Lower limb vein 3D imaging method and system

Abstract

The invention relates to the technical field of medical imaging, and discloses a lower limb vein 3D imaging method, which comprises the following steps: identifying an imaging range of a lower limb vein patient, and collecting a lower limb slice image of the lower limb vein patient; analyzing the pixel distribution condition of the tissue type, and identifying a venous blood pixel intensity factor of the patient with the lower limb vein; constructing a pixel integral and an image of the corrected image, performing image segmentation processing on the corrected image to obtain a segmented image, and determining a vein blood vessel of the patient with the lower limb vein; analyzing the influence degree of the feature type on the lower limb vein patient, and performing dimension reduction processing on the vein features to obtain key vein features; and according to the key vein features, the pixel integral and the image, constructing a lower limb vein 3D image of the lower limb vein patient, analyzing an imaging effect of the lower limb vein 3D image on a vein blood vessel, and based on the imaging effect, outputting a 3D imaging result of the lower limb vein patient. The three-dimensional structure and the spatial relation of the lower limb veins can be visually displayed, and the accuracy of diagnosis results is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical imaging technology, and in particular to a 3D imaging method and system for lower limb veins. Background Art

[0002] Lower limb vein 3D imaging refers to the process of using radiographic imaging technology to depict the lower limb veins and their surrounding structures to generate a three-dimensional image of the lower limb venous system. By using 3D imaging technology, it is possible to comprehensively evaluate the anatomical structure and pathological conditions of the lower limb venous system from multiple angles, which is of great value for diagnosing deep vein thrombosis (DVT), varicose veins and other diseases.

[0003] Traditional lower limb venous imaging mainly uses X-ray venography, which injects contrast agents and uses X-ray imaging technology to display the lower limb venous structure. Although this method can provide a clearer view of the venous cavity, it generates a two-dimensional image and may not be able to intuitively display the three-dimensional structure and spatial relationship of the blood vessels, resulting in a lack of accuracy and reliability in the diagnostic results. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a 3D imaging method and system for lower limb veins, which can intuitively display the three-dimensional structure and spatial relationship of the lower limb veins and improve the accuracy of the diagnosis results.

[0005] In a first aspect, the present invention provides a method for 3D imaging of lower extremity veins, comprising:

[0006] Acquire a patient with lower limb veins to be scanned, identify an imaging range of the patient with lower limb veins, acquire a lower limb slice image of the patient with lower limb veins based on the imaging range, and perform artifact correction processing on the lower limb slice image to obtain a corrected image;

[0007] Collecting an image histogram of the patient with lower limb veins, and identifying the tissue type of the patient with lower limb veins, analyzing the pixel distribution of the tissue type based on the image histogram, and identifying the venous blood pixel intensity factor of the patient with lower limb veins according to the pixel distribution;

[0008] Based on the venous blood pixel intensity factor, construct a pixel integral and image of the corrected image, perform image segmentation processing on the corrected image according to the pixel integral and image to obtain a segmented image, and determine the venous blood vessels of the lower limb vein patient based on the segmented image;

[0009] Extracting the vein features of the venous blood vessels, identifying the feature types corresponding to the vein features, analyzing the degree of influence of the feature types on the lower limb vein patients, and based on the degree of influence, performing dimensionality reduction processing on the vein features to obtain key vein features;

[0010] According to the key vein features and the pixel integral and image, a 3D imaging of the lower limb veins of the lower limb vein patient is constructed, the imaging effect of the 3D imaging of the lower limb veins on the venous blood vessels is analyzed, and based on the imaging effect, the 3D imaging result of the lower limb vein patient is output.

[0011] In a possible implementation manner of the first aspect, performing artifact correction processing on the lower limb slice image to obtain a corrected image includes:

[0012] Analyzing causes of artifacts in the lower limb slice images;

[0013] Based on the cause of the artifact formation, identifying the artifact type of the lower limb slice image;

[0014] According to the artifact type, setting an artifact correction mode for the lower limb slice image;

[0015] Based on the artifact correction method, the lower limb slice image is subjected to artifact correction processing to obtain a corrected image.

[0016] In a possible implementation manner of the first aspect, analyzing the pixel distribution of the tissue type based on the image histogram includes:

[0017] Dispatching radiological image data corresponding to the image histogram;

[0018] identifying tissue characteristics of the tissue type in the radiological image data;

[0019] Based on the tissue characteristics, locate the distribution position of the tissue type in the image histogram;

[0020] Identifying a pixel peak of the tissue type in the image histogram;

[0021] Analyzing peak characteristics of the pixel peaks according to the tissue characteristics and the distribution positions;

[0022] The pixel distribution of the tissue type is analyzed in combination with the tissue characteristics and the peak characteristics.

[0023] In a possible implementation manner of the first aspect, constructing the pixel integral and image of the corrected image based on the venous blood pixel intensity factor includes:

[0024] Based on the venous blood pixel intensity factor, setting a pixel selection threshold of the corrected image;

[0025] Identifying unit pixels of the corrected image;

[0026] identifying venous blood pixels and non-venous blood pixels from the unit pixels according to the pixel selection threshold;

[0027] generating a binary image of the corrected image based on the venous blood pixels and the non-venous blood pixels;

[0028] Based on the binarized image, a pixel integral sum image of the corrected image is constructed.

[0029] In a possible implementation of the first aspect, extracting the vein feature of the venous blood vessel includes:

[0030] identifying a vascular structure of the venous blood vessel, and based on the vascular structure, identifying a vascular branch of the venous blood vessel;

[0031] extracting the branch structure and branch angle of the blood vessel branch;

[0032] Analyzing the hemodynamic characteristics of the venous blood vessel according to the branch structure and the branch angle;

[0033] Identifying the texture characteristics of the blood vessel branches, and analyzing changes in the blood vessel walls of the venous vessels based on the texture characteristics;

[0034] Analyzing the functional status of the venous blood vessels according to the hemodynamic characteristics;

[0035] The vein characteristics of the venous blood vessel are extracted by combining the changes in the blood vessel wall and the functional state.

[0036] In a possible implementation of the first aspect, analyzing the degree of influence of the characteristic type on the lower limb vein patient includes:

[0037] Extracting feature items corresponding to the feature type;

[0038] Analyzing the lower limb vein risk of the lower limb vein patient according to the feature item and the feature type;

[0039] Identify the physiological impact of the leg vein risk on the leg vein patient;

[0040] Calculating a symptom severity coefficient corresponding to the physiological impact;

[0041] According to the symptom severity coefficient, the influence of the characteristic type on the lower limb vein patients is analyzed.

[0042] In a possible implementation of the first aspect, analyzing the lower limb vein risk of the lower limb vein patient according to the feature item and the feature type includes:

[0043] Identifying the venous pathological characteristics of the lower limb vein patient according to the characteristic items and the characteristic types;

[0044] Based on the venous pathological characteristics, identifying the patient risk level of the lower extremity venous patient;

[0045] Analyze the disease type corresponding to the patient's risk level, and analyze the cause of the disease type;

[0046] The lower limb vein risk of the lower limb vein patient is analyzed based on the formation causes and the patient risk level.

[0047] In a possible implementation of the first aspect, performing dimensionality reduction processing on the vein feature based on the influence degree to obtain the key vein feature includes:

[0048] Extracting impact features corresponding to the impact degree;

[0049] Identifying a characteristic attribute corresponding to the vein feature;

[0050] Calculating the feature fit between the feature attribute and the influencing feature;

[0051] According to the feature fit, the vein feature is subjected to dimensionality reduction processing to obtain key vein features.

[0052] In a possible implementation of the first aspect, analyzing the imaging effect of the lower limb vein 3D imaging includes:

[0053] Identifying the venous structure of the lower extremity vein 3D imaging, and analyzing the recognizability and detail distinction of the venous structure;

[0054] Determining the visualization quality of the lower limb vein 3D imaging according to the identifiability and the detail distinction;

[0055] Dispatching lower limb vein patients and venous vessels corresponding to the lower limb vein 3D imaging;

[0056] Analyzing the ability of the lower limb vein 3D imaging to identify the pathology of the lower limb vein patients;

[0057] Based on the 3D imaging of the lower extremity veins, analyzing the diseased vessel display rate of the venous vessels;

[0058] The imaging effect of the lower limb vein 3D imaging is analyzed by combining the visualization quality, the pathology recognition capability and the diseased blood vessel display rate.

[0059] In a second aspect, the present invention provides a lower limb vein 3D imaging system, characterized in that the system comprises:

[0060] An image acquisition module is used to acquire a patient with lower limb veins to be scanned, identify an imaging range of the patient with lower limb veins, acquire a lower limb slice image of the patient with lower limb veins based on the imaging range, and perform artifact correction processing on the lower limb slice image to obtain a corrected image;

[0061] A pixel recognition module is used to collect the image histogram of the patient with lower limb veins and identify the tissue type of the patient with lower limb veins, analyze the pixel distribution of the tissue type based on the image histogram, and identify the venous blood pixel intensity factor of the patient with lower limb veins according to the pixel distribution;

[0062] An image segmentation module, used for constructing a pixel integral and image of the corrected image based on the venous blood pixel intensity factor, performing image segmentation processing on the corrected image according to the pixel integral and image to obtain a segmented image, and determining the venous blood vessels of the lower limb vein patient based on the segmented image;

[0063] A feature extraction module is used to extract the vein features of the venous blood vessels, identify the feature types corresponding to the vein features, analyze the influence of the feature types on the lower limb vein patients, and based on the influence, perform dimensionality reduction processing on the vein features to obtain key vein features;

[0064] The 3D imaging module is used to construct a 3D imaging of the lower limb veins of the patient with lower limb veins according to the key vein features and the pixel integral and image, analyze the imaging effect of the 3D imaging of the lower limb veins on the venous blood vessels, and output the 3D imaging results of the patient with lower limb veins based on the imaging effect.

[0065] Compared with the prior art, the technical principle and beneficial effects of this solution are:

[0066] The embodiment of the present invention can provide a detailed view of the internal structure of the patient's lower limbs by collecting lower limb slice images of the patient's lower limb veins based on the imaging range, help doctors discover and evaluate lesions, provide necessary anatomical information for surgery or interventional treatment, and provide a more accurate data basis for subsequent image processing steps; further, the embodiment of the present invention can provide the distribution of different pixel intensities in the image by collecting the image histogram of the patient's lower limb veins and identifying the tissue type of the patient's lower limb veins, so as to help determine the threshold for image segmentation, thereby more accurately separating the veins and other tissues; secondly, the embodiment of the present invention can quickly calculate the sum of pixel values ​​in an arbitrary shape region by constructing the pixel integral and image of the corrected image based on the venous blood pixel intensity factor, accelerate the regional growing algorithm, help achieve accurate image segmentation and registration, and perform image segmentation based on the pixel integral and image, thereby enhancing the efficiency and quality of feature extraction in the 3D reconstruction process. At the same time, accurate segmentation results can improve the quality of 3D visualization, so that the 3D model more realistically reflects the actual solution. Anatomical structure; Again, the embodiment of the present invention can help doctors understand the anatomical structure of the lower limb vein more intuitively and enhance the diagnostic ability of lower limb venous diseases by extracting the venous features of the venous blood vessels and identifying the feature types corresponding to the venous features. By performing dimensionality reduction processing on the venous features to obtain key venous features, the most important venous features for the diagnosis and treatment of venous diseases can be identified and highlighted, the visualization effect of 3D imaging can be enhanced, and a more accurate anatomical structure can be provided for doctors; Finally, the embodiment of the present invention can comprehensively understand the anatomical structure of the lower limb veins by constructing the lower limb vein 3D imaging of the lower limb vein patient according to the key venous features and the pixel integral and image, provide intuitive imaging basis for the selection of treatment plans and the evaluation of postoperative effects, improve the accuracy of diagnostic effects and the safety of surgery, and output the 3D imaging results of the lower limb vein patient based on the imaging effect, provide accurate venous structure information, and intuitively display the three-dimensional structure and spatial relationship of the lower limb vein vessels, improve the accuracy and reliability of the diagnostic results, and thus improve the quality and efficiency of medical services. Therefore, the 3D imaging method of the lower limb veins proposed by the present invention can intuitively display the three-dimensional structure and spatial relationship of the lower limb veins and improve the accuracy of the diagnostic results. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0069] Figure 1 A schematic diagram of a process of a 3D imaging method for lower extremity veins provided by an embodiment of the present invention;

[0070] Figure 2 A schematic diagram of the modules of a lower limb vein 3D imaging system provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0071] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0072] The embodiment of the present invention provides a 3D imaging method for lower limb veins, and the execution subject of the 3D imaging method for lower limb veins includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present invention. In other words, the 3D imaging method for lower limb veins can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content De li very Network, CDN), and big data and artificial intelligence platforms.

[0073] See also Figure 1 FIG. 1 is a flow chart of a method for 3D imaging of lower extremity veins provided by an embodiment of the present invention. Figure 1 The 3D imaging methods for lower extremity veins described in the present invention include:

[0074] S1. Acquire a patient with lower limb veins to be scanned, and identify the imaging range of the patient with lower limb veins. Based on the imaging range, acquire a lower limb slice image of the patient with lower limb veins, and perform artifact correction processing on the lower limb slice image to obtain a corrected image.

[0075] The embodiment of the present invention can clarify the specific parts that need to be imaged and reduce unnecessary radiation by acquiring a patient with lower limb veins to be scanned and identifying the imaging range of the patient with lower limb veins. The patient with lower limb veins refers to an individual who undergoes an imaging examination of the lower limb vein system, and the imaging range refers to the specific body parts or areas that need special attention and coverage when performing a lower limb vein imaging examination, such as the calf, knee, thigh, etc.

[0076] Optionally, the imaging range identification of the lower extremity vein patient can be determined by the patient's clinical needs.

[0077] Furthermore, the embodiment of the present invention can provide a detailed view of the internal structure of the patient's lower limbs by collecting lower limb slice images of the patient's lower limb veins based on the imaging range, helping doctors to discover and evaluate lesions and provide necessary anatomical information for surgery or interventional treatment. The lower limb slice image refers to a two-dimensional cross-sectional image showing the internal structure of the lower limb obtained by radiological imaging technology.

[0078] Optionally, based on the imaging range, the lower limb slice image acquisition of the lower limb vein patient can be achieved by using magnetic resonance imaging.

[0079] The embodiment of the present invention performs artifact correction processing on the lower limb slice image to obtain a corrected image, which can provide a more accurate data basis for subsequent image processing steps. The corrected image refers to a two-dimensional image after artifact correction processing.

[0080] As an embodiment of the present invention, the artifact correction processing of the lower limb slice image to obtain a corrected image includes: analyzing the cause of artifact formation of the lower limb slice image; identifying the artifact type of the lower limb slice image based on the artifact formation cause; setting an artifact correction method for the lower limb slice image according to the artifact type; and performing artifact correction processing on the lower limb slice image based on the artifact correction method to obtain a corrected image.

[0081] Among them, the cause of artifact formation refers to various reasons that lead to image distortion or erroneous performance, such as patient movement, equipment failure, magnetic field unevenness, etc. The artifact type refers to the artifact attribute obtained after classification according to the morphology and characteristics of the artifact, such as motion artifact, ringing artifact, chemical shift artifact, etc. The artifact correction method refers to the correction measures or algorithms taken for specific artifact types, such as deblurring filters, frequency compensation, etc.

[0082] Optionally, the cause of artifact formation in the lower limb slice image can be analyzed by observing image features, understanding the situation during the scanning process and the technical specifications of the radiological examination equipment. Based on the cause of artifact formation, the artifact type identification of the lower limb slice image can be achieved using image analysis tools, such as OpenCV image processing library tools. According to the artifact type, the artifact correction method setting of the lower limb slice image can be determined by the correction tool built into the device.

[0083] S2. Collect the image histogram of the patient with lower limb veins and identify the tissue type of the patient with lower limb veins. Based on the image histogram, analyze the pixel distribution of the tissue type. According to the pixel distribution, identify the venous blood pixel intensity factor of the patient with lower limb veins.

[0084] The embodiment of the present invention can provide the distribution of different pixel intensities in the image by collecting the image histogram of the patient with lower limb veins and identifying the tissue type of the patient with lower limb veins, helping to determine the threshold for image segmentation, thereby more accurately separating the veins and other tissues. The image histogram refers to a statistical graph that describes the distribution of all pixel values ​​in the image, and the tissue type refers to different lower limb body tissues that can be observed in the radiological image of the lower limb venous system, such as the lower limb muscle groups.

[0085] Optionally, the image histogram acquisition of the lower limb vein patient can be achieved using radiological image viewing software, such as Osirix software, and the tissue type identification of the lower limb vein patient can be obtained through image data analysis software, such as Horos software.

[0086] Furthermore, the embodiments of the present invention can identify pixel intensities that are different from normal tissues and help discover abnormal areas by analyzing the pixel distribution of the tissue type based on the image histogram. The pixel distribution refers to the pixel value distribution characteristics of different tissue types (such as veins, muscles, fat, bones, etc.) in radiological images (such as CT, MRI) on the image.

[0087] As an embodiment of the present invention, the analysis of the pixel distribution of the tissue type based on the image histogram includes: scheduling the radiological image data corresponding to the image histogram; identifying the tissue characteristics of the tissue type in the radiological image data; locating the distribution position of the tissue type in the image histogram based on the tissue characteristics; identifying the pixel peak of the tissue type in the image histogram; analyzing the peak characteristics of the pixel peak according to the tissue characteristics and the distribution position; and analyzing the pixel distribution of the tissue type in combination with the tissue characteristics and the peak characteristics.

[0088] Among them, the radiological image data refers to medical image data obtained through radiological examination, the tissue characteristics refer to the unique properties of different tissues in radiological images, such as signal intensity, density, morphology and texture, etc., the distribution position refers to the pixel value range of different tissue types in the image histogram, the pixel peak refers to the point or area in the histogram that represents the highest frequency of a specific pixel value, and the peak feature refers to the properties of the peak such as shape, height, width and position.

[0089] Optionally, the identification of tissue characteristics of the tissue type in the radiological image data can be automatically achieved using a deep learning algorithm, such as the TensorFlow algorithm. Based on the tissue characteristics, the distribution position of the tissue type in the image histogram can be determined by combining the tissue characteristics and a known pixel value range. Based on the tissue characteristics and the distribution position, the peak feature analysis of the pixel peak can be achieved using MATLAB image processing software.

[0090] In an optional embodiment of the present invention, the pixel peak of the tissue type in the image histogram is identified using the following formula:

[0091]

[0092] Among them, Peak(i) represents the pixel peak value of the tissue type in the image histogram, Q(i) represents the frequency of the i-th pixel value of the tissue type on the image histogram, Q(i-1) represents the frequency of the adjacent pixel values ​​to the left of the i-th pixel value of the tissue type on the image histogram, and Q(i+1) represents the frequency of the adjacent pixel values ​​to the right of the i-th pixel value of the tissue type on the image histogram.

[0093] It should be noted that Peak(i) represents the pixel peak value of the tissue type in the image histogram when Q(i)>Q(i-1) and Q(i+1). Otherwise, Peak(i) is assigned 0, indicating that this is not a peak value.

[0094] Furthermore, the embodiment of the present invention helps to accurately segment the veins in the image by identifying the venous blood pixel intensity factor of the lower limb vein patient based on the pixel distribution, thereby facilitating subsequent vein feature extraction. The venous blood pixel intensity factor refers to a set of parameters or thresholds used to identify and quantify pixel value features in venous blood.

[0095] Optionally, according to the pixel distribution, the identification of venous blood pixel intensity factor of the lower limb vein patient can be achieved by enhancing the contrast of the image using histogram equalization technology.

[0096] S3. Based on the venous blood pixel intensity factor, construct the pixel integral and image of the corrected image, perform image segmentation processing on the corrected image according to the pixel integral and image to obtain a segmented image, and determine the venous blood vessels of the lower limb vein patient based on the segmented image.

[0097] The embodiment of the present invention constructs the pixel integral and image of the corrected image based on the venous blood pixel intensity factor, so as to quickly calculate the sum of pixel values ​​in an area of ​​arbitrary shape, accelerate the region growing algorithm, and help achieve accurate image segmentation and registration. The pixel integral and image refers to a data structure used to quickly calculate the image sub-region features of the radiological images of patients with lower limb veins.

[0098] As an embodiment of the present invention, constructing the pixel integral and image of the corrected image based on the venous blood pixel intensity factor includes: setting a pixel selection threshold of the corrected image based on the venous blood pixel intensity factor; identifying unit pixels of the corrected image; identifying venous blood pixels and non-venous blood pixels from the unit pixels according to the pixel selection threshold; generating a binary image of the corrected image based on the venous blood pixels and the non-venous blood pixels; and constructing the pixel integral and image of the corrected image based on the binary image.

[0099] Among them, the pixel selection threshold refers to the brightness or intensity boundary used to distinguish venous blood pixels from non-venous blood pixels, the unit pixel refers to each individual pixel in the corrected image, the venous blood pixel refers to a pixel identified as venous blood according to the set pixel selection threshold, the non-venous blood pixel refers to a pixel identified as non-venous blood according to the set pixel selection threshold, and the binary image refers to an image obtained by converting the original image into an image containing only two pixel values.

[0100] Optionally, the pixel selection threshold setting of the corrected image based on the venous blood pixel intensity factor can be achieved using an indicator function formula, and the binary image generation of the corrected image based on the venous blood pixels and the non-venous blood pixels can be determined by a multi-threshold segmentation method.

[0101] Furthermore, the embodiment of the present invention performs image segmentation processing on the corrected image according to the pixel integral and image to obtain a segmented image, which can enhance the feature extraction efficiency and quality in the 3D reconstruction process. At the same time, accurate segmentation results can improve the quality of 3D visualization, so that the 3D model more realistically reflects the actual anatomical structure. The segmented image refers to an image of a specific area or object extracted from the original image by image segmentation technology, such as an image of the lower limb veins.

[0102] Optionally, the image segmentation processing of the corrected image based on the pixel integral and image can be implemented using a region growing algorithm.

[0103] The embodiment of the present invention determines the venous vessels of the lower limb vein patient based on the segmented image, and can accurately visualize the structure and direction of the lower limb vein vessels in three-dimensional space, helping doctors obtain more comprehensive information about the patient's lower limb veins, thereby improving the accuracy of diagnosis and treatment effects. The venous vessels refer to specific tissue structures, such as venous lumens, identified and extracted from the radiological image data of the lower limb vein patient through image segmentation technology.

[0104] S4. Extract the vein features of the venous blood vessels, identify the feature types corresponding to the vein features, analyze the degree of influence of the feature types on the lower limb vein patients, and based on the degree of influence, perform dimensionality reduction processing on the vein features to obtain key vein features.

[0105] The embodiment of the present invention can help doctors understand the anatomical structure of the lower limb veins more intuitively and enhance the diagnostic ability of lower limb venous diseases by extracting the venous features of the venous vessels and identifying the feature types corresponding to the venous features. The venous features refer to a series of quantitative and qualitative attributes extracted from the radiological imaging data of the lower limb venous vessels, such as geometric features, hemodynamic features, signal features, etc.

[0106] As an embodiment of the present invention, the extraction of the venous characteristics of the venous blood vessel includes: identifying the vascular structure of the venous blood vessel, and based on the vascular structure, identifying the vascular branches of the venous blood vessel; extracting the branch structure and branch angles of the vascular branches; analyzing the hemodynamic characteristics of the venous blood vessel based on the branch structure and the branch angle; identifying the texture characteristics of the vascular branches, and based on the texture characteristics, analyzing the changes in the vascular wall of the venous blood vessel; analyzing the functional status of the venous blood vessel based on the hemodynamic characteristics; and extracting the venous characteristics of the venous blood vessel in combination with the changes in the vascular wall and the functional status.

[0107] Among them, the vascular structure refers to the anatomical morphology of the veins, including the direction, distribution, connection relationship, etc. of the blood vessels; the vascular branches refer to the smaller blood vessels branching out from the main trunk of the veins; the branch structure refers to the morphology and distribution of the vascular branches; the branch angle refers to the angle between the branch and the main blood vessel or other branches; the hemodynamic characteristics refer to the characteristics of blood flow in the veins, including flow velocity, flow rate, direction, etc.; the texture characteristics refer to the surface characteristics of the blood vessel wall, such as smoothness, roughness, etc.; the changes in the blood vessel wall refer to pathological changes such as the thickness, elasticity, inflammation, etc. of the blood vessel wall; the functional state refers to the physiological functional state of the veins, such as whether they are unobstructed, and whether there is stenosis or blockage.

[0108] Optionally, the branch structure extraction of the blood vessel branch can be achieved by using the intersection detection method, the hemodynamic characteristics analysis of the venous blood vessel based on the branch structure and the branch angle can be determined by using hemodynamic simulation software, and the analysis of the changes in the vascular wall of the venous blood vessel based on the texture characteristics can be achieved by comparing and analyzing the texture characteristics of the blood vessel branch and the texture characteristics of the normal blood vessel wall.

[0109] Furthermore, the embodiment of the present invention can identify the key venous features that affect the patients with lower limb vein disease and improve the accuracy of venous structure identification by analyzing the degree of influence of the feature types on the patients with lower limb vein disease, wherein the degree of influence refers to the specific scope and severity of the influence of different feature types on the health status of patients with lower limb vein disease.

[0110] As an embodiment of the present invention, the analysis of the degree of influence of the feature type on the lower limb vein patient includes: extracting feature items corresponding to the feature type; analyzing the lower limb vein risk of the lower limb vein patient based on the feature items and the feature type; identifying the physiological impact of the lower limb vein risk on the lower limb vein patient; calculating the symptom severity coefficient corresponding to the physiological impact; and analyzing the degree of influence of the feature type on the lower limb vein patient based on the symptom severity coefficient.

[0111] Among them, the characteristic items refer to specific parameters or indicators extracted from the vein characteristic types, such as blood flow velocity and flow, venous valve function, degree of varicose veins, etc. The lower limb venous risk refers to the potential health risks related to the venous characteristic items, such as phlebitis. The physiological impact refers to the specific impact of lower limb venous risk on the physical function and health status of lower limb venous patients, such as skin changes. The symptom severity coefficient refers to the degree coefficient obtained after quantitative evaluation of the patient's venous disease symptoms.

[0112] Optionally, the lower limb venous risk analysis corresponding to the feature type based on the feature item can be achieved by utilizing the correlation between the feature item and the lower limb venous risk, and the identification of the physiological impact of the lower limb venous risk on the lower limb venous patient can be obtained through CT imaging examination technology.

[0113] In an optional embodiment of the present invention, the symptom severity coefficient corresponding to the physiological impact is calculated using the following formula:

[0114]

[0115] Among them, C represents the symptom severity coefficient corresponding to the physiological impact, B i represents the score of physiological impact in the i-th symptom severity assessment, C irepresents the weight coefficient in the i-th symptom severity assessment, D j represents the adjustment coefficient corresponding to the jth physiological impact, n represents the total number of evaluations corresponding to the physiological impact, i represents the evaluation number index, m represents the total number corresponding to the physiological impact, and j represents the index number of the physiological impact.

[0116] As another embodiment of the present invention, analyzing the lower limb vein risk of the lower limb vein patient according to the feature items and the feature types includes: identifying the venous pathology characteristics of the lower limb vein patient according to the feature items and the feature types; identifying the patient risk level of the lower limb vein patient based on the venous pathology characteristics; analyzing the disease type corresponding to the patient risk level, and analyzing the formation cause corresponding to the disease type; analyzing the lower limb vein risk of the lower limb vein patient according to the formation cause and the patient risk level.

[0117] Among them, the venous pathological characteristics refer to the characteristics that describe abnormal conditions in the lower limb venous system, such as stenosis; the patient risk level refers to dividing lower limb venous patients into different risk groups according to the venous pathological characteristics and other relevant factors; the disease type refers to the specific disease that the patient may suffer from, such as deep vein thrombosis, varicose veins, etc.; the cause of formation refers to the specific cause of lower limb venous disease, such as genetic factors.

[0118] Optionally, the identification of patient risk level of the lower limb vein patients based on the venous pathological characteristics can be achieved using a risk scoring model.

[0119] The embodiment of the present invention obtains key vein features by performing dimensionality reduction processing on the vein features based on the degree of influence, and can identify and highlight the vein features that are most important for the diagnosis and treatment of venous diseases, enhance the visualization effect of 3D imaging, and provide doctors with more accurate anatomical structures. The key vein features refer to vein attributes that are crucial for diagnosis, treatment planning, disease monitoring and research, such as lesion characteristics.

[0120] As an embodiment of the present invention, based on the influence degree, the vein feature is subjected to dimensionality reduction processing to obtain key vein features, including: extracting the influencing feature corresponding to the influence degree; identifying the feature attribute corresponding to the vein feature; calculating the feature fit between the feature attribute and the influencing feature; and according to the feature fit, the vein feature is subjected to dimensionality reduction processing to obtain key vein features.

[0121] Among them, the influencing feature refers to the feature item that has a significant impact on the identification of the degree of influence of lower limb vein patients, the feature attribute refers to the specific property or category of the feature, such as geometric property, morphological property, hemodynamic property, and the feature fit refers to the correlation or matching degree between the feature attribute and the influencing feature.

[0122] In an optional embodiment of the present invention, the feature fit between the feature attribute and the influencing feature is calculated using the following formula:

[0123]

[0124] Among them, K represents the degree of fit between the feature attribute and the feature influencing the feature, and E a represents the attribute value of the ath feature attribute, F a Indicates the attribute value of the a-th feature attribute, max(E a ,F a ) represents the maximum value of the attribute value of the ath feature attribute and its corresponding attribute value affecting the feature, r represents the number of identical features of the feature attribute and its corresponding attribute affecting the feature, and a represents the serial number of the feature attribute.

[0125] S5. Construct a 3D imaging of the lower limb veins of the patient with lower limb veins according to the key vein features and the pixel integral and image, analyze the imaging effect of the 3D imaging of the lower limb veins, and output the 3D imaging result of the patient with lower limb veins based on the imaging effect.

[0126] The embodiment of the present invention constructs 3D imaging of the lower limb veins of the patient with lower limb veins based on the key vein features and the pixel integral and image, so as to fully understand the anatomical structure of the lower limb veins, provide intuitive imaging basis for the selection of treatment plans and the evaluation of postoperative effects, and improve the accuracy of diagnostic effects and the safety of surgery. The 3D imaging of the lower limb veins refers to a digital representation of the lower limb vein structure presented in three-dimensional space.

[0127] Optionally, the construction of the 3D imaging of the lower limb veins of the lower limb vein patient based on the key vein features and the pixel integral and image can be achieved using a three-dimensional reconstruction algorithm, such as a Marching Cubes algorithm.

[0128] Furthermore, the embodiment of the present invention can verify the imaging quality of the lower limb vein 3D imaging by analyzing the imaging effect of the lower limb vein 3D imaging, and determine the application scope of the lower limb vein 3D imaging according to the imaging content. The imaging effect refers to the visual quality and information content of the medical image.

[0129] As an embodiment of the present invention, the analysis of the imaging effect of the lower limb vein 3D imaging includes: identifying the venous structure of the lower limb vein 3D imaging, and analyzing the recognizability and detail distinction of the venous structure; determining the visualization quality of the lower limb vein 3D imaging based on the recognizability and detail distinction; scheduling lower limb vein patients and venous vessels corresponding to the lower limb vein 3D imaging; analyzing the pathological recognition ability of the lower limb vein 3D imaging for the lower limb vein patients; based on the lower limb vein 3D imaging, analyzing the diseased vessel display rate of the venous vessels; and analyzing the imaging effect of the lower limb vein 3D imaging in combination with the visualization quality, the pathological recognition ability and the diseased vessel display rate.

[0130] Optionally, the analysis of the recognizability and detail distinction of the venous structure can be achieved using image resolution, and the analysis of the pathological recognition ability of the lower limb vein 3D imaging for the lower limb vein patient can be determined by constructing a three-dimensional model of the lower limb veins, and the analysis of the display rate of venous diseased vessels based on the lower limb vein 3D imaging can be achieved using phase imaging.

[0131] The embodiment of the present invention outputs the 3D imaging results of the lower limb vein patient based on the imaging effect, can provide accurate vein structure information, and intuitively display the three-dimensional structure and spatial relationship of the lower limb veins, thereby improving the accuracy and reliability of the diagnosis results, thereby improving the quality and efficiency of medical services. The 3D imaging results refer to a three-dimensional image or model obtained by radiological imaging technology, such as the three-dimensional morphology of the lower limb bones.

[0132] It can be seen that the embodiment of the present invention can provide a detailed view of the internal structure of the patient's lower limbs by collecting the lower limb slice images of the patient's lower limb vein based on the imaging range, help doctors discover and evaluate lesions, provide necessary anatomical information for surgery or interventional treatment, and provide a more accurate data basis for subsequent image processing steps; further, the embodiment of the present invention can provide the distribution of different pixel intensities in the image by collecting the image histogram of the patient's lower limb vein and identifying the tissue type of the patient's lower limb vein, so as to help determine the threshold for image segmentation, thereby more accurately separating the veins and other tissues; secondly, the embodiment of the present invention can quickly calculate the sum of pixel values ​​in an arbitrary shape region by constructing the pixel integral and image of the corrected image based on the venous blood pixel intensity factor, accelerate the regional growing algorithm, help achieve accurate image segmentation and registration, and perform image segmentation based on the pixel integral and image, thereby enhancing the efficiency and quality of feature extraction in the 3D reconstruction process. At the same time, accurate segmentation results can improve the quality of 3D visualization, so that the 3D model more realistically reflects the actual Second, the embodiment of the present invention can help doctors understand the anatomical structure of the lower limb vein more intuitively, enhance the diagnostic ability of lower limb venous diseases, and obtain key venous features by performing dimensionality reduction processing on the venous features, which can identify and highlight the most important venous features for the diagnosis and treatment of venous diseases, enhance the visualization effect of 3D imaging, and provide doctors with more accurate anatomical structures; Finally, the embodiment of the present invention can comprehensively understand the anatomical structure of the lower limb vein by constructing the lower limb vein 3D imaging of the lower limb vein patient according to the key venous features and the pixel integral and image, provide intuitive imaging basis for the selection of treatment plans and the evaluation of postoperative effects, improve the accuracy of diagnostic effects and the safety of surgery, and output the 3D imaging results of the lower limb vein patient based on the imaging effect, which can provide accurate venous structure information, intuitively display the three-dimensional structure and spatial relationship of the lower limb vein vessels, improve the accuracy and reliability of the diagnostic results, and thus improve the quality and efficiency of medical services. Therefore, the 3D imaging method of the lower limb vein proposed by the present invention can intuitively display the three-dimensional structure and spatial relationship of the lower limb vein, and improve the accuracy of the diagnostic results.

[0133] like Figure 2 Shown is a system functional module diagram of the lower limb vein 3D imaging of the present invention.

[0134] The lower limb vein 3D imaging system 200 of the present invention can be installed in an electronic device. According to the functions to be implemented, the lower limb vein 3D imaging system can include an image acquisition module 201, a pixel recognition module 202, an image segmentation module 203, a feature extraction module 204, and a 3D imaging module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0135] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0136] The image acquisition module 201 is used to acquire a patient with lower limb veins to be scanned, identify an imaging range of the patient with lower limb veins, acquire a lower limb slice image of the patient with lower limb veins based on the imaging range, and perform artifact correction processing on the lower limb slice image to obtain a corrected image;

[0137] The pixel identification module 202 is used to collect the image histogram of the patient with lower limb veins, identify the tissue type of the patient with lower limb veins, analyze the pixel distribution of the tissue type based on the image histogram, and identify the venous blood pixel intensity factor of the patient with lower limb veins according to the pixel distribution;

[0138] An image segmentation module 203 is used to construct a pixel integral and image of the corrected image based on the venous blood pixel intensity factor, perform image segmentation processing on the corrected image according to the pixel integral and image to obtain a segmented image, and determine the venous blood vessels of the lower limb vein patient based on the segmented image;

[0139] The feature extraction module 204 is used to extract the vein features of the venous blood vessels, identify the feature types corresponding to the vein features, analyze the influence of the feature types on the lower limb vein patients, and perform dimensionality reduction processing on the vein features based on the influence to obtain key vein features;

[0140] The 3D imaging module 205 is used to construct a 3D imaging of the lower limb veins of the patient with lower limb veins based on the key vein features and the pixel integral and image, analyze the imaging effect of the 3D imaging of the lower limb veins on the venous blood vessels, and output the 3D imaging results of the patient with lower limb veins based on the imaging effect.

[0141] In detail, each module in the lower limb vein 3D imaging system 200 in the embodiment of the present invention is used in the same manner as described above. Figure 1 The lower limb vein 3D imaging method described in the present invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0142] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for 3D imaging of lower extremity veins, characterized in that: The method comprises: Acquire a patient with lower limb veins to be scanned, identify an imaging range of the patient with lower limb veins, acquire a lower limb slice image of the patient with lower limb veins based on the imaging range, and perform artifact correction processing on the lower limb slice image to obtain a corrected image; Collecting an image histogram of the patient with lower limb veins, and identifying the tissue type of the patient with lower limb veins, analyzing the pixel distribution of the tissue type based on the image histogram, and identifying the venous blood pixel intensity factor of the patient with lower limb veins according to the pixel distribution; Based on the venous blood pixel intensity factor, construct a pixel integral and image of the corrected image, perform image segmentation processing on the corrected image according to the pixel integral and image to obtain a segmented image, and determine the venous blood vessels of the lower limb vein patient based on the segmented image; Extracting the vein features of the venous blood vessels, identifying the feature types corresponding to the vein features, analyzing the degree of influence of the feature types on the lower limb vein patients, and based on the degree of influence, performing dimensionality reduction processing on the vein features to obtain key vein features; According to the key vein features and the pixel integral and image, a 3D imaging of the lower limb veins of the lower limb vein patient is constructed, the imaging effect of the 3D imaging of the lower limb veins on the venous blood vessels is analyzed, and based on the imaging effect, the 3D imaging result of the lower limb vein patient is output.

2. The method according to claim 1, characterized in that The step of performing artifact correction processing on the lower limb slice image to obtain a corrected image includes: Analyzing causes of artifacts in the lower limb slice images; Based on the cause of the artifact formation, identifying the artifact type of the lower limb slice image; According to the artifact type, setting an artifact correction mode for the lower limb slice image; Based on the artifact correction method, the lower limb slice image is subjected to artifact correction processing to obtain a corrected image.

3. The method according to claim 1, characterized in that The analyzing the pixel distribution of the tissue type based on the image histogram includes: Dispatching radiological image data corresponding to the image histogram; identifying tissue characteristics of the tissue type in the radiological image data; Based on the tissue characteristics, locate the distribution position of the tissue type in the image histogram; Using the following formula to identify the pixel peak of the tissue type in the image histogram; Analyzing peak characteristics of the pixel peaks according to the tissue characteristics and the distribution positions; The pixel distribution of the tissue type is analyzed in combination with the tissue characteristics and the peak characteristics.

4. The method according to claim 1, characterized in that The step of constructing the pixel integral and image of the corrected image based on the venous blood pixel intensity factor comprises: Based on the venous blood pixel intensity factor, setting a pixel selection threshold of the corrected image; Identifying unit pixels of the corrected image; identifying venous blood pixels and non-venous blood pixels from the unit pixels according to the pixel selection threshold; generating a binary image of the corrected image based on the venous blood pixels and the non-venous blood pixels; Based on the binarized image, a pixel integral sum image of the corrected image is constructed.

5. The method according to claim 1, characterized in that The step of extracting the vein feature of the venous blood vessel comprises: identifying a vascular structure of the venous blood vessel, and based on the vascular structure, identifying a vascular branch of the venous blood vessel; extracting the branch structure and branch angle of the blood vessel branch; Analyzing the hemodynamic characteristics of the venous blood vessel according to the branch structure and the branch angle; Identifying the texture characteristics of the blood vessel branches, and analyzing changes in the blood vessel walls of the venous vessels based on the texture characteristics; Analyzing the functional status of the venous blood vessels according to the hemodynamic characteristics; The vein characteristics of the venous blood vessel are extracted by combining the changes in the blood vessel wall and the functional state.

6. The method according to claim 1, characterized in that The analyzing the influence of the characteristic type on the lower limb vein patient includes: Extracting feature items corresponding to the feature type; Analyzing the lower limb vein risk of the lower limb vein patient according to the feature item and the feature type; Identify the physiological impact of the leg vein risk on the leg vein patient; Calculating a symptom severity coefficient corresponding to the physiological impact; According to the symptom severity coefficient, the influence of the characteristic type on the lower limb vein patients is analyzed.

7. The method according to claim 6, characterized in that The analyzing the lower limb vein risk of the lower limb vein patient according to the feature item and the feature type includes: Identifying the venous pathological characteristics of the lower limb vein patient according to the characteristic items and the characteristic types; Based on the venous pathological characteristics, identifying the patient risk level of the lower extremity venous patient; Analyze the disease type corresponding to the patient's risk level, and analyze the cause of the disease type; The lower limb vein risk of the lower limb vein patient is analyzed based on the formation causes and the patient risk level.

8. The method according to claim 1, characterized in that Based on the influence degree, the venous feature is subjected to dimensionality reduction processing to obtain key venous features, including: Extracting impact features corresponding to the impact degree; Identifying a characteristic attribute corresponding to the vein feature; Calculating the feature fit between the feature attribute and the influencing feature; According to the feature fit, the vein feature is subjected to dimensionality reduction processing to obtain key vein features.

9. The method according to claim 1, characterized in that: The analyzing the imaging effect of the lower extremity vein 3D imaging includes: Identifying the venous structure of the lower extremity vein 3D imaging, and analyzing the recognizability and detail distinction of the venous structure; Determining the visualization quality of the lower limb vein 3D imaging according to the identifiability and the detail distinction; Dispatching lower limb vein patients and venous vessels corresponding to the lower limb vein 3D imaging; Analyzing the ability of the lower limb vein 3D imaging to identify the pathology of the lower limb vein patients; Based on the 3D imaging of the lower extremity veins, analyzing the diseased vessel display rate of the venous vessels; The imaging effect of the lower limb vein 3D imaging is analyzed by combining the visualization quality, the pathology recognition capability and the diseased blood vessel display rate.

10. A 3D imaging system for lower extremity veins, the system implementing the method according to claim 1, characterized in that: The system comprises: An image acquisition module is used to acquire a patient with lower limb veins to be scanned, identify an imaging range of the patient with lower limb veins, acquire a lower limb slice image of the patient with lower limb veins based on the imaging range, and perform artifact correction processing on the lower limb slice image to obtain a corrected image; A pixel recognition module is used to collect the image histogram of the patient with lower limb veins and identify the tissue type of the patient with lower limb veins, analyze the pixel distribution of the tissue type based on the image histogram, and identify the venous blood pixel intensity factor of the patient with lower limb veins according to the pixel distribution; An image segmentation module, used for constructing a pixel integral and image of the corrected image based on the venous blood pixel intensity factor, performing image segmentation processing on the corrected image according to the pixel integral and image to obtain a segmented image, and determining the venous blood vessels of the lower limb vein patient based on the segmented image; A feature extraction module is used to extract the vein features of the venous blood vessels, identify the feature types corresponding to the vein features, analyze the influence of the feature types on the lower limb vein patients, and based on the influence, perform dimensionality reduction processing on the vein features to obtain key vein features; The 3D imaging module is used to construct a 3D imaging of the lower limb veins of the patient with lower limb veins according to the key vein features and the pixel integral and image, analyze the imaging effect of the 3D imaging of the lower limb veins on the venous blood vessels, and output the 3D imaging results of the patient with lower limb veins based on the imaging effect.

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