of an image subtraction system according to some embodiments of the present application;

[0009] Figure 2 is an exemplary block diagram of an image subtraction system according to some embodiments of the present application;

[0010] Figure 3 is an exemplary flowchart of a process of obtaining a combined mask image according to some embodiments of the present application;

[0011] Figure 4 is an exemplary flowchart of an image subtraction process according to some embodiments of the present application;

[0012] Figure 5 is an exemplary flowchart of a process of obtaining a combined mask image according to some embodiments of the present application;

[0013] Figure 6 is an exemplary flowchart of a process of obtaining a fitted mask image according to some embodiments of the present application;

[0014] Figure 7 is an exemplary flowchart of a process of obtaining a combined mask image according to some embodiments of the present application;

[0015] Figures 8-10 is a set of subtraction images obtained using different methods;

[0016] Figures 11-13 is another set of subtraction images obtained using different methods. DETAILED DESCRIPTION

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can also be applied to other similar scenarios without creative labor on the basis of these drawings. Unless the context clearly indicates otherwise or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0018] As shown in the present application and claims, unless the context clearly indicates otherwise or otherwise stated, the words "one", "a", "an", and / or "the" do not specify a singular form, but can also include a plural form. Generally, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0019] It should be understood that the terms "system", "device", "unit", "component", "module" and / or "block" used herein are a method of distinguishing different components, elements, parts, portions or components at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0020] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. At the same time, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0021] Figure 1 is a schematic diagram of an application scenario of an exemplary image subtraction system according to some embodiments of the present application. As shown in Figure 1 , the image subtraction system 100 can include a medical device 110, a network 120, a terminal device 130, a processing device 140, and a storage device 150. The components of the image subtraction system 100 can be connected in various ways. For example only, as shown in Figure 1 , the processing device 140 can be connected to the medical device 110 through the network 120. For another example, the processing device 140 can be directly connected to the medical device 110 (as shown by the dashed bidirectional arrow connecting the processing device 140 and the medical device 110). For another example, the terminal device (e.g., 131, 132, 133, etc.) can be directly connected to the processing device 140 (as shown by the dashed bidirectional arrow connecting the terminal device 130 and the processing device 140), or can be connected to the processing device 140 through the network 120.

[0022] The medical device 110 can be used to scan a target object or a portion thereof located within its detection region and generate an image related to the target object or the portion thereof. In some embodiments, the target object can include a human body, an animal (e.g., a laboratory mouse or other animal), a phantom, etc., or any combination thereof. In some embodiments, the target object can include a specific portion of a human body, such as a head, a chest, an abdomen, a limb, etc., or any combination thereof. In some embodiments, the target object can include a specific organ, such as a heart, a thyroid, an esophagus, a trachea, a stomach, a liver, a lung, a gallbladder, a small intestine, a colon, a bladder, a ureter, a uterus, a fallopian tube, etc. In some embodiments, the medical device 110 can include a digital subtraction angiography (DSA) device, a computed tomography (CT) device, a magnetic resonance imaging (MRI) device, a positron emission computed tomography (PET) device, a single-photon emission computed tomography (SPECT) device, etc., or any combination thereof.

[0023] The network 120 can facilitate the exchange of information and / or data. In some embodiments, one or more components of the image subtraction system 100 (e.g., the medical device 110, the terminal device 130, the processing device 140, the storage device 150, etc.) can exchange information and / or data with other components in the image subtraction system 100 through the network 120. For example, the processing device 140 can obtain the mask image and the contrast image from the medical device 110 via the network 120. In some embodiments, the network 120 can be any type of wired or wireless network or a combination thereof. By way of example only, the network 120 can include a cable network, a wireline network, a fiber-optic network, a telecommunication network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, a near-field communication (NFC) network, etc., or any combination thereof. In some embodiments, the network 120 can include one or more network access points. For example, the network 120 can include wired and / or wireless network access points, such as base stations and / or Internet exchange points. Through the network access points, one or more components of the image subtraction system 100 can connect to the network 120 to exchange data and / or information. TM TM

[0024] ​​The terminal device 130 can enable a user to interact with other components in the image subtraction system 100. For example, a user can send a request to access data and images of the medical device 110 to the processing device 140 through the terminal device 130. For another example, the terminal device 130 can also receive data and images acquired by the medical device 110 through the network 120. In some embodiments, the terminal device 130 can include a mobile device 131, a tablet 132, a laptop 133, or the like, or any combination thereof. In some embodiments, the mobile device 131 can include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home device can include a smart lighting device, a control device of a smart electrical device, a smart monitoring device, a smart television, a smart camera, an intercom, or the like, or any combination thereof. In some embodiments, the wearable device can include a smart bracelet, smart footwear, smart glasses, a smart helmet, a smart watch, smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device can include a smart phone, a personal digital assistant (PDA), a gaming device, a navigation device, a point of sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and / or the augmented reality device can include a virtual reality helmet, a virtual reality glasses, a virtual reality eyeshield, an augmented reality helmet, an augmented reality glasses, an augmented reality eyeshield, or the like, or any combination thereof. For example, the virtual reality device and / or the augmented reality device can include Google Glass TM , Oculus Rift TM , Hololens TM , Gear VR TM , or the like.

[0025] The processing device 140 can process information and / or data obtained from the medical device 110, the terminal device 130, and / or the storage device 150. For example, the processing device 140 can obtain a plurality of mask images generated by the medical device 110 scanning a target object, and combine at least two mask images of the plurality of mask images to obtain a combined mask image. For another example, the processing device 140 can obtain a contrast image generated by the medical device 110 scanning the target object, and perform pixel displacement on a mask image based on the contrast image to obtain a fitted mask image. For yet another example, the processing device 140 can obtain a contrast image generated by the medical device 110 scanning the target object, and subtract the combined mask image from the contrast image to obtain a subtraction image. In some embodiments, the processing device 140 can be a single server or a group of servers. The group of servers can be centralized or distributed. In some embodiments, the processing device 140 can be local or remote. For example, the processing device 140 can access information and / or data from the medical device 110, the terminal device 130, and / or the storage device 150 through the network 120. For another example, the processing device 140 can directly connect to the medical device 110, the terminal device 130, and / or the storage device 150 to access information and / or data. In some embodiments, the processing device 140 can include one or more processing units (e.g., single-core processing engines or multi-core processing engines). By way of example only, the processing device 140 can include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, etc., or any combination thereof. In some embodiments, the processing device 140 can be implemented on a cloud platform. For example, the cloud platform can include one or a combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, a cross-cloud, a multi-cloud, etc. In some embodiments, the processing device 140 can be part of the medical device 110 or the terminal device 130.

[0026] The storage device 150 can store data, instructions, and / or any other information. In some embodiments, the storage device 150 can store data obtained from the medical device 110, the terminal device 130, and / or the processing device 140. For example, the storage device 150 can store scan parameters of the medical device 110. For another example, the storage device 150 can store the mask image and the contrast image generated by the medical device 110 scanning the target object. For yet another example, the storage device 150 can store the combined mask image and the fitted mask image generated by the processing device 140. In some embodiments, the storage device 150 can store data and / or instructions that the processing device 140 can execute or use to perform the exemplary methods described in the present application. In some embodiments, the storage device 150 can include one or a combination of a mass storage, a removable storage, a volatile read-write memory, a read-only memory (ROM), etc. The mass storage can include a disk, an optical disk, a solid state disk, a mobile storage, etc. The removable storage can include a flash drive, a floppy disk, an optical disk, a memory card, a ZIP disk, a magnetic tape, etc. The volatile read-write memory can include a random access memory (RAM). The RAM can include a dynamic random access memory (DRAM), a double data rate synchronous dynamic random access memory (DDR-SDRAM), a static random access memory (SRAM), a thyristor random access memory (T-RAM), a zero-capacitor random access memory (Z-RAM), etc. The ROM can include a mask read-only memory (MROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disk read-only memory (CD-ROM), a digital versatile disk, etc. In some embodiments, the storage device 150 can be implemented by a cloud platform described in the present application.

[0027] In some embodiments, the storage device 150 can be connected to the network 120 to enable communication between one or more components in the image subtraction system 100 (e.g., the medical device 110, the processing device 140, the terminal device 130, etc.). One or more components in the image subtraction system 100 can read data or instructions in the storage device 150 through the network 120. In some embodiments, the storage device 150 can be a part of the processing device 140 or can be independent and directly or indirectly connected to the processing device 140.

[0028] It should be noted that the above description of the image subtraction system 100 is for illustrative purposes only and is not intended to limit the scope of the present application. It can be appreciated that various modifications and changes can be made to the application of the above-described system in form and detail by those skilled in the art without departing from the principle of the system, upon learning the principle of the system. However, these changes and modifications do not depart from the scope of the present application. For example, the medical device 110, the processing device 140 and the terminal device 130 can share one storage device 150, or have their own storage devices.

[0029] Figure 2 is an exemplary block diagram of an image subtraction system according to some embodiments of the present application. In some embodiments, the image subtraction system 200 can be implemented by the processing device 140. As shown in Figure 2 , the image subtraction system 200 can include an acquisition module 210 and a subtraction module 220.

[0030] The acquisition module 210 can be configured to acquire a mask image and an angiogram image.

[0031] In some embodiments, the acquisition module 210 can acquire a plurality of mask images and an angiogram image. The acquisition module 210 can combine at least two mask images of the plurality of mask images based on the angiogram image to obtain a combined mask image. In some embodiments, the acquisition module 210 can acquire at least two mask images of the plurality of mask images based on the angiogram image. The acquisition module 210 can acquire a low frequency component and a high frequency component of each of the at least two mask images. The acquisition module 210 can combine the low frequency component of at least one mask image of the at least two mask images and the high frequency component of each of the at least two mask images to obtain the combined mask image.

[0032] In some embodiments, the mask image can be a fitted mask image. In some embodiments, the mask image can be a real mask image. In some embodiments, the acquisition module 210 can obtain a fitted mask image by pixel shifting a real mask image or a fitted mask image according to the angiogram image. In some embodiments, the acquisition module 210 can pre-process the real mask image and the angiogram image. The pre-processing includes but is not limited to log transformation, noise reduction processing or regularization processing.

[0033] The subtraction module 220 can be configured to subtract the mask image from the angiogram image to obtain a subtraction image.

[0034] In some embodiments, the subtraction module 220 can be configured to subtract the combined mask image from the angiography image to obtain a subtraction image. In some embodiments, the subtraction module 220 can be configured to pixel shift the combined mask image according to the angiography image before subtracting the combined mask image from the angiography image to obtain the subtraction image. In some embodiments, the subtraction module 220 can be configured to post-process the subtraction image. The post-processing includes, but is not limited to, LUT (Lookup Table) curve, non-linear S-shaped curve, vessel enhancement, etc.

[0035] It should be understood that, Figure 2 The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented in hardware, software, or a combination of software and hardware. The hardware portion can be implemented with special logic, while the software portion can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above-mentioned method and system can be implemented using computer executable instructions and / or included in processor control code, such as provided on a carrier medium, such as a magnetic disk, CD or DVD-ROM, programmable memory, such as read-only memory (firmware), or data carrier, such as optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuit, such as ultra-large scale integrated circuit or gate array, semiconductor, such as logic chip, transistor, or programmable hardware device, such as field programmable gate array, programmable logic device, but also by software, for example, executed by various types of processors, and also by a combination of the above-mentioned hardware circuit and software (for example, firmware).

[0036] It should be noted that the above description of the system and its modules is for convenience of description only, and cannot limit the present application to the scope of the embodiments. It can be understood that, for those skilled in the art, after understanding the principle of the system, the modules can be combined arbitrarily, or connected with other modules to form a subsystem, without departing from the principle. For example, in some embodiments, the acquisition module 210 can include two units, such as a mask image acquisition unit and an angiography image acquisition unit, to acquire the mask image and the angiography image respectively. For another example, each module can share a storage device, and each module can also have its own storage device. Such variations are within the scope of the present application.

[0037] Figure 3is an example flowchart of acquiring combined mask images according to some embodiments of the present application. In some embodiments, flow 300 can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions run on a processing device to simulate the hardware), etc., or any combination thereof. In some embodiments, flow 300 can be implemented as a set of instructions stored in storage device 150 (e.g., an application program). Processing device 140 and / or Figure 2 modules in system 200 can execute the set of instructions and cause processing device 140 and / or the modules to perform flow 300 when the instructions are executed. The operations of the illustrated processes presented below are intended to be illustrative. In some embodiments, flow 300 can be accomplished with one or more additional operations not described, and / or without one or more of the Figure 3 operations discussed in this application. Additionally, the order in which the operations of the processes are illustrated is not limiting.

[0038] In step 310, processing device 140 can acquire a contrast image and a plurality of mask images. In some embodiments, step 310 can be performed by acquisition module 210 in system 200.

[0039] In some embodiments, the contrast image can be an image of the target object scanned after the target object receives a contrast agent. In some embodiments, the contrast image can be acquired by medical device 110 scanning the target object. In some embodiments, the contrast image can be acquired by another medical device scanning the target object. In some embodiments, processing device 140 can acquire the contrast image from one or more of medical device 110, another medical device, terminal device 130, and storage device 150.

[0040] In some embodiments, the plurality of mask images can include a true mask image. The true mask image can be an image of the target object scanned before the target object receives a contrast agent. In some embodiments, the true mask image can be acquired by medical device 110 scanning the target object. In some embodiments, the true mask image can be acquired by another medical device scanning the target object. In some embodiments, processing device 140 can acquire the true mask image from one or more of medical device 110, another medical device, terminal device 130, and storage device 150.

[0041] In some embodiments, the contrast image and the true mask image are of the same region of interest of the same target object scanned by the same medical device. In some embodiments, the contrast image and the true mask image can have a time interval between their scanning times, which can be a few minutes, such as 2 minutes, 3.5 minutes, 5 minutes, 7 minutes, or 10 minutes, etc.

[0042] In some embodiments, the processing device 140 can pre-process the contrast image and the real mask image. The pre-processing can include one or more of log transformation, noise reduction processing, or regularization processing on the images.

[0043] In some embodiments, the plurality of mask images can include a fitted mask image. The fitted mask image can be an image obtained after pixel displacement of the mask images (including the real mask image and the fitted mask image) based on the contrast image. The process of obtaining the fitted mask image can refer to Figure 6 .

[0044] In some embodiments, the plurality of mask images can include both the real mask image and the fitted mask image.

[0045] In step 320, the processing device 140 can combine at least two mask images in the plurality of mask images based on the contrast image to obtain a combined mask image. In some embodiments, step 320 can be performed by the obtaining module 210 in the system 200.

[0046] In some embodiments, the processing device 140 can obtain a physiological parameter of the target object when the contrast image and the plurality of mask images are acquired, and determine the at least two mask images based on the physiological parameter of the target object. In some embodiments, the physiological parameter can be a physiological parameter related to heartbeat or respiration. In some embodiments, the physiological parameter can be derived from one or more of a photoplethysmography (PPG) signal, an electrocardiography (ECG) signal, or a ballistocardiogram (BCG) signal, a respiratory motion, and the like. In some embodiments, the physiological parameter can include an amplitude, a phase, a feature point position (e.g., a Q wave, an R wave, and an S wave in an ECG signal, a wave peak and a wave trough in a PPG signal, and the like), a period, and the like of one or more of the physiological signals. In some embodiments, when the physiological signal of the target object is acquired, the device for acquiring the physiological signal can be placed at a position outside the scanning area of the medical device 110, such as a finger, a wrist, an ankle, an arm, a leg, and the like of the target object.

[0047] In some embodiments, the processing device 140 can obtain the physiological parameter of the target object corresponding to the acquisition of the contrast image. The processing device can also obtain the physiological parameter of the target object corresponding to the acquisition of each of the plurality of mask images. The processing device 140 can determine at least two mask images according to the similarity of the physiological parameter of the target object corresponding to the acquisition of the contrast image and the acquisition of each of the plurality of mask images. In some embodiments, the processing device 140 can determine at least two mask images according to the similarity of one or more of the physiological signal parameters of the target object corresponding to the acquisition of the contrast image and the acquisition of each of the plurality of mask images, such as the amplitude of the physiological signal, the position of the cycle, the distance from the position of the feature point, etc.

[0048] In some embodiments, the processing device 140 can determine a mask image with the highest similarity according to the similarity of the physiological signal parameters of the target object corresponding to the acquisition of the contrast image and the acquisition of the plurality of mask images. In some embodiments, the mask image with the highest similarity can be directly used as the mask image without combination.

[0049] In some embodiments, the processing device 140 can determine at least two mask images according to the similarity of the physiological signal parameters of the target object corresponding to the acquisition of the contrast image and the acquisition of each of the plurality of mask images and a similarity threshold, for example, the mask images greater than the similarity threshold are used as the at least two mask images. In some embodiments, the processing device 140 can sort the similarity of the physiological signal parameters of the target object corresponding to the acquisition of the contrast image and the acquisition of each of the plurality of mask images in descending order, and the first N (for example, a natural number within 2-10) mask images can be selected as the at least two mask images. In some embodiments, the processing device 140 can combine the at least two mask images to obtain a combined mask image. For the process of obtaining the combined mask image, please refer to Figure 5 .

[0050] In some embodiments, the processing device 140 can obtain the similarity of each of the plurality of mask images and the contrast image, and determine the at least two mask images based on the similarity.

[0051] In some embodiments, the processing device 140 can use the method of determining the similarity between images, such as maximum subtraction histogram energy, gradient similarity method, mutual information, Person correlation coefficient, etc., to determine the similarity of each of the plurality of mask images and the contrast image.

[0052] In some embodiments, the processing device 140 can determine the similarity between each of the plurality of mask images and the contrast image according to the maximum subtraction histogram energy. The processing device 140 can convert the contrast image and each of the plurality of mask images into a gray scale histogram. The horizontal axis of the gray scale histogram is the gray scale value (e.g., 0-255), and the vertical axis is the number or proportion of pixels with the same gray scale value. The processing device 140 can determine the similarity between each of the plurality of mask images and the contrast image according to the similarity between the gray scale histogram converted from the contrast image and the gray scale histogram converted from each of the plurality of mask images.

[0053] In some embodiments, the processing device 140 can determine the similarity between each of the plurality of mask images and the contrast image according to the gradient similarity method. For each of the plurality of mask images, the processing device 140 can subtract the pixel matrix corresponding to the mask image from the pixel matrix corresponding to the contrast image to obtain a pixel matrix corresponding to a template image, such as matrix [M, N]. The processing device 140 can process the pixel matrix [M, N] corresponding to the template image with a step size of n (e.g., a natural number within 1-10). In some embodiments, when the step size is 1, the processing device 140 can subtract the pixel value (e.g., the gray scale value) of the Nth column (or the Mth row) from the pixel value of the N-1th column (or the M-1th row), i.e., subtract the pixel value (e.g., the gray scale value) of the adjacent previous column (or the adjacent previous row) from the pixel value (e.g., the gray scale value) of the adjacent next column (or the adjacent next row) to obtain a gradient matrix of [M-1, N-1]. The processing device 140 can accumulate the gradient matrix [M-1, N-1] to obtain the gradient value of the template image. Further, the processing device 140 can determine the similarity between each of the plurality of mask images and the contrast image according to the gradient value of the template image obtained by subtracting the contrast image from each of the plurality of mask images.

[0054] In some embodiments, the processing device 140 can determine at least two mask images based on the similarity between each of the plurality of mask images and the contrast image. In some embodiments, the processing device 140 can sort the similarity between each of the plurality of mask images and the contrast image in descending order, and can select the first N (e.g., a natural number within 2-10) mask images as the at least two mask images. In some embodiments, the processing device 140 can obtain a similarity threshold, and can select the plurality of mask images with a similarity between the mask image and the contrast image greater than the similarity threshold as the at least two mask images. In some embodiments, the processing device 140 can combine the at least two mask images to obtain a combined mask image. The process of obtaining the combined mask image is described in detail in Figure 5 .

[0055] In some embodiments, before determining the similarity of each of the plurality of mask images to the contrast image, the processing device 140 can pre-process each of the plurality of mask images and the contrast image. The pre-processing can include one or more of log transformation, noise reduction processing, or regularization processing on the images.

[0056] In some embodiments, the processing device 140 can obtain the low frequency component and the high frequency component of each of the at least two mask images. The processing device 140 can combine the low frequency component of at least one of the at least two mask images and the high frequency component of each of the at least two mask images to obtain a combined mask image. In some embodiments, the low frequency component can be related to the portion of the mask image other than the region of interest (e.g., bone, muscle). The high frequency component can be related to the portion of the region of interest (e.g., vessel edge). In some embodiments, the high frequency component can also be related to artifacts caused by motion.

[0057] In some embodiments, the processing device 140 can filter each of the at least two mask images with different bandwidths to obtain a base frequency portion and a high frequency portion corresponding to the portion of the region of interest (e.g., vessel). The base frequency portion can be the low frequency component of the mask image and the high frequency portion can be the high frequency component of the mask image. The processing device 140 can combine the base frequency portion of at least one of the at least two mask images and the high frequency portion of each of the at least two mask images to obtain a combined mask image. In some embodiments, the processing device 140 can obtain the base frequency portion of the mask image with the highest similarity to the contrast image as the base frequency portion of the combined mask image. The processing device 140 can also obtain the high frequency portion of each of the at least two mask images. The processing device 140 can combine the base frequency portion of the combined mask image and the high frequency portion of each of the at least two mask images to obtain a combined mask image. In some embodiments, the processing device 140 can combine the base frequency portion of at least two of the at least two mask images (which can be determined according to the similarity or the corresponding motion phase) as the base frequency portion of the combined mask image. The processing device 140 can combine the high frequency portion of each of the at least two mask images as the high frequency portion of the combined mask image. The processing device 140 can combine the base frequency portion of the combined mask image and the high frequency portion of the combined mask image to obtain a combined mask image. In some embodiments, the processing device 140 can combine the base frequency portion and the high frequency portion of each of the at least two mask images to obtain a combined mask image.

[0058] In some embodiments, the processing device 140 can perform pyramid decomposition on each of the at least two mask images. Each time the pyramid decomposition is performed, the image can be divided into a level, for example, when a three-level pyramid decomposition is performed, the mask image can be decomposed three times, and after each time of pyramid decomposition, a high frequency component of a first level and a low frequency component of the first level can be obtained, the low frequency component of the first level can be taken as an input image for the second time of pyramid decomposition, and the process can be repeated, and the low frequency component of each level can be taken as an input image for the next level of pyramid decomposition, and after three times of pyramid decomposition, three levels of high frequency components and three levels of low frequency components of the mask image can be obtained. In some embodiments, the pyramid decomposition can be four levels or five levels. In this case, the high frequency component of the highest level of pyramid decomposition can better reflect the edge features of the region of interest (e.g., blood vessels).

[0059] The processing device 140 can obtain the first level low frequency component of the mask image with the highest similarity to the contrast image among the at least two mask images. In some embodiments, the first level low frequency component of the mask image with the highest similarity to the contrast image among the at least two mask images can be taken as a base frequency part of the combined mask image. In some embodiments, the combination of the first level low frequency components of at least two mask images (which can be determined according to the similarity or the corresponding motion phase) among the at least two mask images can be taken as the base frequency part of the combined mask image. In some embodiments, the combination of the first level low frequency components of each of the at least two mask images can be taken as the base frequency part of the combined mask image.

[0060] The processing device 140 can obtain a certain level high frequency component of each of the at least two mask images. The processing device 140 can combine the certain level high frequency components of the at least two mask images to obtain a combined high frequency component. In some embodiments, the certain level high frequency component of each of the at least two mask images can be the high frequency component of the same level. In some embodiments, the certain level high frequency component of the at least two mask images can be the high frequency component that best reflects the edge features of the region of interest. In some embodiments, the certain level high frequency component of the at least two mask images can be the high frequency component of the fourth level or the fifth level of pyramid decomposition. In some embodiments, based on the similarity of each of the at least two mask images to the contrast image, the processing device 140 can determine a weight coefficient of the certain level high frequency component of each of the at least two mask images. The higher the similarity, the greater the weight coefficient.

[0061] The processing device 140 can combine the base frequency part of the combined mask image and the combined high frequency component to obtain a combined mask image.

[0062] Figure 4is an exemplary flowchart of an image subtraction process according to some embodiments of the present application. In some embodiments, flow 400 can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions run on a processing device to simulate the hardware), etc., or any combination thereof. In some embodiments, flow 400 can be implemented as a set of instructions stored in storage device 150 (e.g., an application program). Processing device 140 and / or Figure 2 modules in system 200 can execute the set of instructions and cause processing device 140 and / or the modules to perform flow 400 when the instructions are executed. The operations of the illustrated processes presented below are intended to be illustrative. In some embodiments, flow 400 can be accomplished with one or more additional operations not described, and / or without one or more of the Figure 4 operations discussed in this application. Additionally, the order in which the operations of the processes are illustrated is not limiting.

[0063] In step 410, processing device 140 can obtain a contrast image. In some embodiments, step 410 can be performed by obtaining module 210 in system 200.

[0064] The contrast image can be an image of the target object scanned after the target object receives a contrast agent. In some embodiments, the contrast image and the real mask image are obtained by the same medical device scanning the same region of interest of the same target object. In some embodiments, the scanning time of the contrast image and the real mask image can have a time interval, which can be several minutes, such as 2 minutes, 3.5 minutes, 5 minutes, 7 minutes, or 10 minutes, etc.

[0065] In some embodiments, the contrast image can be obtained by medical device 110 scanning the target object. In some embodiments, the contrast image can be obtained by other medical devices scanning the target object. In some embodiments, processing device 140 can obtain the contrast image from one or more of medical device 110, other medical devices, terminal device 130, and storage device 150.

[0066] In some embodiments, processing device 140 can pre-process the contrast image before the subtraction operation. The pre-processing can include one or more of log transformation, noise reduction processing, or regularization processing on the image.

[0067] In step 420, processing device 140 can obtain a combined mask image. In some embodiments, step 420 can be performed by obtaining module 210 in system 200.

[0068] In some embodiments, the combined mask image can be a mask image obtained by combining at least two mask images. In some embodiments, the processing device 140 can extract, from each of the at least two mask images, one or more of, but not limited to, intensity, gradient, pattern, texture, contour, noise map, motion content of the layer, etc. In some embodiments, the combining of the at least two mask images by the processing device 140 can include, but not limited to, superimposing, adding, subtracting, multiplying, dividing, filtering, and combining the above-mentioned extracted parts.

[0069] In some embodiments, the processing device 140 can perform one or more levels of pyramid decomposition on each of the at least two mask images to obtain one low-frequency component and multiple high-frequency components, and then combine the low-frequency component and / or the high-frequency component in each of the at least two mask images to obtain the combined mask image. The specific combining process can refer to Figure 5 .

[0070] In some embodiments, the processing device 140 can perform filtering with different bandwidths on each of the at least two mask images to obtain a base frequency part and a high frequency part corresponding to a region of interest (e.g. blood vessels), and then combine the base frequency part and / or the high frequency part in each of the at least two mask images to obtain the combined mask image.

[0071] In step 430, the processing device 140 can perform pixel displacement on the combined mask image based on the contrast image to match the contrast image. In some embodiments, step 430 can be performed by the subtraction module 210 in the system 200.

[0072] In some embodiments, the processing device 140 can obtain corresponding feature points on the combined mask image and the contrast image. In some embodiments, the feature points can be anatomical feature points (e.g. pixel points on the edge of blood vessels, branch points of blood vessel branches). In some embodiments, the processing device 140 can divide the combined mask image and the contrast image into grid blocks, for example, 6*9 grid blocks. In this case, the feature points can include the center points of the grid blocks, and can also include one or more points on the frame of the grid blocks.

[0073] In some embodiments, the processing device 140 can match the combined mask image to the contrast image using the feature points described above as control points. In the matching process, the processing device 140 can obtain the displacement of each control point, and can obtain the displacement of each pixel point on the combined mask image based on the displacement of each control point described above, and warp the entire image based on this, such as affine transformation, so as to perform pixel displacement on the entire combined mask image. The more the number of feature points or control points, the more accurate the pixel displacement solved based on the feature points or control points, and the calculation amount will also increase accordingly. The number of feature points or control points can be determined according to actual needs and calculation amount.

[0074] In some embodiments, other pixel displacement algorithms can be used to perform pixel displacement on the combined mask image to match the contrast image, including but not limited to a pixel displacement algorithm based on triangular subdivision or a pixel displacement algorithm based on elastic transformation. In some embodiments, a pixel displacement model can be used to perform pixel displacement on the combined mask image to match the contrast image.

[0075] In step 440, the processing device 140 can obtain a subtraction image by subtracting the combined mask image from the contrast image. In some embodiments, step 440 can be performed by the subtraction module 210 in the system 200.

[0076] In some embodiments, the subtraction image obtained by subtracting the combined mask image from the contrast image can include a region of interest containing contrast agent, such as a blood vessel portion containing contrast agent. In some embodiments, the processing device 140 can post-process the subtraction image. Post-processing includes but is not limited to LUT curve, nonlinear S-shaped curve, vessel enhancement, etc.

[0077] It should be noted that the above description is for convenience of description only, and cannot limit the present application to the scope of the embodiments. For those of ordinary skill in the art, various changes and modifications can be made according to the description of the present application. However, these changes and modifications will not deviate from the scope of the present application. For example, in some embodiments, if the similarity between the combined mask image and the contrast image is high, step 530 can be omitted, i.e. the contrast image is directly subtracted from the combined mask image to obtain the subtraction image, without performing pixel displacement on the combined mask image. This can speed up the process of obtaining the subtraction image.

[0078] Figure 5is an exemplary flowchart of a process for acquiring combined mask images, according to some embodiments of the present application. In some embodiments, flow 500 can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions run on a processing device to simulate the hardware), etc., or any combination thereof. In some embodiments, flow 500 can be implemented as a set of instructions stored in storage device 150 (e.g., an application program). Processing device 140 and / or Figure 2 the modules in system 200 can execute the set of instructions and the processing device 140 and / or modules can be configured to perform flow 500 when the instructions are executed. The operations presented in the illustrated process are intended to be illustrative. In some embodiments, flow 500 can be accomplished with one or more additional operations not described, and / or without one or more of the Figure 5 operations discussed in this application. Additionally, the order in which the operations are presented is not limiting.

[0079] In step 510, processing device 140 can perform one or more levels of pyramid decomposition on each of the at least two mask images, each level of pyramid decomposition resulting in one layer of high frequency components and one layer of low frequency components. In some embodiments, step 510 can be performed by acquisition module 210 in system 200.

[0080] In some embodiments, processing device 140 can use a Gaussian kernel function to downsample each of the at least two mask images, for example, to convert a 1028*1028 pixel matrix to a 256*256 pixel matrix, and multiple downsampling can result in a multi-layer pixel matrix with a pyramid-like distribution.

[0081] In some embodiments, the main steps of pyramid decomposition include low-pass filtering, downsampling, upsampling, band-pass filtering, and each time a pyramid decomposition is completed, the image can be divided into one level, for example, when a three-level pyramid decomposition is performed, the mask image can be decomposed three times, and after each pyramid decomposition, a first layer of high frequency components and a first layer of low frequency components are obtained, the low frequency components of the first level are used as the input image for the second pyramid decomposition, and so on, and the low frequency components of each level are used as the input image for the next level of pyramid decomposition, and after three pyramid decompositions, three levels of high frequency components and three levels of low frequency components of the mask image are obtained.

[0082] In some embodiments, the pyramid decomposition is four or five levels. In this case, the high frequency components of the highest level of pyramid decomposition can better reflect the edge features of the region of interest (e.g., blood vessels).

[0083] In step 520, the processing device 140 can obtain the first layer low frequency component of the mask image that has the highest similarity with the contrast image among the at least two mask images. In some embodiments, step 520 can be performed by the obtaining module 210 in the system 200.

[0084] In some embodiments, the first layer low frequency component of the mask image that has the highest similarity with the contrast image among the at least two mask images can be used as the base frequency part of the combined mask image.

[0085] In step 530, the processing device 140 can obtain a certain layer high frequency component of the at least two mask images. In some embodiments, the certain layer high frequency component of the at least two mask images can be the same layer high frequency component of each of the two mask images. In some embodiments, step 530 can be performed by the obtaining module 210 in the system 200.

[0086] In some embodiments, the certain layer high frequency component of the at least two mask images can be the high frequency component that best reflects the edge features of the region of interest. In some embodiments, the certain layer high frequency component of the at least two mask images can be the high frequency component of the 4th layer or the 5th layer of the pyramid decomposition.

[0087] In step 540, the processing device 140 can combine the certain layer high frequency component of the at least two mask images to obtain a combined high frequency component. In some embodiments, step 540 can be performed by the obtaining module 210 in the system 200.

[0088] In some embodiments, based on the similarity of each of the at least two mask images with the contrast image, the processing device 140 can determine a weight coefficient of the certain layer high frequency component of each of the at least two mask images. The higher the similarity, the greater the weight coefficient. In some embodiments, the certain layer high frequency component of each of the at least two mask images can be the high frequency component of the same layer (e.g. both the 4th layer or the 5th layer).

[0089] In some embodiments, the at least two mask images can be two mask images, a first frame mask image and a second frame mask image, and the weight coefficients of the two mask images are P and Q respectively. The combined high frequency component can be the sum of P / (P+Q) of the certain layer high frequency component of the first frame mask image and Q / (P+Q) of the certain layer high frequency component of the second frame mask image. In some embodiments, the at least two mask images can be more than two mask images, and the calculation of the combined high frequency component is similar to the above.

[0090] In step 550, the processing device 140 can combine the first layer low frequency components and the combined high frequency components to obtain the combined mask image. In some embodiments, step 550 can be performed by the obtaining module 210 in the system 200.

[0091] Since the combined mask image is based on the low frequency components of the most similar mask images and combined with the combined high frequency components of the at least two mask images that have high similarity and can best reflect the edge features of the region of interest (the portion containing contrast agent in the contrast process), the combined mask image is a mask image that is very similar to the contrast image, and can effectively reduce the influence of motion artifacts between the contrast image and the mask image on the subtraction image.

[0092] Figure 6 is an exemplary flowchart of a process for obtaining a fitted mask image according to some embodiments of the present application. In some embodiments, the flow 600 can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions run on a processing device to simulate hardware), etc., or any combination thereof. In some embodiments, the flow 600 can be implemented as a set of instructions stored in the storage device 150 (e.g., an application program). The processing device 140 and / or Figure 2 the modules in the system 200 can execute the set of instructions and the processing device 140 and / or the modules can be configured to perform the flow 600 when the instructions are executed. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the flow 600 can be accomplished with one or more additional operations not described, and / or without one or more of the Figure 6 operations discussed in this application. Additionally, the order in which the operations are presented is not limiting.

[0093] In step 610, the processing device 140 can obtain a fitted mask image. In some embodiments, step 610 can be performed by the obtaining module 210 in the system 200.

[0094] In some embodiments, the fitted mask image can be a real mask image. In some embodiments, the fitted mask image can be a fitted mask image.

[0095] In step 620, the processing device 140 can obtain a frame of fitted contrast image. In some embodiments, step 620 can be performed by the obtaining module 210 in the system 200.

[0096] In some embodiments, the fitted contrast image can be used by the fitted mask image to fit a fitted mask image corresponding to the fitted contrast image according to the fitted contrast image.

[0097] In step 630, the processing device 140 can perform pixel displacement on the one fitting mask image to match the one fitting contrast image based on the one fitting contrast image to obtain the fitting mask image. In some embodiments, step 630 can be performed by the acquisition module 210 in the system 200.

[0098] In some embodiments, the processing device 140 can obtain corresponding feature points on the one fitting mask image and the one fitting contrast image. In some embodiments, the feature points can be anatomical feature points (e.g., pixel points on the edge of a blood vessel, branch points of a blood vessel branch). In some embodiments, the processing device 140 can divide the one fitting mask image and the one fitting contrast image into grid blocks, for example, 6*9 grid blocks. In this case, the feature points can include the center points of the grid blocks, and can also include one or more points on the frame of the grid blocks.

[0099] In some embodiments, the processing device 140 can match the one fitting mask image to the one fitting contrast image using the above-mentioned feature points as control points. In the matching process, the processing device 140 can obtain the displacement of each control point, and can obtain the displacement of each pixel point on the one fitting mask image based on the displacement of each control point, and based on this, the entire image is warped, for example, affine transformation, so as to perform pixel displacement on the entire image of the one fitting mask image. The more the number of feature points or control points, the more accurate the pixel displacement solved based on the feature points or control points, and the calculation amount will also increase accordingly. The number of feature points or control points can be determined according to actual needs and calculation amount.

[0100] In some embodiments, other pixel displacement methods can be used to perform pixel displacement on the combined mask image to match the contrast image. For example, using a pixel displacement model to perform pixel displacement on the combined mask image to match the contrast image.

[0101] In some embodiments, multiple fitting mask images can be iteratively generated based on one real mask image and multiple contrast images obtained by continuous scanning. For example, after obtaining the first contrast image, the one real mask image can be pixel-displaced based on the first contrast image to obtain the first fitting mask image. Then, after obtaining the second contrast image, the first fitting mask image can be pixel-displaced based on the second contrast image to obtain the second fitting mask image. In this way, after obtaining the Nth contrast image, the N-1th fitting mask image can be pixel-displaced based on the Nth contrast image to obtain the Nth fitting mask image.

[0102] In some embodiments, the first fitted mask image to the Nth fitted mask image can constitute a candidate mask template set. In some embodiments, the candidate mask template set can further include the one real real mask image described above. When the (N+1)th frame of the angiogram image is acquired, the processing device 140 can determine at least two mask images from the candidate mask template set based on the similarity to generate a combined mask image.

[0103] Figure 7 is an exemplary flowchart of a process for acquiring a combined mask image according to some embodiments of the present application. In some embodiments, the flow 700 can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (such as instructions run on a processing device to simulate the hardware), etc., or any combination thereof. In some embodiments, the flow 700 can be implemented as a set of instructions stored in the storage device 150 (e.g., an application program). The processing device 140 and / or Figure 2 the modules in the processing device 140 can execute the set of instructions and, as a result, the processing device 140 and / or the modules can be configured to perform the flow 700. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the flow 700 can be accomplished with one or more additional operations not described, and / or without one or more of the Figure 7 operations illustrated. For example, one or more of the operations described below can be eliminated, and / or one or more other operations can be added in various places as desired. Similarly, elements of the described operations can be performed in a different order than other operations described herein.

[0104] The processing device 140 can acquire three mask images (A, B and C) and one angiogram image (D). The similarities N1, N2 and N3 of the three mask images (A, B and C) and the angiogram image (D) are 0.7, 0.8 and 0.9 respectively. The processing device 140 can select two mask images B and C with high similarity and perform pyramid decomposition on them to obtain the high frequency component h1 and the base frequency component f1 of the mask image B, and the high frequency component h2 and the base frequency component f2 of the mask image C. The processing device 140 can select the base frequency component f2 of the mask image C with the highest similarity as the base frequency component of the combined mask image, combine the weights of the high frequency component h1 of the mask image B and the high frequency component h2 of the mask image C as the high frequency component of the combined mask image, and then combine the base frequency component and the high frequency component to obtain the combined mask image. Specifically, the combined mask image can be obtained according to the formula f2+N3 / (N2+N3)*h2+N2 / (N2+N3)*h1. The processing device 140 can perform pixel displacement on the combined mask image based on the angiogram image D to obtain a new mask image for subtraction.

[0105] Figures 8-10 are subtraction images obtained using different methods. Among them, Figure 8The subtraction image in the image is obtained by directly subtracting the mask image from the contrast image. Figure 9 The subtraction image is obtained by subtracting the combined mask image from the contrast image. Details on obtaining the combined mask image can be found in this application. Figures 3-7 The example shown. Figure 10 The subtraction image in the image is obtained by pixel-shifting the composite mask image based on the contrast image, and then subtracting the pixel-shifted composite mask image from the contrast image. Figure 8 and Figure 9 Compared to the subtraction image obtained from the masked image, it is obvious that the subtraction image obtained from the combined masked image has fewer motion artifacts. Figure 9 and Figure 10 The subtraction image obtained after pixel shifting of the combined mask image further reduces motion artifacts.

[0106] Figures 11-13 This is another set of subtraction images obtained using different methods. Figure 11 The subtraction image in the image is obtained by directly subtracting the mask image from the contrast image. Figure 12 The subtraction image in the image is obtained by pixel-shifting the mask image based on the contrast image, and then subtracting the pixel-shifted mask image from the contrast image. Figure 13 The subtraction image is obtained by subtracting the combined mask image from the contrast image. Details on obtaining the combined mask image can be found in this application. Figures 3-7 The illustrated embodiment. According to... Figure 12 and Figure 13 Compared to the subtraction image obtained by pixel displacement of the mask image, it is obvious that the subtraction image obtained by combining the mask images has fewer motion artifacts.

[0107] It should be noted that the above description is for convenience only and should not limit this application to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principle of the system, can make various modifications and changes in form and detail to the application fields of the above methods and systems without departing from this principle.

[0108] The beneficial effects that the embodiments of this application may bring include, but are not limited to: (1) This application proposes a method for obtaining combined mask images, which can combine at least two mask images based on the angiographic image to reduce the influence of motion artifacts on the subtraction image; (2) Multiple fitted mask images can be obtained based on one real mask image and multiple angiographic images, reducing the time required to obtain multiple real masks. It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced can be any one or a combination of the above, or any other possible beneficial effects.

[0109] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0110] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0111] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may be embodied as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0112] Furthermore, unless expressly stated in the claims, the order of elements and sequences processed in this application, the use of numbers and letters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although some inventive embodiments that are currently considered useful have been discussed through various examples in the foregoing disclosure, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. Rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the system components described above can be implemented by hardware devices, they can also be implemented solely by software solutions, such as installing the described system on existing servers or mobile devices.

[0113] For simplicity and to facilitate understanding of the terms used herein, a description of an embodiment of the application is sometimes referred to in the foregoing description as a single instance of a feature, drawing, or description of an embodiment. However, this method of disclosure is not intended to limit the scope of the application to only a single embodiment of the features described. Indeed, many of the features described herein are applicable to a wide variety of embodiments and can be used in combinations with each other in embodiments of the application.

[0114] Each patent, patent application, publication, document, article, book, instruction manual, and / or other material cited in this application is hereby incorporated by reference in its entirety, except to the extent that the incorporation of a document or material would be inconsistent with the patenting of this application or would otherwise render this application invalid. To the extent that any meaning or definition of a term in any document incorporated by reference in this application differs from the meaning or definition of the same term in this application, the meaning or definition assigned to that term in this application shall control. It is specifically intended that any document or material cited in this application shall not be incorporated by reference in the event that any conflicting material in such document or material is incompatible with this application.

[0115] Finally, it should be understood that the embodiments described herein are intended to be illustrative only and that the scope of the application should not be limited to specific embodiments presented herein. In other words, although the present application has been described in considerable detail with reference to certain embodiments thereof, other versions are possible that will become apparent to those skilled in the art upon reading the description herein. Configurations, steps, and / or elements in the above-described embodiments can be combined, eliminated, modified, or taken during different stages or phases, and can be substituted for one another. Refinements, combinations, and / or modifications can be technically convenient or desirable in particular circumstances. Accordingly, the application should not be limited to only those embodiments described herein, but should be given broad coverage.

Claims

1. An image subtraction method, characterized in that, The method includes: Acquire contrast images and multiple masked images; Based on the imaging image, at least two mask images are obtained from the plurality of mask images; Each of the at least two masked images is subjected to one or more levels of pyramid decomposition, and each level of pyramid decomposition yields a high-frequency component and a low-frequency component. Obtain the first layer low-frequency component of the mask image that has the highest similarity to the imaging image among the at least two mask images; Obtain a high-frequency component of a certain layer from the at least two masked images; Combine the high-frequency components of a certain layer of the at least two masked images to obtain the combined high-frequency components; The combined low-frequency component of the first layer and the combined high-frequency component are combined to obtain a combined mask image.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the similarity between each of the multiple mask images and the imaging image; The at least two masked images are determined based on the similarity.

3. The method according to claim 1, characterized in that, The pyramid is divided into 4 or 5 levels.

4. The method according to claim 1, characterized in that, The high-frequency component of a certain layer of the at least two masked images is the high-frequency component of the highest layer.

5. The method according to claim 1, characterized in that, The multiple mask images include fitted mask images.

6. The method according to claim 5, characterized in that, The fitted mask is obtained according to the following process: Obtain a fitting mask image, wherein the fitting mask image is either a real mask image or a fitted mask image; Obtain a single frame of the imaging image used for fitting; Based on the one frame of the imaging image used for fitting, the pixel shift of the one frame of the imaging image used for fitting is performed to match the one frame of the imaging image used for fitting, thereby obtaining the fitting mask image.

7. The method according to claim 1, characterized in that, The method further includes: The subtracted image is obtained by subtracting the combined mask image from the contrast image.

8. The method according to claim 7, characterized in that, Before subtracting the combined mask image from the contrast image, the method further includes: Based on the contrast image, the combined mask image is pixel-shifted to match the contrast image.

9. A system for image subtraction, characterized in that, The system includes at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is configured to execute at least a portion of the computer instructions to implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium storing computer instructions that, when read by a computer, execute the method as described in any one of claims 1-8.

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