A speckle image classification system, electronic device and readable storage medium

Abstract

The present disclosure relates to a speckle image classification system, an electronic device and a readable storage medium, the speckle image classification system comprising a handheld image acquisition device and an image processing device, which are respectively used for acquiring a skin surface of a target human body region to obtain a speckle image to be classified, and segmenting the speckle image to obtain a sub-image set composed of a plurality of sub-images of a preset size, and inputting the sub-image set into a trained classifier to perform image classification, so as to obtain a classification result corresponding to the speckle image, and the classification result is used to determine the health condition of the target human body region. The present disclosure can realize flexible, convenient and fast acquisition of speckle images through a handheld device, and can extract features in the speckle images through a trained classifier to perform classification, so as to determine the health condition of the target human body region, and realize convenient health diagnosis according to the structural imaging of skin tissue.

Description

Technical Field

[0001] This disclosure relates to the field of imaging, and more particularly to a speckle image classification system, electronic device, and readable storage medium. Background Technology

[0002] Currently, laser speckle imaging technology is used for dynamic blood flow monitoring. However, the information contained in speckle images actually extends far beyond static and dynamic tissue information; it also includes structural information within the tissue. But because there is no linear correspondence between the morphology of laser speckle and the morphology of tissue structure, it is difficult to intuitively extract tissue structural information from speckle images and further apply it to fields such as health monitoring and clinical medicine. Summary of the Invention

[0003] In view of this, this disclosure proposes a speckle image classification system, electronic device, and readable storage medium, which are designed to classify speckle images based on tissue structure information in the images.

[0004] According to a first aspect of this disclosure, a speckle image classification system is provided, the system comprising:

[0005] A handheld image acquisition device is used to acquire speckle images of skin tissue in a target human body area to obtain images to be classified.

[0006] An image processing device is used to segment the speckle image to obtain a set of sub-images consisting of multiple sub-images of preset sizes, and input the set of sub-images into a trained classifier for image classification to obtain a classification result corresponding to the speckle image. The classification result is used to determine the health status of the target human body region.

[0007] In one possible implementation, the handheld image acquisition device includes:

[0008] The laser module is used to generate and emit laser light into the skin tissue;

[0009] The image acquisition module is used to acquire speckle images formed by the scattering of the laser through the skin tissue.

[0010] In one possible implementation, the laser module includes a laser source, an optical fiber, a collimator, a polarizer, and a reflector.

[0011] In one possible implementation, the training process of the classifier includes:

[0012] Acquire speckle images of multiple samples;

[0013] Each sample speckle image is segmented into multiple sub-images of a preset size to obtain a sample image set;

[0014] The classifier is trained based on multiple sets of sample images and the annotation results corresponding to each set of sample images.

[0015] In one possible implementation, the training process of the classifier further includes:

[0016] The multiple sample speckle images are subjected to noise reduction and normalization processing.

[0017] In one possible implementation, training the classifier based on a plurality of sample image sets and the annotation results corresponding to each sample image set includes:

[0018] Each of the sample image sets is input into the classifier to obtain the corresponding prediction result;

[0019] The classifier loss is determined based on the difference between the prediction result and the annotation result corresponding to each sample image;

[0020] Adjust the classifier parameters based on the classifier loss.

[0021] According to a second aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to, when executing the instructions stored in the memory, implement the method performed by the image processing apparatus in the above system.

[0022] According to a third aspect of this disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, implement the method executed by the image processing apparatus in the above-described system.

[0023] According to a fourth aspect of this disclosure, a computer program product is provided, including computer-readable code or a non-volatile computer-readable storage medium carrying the computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the method executed by the image processing device in the above system.

[0024] In this embodiment, the speckle image classification system includes a handheld image acquisition device and an image processing device. The former acquires speckle images of the skin surface of a target human body region to be classified, and the latter segments the speckle images to obtain a set of sub-images composed of multiple sub-images of preset sizes. The sub-image set is then input into a trained classifier for image classification to obtain a classification result corresponding to the speckle image. This classification result is used to determine the health status of the target human body region. This disclosure enables flexible, convenient, and rapid acquisition of speckle images using a handheld device, and the extraction of features from the speckle images by a trained classifier for classification to determine the health status of the target human body region, thus facilitating health diagnosis based on structural imaging of skin tissue.

[0025] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0026] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0027] Figure 1 A schematic diagram of a speckle image classification system according to an embodiment of the present disclosure is shown;

[0028] Figure 2 A schematic diagram of a handheld image acquisition device according to an embodiment of the present disclosure is shown;

[0029] Figure 3 A signaling flowchart of an image classification process according to an embodiment of the present disclosure is shown;

[0030] Figure 4 A schematic diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0031] Figure 5 A schematic diagram of another electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0032] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0033] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0034] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0035] Figure 1 A schematic diagram of a speckle image classification system according to an embodiment of the present disclosure is shown. Figure 1 As shown, the speckle image classification system of this disclosure may include a handheld image acquisition device 10 and an image processing device 11.

[0036] In one possible implementation, the handheld image acquisition device 10 is used to acquire skin tissue from a target human body region to obtain a speckle image to be classified. The target human body region can be any area requiring health detection, such as the back region, face region, or limb region. The speckle image is obtained by acquiring the scattered light reflected back from the skin surface of the target human body region after laser irradiation. Specifically, after the laser irradiates the skin surface of the target human body region, the incident light is backscattered by scattering particles in the surface layer of human tissue within the skin. Due to the different optical path differences between the backscattered light from different tissues reaching the camera imaging surface, random interference phenomena are formed between the different scattered light rays on the imaging surface, which manifests as a particle pattern of varying brightness in spatial distribution, thus obtaining the speckle image. Optionally, the tissue undergoing backscattering can be melanocytes in the epidermis, etc.

[0037] Optionally, the handheld image acquisition device 10 can be a handheld integrated device, which may include two parts: a laser module and an image acquisition module. The laser module is used to generate and emit laser light onto skin tissue. The image acquisition module is used to acquire speckle images formed by the scattering of the laser light through the skin tissue. To ensure the quality and effect of the laser emitted onto the skin tissue, the laser module may further include a laser source, optical fiber, collimator, polarizer, and reflector. The image acquisition module may include a polarizer and an industrial camera.

[0038] Figure 2 A schematic diagram of a handheld image acquisition device according to an embodiment of the present disclosure is shown. Figure 2As shown, the handheld image acquisition device 10 of this embodiment may include a laser module and an image acquisition module. The laser module includes a laser source 20, an optical fiber 21, a collimator 22, a polarizer 23, and a reflector 24. The laser source 20 generates laser light when it is turned on. The optical fiber 21 acts as a light transmission tool to guide the laser light generated by the laser source 20 into the collimator 22. The collimator 22 converts the divergent light guided by the optical fiber 21 into parallel light through collimation. The polarizer 23 then shields the parallel light from interfering rays, retaining the desired beam. The reflector 24 reflects the beam output by the polarizer 23 to the skin tissue of the target human body area. The image acquisition module includes a polarizer 23 and an industrial camera 25. The polarizer 23 in the image acquisition module filters out interfering rays in the light returned from the skin tissue, and the industrial camera 25 acquires the light returned through the polarizer 23 to obtain a speckle image.

[0039] Optionally, the handheld image acquisition device 10, or its image acquisition module, may further include a communication unit for transmitting the acquired speckle image to the image processing device 11. Furthermore, the handheld image acquisition device 10 may also pre-set the format of the acquired speckle image, for example, acquiring an image with exposure within a preset range and a pixel size of 1024×1280 as the speckle image, and deleting the image if the acquired image format does not meet the preset format requirements.

[0040] In one possible implementation, the image processing device 11 in the speckle image classification system can be connected to the handheld image acquisition device 10 via wired or wireless communication. After the handheld image acquisition device 10 acquires a speckle image, it performs image processing on the speckle image. Optionally, the image processing device 11 may perform image processing on the speckle image by first segmenting the speckle image to obtain a set of sub-images composed of multiple sub-images of preset sizes, and then inputting the set of sub-images into a trained classifier for image classification to obtain the classification result corresponding to the speckle image. The classification result can be used to determine the health status of the target human body region. For example, the classification result can be a label that directly represents the health status of the target human body region, such as "excellent," "good," and "poor," or a label that represents the specific health status of the target human body region, such as "normal," "nodules present," and "cysts present," or a label that indirectly represents the health status of the target human body region, such as "no suspicious areas" or "suspicious areas present." Alternatively, the classification result can also be a numerical value representing the health status, such as an integer value between "0" and "10," where a larger value indicates a healthier target human body region.

[0041] Optionally, the preset size determined by the image processing device 11 can be 128×128, meaning the electronic device can segment the speckle image to obtain multiple sub-images of size 128×128, forming a set of sub-images. Before segmenting the speckle image, to improve processing efficiency, denoising and normalization can be performed on the speckle image. This denoising process can be any denoising method, such as denoising based on median filters, adaptive Wiener filters, etc. Alternatively, it can be a denoising method using models such as non-local self-similarity (NSS), sparse models, gradient models, and Markov random field (MRF) models, or a denoising method based on machine learning. The normalization process is used to unify the data in the speckle image, and can convert each pixel value in the speckle image into a value between 0 and 1 through linear normalization, standard deviation normalization, and some non-linear normalization methods.

[0042] Furthermore, the image processing device 11 can perform image classification based on the sub-image set using a trained classifier to obtain the corresponding classification result. The classifier can be any type of classifier, such as a regression classifier, a Naive Bayes network, a rule-based classifier, a distance-based classifier, or other statistical or structured classifiers. Optionally, the classifier can be a convolutional neural network consisting of 4-5 convolutional layers with a stride of 2-5, where each pair of convolutional layers can be connected by an activation function.

[0043] In one possible implementation, the image processing apparatus 11 can be an electronic device such as a terminal device or a server capable of image processing. The terminal device can be any fixed or mobile terminal, such as user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, or wearable device. The server can be a single server or a server cluster consisting of multiple servers. The electronic device can implement the image processing process of this embodiment by having a processor call computer-readable instructions stored in memory.

[0044] Optionally, the classifier training process can be completed directly in the image processing device 11, or trained in other devices and then deployed in the image processing device 11. This training process may include first acquiring multiple sample speckle images, then segmenting each sample speckle image into multiple sub-images of preset sizes to obtain a set of sample images. The classifier is then trained based on the multiple sample image sets and the annotation results corresponding to each sample image set. The annotation results corresponding to each sample image set represent the actual classification result of the sample speckle image, characterizing the actual health status of the human body region corresponding to that sample speckle image. To improve the accuracy of the trained classifier, the training process may further include performing noise reduction and normalization processing on the multiple sample speckle images before segmenting them. This noise reduction and normalization processing can be any feasible processing method.

[0045] Furthermore, the process of training a classifier based on multiple sets of sample images and the corresponding annotations for each set can be described as follows: Each set of sample images is input into the classifier to obtain a corresponding prediction result. The classifier loss is then determined based on the difference between the prediction and annotation results for each sample image, and the classifier parameters are adjusted accordingly. This classifier loss can be determined based on the mean, variance, and other values ​​of the difference between the prediction and annotation results for each sample image. The training process ends when the classifier loss meets a preset convergence condition.

[0046] Optionally, after inputting the sub-image set into the classifier to obtain the classification result, the image processing device 11 can return the classification result or the corresponding health status to the handheld image acquisition device 10. The handheld image acquisition device 10 may also include a display device or an audio playback device for intuitively notifying the user of the classification result or the corresponding health status in the form of text, images, or audio.

[0047] Figure 3 A signaling flowchart of an image classification process according to an embodiment of the present disclosure is shown. Figure 3 As shown, the process of image classification by a speckle image classification system may include the following steps:

[0048] Step S10: The handheld image acquisition device can acquire images by hand, obtain speckle images that meet the preset requirements, and send the speckle images to the image processing device.

[0049] Step S20: The image processing device classifies the speckle image using a trained classifier deployed therein, and obtains the classification result.

[0050] Step S30: The image processing device sends the classification result or the health status corresponding to the classification result to the handheld image acquisition device to notify the user of the health status of the target human body area.

[0051] Based on the aforementioned technical features, embodiments of this disclosure can integrate a laser light source, optical fiber, polarizer, collimator, industrial camera, and reflector into a handheld image acquisition device, reducing the size of the device for acquiring speckle images. This allows for flexible, free, and convenient acquisition of speckle images of skin tissue from different angles and positions via a handheld device. Furthermore, by transmitting the acquired speckle images to an image processing device, a classifier in the image processing device extracts tissue structure information from the speckle images, further classifying the images to obtain accurate classification results for characterizing health status and achieving the purpose of assisting medical diagnosis.

[0052] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0053] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium can be volatile or non-volatile.

[0054] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0055] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0056] Figure 4 A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0057] Reference Figure 4 The electronic device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0058] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0059] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0060] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0061] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0062] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0063] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0064] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0065] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0066] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0067] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0068] Figure 5 A schematic diagram of another electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 may be provided as a server or a terminal device. (Refer to...) Figure 5 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0069] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0070] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0071] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0072] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0073] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0074] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0075] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0076] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0077] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0079] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A speckle image classification system, characterized in that, The system includes: A handheld image acquisition device is used to acquire speckle images of skin tissue in a target human body area to obtain images to be classified. An image processing device is used to segment the speckle image to obtain a set of sub-images consisting of multiple sub-images of preset sizes, and input the set of sub-images into a trained classifier for image classification to obtain a classification result corresponding to the speckle image. The classification result is used to determine the health status of the target human body region. The training process of the classifier includes: Acquire speckle images of multiple samples; Each sample speckle image is segmented into multiple sub-images of a preset size to obtain a sample image set; Each of the sample image sets is input into the classifier to obtain the corresponding prediction result; The classifier loss is determined based on the difference between the prediction result and the annotation result corresponding to each sample image; Adjust the classifier parameters based on the classifier loss.

2. The system according to claim 1, characterized in that, The handheld image acquisition device includes: The laser module is used to generate and emit laser light into the skin tissue; The image acquisition module is used to acquire speckle images formed by the scattering of the laser through the skin tissue.

3. The system according to claim 2, characterized in that, The laser module includes a laser source, optical fiber, collimator, polarizer, and reflector.

4. The system according to claim 1, characterized in that, The training process for the classifier also includes: The multiple sample speckle images are subjected to noise reduction and normalization processing.

5. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute, when executing instructions stored in the memory, a method for implementing the image processing apparatus according to any one of claims 1 to 4.

6. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they perform a method for implementing the image processing apparatus according to any one of claims 1 to 4.

Images