Analysis and comparison method and system of cardiovascular image based on model

Abstract

The invention discloses a model-based cardiovascular image analysis, comparison and recognition method and system, and the method comprises the steps: obtaining a current cardiovascular image and a cardiovascular image with a determined focus, extracting the image information, and marking the position of a known focus on the current cardiovascular image, the image information comprises pixel points at all positions on the image; setting a cardiovascular image comparison model, and calculating the comprehensive contrast amount between the current cardiovascular image and the cardiovascular image of the determined focus according to the marked known focus position and the image information; and when the comprehensive contrast amount exceeds a measurement threshold value, taking a focus result corresponding to the cardiovascular image of the determined focus as a final focus result of the current cardiovascular image.

Description

Technical Field

[0001] The present invention belongs to the technical field of cardiovascular image analysis, and more specifically, relates to a model-based cardiovascular image analysis, comparison and recognition method and system. Background Art

[0002] The current state of the art in cardiovascular image analysis is mainly focused on the following aspects: Imaging Technology: MRI (magnetic resonance imaging) and CT (computed tomography): Used to obtain high-resolution images of the heart and blood vessels to help doctors assess cardiovascular health.

[0003] Ultrasound imaging: Widely used for real-time monitoring of cardiac structure and function, particularly in the diagnosis of heart disease.

[0004] Image processing and analysis technology: Image segmentation: Use algorithms (such as thresholding, region growing, deep learning, etc.) to automatically or semi-automatically segment cardiac and vascular structures to improve diagnostic accuracy.

[0005] Feature extraction: Extract features of cardiac function (e.g., ventricular volume, ejection fraction) and lesions (e.g., atherosclerotic plaques) for further analysis.

[0006] Due to the complexity of cardiovascular structure, the image processing accuracy of existing technologies is low and lesion results cannot be automatically obtained. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a model-based cardiovascular image analysis, comparison and recognition method, comprising: Acquire a current cardiovascular image and a cardiovascular image with a confirmed lesion, extract image information respectively, and mark the known lesion position on the current cardiovascular image, wherein the image information includes pixel points at all positions on the image; Setting a cardiovascular image contrast model, and calculating a comprehensive contrast amount between a current cardiovascular image and a cardiovascular image of a determined lesion based on the marked known lesion location and the image information; When the comprehensive contrast value exceeds the measurement threshold, the lesion result corresponding to the cardiovascular image with the determined lesion is used as the final lesion result of the current cardiovascular image.

[0008] Furthermore, the cardiovascular image contrast model includes: , in, For current cardiovascular images and cardiovascular images of identified lesions The comprehensive contrast between the two cardiovascular images is used to capture the differences in structure, texture, topological changes, and temporal evolution. Define the domain for the image, For time Pixels The adaptive weight function is For time Pixels The morphological energy evolution field function is used to describe the physiological signals and structural evolution in current cardiovascular images. For current cardiovascular images and cardiovascular images of identified lesions Between pixels The image feature matching function at is the spatiotemporal coupled topological change function of the cardiovascular structure angle change, is the adjustment factor for the angle change, is the geometric angle change of the cardiovascular structure, For The geometric angle change of the cardiovascular structure in the vertical direction, For the pixel The spatial modulation function at is used to capture the spatial distribution of image features at different scales.

[0009] Further, time Pixels The adaptive weight function include: , in, is the weight of the first-order gradient, For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor of the first-order gradient, is the weight of the second-order gradient, Define the domain for the image subdomain of For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor for the second-order gradient.

[0010] Furthermore, the spatiotemporal coupling topological change function of the angle change of cardiovascular structure include: , in, is the first adjustment factor of the spatiotemporal coupling topological change function, is the second adjustment factor of the spatiotemporal coupling topological change function, It is the third adjustment factor of the spatiotemporal coupling topological change function.

[0011] Furthermore, at the pixel The spatial modulation function at include: , in, is the first adjustment factor of the spatial modulation function, For current cardiovascular images The known lesion location on is the second adjustment factor of the spatial modulation function, For current cardiovascular images The pixels on is the third adjustment factor of the spatial modulation function.

[0012] Further, time Pixels The morphological energy evolution field function include: , in, For images in the current cardiovascular Upper pixel Over time Varying signal strength, is the first adjustment factor of the morphological energy evolution field function, is the second adjustment factor of the morphological energy evolution field function, is the third adjustment factor of the morphological energy evolution field function, is the fourth adjustment factor of the morphological energy evolution field function, is the fifth adjustment factor of the morphological energy evolution field function, It is the sixth adjustment factor of the morphological energy evolution field function.

[0013] Further, current cardiovascular images and cardiovascular images of identified lesions Between pixels Image feature matching function at for: , in, For current cardiovascular images In pixels The mean value near Cardiovascular images of identified lesions In pixels The mean value near is the first constant, used to stabilize the calculation, For current cardiovascular images Cardiovascular images with identified lesions In pixels The covariance at is the second constant, used to stabilize the calculation, For current cardiovascular images In pixels The standard deviation of Cardiovascular images of identified lesions In pixels The standard deviation of .

[0014] The present invention also proposes a model-based cardiovascular image analysis, comparison and recognition system, comprising: An image information acquisition module is used to acquire a current cardiovascular image and a cardiovascular image with a confirmed lesion, and to extract image information respectively, and mark the known lesion position on the current cardiovascular image, wherein the image information includes pixel points at all positions on the image; A model setting module is used to set a cardiovascular image contrast model, and calculate a comprehensive contrast amount between a current cardiovascular image and a cardiovascular image of a determined lesion according to the marked known lesion location and the image information; The lesion determination module is used to use the lesion result corresponding to the cardiovascular image with the determined lesion as the final lesion result of the current cardiovascular image when the comprehensive contrast value exceeds the measurement threshold.

[0015] Furthermore, the cardiovascular image contrast model includes: , in, For current cardiovascular images and cardiovascular images of identified lesions The comprehensive contrast between the two cardiovascular images is used to capture the differences in structure, texture, topological changes, and temporal evolution. Define the domain for the image, For time Pixels The adaptive weight function is For time Pixels The morphological energy evolution field function is used to describe the physiological signals and structural evolution in current cardiovascular images. For current cardiovascular images and cardiovascular images of identified lesions Between pixels The image feature matching function at is the spatiotemporal coupled topological change function of the cardiovascular structure angle change, is the adjustment factor for the angle change, is the geometric angle change of the cardiovascular structure, For The geometric angle change of the cardiovascular structure in the vertical direction, For the pixel The spatial modulation function at is used to capture the spatial distribution of image features at different scales.

[0016] Further, time Pixels The adaptive weight function include: , in, is the weight of the first-order gradient, For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor of the first-order gradient, is the weight of the second-order gradient, Define the domain for the image subdomain of For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor for the second-order gradient.

[0017] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art: Through the above technical solutions, the present invention further enhances the accuracy of cardiovascular image analysis through innovative designs such as adaptive regional weights, topological coupling, and multi-level spatiotemporal modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the method of embodiment 1 of the present invention; Figure 2 is a structural diagram of a system in Embodiment 2 of the present invention; DETAILED DESCRIPTION

[0019] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0020] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0021] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0022] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0023] The display screen is used to display the user interface of each application.

[0024] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0025] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a model-based cardiovascular image analysis, comparison and recognition method, comprising: Step 101, obtaining a current cardiovascular image and a cardiovascular image with a confirmed lesion, respectively extracting image information, and marking the known lesion position on the current cardiovascular image, wherein the image information includes pixel points at all positions on the image; Step 102, setting a cardiovascular image contrast model, and calculating a comprehensive contrast amount between the current cardiovascular image and the cardiovascular image of the determined lesion according to the marked known lesion location and the image information; Specifically, the cardiovascular image contrast model includes: , in, For current cardiovascular images and cardiovascular images of identified lesions The comprehensive contrast between the two cardiovascular images is used to capture the differences in structure, texture, topological changes, and temporal evolution. Define the domain for the image, For time Pixels The adaptive weight function is For time Pixels The morphological energy evolution field function is used to describe the physiological signals and structural evolution in current cardiovascular images. For current cardiovascular images and cardiovascular images of identified lesions Between pixels The image feature matching function at is the spatiotemporal coupled topological change function of the cardiovascular structure angle change, is the adjustment factor for the angle change, is the geometric angle change of the cardiovascular structure, For The geometric angle change of the cardiovascular structure in the vertical direction, For the pixel The spatial modulation function at is used to capture the spatial distribution of image features at different scales.

[0026] Specifically, time Pixels The adaptive weight function include: , in, is the weight of the first-order gradient, For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor of the first-order gradient, is the weight of the second-order gradient, Define the domain for the image subdomain of For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor for the second-order gradient.

[0027] Specifically, the spatiotemporal coupling topological change function of the angle change of cardiovascular structure include: , in, is the first adjustment factor of the spatiotemporal coupling topological change function, is the second adjustment factor of the spatiotemporal coupling topological change function, It is the third adjustment factor of the spatiotemporal coupling topological change function.

[0028] Specifically, at the pixel The spatial modulation function at include: , in, is the first adjustment factor of the spatial modulation function, For current cardiovascular images The known lesion location on is the second adjustment factor of the spatial modulation function, For current cardiovascular images The pixels on is the third adjustment factor of the spatial modulation function.

[0029] Specifically, time Pixels The morphological energy evolution field function include: , in, For images in the current cardiovascular Upper pixel Over time Varying signal strength, is the first adjustment factor of the morphological energy evolution field function, is the second adjustment factor of the morphological energy evolution field function, is the third adjustment factor of the morphological energy evolution field function, is the fourth adjustment factor of the morphological energy evolution field function, is the fifth adjustment factor of the morphological energy evolution field function, It is the sixth adjustment factor of the morphological energy evolution field function.

[0030] Specific, current cardiovascular images and cardiovascular images of identified lesions Between pixels Image feature matching function at for: , in, For current cardiovascular images In pixels The mean value near Cardiovascular images of identified lesions In pixels The mean value near is the first constant, used to stabilize the calculation, For current cardiovascular images Cardiovascular images with identified lesions In pixels The covariance at is the second constant, used to stabilize the calculation, For current cardiovascular images In pixels The standard deviation of Cardiovascular images of identified lesions In pixels The standard deviation of .

[0031] Step 103: when the comprehensive contrast value exceeds the measurement threshold, the lesion result corresponding to the cardiovascular image with the determined lesion is used as the final lesion result of the current cardiovascular image.

[0032] Example 2 like Figure 2 As shown, an embodiment of the present invention further provides a model-based cardiovascular image analysis, comparison and recognition system, comprising: An image information acquisition module is used to acquire a current cardiovascular image and a cardiovascular image with a confirmed lesion, and to extract image information respectively, and mark the known lesion position on the current cardiovascular image, wherein the image information includes pixel points at all positions on the image; A model setting module is used to set a cardiovascular image contrast model, and calculate a comprehensive contrast amount between a current cardiovascular image and a cardiovascular image of a determined lesion according to the marked known lesion location and the image information; Specifically, the cardiovascular image contrast model includes: , in, For current cardiovascular images and cardiovascular images of identified lesions The comprehensive contrast between the two cardiovascular images is used to capture the differences in structure, texture, topological changes, and temporal evolution. Define the domain for the image, For time Pixels The adaptive weight function is For time Pixels The morphological energy evolution field function is used to describe the physiological signals and structural evolution in current cardiovascular images. For current cardiovascular images and cardiovascular images of identified lesions Between pixels The image feature matching function at is the spatiotemporal coupled topological change function of the cardiovascular structure angle change, is the adjustment factor for the angle change, is the geometric angle change of the cardiovascular structure, For The geometric angle change of the cardiovascular structure in the vertical direction, For the pixel The spatial modulation function at is used to capture the spatial distribution of image features at different scales.

[0033] Specifically, time Pixels The adaptive weight function include: , in, is the weight of the first-order gradient, For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor of the first-order gradient, is the weight of the second-order gradient, Define the domain for the image subdomain of For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor for the second-order gradient.

[0034] Specifically, the spatiotemporal coupling topological change function of the angle change of cardiovascular structure include: , in, is the first adjustment factor of the spatiotemporal coupling topological change function, is the second adjustment factor of the spatiotemporal coupling topological change function, It is the third adjustment factor of the spatiotemporal coupling topological change function.

[0035] Specifically, at the pixel The spatial modulation function at include: , in, is the first adjustment factor of the spatial modulation function, For current cardiovascular images The known lesion location on is the second adjustment factor of the spatial modulation function, For current cardiovascular images The pixels on is the third adjustment factor of the spatial modulation function.

[0036] Specifically, time Pixels The morphological energy evolution field function include: , in, For images in the current cardiovascular Upper pixel Over time Varying signal strength, is the first adjustment factor of the morphological energy evolution field function, is the second adjustment factor of the morphological energy evolution field function, is the third adjustment factor of the morphological energy evolution field function, is the fourth adjustment factor of the morphological energy evolution field function, is the fifth adjustment factor of the morphological energy evolution field function, It is the sixth adjustment factor of the morphological energy evolution field function.

[0037] Specific, current cardiovascular images and cardiovascular images of identified lesions Between pixels Image feature matching function at for: , in, For current cardiovascular images In pixels The mean value near Cardiovascular images of identified lesions In pixels The mean value near is the first constant, used to stabilize the calculation, For current cardiovascular images Cardiovascular images with identified lesions In pixels The covariance at is the second constant, used to stabilize the calculation, For current cardiovascular images In pixels The standard deviation of Cardiovascular images of identified lesions In pixels The standard deviation of .

[0038] The lesion determination module is used to use the lesion result corresponding to the cardiovascular image with the determined lesion as the final lesion result of the current cardiovascular image when the comprehensive contrast value exceeds the measurement threshold.

[0039] Example 3 The embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the model-based cardiovascular image analysis, comparison and recognition method.

[0040] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0041] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining a current cardiovascular image and a cardiovascular image with a determined lesion, and extracting image information respectively, marking the known lesion position on the current cardiovascular image, wherein the image information includes pixel points at all positions on the image; Step 102, setting a cardiovascular image contrast model, and calculating a comprehensive contrast amount between the current cardiovascular image and the cardiovascular image of the determined lesion according to the marked known lesion location and the image information; Specifically, the cardiovascular image contrast model includes: , in, For current cardiovascular images and cardiovascular images of identified lesions The comprehensive contrast between the two cardiovascular images is used to capture the differences in structure, texture, topological changes, and temporal evolution. Define the domain for the image, For time Pixels The adaptive weight function is For time Pixels The morphological energy evolution field function is used to describe the physiological signals and structural evolution in current cardiovascular images. For current cardiovascular images and cardiovascular images of identified lesions Between pixels The image feature matching function at is the spatiotemporal coupled topological change function of the cardiovascular structure angle change, is the adjustment factor for the angle change, is the geometric angle change of the cardiovascular structure, For The geometric angle change of the cardiovascular structure in the vertical direction, For the pixel The spatial modulation function at is used to capture the spatial distribution of image features at different scales.

[0042] Specifically, time Pixels The adaptive weight function include: , in, is the weight of the first-order gradient, For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor of the first-order gradient, is the weight of the second-order gradient, Define the domain for the image subdomain of For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor for the second-order gradient.

[0043] Specifically, the spatiotemporal coupling topological change function of the angle change of cardiovascular structure include: , in, is the first adjustment factor of the spatiotemporal coupling topological change function, is the second adjustment factor of the spatiotemporal coupling topological change function, It is the third adjustment factor of the spatiotemporal coupling topological change function.

[0044] Specifically, at the pixel The spatial modulation function at include: , in, is the first adjustment factor of the spatial modulation function, For current cardiovascular images The known lesion location on is the second adjustment factor of the spatial modulation function, For current cardiovascular images The pixels on is the third adjustment factor of the spatial modulation function.

[0045] Specifically, time Pixels The morphological energy evolution field function include: , in, For images in the current cardiovascular Upper pixel Over time Varying signal strength, is the first adjustment factor of the morphological energy evolution field function, is the second adjustment factor of the morphological energy evolution field function, is the third adjustment factor of the morphological energy evolution field function, is the fourth adjustment factor of the morphological energy evolution field function, is the fifth adjustment factor of the morphological energy evolution field function, It is the sixth adjustment factor of the morphological energy evolution field function.

[0046] Specific, current cardiovascular images and cardiovascular images of identified lesions Between pixels Image feature matching function at for: , in, For current cardiovascular images In pixels The mean value near Cardiovascular images of identified lesions In pixels The mean value near is the first constant, used to stabilize the calculation, For current cardiovascular images Cardiovascular images with identified lesions In pixels The covariance at is the second constant, used to stabilize the calculation, For current cardiovascular images In pixels The standard deviation of Cardiovascular images of identified lesions In pixels The standard deviation of .

[0047] Step 103: when the comprehensive contrast value exceeds the measurement threshold, the lesion result corresponding to the cardiovascular image with the determined lesion is used as the final lesion result of the current cardiovascular image.

[0048] Example 4 An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the model-based cardiovascular image analysis, comparison and recognition method.

[0049] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0050] Among them, the storage medium can be used to store software programs and modules, such as a model-based cardiovascular image analysis, comparison and recognition method in the embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned model-based cardiovascular image analysis, comparison and recognition method. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0051] The processor can call the information and application program stored in the storage medium through the transmission system to perform the following steps: Step 101, obtaining a current cardiovascular image and a cardiovascular image with a determined lesion, and extracting image information respectively, marking the known lesion position on the current cardiovascular image, wherein the image information includes pixel points at all positions on the image; Step 102, setting a cardiovascular image contrast model, and calculating a comprehensive contrast amount between the current cardiovascular image and the cardiovascular image of the determined lesion according to the marked known lesion location and the image information; Specifically, the cardiovascular image contrast model includes: , in, For current cardiovascular images and cardiovascular images of identified lesions The comprehensive contrast between the two cardiovascular images is used to capture the differences in structure, texture, topological changes, and temporal evolution. Define the domain for the image, For time Pixels The adaptive weight function is For time Pixels The morphological energy evolution field function is used to describe the physiological signals and structural evolution in current cardiovascular images. For current cardiovascular images and cardiovascular images of identified lesions Between pixels The image feature matching function at is the spatiotemporal coupled topological change function of the cardiovascular structure angle change, is the adjustment factor for the angle change, is the geometric angle change of the cardiovascular structure, For The geometric angle change of the cardiovascular structure in the vertical direction, For the pixel The spatial modulation function at is used to capture the spatial distribution of image features at different scales.

[0052] Specifically, time Pixels The adaptive weight function include: , in, is the weight of the first-order gradient, For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor of the first-order gradient, is the weight of the second-order gradient, Define the domain for the image subdomain of For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor for the second-order gradient.

[0053] Specifically, the spatiotemporal coupling topological change function of the angle change of cardiovascular structure include: , in, is the first adjustment factor of the spatiotemporal coupling topological change function, is the second adjustment factor of the spatiotemporal coupling topological change function, It is the third adjustment factor of the spatiotemporal coupling topological change function.

[0054] Specifically, at the pixel The spatial modulation function at include: , in, is the first adjustment factor of the spatial modulation function, For current cardiovascular images The known lesion location on is the second adjustment factor of the spatial modulation function, For current cardiovascular images The pixels on is the third adjustment factor of the spatial modulation function.

[0055] Specifically, time Pixels The morphological energy evolution field function include: , in, For images in the current cardiovascular Upper pixel Over time Varying signal strength, is the first adjustment factor of the morphological energy evolution field function, is the second adjustment factor of the morphological energy evolution field function, is the third adjustment factor of the morphological energy evolution field function, is the fourth adjustment factor of the morphological energy evolution field function, is the fifth adjustment factor of the morphological energy evolution field function, It is the sixth adjustment factor of the morphological energy evolution field function.

[0056] Specific, current cardiovascular images and cardiovascular images of identified lesions Between pixels Image feature matching function at for: , in, For current cardiovascular images In pixels The mean value near Cardiovascular images of identified lesions In pixels The mean value near is the first constant, used to stabilize the calculation, For current cardiovascular images Cardiovascular images with identified lesions In pixels The covariance at is the second constant, used to stabilize the calculation, For current cardiovascular images In pixels The standard deviation of Cardiovascular images of identified lesions In pixels The standard deviation of .

[0057] Step 103: when the comprehensive contrast value exceeds the measurement threshold, the lesion result corresponding to the cardiovascular image with the determined lesion is used as the final lesion result of the current cardiovascular image.

[0058] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0059] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0060] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0061] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0062] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0063] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0064] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A model-based cardiovascular image analysis, comparison and recognition method, characterized in that: include: Acquire a current cardiovascular image and a cardiovascular image with a confirmed lesion, extract image information respectively, and mark the known lesion position on the current cardiovascular image, wherein the image information includes pixel points at all positions on the image; Setting a cardiovascular image contrast model, and calculating a comprehensive contrast amount between a current cardiovascular image and a cardiovascular image of a determined lesion based on the marked known lesion location and the image information; When the comprehensive contrast value exceeds the measurement threshold, the lesion result corresponding to the cardiovascular image with the determined lesion is used as the final lesion result of the current cardiovascular image.

2. A method for analyzing, comparing and identifying cardiovascular images based on a model as claimed in claim 1, characterized in that: The cardiovascular image contrast model comprises: ， in, For current cardiovascular images and cardiovascular images of identified lesions The comprehensive contrast between the two cardiovascular images is used to capture the differences in structure, texture, topological changes, and temporal evolution. Define the domain for the image, For time Pixels The adaptive weight function is For time Pixels The morphological energy evolution field function is used to describe the physiological signals and structural evolution in current cardiovascular images. For current cardiovascular images and cardiovascular images of identified lesions Between pixels The image feature matching function at is the spatiotemporal coupled topological change function of the cardiovascular structure angle change, is the adjustment factor for the angle change, is the geometric angle change of the cardiovascular structure, For The geometric angle change of the cardiovascular structure in the vertical direction, For the pixel The spatial modulation function at is used to capture the spatial distribution of image features at different scales.

3. A method for analyzing, comparing and identifying cardiovascular images based on a model as claimed in claim 2, characterized in that: time Pixels The adaptive weight function include: ， in, is the weight of the first-order gradient, For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor of the first-order gradient, is the weight of the second-order gradient, Define the domain for the image subdomain of For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor for the second-order gradient.

4. The method for analyzing, comparing and identifying cardiovascular images based on a model as claimed in claim 2, characterized in that: Spatiotemporal coupling of topological change functions for angle changes in cardiovascular structures include: ， in, is the first adjustment factor of the spatiotemporal coupling topological change function, is the second adjustment factor of the spatiotemporal coupling topological change function, It is the third adjustment factor of the spatiotemporal coupling topological change function.

5. The method for analyzing, comparing and identifying cardiovascular images based on a model as claimed in claim 2, characterized in that: In pixels The spatial modulation function at include: ， in, is the first adjustment factor of the spatial modulation function, For current cardiovascular images The known lesion location on is the second adjustment factor of the spatial modulation function, For current cardiovascular images The pixels on is the third adjustment factor of the spatial modulation function.

6. The method for analyzing, comparing and identifying cardiovascular images based on a model as claimed in claim 2, characterized in that: time Pixels The morphological energy evolution field function include: ， in, For images in the current cardiovascular Upper pixel Over time Varying signal strength, is the first adjustment factor of the morphological energy evolution field function, is the second adjustment factor of the morphological energy evolution field function, is the third adjustment factor of the morphological energy evolution field function, is the fourth adjustment factor of the morphological energy evolution field function, is the fifth adjustment factor of the morphological energy evolution field function, It is the sixth adjustment factor of the morphological energy evolution field function.

7. The method for analyzing, comparing and identifying cardiovascular images based on a model as claimed in claim 2, characterized in that: Current Cardiovascular Images and cardiovascular images of identified lesions Between pixels Image feature matching function at for: ， in, For current cardiovascular images In pixels The mean value near Cardiovascular images of identified lesions In pixels The mean value near is the first constant, used to stabilize the calculation, For current cardiovascular images Cardiovascular images with identified lesions In pixels The covariance at is the second constant, used to stabilize the calculation, For current cardiovascular images In pixels The standard deviation of Cardiovascular images of identified lesions In pixels The standard deviation at .

8. A model-based cardiovascular image analysis, comparison and recognition system, characterized in that: include: An image information acquisition module is used to acquire a current cardiovascular image and a cardiovascular image with a confirmed lesion, and to extract image information respectively, and mark the known lesion position on the current cardiovascular image, wherein the image information includes pixel points at all positions on the image; A model setting module is used to set a cardiovascular image contrast model, and calculate a comprehensive contrast amount between a current cardiovascular image and a cardiovascular image of a determined lesion according to the marked known lesion location and the image information; The lesion determination module is used to use the lesion result corresponding to the cardiovascular image with the determined lesion as the final lesion result of the current cardiovascular image when the comprehensive contrast value exceeds the measurement threshold.

9. The model-based cardiovascular image analysis, comparison and recognition system according to claim 8, characterized in that: The cardiovascular image contrast model comprises: ， in, For current cardiovascular images and cardiovascular images of identified lesions The comprehensive contrast between the two cardiovascular images is used to capture the differences in structure, texture, topological changes, and temporal evolution. Define the domain for the image, For time Pixels The adaptive weight function is For time Pixels The morphological energy evolution field function is used to describe the physiological signals and structural evolution in current cardiovascular images. For current cardiovascular images and cardiovascular images of identified lesions Between pixels The image feature matching function at is the spatiotemporal coupled topological change function of the cardiovascular structure angle change, is the adjustment factor for the angle change, is the geometric angle change of the cardiovascular structure, For The geometric angle change of the cardiovascular structure in the vertical direction, For the pixel The spatial modulation function at is used to capture the spatial distribution of image features at different scales.

10. The model-based cardiovascular image analysis, comparison and recognition system according to claim 8, characterized in that: time Pixels The adaptive weight function include: ， in, is the weight of the first-order gradient, For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor of the first-order gradient, is the weight of the second-order gradient, Define the domain for the image subdomain of For time Current cardiovascular images In pixels The first-order gradient at is the adjustment factor for the second-order gradient.

Images