Image processing method and electronic equipment

Abstract

The invention provides an image processing method and electronic equipment, which can be applied to the technical field of computer vision. The method comprises the following steps: fitting at least one target maximum value of a measured value histogram of an image to be processed to obtain Gaussian peaks corresponding to the at least one target maximum value; performing Gaussian filtering on the to-be-processed image to obtain density image data used for representing the tissue density in the to-be-processed image; and based on a threshold interval determined according to the target object, processing the measured value histogram, the Gaussian peak and the density image data to obtain an image processing result, the image processing result comprising a sub-image related to the target object and an image feature of the sub-image in the to-be-processed image.

Description

Technical Field

[0001] This disclosure relates to the field of computer vision technology, and more specifically to an image processing method and an electronic device. Background Technology

[0002] With the development of imaging and image processing technologies, imaging the research subject and then processing the image can significantly improve the efficiency and accuracy of the analysis. Taking the medical field as an example, medical image processing is a core supporting technology for clinical diagnosis and treatment. Computed tomography (CT) technology, since its inception, has gradually become one of the most important tools in modern medical imaging. The essence of a CT image is a digital grayscale matrix, where the grayscale value of each pixel directly corresponds to the linear attenuation coefficient of human tissue. Therefore, CT technology can be used to non-invasively and quantitatively measure the density of tissues in the body, which has significant reference value.

[0003] In the process of implementing this disclosure, it was found that the relevant technology has at least the following problems: the existing grayscale analysis technology for CT images commonly uses histogram analysis and local grayscale value analysis, such as calculating the mean and standard deviation, and the analysis results are relatively simple and have high repeatability. Summary of the Invention

[0004] In view of the above problems, this disclosure provides an image processing method and an electronic device.

[0005] According to a first aspect of this disclosure, an image processing method is provided, comprising: fitting at least one target maximum of a metric histogram of an image to be processed to obtain Gaussian peaks corresponding to each of the at least one target maximum, wherein the independent variable of the metric histogram represents the metric value of a voxel in the image to be processed, the dependent variable of the metric histogram represents the frequency corresponding to the metric value, and the target maximum is determined from multiple maxima of the metric histogram based on preset filtering conditions; performing Gaussian filtering on the image to be processed to obtain density image data representing the tissue density in the image to be processed; and processing the metric histogram, Gaussian peaks, and density image data based on a threshold interval determined according to a target object to obtain an image processing result, wherein the image processing result includes a sub-image related to the target object and image features of the sub-image in the image to be processed.

[0006] According to embodiments of this disclosure, the threshold interval includes a first threshold interval and a second threshold interval, where the first threshold interval represents the range of measure values ​​of the target object, and the second threshold interval represents the range of density values ​​of the target object. Based on the threshold interval determined according to the target object, the measure value histogram, Gaussian peak, and density image data are processed to obtain an image processing result, including: processing the measure value histogram and Gaussian peak based on the first threshold interval to obtain a first parsing sub-result of the image to be processed; processing the density image data based on the second threshold interval to obtain a second parsing sub-result of the image to be processed; and determining the image processing result based on the first parsing sub-result and the second parsing sub-result.

[0007] According to embodiments of this disclosure, based on a first threshold interval, the histogram of measurement values ​​and the Gaussian peak are processed to obtain a first analytical sub-result of the image to be processed, including: based on the first threshold interval, truncating the range of measurement values ​​of the histogram of measurement values ​​and the Gaussian peak to obtain the histogram of measurement values ​​to be analyzed and the Gaussian peak to be analyzed; and performing data statistics on the histogram of measurement values ​​to be analyzed and the Gaussian peak to be analyzed to obtain the first analytical sub-result.

[0008] According to embodiments of this disclosure, the first parsing sub-result includes at least one of the following: the average value of the measure values ​​of each voxel represented in the histogram of the measure values ​​to be analyzed and the Gaussian peak to be analyzed; the standard deviation of the measure values ​​of each voxel represented in the histogram of the measure values ​​to be analyzed and the Gaussian peak to be analyzed; the sum of the frequencies of the measure values ​​of each voxel represented in the histogram of the measure values ​​to be analyzed and the Gaussian peak to be analyzed; and the percentage of the sum of the frequencies of the measure values ​​of each voxel represented in the histogram of the measure values ​​to be analyzed and the Gaussian peak to be analyzed to the sum of the frequencies of the measure values ​​of each voxel in the image to be processed.

[0009] According to embodiments of this disclosure, density image data is processed based on a second threshold interval to obtain a second parsing sub-result of the image to be processed, including: performing binary transformation on the density values ​​of multiple voxels in the density image data based on the second threshold interval to obtain binary density image data; and performing data statistics on at least one connected component determined based on the binary density image data to obtain the second parsing sub-result.

[0010] According to embodiments of this disclosure, the second parsing result includes at least one of the following: the center position of each of at least one connected component, the volume of each of at least one connected component, the surface area of ​​each of at least one connected component, the average value of the measure values ​​of a plurality of voxels in the density image data corresponding to each of at least one connected component, the standard deviation of the measure values ​​of a plurality of voxels in the density image data corresponding to each of at least one connected component, the maximum value of the measure values ​​of a plurality of voxels in the density image data corresponding to each of at least one connected component, and the minimum value of the measure values ​​of a plurality of voxels in the density image data corresponding to each of at least one connected component.

[0011] According to embodiments of this disclosure, the target maximum is determined as follows: the histogram of measure values ​​is traversed in order of magnitude to determine multiple maxima of the histogram; the multiple maxima are filtered in order of magnitude of the measure values ​​to obtain the target maximum that satisfies preset filtering conditions; the preset filtering conditions include: the frequency of the maximum is greater than a preset frequency; when the maximum is not the first maximum, the distance between the measure value corresponding to the maximum and the measure value of at least one previously determined target maximum is greater than a preset spacing; and the prominence of the maximum is greater than a preset prominence, the prominence being determined based on the difference between the frequency of the maximum and the frequency of the minimum adjacent to the maximum.

[0012] According to embodiments of this disclosure, fitting at least one target maximum value of the histogram of the measurement values ​​of the image to be processed to obtain Gaussian peaks corresponding to each of the at least one target maximum value includes: for each target maximum value, determining a neighborhood corresponding to the target maximum value from the histogram of the measurement values ​​based on the target maximum value and a preset neighborhood size; and determining a Gaussian peak conforming to a Gaussian distribution from the neighborhood to decompose the histogram of the measurement values ​​using at least one Gaussian peak.

[0013] According to embodiments of this disclosure, decomposing a measure value histogram using at least one Gaussian peak includes: narrowing the range of independent variables of the Gaussian peaks with overlapping portions when there is an overlap between the ranges of independent variables corresponding to at least one target maximum, until there is no overlap between the ranges of independent variables; and representing the measure value histogram using at least one Gaussian peak.

[0014] According to embodiments of this disclosure, the measurement value includes one of the following: grayscale value, voxel value, and calibration reference value.

[0015] A second aspect of this disclosure provides an image processing apparatus, comprising: a data fitting module for fitting at least one target maximum of a histogram of a measure value of an image to be processed to obtain Gaussian peaks corresponding to each of the at least one target maximum, wherein the independent variable of the measure value histogram represents the measure value of a voxel in the image to be processed, the dependent variable of the measure value histogram represents the frequency corresponding to the measure value, and the target maximum is determined from multiple maxima of the measure value histogram based on preset filtering conditions; a Gaussian filtering module for performing Gaussian filtering on the image to be processed to obtain density image data representing the tissue density in the image to be processed; and an image processing module for processing the measure value histogram, Gaussian peaks, and density image data based on a threshold interval determined according to a target object to obtain an image processing result, wherein the image processing result includes sub-images related to the target object and image features of the sub-images in the image to be processed.

[0016] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0017] A fourth aspect of this disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0018] The fifth aspect of this disclosure also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0019] According to embodiments of this disclosure, a Gaussian peak is obtained by fitting the target maxima of the histogram of measurement values. A density image is generated by Gaussian filtering, and multi-dimensional processing is performed in conjunction with the threshold range of the target object, enabling accurate image analysis. Filtering the target maxima and fitting the Gaussian peak can eliminate noise interference and focus on effective signals. The density image can reflect tissue density characteristics. Multi-dimensional collaborative processing based on the above indicators can avoid the limitations of single-indicator analysis, accurately extract target sub-images and features, provide objective and reliable image evidence for image processing, and reduce subjective judgment errors. Attached Figure Description

[0020] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0021] Figure 1 The illustration schematically depicts an application scenario of the image processing method and electronic device according to embodiments of the present disclosure;

[0022] Figure 2 A flowchart illustrating an image processing method according to an embodiment of the present disclosure is shown schematically.

[0023] Figure 3 The flowchart illustrating the image processing method according to an embodiment of the present disclosure processes the image to be processed.

[0024] Figure 4 A schematic block diagram of an image processing apparatus according to an embodiment of the present disclosure is shown; and

[0025] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing an image processing method according to an embodiment of the present disclosure. Detailed Implementation

[0026] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0030] In the technical solution disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0031] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this disclosure all offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0032] Embodiments of this disclosure provide an image processing method, comprising: fitting at least one target maximum value of a metric histogram of an image to be processed to obtain Gaussian peaks corresponding to each of the at least one target maximum value, wherein the independent variable of the metric histogram represents the metric value of a voxel in the image to be processed, the dependent variable of the metric histogram represents the frequency corresponding to the metric value, and the target maximum value is determined from multiple maxima of the metric histogram based on preset filtering conditions; performing Gaussian filtering on the image to be processed to obtain density image data representing the tissue density in the image to be processed; and processing the metric histogram, Gaussian peaks, and density image data based on a threshold interval determined according to a target object to obtain an image processing result, wherein the image processing result includes sub-images related to the target object and image features of the sub-images in the image to be processed.

[0033] Figure 1 The illustration shows an application scenario of the image processing method and electronic device according to embodiments of the present disclosure.

[0034] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0035] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0036] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0037] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0038] It should be noted that the image processing method provided in this embodiment can generally be executed by server 105. Correspondingly, the image processing apparatus provided in this embodiment can generally be located in server 105. The image processing method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the image processing apparatus provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0039] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0040] The following will be based on Figure 1 The described scene, through Figures 2-3 The image processing method according to the embodiments of this disclosure will be described in detail.

[0041] Figure 2 A flowchart illustrating an image processing method according to an embodiment of the present disclosure is shown schematically.

[0042] like Figure 2 As shown, the image processing method of this embodiment includes operations S210 to S230.

[0043] In operation S210, at least one target maximum value of the histogram of the measure values ​​of the image to be processed is fitted to obtain the Gaussian peak corresponding to each of the at least one target maximum value.

[0044] The images to be processed can include images obtained by imaging the object under study in various fields using imaging techniques. For example, CT images obtained by imaging lesions or organs in the medical field, and CT images obtained by imaging industrial devices in the industrial field.

[0045] The independent variable of the measure value histogram represents the measure value of voxels in the image to be processed, and the dependent variable represents the frequency corresponding to the measure value. The target maximum is determined from multiple maxima of the measure value histogram based on preset filtering conditions. A voxel can represent the smallest segmentation unit in the image to be processed. For example, in the case of a two-dimensional image, a voxel is a pixel in that two-dimensional image.

[0046] In operation S220, Gaussian filtering is performed on the image to be processed to obtain density image data that represents the tissue density in the image to be processed.

[0047] In operation S230, based on the threshold range determined according to the target object, the histogram of the measurement value, the Gaussian peak and the density image data are processed to obtain the image processing result.

[0048] By fitting at least one target maximum in the measure histogram, the measure histogram can be decomposed to obtain Gaussian peaks corresponding to each target maximum, wherein each Gaussian peak conforms to a Gaussian distribution, and the peak value of each Gaussian peak is its corresponding target maximum. The fitting process involves fitting the target maximum to its left and right neighborhoods to obtain Gaussian peaks corresponding to that target maximum that conform to a Gaussian distribution.

[0049] By applying Gaussian filtering to the image to be processed based on its dimensions, the tissue density in the corresponding region of the image to be processed can be determined based on the voxel measurement values, thus obtaining density image data.

[0050] In one example, when the image to be processed is a three-dimensional image, the Gaussian filtering method can be a three-dimensional Gaussian filter.

[0051] In another example, when the image to be processed is a two-dimensional image, the Gaussian filtering method can correspondingly select a two-dimensional Gaussian filter.

[0052] The target object can represent the tissue, lesion, organ, or other part to be analyzed in this image processing. A threshold range can be determined based on the target object's metric value range in the image to be processed and its density value range in the density image data. The metric value histogram, Gaussian peak, and density image data are then processed based on this threshold range to obtain image processing results related to the target object. The image processing results include sub-images related to the target object within the image to be processed and the image features of these sub-images.

[0053] According to embodiments of this disclosure, a Gaussian peak is obtained by fitting the target maxima of the histogram of measurement values. A density image is generated by Gaussian filtering, and multi-dimensional processing is performed in conjunction with the threshold range of the target object, enabling accurate image analysis. Filtering the target maxima and fitting the Gaussian peak can eliminate noise interference and focus on effective signals. The density image can reflect tissue density characteristics. Multi-dimensional collaborative processing based on the above indicators can avoid the limitations of single-indicator analysis, accurately extract target sub-images and features, provide objective and reliable image evidence for image processing, and reduce subjective judgment errors.

[0054] According to embodiments of this disclosure, the threshold interval includes a first threshold interval and a second threshold interval, where the first threshold interval represents the range of measure values ​​of the target object, and the second threshold interval represents the range of density values ​​of the target object. Based on the threshold interval determined according to the target object, the measure value histogram, Gaussian peak, and density image data are processed to obtain an image processing result, including: processing the measure value histogram and Gaussian peak based on the first threshold interval to obtain a first parsing sub-result of the image to be processed; processing the density image data based on the second threshold interval to obtain a second parsing sub-result of the image to be processed; and determining the image processing result based on the first parsing sub-result and the second parsing sub-result.

[0055] The first threshold interval is the range of measure values ​​in the image to be processed, and the second threshold interval is the range of density values ​​in the density image data. Therefore, based on the first threshold interval and the second threshold interval respectively, the measure values ​​and density values ​​can be filtered to obtain the parts of the above measure values ​​and density values ​​that are related to the measure values ​​and density values ​​of the target object.

[0056] Specifically, based on the first threshold interval, the histogram of the measurement value and the Gaussian peak are processed, that is, the portion of the measurement value that is within the first threshold interval is selected to obtain the first parsing sub-result of the image to be processed. The first parsing sub-result is used to represent the feature information related to the measurement value of the target object.

[0057] Based on the second threshold interval, the density image data is processed by distinguishing between voxels whose density values ​​are within the second threshold interval and those whose density values ​​are outside the second threshold interval, thus obtaining the second parse sub-result of the image to be processed. The second parse sub-result is used to represent the geometric information of the target object in space, such as its contour and shape.

[0058] According to embodiments of this disclosure, the threshold interval is divided into a first threshold interval corresponding to the metric value and a second threshold interval corresponding to the density value. After processing the parse sub-results separately, they are fused to form a dual verification mechanism of metric value features and density features. Dual verification can effectively reduce missed and false positives, significantly improving the reliability of image processing results, and is particularly suitable for image scenarios with complex tissue types.

[0059] According to embodiments of this disclosure, based on a first threshold interval, the histogram of measurement values ​​and the Gaussian peak are processed to obtain a first analytical sub-result of the image to be processed, including: based on the first threshold interval, truncating the range of measurement values ​​of the histogram of measurement values ​​and the Gaussian peak to obtain the histogram of measurement values ​​to be analyzed and the Gaussian peak to be analyzed; and performing data statistics on the histogram of measurement values ​​to be analyzed and the Gaussian peak to be analyzed to obtain the first analytical sub-result.

[0060] The first threshold interval includes the upper limit and lower limit of the measure value. It can retain the portion of the measure value between the upper limit and lower limit of the measure value in the measure value histogram and Gaussian peak, so as to truncate the measure value range of the measure value histogram and Gaussian peak to obtain the measure value histogram and Gaussian peak to be analyzed.

[0061] By performing statistical analysis on the histogram of the measure values ​​to be analyzed and the Gaussian peak to be analyzed, the data characteristics of each can be obtained, and these characteristics can be used as the first analytical sub-result.

[0062] According to embodiments of this disclosure, by extracting the histogram and Gaussian peak of the measurement value to be analyzed based on a first threshold interval before statistical analysis, the focus can be placed on the measurement value range corresponding to the target object, eliminating interference from irrelevant regional data. Through refined statistical processing after extraction, it is ensured that the first analytical sub-result only reflects the measurement value characteristics of the target object, avoiding statistical errors caused by redundant data and improving the accuracy of measurement value-related indicators.

[0063] According to embodiments of this disclosure, the image to be processed consists of multiple voxels, and the measurement value of each voxel includes one of the following: gray value, voxel value, or calibration reference value. The calibration reference value represents the standardized value of the CT image, i.e., the Hounsfield Unit (HU).

[0064] The first analytical sub-result includes at least one of the following: the average value of the individual voxels represented in the histogram of the measures to be analyzed and the Gaussian peak to be analyzed; the standard deviation of the individual voxels represented in the histogram of the measures to be analyzed and the Gaussian peak to be analyzed; the sum of the frequencies of the individual voxels represented in the histogram of the measures to be analyzed and the Gaussian peak to be analyzed; and the percentage of the sum of the frequencies of the individual voxels represented in the histogram of the measures to be analyzed and the Gaussian peak to be analyzed to the sum of the frequencies of the individual voxels in the image to be processed.

[0065] The aforementioned average value can be determined by the sum of the measure values ​​and the total number of voxels in the histogram of the measure values ​​to be analyzed. Specifically, the average value is obtained by summing the measure values ​​of all voxels and then calculating the ratio of this sum to the total number of voxels. The average value for multiple voxels represented by the Gaussian peak to be analyzed is calculated in the same way, except that the required data are the measure values ​​and the total number of voxels in the Gaussian peak to be analyzed.

[0066] Similarly, the standard deviation and the sum of the frequencies of the measures represented by the histogram of the measures to be analyzed and the multiple voxels represented by the Gaussian peak to be analyzed can be calculated separately according to the calculation methods of standard deviation and sum.

[0067] The aforementioned percentage can be obtained by calculating the ratio between the sum of the frequencies of the individual voxels represented in the histogram of the measured values ​​to be analyzed and the sum of the frequencies of the measured values ​​of the voxels in the image to be processed. This ratio is used to measure the proportion of the measured values ​​in the first threshold interval in the image to be processed, thereby determining the proportion of the target object in the image to be processed.

[0068] According to embodiments of this disclosure, density image data is processed based on a second threshold interval to obtain a second parsing sub-result of the image to be processed, including: performing binary transformation on the density values ​​of multiple voxels in the density image data based on the second threshold interval to obtain binary density image data; and performing data statistics on at least one connected component determined based on the binary density image data to obtain the second parsing sub-result.

[0069] The second threshold interval includes an upper limit and a lower limit for density values. Based on the second threshold interval, multiple voxels in the density image data can be binarized, so that voxels with density values ​​within the second threshold interval, i.e., the region or shape where the target object is located, can be distinguished from voxels outside the region or shape where the target object is located, which facilitates subsequent analysis.

[0070] Specifically, the density values ​​of multiple voxels can be binary converted by converting the measure values ​​within the second threshold range to 1 and the measure values ​​outside the second threshold range to 0. After the conversion, the binary density image data is actually a label image composed of 0s and 1s. Regions filled with 0s can be regarded as regions unrelated to the target object, and regions filled with 1s can be regarded as regions related to the target object.

[0071] In one example, at least one connected component can be determined based on the positional relationship of 0 and 1 in the label image. Each connected component is filled with the same data (0 or 1), and the data filled outside the edges of each connected component is different from the data filled inside the connected component.

[0072] The data of the above connected components are statistically analyzed to obtain a second parse sub-result, wherein the second parse sub-result includes at least one of the following: the center position of each of the at least one connected component, the volume of each of the at least one connected component, the surface area of ​​each of the at least one connected component, the average value of the measure values ​​of multiple voxels in the density image data corresponding to each of the at least one connected component, the standard deviation of the measure values ​​of multiple voxels in the density image data corresponding to each of the at least one connected component, the maximum value of the measure values ​​of multiple voxels in the density image data corresponding to each of the at least one connected component, and the minimum value of the measure values ​​of multiple voxels in the density image data corresponding to each of the at least one connected component.

[0073] In another example, after obtaining the above connected components, the volume of each connected component can be calculated separately, and connected components with a volume smaller than a preset volume can be deleted to avoid connected components that are too small due to data distortion.

[0074] The center of a connected component can be determined by one of the following: the centroid of the geometry corresponding to the connected component, the center of the smallest circumcircle corresponding to the connected component, or the center of the axis-aligned bounding box.

[0075] The volume and surface area of ​​a connected region can be obtained through geometric calculations based on the range of the label image corresponding to the connected region.

[0076] In the image to be processed, the region corresponding to each connected component can be determined, and the mean, standard deviation, maximum and minimum values ​​of the measure values ​​of multiple voxels in the region can be calculated. The above calculation results are used as the mean, standard deviation, maximum and minimum values ​​of the measure values ​​of multiple voxels in the density image data corresponding to the connected component.

[0077] According to embodiments of this disclosure, binarizing density image data based on a second threshold interval and analyzing connected components can clearly segment regions with density matching the target object, and connected component analysis can locate the spatial distribution of the target region. Binarization simplifies density data, facilitating rapid screening of target regions, while connected component analysis avoids misidentifying discrete noise with similar densities as target tissue, improving the spatial localization accuracy of the second parsing result.

[0078] Figure 3 The flowchart illustrating the image processing method according to an embodiment of the present disclosure processes the image to be processed.

[0079] like Figure 3 As shown, on the one hand, histogram data statistics can be performed on the image to be processed to obtain a measure value histogram, and then histogram decomposition can be performed on the measure value histogram to obtain multiple Gaussian peaks. Based on the first threshold interval, the Gaussian peaks and the measure value histogram are processed and analyzed respectively to obtain the first parsing sub-result of the image to be processed.

[0080] On the other hand, Gaussian filtering can be applied to the image to be processed to obtain density image data of the image to be processed. Based on the second threshold interval, the density image data is processed to perform binary transformation on the density values ​​of multiple voxels in the density image data, thereby obtaining multiple connected components. The multiple connected components are processed and analyzed to obtain the second parsing result of the image to be processed.

[0081] After obtaining the first and second parse sub-results, the image processing results can be obtained by combining the first and second parse sub-results.

[0082] According to embodiments of this disclosure, the target maximum is determined as follows: the histogram of measure values ​​is traversed in order of magnitude to determine multiple maxima of the histogram; the multiple maxima are filtered in order of magnitude of the measure values ​​to obtain the target maximum that satisfies preset filtering conditions; the preset filtering conditions include: the frequency of the maximum is greater than a preset frequency; when the maximum is not the first maximum, the distance between the measure value corresponding to the maximum and the measure value of at least one previously determined target maximum is greater than a preset spacing; and the prominence of the maximum is greater than a preset prominence, the prominence being determined based on the difference between the frequency of the maximum and the frequency of the minimum adjacent to the maximum.

[0083] We can iterate through each measure value in the histogram from smallest to largest, i.e., from left to right, to identify multiple maxima in the histogram. Specifically, if the frequency corresponding to a measure value is higher than the frequencies of its two adjacent measure values, then that frequency can be identified as a maximum.

[0084] Since the aforementioned maxima can be very close, directly decomposing the measure value histogram based on these maxima would result in overly dense subgraphs that cover too small a range of measure values, leading to poor decomposition. Therefore, several representative target maxima that meet the criteria can be selected from the multiple maxima, and the measure value histogram can be decomposed based on these target maxima to ensure the effectiveness of the decomposition.

[0085] Based on the same order as when traversing each measure value in the measure histogram, multiple maxima are filtered according to preset filtering conditions to obtain the target maxima that satisfy the preset filtering conditions.

[0086] In the preset filtering conditions, the preset frequency is to ensure that the absolute frequency of the target maximum is large enough, so as to avoid the situation where the smaller frequency in the middle is determined as the target maximum in some regions because the frequencies of the adjacent measure values ​​are too small.

[0087] The preset filtering conditions also include ensuring that, when the maximum is not the first maximum, the distance between the measure value corresponding to the maximum and the measure value between the previously determined target maximum is greater than the preset interval, so as to ensure that the distance between the two target maximums is not too small, and to avoid the multiple subgraphs obtained by decomposition being too dense and covering too small a range of measure values ​​due to the distance between the two target maximums being too small, resulting in poor decomposition and fitting effects.

[0088] Furthermore, the preset filtering conditions include a maxima prominence greater than a preset prominence. This prominence is determined based on the difference between the frequency of the maxima and the frequency of the adjacent minima. The method for determining the minima is similar to that for the maxima; that is, if the frequency corresponding to a measure value is lower than the frequencies of its two adjacent measure values, then that frequency can be determined as the minima. For a maxima, its two adjacent minima are determined, and the larger of the two minima is selected. The frequency difference between the larger minima and the maxima is calculated, and this result is used as the prominence. By using prominence, the selected target maxima become more prominent, facilitating subsequent Gaussian fitting to obtain Gaussian peaks.

[0089] According to embodiments of this disclosure, by filtering target maxima based on multiple conditions such as frequency, spacing, and prominence, noisy maxima in the measure value histogram can be effectively eliminated. The filtered target maxima more closely match the signal characteristics of the real tissue, providing high-quality objects for subsequent Gaussian fitting, reducing the interference of noise on the fitting results, and improving the accuracy of Gaussian peaks.

[0090] According to embodiments of this disclosure, fitting at least one target maximum value of the histogram of the measurement values ​​of the image to be processed to obtain Gaussian peaks corresponding to each of the at least one target maximum value includes: for each target maximum value, determining a neighborhood corresponding to the target maximum value from the histogram of the measurement values ​​based on the target maximum value and a preset neighborhood size; and determining a Gaussian peak conforming to a Gaussian distribution from the neighborhood to decompose the histogram of the measurement values ​​using at least one Gaussian peak.

[0091] The preset neighborhood size represents the maximum range that can be referenced when fitting the target maximum. Based on the preset neighborhood size, the neighborhood corresponding to the target maximum is determined in the metric histogram with the target maximum as the center. That is, in the metric histogram, the range is defined on the left and right sides of the metric value corresponding to the target maximum with half of the preset neighborhood size, and the neighborhood corresponding to the target maximum is determined.

[0092] From this neighborhood, the frequency corresponding to the measure value is used as the mode to determine the maximum range that conforms to the Gaussian distribution. The distribution within this range is then fitted to obtain the Gaussian peak corresponding to the target maximum value.

[0093] After identifying the Gaussian peaks corresponding to at least one target maximum, these Gaussian peaks can be used as the decomposition results of the measure value histogram.

[0094] According to embodiments of this disclosure, a Gaussian peak is fitted after determining a neighborhood for each target maximum, achieving a refined local fit. The defined neighborhood range avoids the influence of irrelevant cross-regional data on fitting accuracy, enabling each Gaussian peak to accurately match the signal distribution around the corresponding target maximum, improving the clarity of histogram decomposition, and laying a reliable foundation for subsequent analysis.

[0095] According to embodiments of this disclosure, decomposing a measure value histogram using at least one Gaussian peak includes: narrowing the range of independent variables of the Gaussian peaks with overlapping portions when there is an overlap between the ranges of independent variables corresponding to at least one target maximum, until there is no overlap between the ranges of independent variables; and representing the measure value histogram using at least one Gaussian peak.

[0096] If there is an overlap between the independent variable ranges of Gaussian peaks corresponding to at least one target maximum, and the measure value histogram is directly decomposed based on the Gaussian peaks with the overlapping parts, the overlapping parts will be divided into multiple Gaussian peaks at the same time, resulting in data duplication in adjacent Gaussian peaks. Therefore, the independent variable ranges of the Gaussian peaks with the overlapping parts can be reduced until there is no longer any overlap between the independent variables of adjacent Gaussian peaks.

[0097] After the above processing, the histogram of the measure value can be decomposed and represented by at least one Gaussian peak with no overlapping parts.

[0098] According to embodiments of this disclosure, reducing the range of overlapping Gaussian peaks to non-overlapping areas avoids signal confusion caused by overlapping regions. Each processed Gaussian peak corresponds to an independent signal component, ensuring that the histogram decomposition results are clear and distinguishable, and subsequent analysis based on Gaussian peak measurements is more accurate, especially suitable for images with high tissue signal overlap.

[0099] Based on the above image processing method, this disclosure also provides an image processing apparatus. The following will be combined with... Figure 4 The device is described in detail.

[0100] Figure 4 A schematic block diagram of an image processing apparatus according to an embodiment of the present disclosure is shown.

[0101] like Figure 4 As shown, the image processing device 400 of this embodiment includes a data fitting module 410, a Gaussian filtering module 420, and an image processing module 430.

[0102] The data fitting module 410 is used to fit at least one target maximum value of the histogram of the measure values ​​of the image to be processed, to obtain Gaussian peaks corresponding to each of the at least one target maximum value. Here, the independent variable of the measure value histogram represents the measure value of a voxel in the image to be processed, the dependent variable represents the frequency corresponding to the measure value, and the target maximum value is determined from multiple maxima of the measure value histogram based on preset filtering conditions. In one embodiment, the data fitting module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0103] The Gaussian filtering module 420 is used to perform Gaussian filtering on the image to be processed, obtaining density image data representing the tissue density in the image to be processed. In one embodiment, the Gaussian filtering module 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0104] The image processing module 430 processes the histogram of metric values, Gaussian peaks, and density image data based on a threshold range determined according to the target object, to obtain an image processing result. The image processing result includes sub-images related to the target object and image features of these sub-images within the image to be processed. In one embodiment, the image processing module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0105] According to embodiments of the present disclosure, the image processing module 430 includes a first processing submodule, a second processing submodule, and a result summarization submodule.

[0106] The first processing submodule is used to process the histogram of the measure value and the Gaussian peak based on the first threshold interval to obtain the first parsing sub-result of the image to be processed.

[0107] The second processing submodule is used to process the density image data based on the second threshold interval to obtain the second parsing sub-result of the image to be processed.

[0108] The results summary submodule is used to determine the image processing results based on the first and second parsing sub-results.

[0109] According to embodiments of this disclosure, the first processing submodule includes an interception unit and a first statistics unit.

[0110] The truncation unit is used to truncate the range of measure values ​​of the measure value histogram and the Gaussian peak based on the first threshold interval, so as to obtain the measure value histogram and the Gaussian peak to be analyzed.

[0111] The first statistical unit is used to perform data statistics on the histogram of the measure values ​​to be analyzed and the Gaussian peak to be analyzed, and to obtain the first analytical sub-result.

[0112] According to embodiments of this disclosure, the second processing submodule includes a binary conversion unit and a second statistical unit.

[0113] The binary conversion unit is used to perform binary conversion on the density values ​​of multiple voxels in the density image data based on a second threshold interval to obtain binary density image data.

[0114] The second statistical unit is used to perform data statistics on at least one connected component determined based on binary density image data to obtain a second parse sub-result.

[0115] According to embodiments of the present disclosure, the image processing apparatus 400 further includes a histogram traversal module and a maximum value filtering module.

[0116] The histogram traversal module is used to traverse the histogram of measure values ​​in order of magnitude and determine the multiple maxima of the histogram of measure values.

[0117] The maximum value filtering module is used to filter multiple maxima according to the order of the measure values ​​to obtain target maxima that meet preset filtering conditions. The preset filtering conditions include: the frequency of the maxima is greater than the preset frequency; if the maxima is not the first maxima, the distance between the measure value corresponding to the maxima and the measure value of at least one previously determined target maxima is greater than the preset spacing; and the prominence of the maxima is greater than the preset prominence, which is determined based on the difference between the frequency of the maxima and the frequency of the minima adjacent to the maxima.

[0118] According to embodiments of this disclosure, the data fitting module 410 includes a neighborhood determination submodule and a Gaussian peak determination submodule.

[0119] The neighborhood determination submodule is used to determine the neighborhood corresponding to each target maximum value from the metric value histogram based on the target maximum value and a preset neighborhood size.

[0120] The Gaussian peak determination submodule is used to identify Gaussian peaks that conform to a Gaussian distribution from the neighborhood, so as to decompose the measure value histogram using at least one Gaussian peak.

[0121] According to embodiments of this disclosure, the Gaussian peak determination submodule includes a range reduction unit and a histogram decomposition unit.

[0122] The range reduction unit is used to reduce the range of independent variables of Gaussian peaks with overlapping portions when there is an overlap between the ranges of independent variables of Gaussian peaks corresponding to at least one target maximum, until there is no overlap between the ranges of independent variables.

[0123] Histogram decomposition unit, used to represent the histogram of measure values ​​using at least one Gaussian peak.

[0124] According to embodiments of this disclosure, any plurality of modules among the data fitting module 410, Gaussian filtering module 420, and image processing module 430 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules may be combined with at least a portion of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the data fitting module 410, Gaussian filtering module 420, and image processing module 430 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the data fitting module 410, Gaussian filtering module 420, and image processing module 430 may be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0125] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing an image processing method according to an embodiment of the present disclosure.

[0126] like Figure 5As shown, an electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0127] RAM 503 stores various programs and data required for the operation of electronic device 500. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0128] According to embodiments of this disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0129] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0130] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 502 and / or RAM 503 and / or one or more memories other than ROM 502 and RAM 503 described above.

[0131] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this disclosure.

[0132] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0133] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0134] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0135] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0136] 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 this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0137] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0138] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. An image processing method, characterized in that, The method includes: Fit at least one target maximum of the histogram of the measure values ​​of the image to be processed to obtain Gaussian peaks corresponding to each of the at least one target maximum, wherein the independent variable of the histogram of the measure values ​​represents the measure value of the voxel in the image to be processed, the dependent variable of the histogram of the measure values ​​represents the frequency corresponding to the measure value, and the target maximum is determined from multiple maxima of the histogram of the measure values ​​based on preset filtering conditions; The image to be processed is subjected to Gaussian filtering to obtain density image data representing the tissue density in the image to be processed; and Based on the threshold range determined according to the target object, the histogram of the measurement value, the Gaussian peak and the density image data are processed to obtain the image processing result, wherein the image processing result includes the sub-image related to the target object in the image to be processed and the image features of the sub-image.

2. The method according to claim 1, characterized in that, The threshold interval includes a first threshold interval and a second threshold interval, wherein the first threshold interval represents the range of measurement values ​​of the target object, and the second threshold interval represents the range of density values ​​of the target object; The image processing results are obtained by processing the histogram of the measurement value, the Gaussian peak, and the density image data based on a threshold range determined according to the target object, including: Based on the first threshold interval, the histogram of the measurement value and the Gaussian peak are processed to obtain the first parsing sub-result of the image to be processed; Based on the second threshold interval, the density image data is processed to obtain a second parsing sub-result of the image to be processed; and The image processing result is determined based on the first parsing sub-result and the second parsing sub-result.

3. The method according to claim 2, characterized in that, The first parsing sub-result of the image to be processed, based on the first threshold interval, involves processing the histogram of the measurement value and the Gaussian peak, including: Based on the first threshold interval, the histogram of the measurement values ​​and the range of the Gaussian peak are truncated to obtain the histogram of the measurement values ​​to be analyzed and the Gaussian peak to be analyzed; and The histogram of the measured value to be analyzed and the Gaussian peak to be analyzed are statistically analyzed to obtain the first analytical sub-result.

4. The method according to claim 3, characterized in that, The measurement value includes one of the following: gray value, voxel value, or calibration reference value; The first parsing sub-result includes at least one of the following: The average value of the individual voxels represented by the histogram of the measured values ​​to be analyzed and the Gaussian peak to be analyzed, the standard deviation of the individual voxels represented by the histogram of the measured values ​​to be analyzed and the Gaussian peak to be analyzed, the sum of the frequencies of the individual voxels represented by the histogram of the measured values ​​to be analyzed and the Gaussian peak to be analyzed, and the percentage of the sum of the frequencies of the individual voxels represented by the histogram of the measured values ​​to be analyzed and the Gaussian peak to be analyzed to the sum of the frequencies of the individual voxels in the image to be processed.

5. The method according to claim 2, characterized in that, The process of processing the density image data based on the second threshold interval to obtain the second parsing sub-result of the image to be processed includes: Based on the second threshold interval, the density values ​​of multiple voxels in the density image data are binarized to obtain binary density image data; and Data statistics are performed on at least one connected component determined based on the binary density image data to obtain a second parse sub-result.

6. The method according to claim 5, characterized in that, The second parsing sub-result includes at least one of the following: The center position of at least one of the connected components, the volume of at least one of the connected components, the surface area of ​​at least one of the connected components, the average value of the measure values ​​of multiple voxels in the density image data corresponding to at least one of the connected components, the standard deviation of the measure values ​​of multiple voxels in the density image data corresponding to at least one of the connected components, the maximum value of the measure values ​​of multiple voxels in the density image data corresponding to at least one of the connected components, and the minimum value of the measure values ​​of multiple voxels in the density image data corresponding to at least one of the connected components.

7. The method according to claim 1, characterized in that, The target maximum value is determined in the following way: The histogram of measure values ​​is traversed in order of magnitude to determine multiple maxima of the histogram of measure values. According to the order of the magnitude of the measured values, multiple maximum values ​​are filtered to obtain the target maximum value that satisfies the preset filtering conditions; The preset filtering conditions include: the frequency of the maximum value is greater than a preset frequency; when the maximum value is not the first maximum value, the distance between the measure value corresponding to the maximum value and the measure value of at least one previously determined target maximum value is greater than a preset spacing; and the prominence of the maximum value is greater than a preset prominence, wherein the prominence is determined based on the difference between the frequency of the maximum value and the frequency of the minimum value adjacent to the maximum value.

8. The method according to claim 1, characterized in that, The step of fitting at least one target maximum of the histogram of the measure values ​​of the image to be processed to obtain a Gaussian peak corresponding to each of the at least one target maximum includes: For each target maximum, based on the target maximum and a preset neighborhood size, a neighborhood corresponding to the target maximum is determined from the metric histogram; and Determine Gaussian peaks that conform to a Gaussian distribution from the neighborhood, and decompose the measure value histogram using at least one of the Gaussian peaks.

9. The method according to claim 8, characterized in that, The step of decomposing the histogram of the measure values ​​using at least one of the Gaussian peaks includes: If there is an overlap between the independent variable ranges of Gaussian peaks corresponding to at least one of the target maxima, the independent variable ranges of the overlapping Gaussian peaks are narrowed until there is no overlap between the independent variable ranges; and The histogram of the measured values ​​is represented by at least one of the Gaussian peaks.

10. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 9.

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