WO2021030994A1 - 基于vrds ai静脉影像的识别方法及产品 - Google Patents

基于vrds ai静脉影像的识别方法及产品 Download PDF

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Publication number
WO2021030994A1
WO2021030994A1 PCT/CN2019/101164 CN2019101164W WO2021030994A1 WO 2021030994 A1 WO2021030994 A1 WO 2021030994A1 CN 2019101164 W CN2019101164 W CN 2019101164W WO 2021030994 A1 WO2021030994 A1 WO 2021030994A1
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vein
sub
image
vena cava
inferior vena
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PCT/CN2019/101164
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English (en)
French (fr)
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李斯图尔特平
李戴维伟
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未艾医疗技术(深圳)有限公司
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Priority to PCT/CN2019/101164 priority Critical patent/WO2021030994A1/zh
Priority to CN201980099716.2A priority patent/CN114364323A/zh
Publication of WO2021030994A1 publication Critical patent/WO2021030994A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves

Definitions

  • This application relates to the technical field of medical imaging devices, and in particular to a method and product based on VRDS AI vein image recognition.
  • the embodiments of the present application provide a method and product for identifying a vein image based on VRDS AI, in order to improve the security of identification by a medical imaging device.
  • an embodiment of the present application provides a method for identifying vein images based on VRDS AI, which is applied to a medical imaging device; the method includes:
  • an embodiment of the present application provides a medical imaging device, which includes a processing unit and a communication unit, wherein:
  • the processing unit is used to obtain a scanned image of a target user's target part including the inferior vena cava through the communication unit; and used to process the scanned image to obtain a target image of the inferior vena cava;
  • the target image generates a first feature data set of the inferior vena cava, the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user; and the first feature data is used to
  • the collection is compared with the pre-stored original feature data collection to obtain a comparison result; and used to perform a preset operation according to the comparison result.
  • an embodiment of the present application provides a medical imaging device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured by the above Executed by a processor, the above-mentioned program includes instructions for executing steps in any method of the first aspect of the embodiments of the present application.
  • an embodiment of the present application provides a computer-readable storage medium, wherein the foregoing computer-readable storage medium stores a computer program for electronic data exchange, wherein the foregoing computer program enables a computer to execute In one aspect, some or all of the steps described in any method.
  • embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute For example, some or all of the steps described in any method of the first aspect.
  • the computer program product may be a software installation package.
  • the medical imaging device first obtains a scanned image of the target user's target part including the inferior vena cava, and secondly, processes the scanned image to obtain a target image of the inferior vena cava, and again, generates the inferior vena cava based on the target image
  • the first feature data set of veins the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user, and then the first feature data set is compared with the pre-stored original feature data set to obtain the comparison result, Finally, perform preset operations based on the comparison results.
  • the medical imaging device of this application analyzes the image features of the user's inferior vena cava and compares the image features to achieve identity recognition to complete the preset operation. Due to the physiological feature verification mechanism on the surface of the human body, the image features of the inferior vena cava inside the human body are difficult to be forged by illegal users, which is beneficial to improve the security of identity recognition.
  • FIG. 1 is a schematic structural diagram of a medical image intelligent analysis and processing system based on VRDS Ai according to an embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a method for identifying vein images based on VRDS AI according to an embodiment of the present application
  • FIG. 3 is a schematic structural diagram of a medical imaging device provided by an embodiment of the present application.
  • Fig. 4 is a block diagram of functional units of a medical imaging device provided by an embodiment of the present application.
  • the medical imaging devices involved in the embodiments of this application refer to various instruments that use various media as information carriers to reproduce the internal structure of the human body as images.
  • the image information and the actual structure of the human body have spatial and temporal distributions.
  • DICOM data refers to the original image file data collected by medical equipment that reflects the internal structural characteristics of the human body. It can include electronic computed tomography CT, magnetic resonance MRI, diffusion tensor imaging DTI, and positron emission computed tomography PET-
  • image source refers to the Texture2D/3D image volume data generated by analyzing the original DICOM data.
  • VRDS refers to the Virtual Reality Doctor system (VRDS for short).
  • FIG. 1 is a schematic structural diagram of a VRDS Ai medical image intelligent analysis and processing system 100 based on an embodiment of the present application.
  • the system 100 includes a medical imaging device 110 and a network database 120.
  • the medical imaging device 110 may include The local medical imaging device 111 and/or the terminal medical imaging device 112, the local medical imaging device 111 or the terminal medical imaging device 112 are used to based on the original DICOM data, based on the identity recognition based on the VRDS AI vein image presented in the embodiment of this application , To perform the identification, positioning, four-dimensional volume rendering and identification comparison and identity recognition of the human inferior vena cava, to achieve four-dimensional stereo imaging effects (the four-dimensional medical image specifically refers to the medical image including the internal spatial structure characteristics of the displayed tissue and the external space Structural characteristics, the internal spatial structural characteristics mean that the slice data inside the tissue is not lost, that is, the medical imaging device can present the internal structure of target organs, blood vessels and other tissues, and the external spatial structural characteristics refer to the environmental characteristics between tissues
  • the transfer function result may include the transfer function result of the surface of the internal organs and the tissue structure in the internal organs of the human body, and the transfer function result of the cube space, such as The cube edit box and arc edit array quantity, coordinates, color, transparency and other information required by the transfer function.
  • the network database 120 may be, for example, a cloud server.
  • the network database 120 is used to store the image source generated by analyzing the original DICOM data and the transfer function result of the four-dimensional human body image edited by the local medical imaging device 111.
  • the image source may be from multiple sources.
  • a local medical imaging device 111 to realize interactive diagnosis of multiple doctors.
  • HMDS Head mounted Displays Set
  • the operating actions refer to the user’s actions through the medical imaging device.
  • An external intake device such as a mouse, keyboard, etc., controls the operation of the four-dimensional human body image to achieve human-computer interaction.
  • the operation action includes at least one of the following: (1) Change the color and/or of a specific organ/tissue Transparency, (2) positioning zoom view, (3) rotating view, realizing multi-view 360-degree observation of four-dimensional human body image, (4) "entering" human organs to observe internal structure, real-time clipping effect rendering, (5) moving up and down view.
  • FIG 2 is a schematic flowchart of a VRDS AI vein image recognition method according to an embodiment of the present application, which is applied to the medical imaging device described in Figure 1; as shown in the figure, the VRDS AI vein image Image recognition methods include:
  • the medical imaging device acquires a scanned image of a target part of the target user including the inferior vena cava;
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the medical imaging device processes the scanned image to obtain a target image of the inferior vena cava
  • the medical imaging device generates a first characteristic data set of the inferior vena cava according to the target image, where the first characteristic data set is used to reflect the physiological characteristics of the inferior vena cava of the target user;
  • the physiological characteristics of the inferior vena cava refer to specific types of characteristic data that can reflect the unique identity of the user set based on a priori data.
  • the formation of the inferior vena cava of the human body includes complex connection processes and various embryonic stages.
  • the degenerative process of veins is the main conduit through which the veins of the lower extremities and abdominal organs return to the right atrium. Therefore, R&D personnel can use big data test analysis to find the uniqueness that can be used to identify the user’s identity from the various physiological characteristics of the inferior vena cava Single or multiple types of characteristic data.
  • This setting principle is similar to the setting principle of fingerprint features, but it is more complicated than the setting principle of fingerprint features, because fingerprints are the surface tissue of the user's skin, and their physiological characteristics are only limited to the associated features of the fingerprint texture, and are generally two-dimensional. Due to its high degree of complexity in distribution and the complicated internal environment of the human body, traditional two-dimensional image analysis methods are difficult to extract effective physiological characteristic data. Medical imaging devices are required to extract comprehensive and accurate images of the inferior vena cava. Based on this data, we can further analyze the image to obtain effective physiological characteristic data.
  • the medical imaging device compares the first feature data set with a pre-stored original feature data set to obtain a comparison result
  • the original feature data set includes a single type of feature data
  • the original feature data set includes multiple types of feature data
  • the comparing the first feature data set with a pre-stored original feature data set includes: calculating the data of each category The reference matching degree of the feature data; the weighted summation is assigned according to the matching degree weights of the multiple types of feature data to obtain the comprehensive matching degree; the comparison result is obtained according to the comprehensive matching degree and the preset matching degree threshold.
  • the matching weight distribution of the multiple types of feature data can be empirical values, or the age range of the target user can be predicted based on the target image of the inferior vena cava of the current user, and then the weight corresponding to the age range can be queried Distribution, which can avoid the influence of the inferior vena cava differences of different ages on the recognition results and improve the accuracy of comparison.
  • multiple types of feature data include feature data A, feature data B, feature data C, and feature data D.
  • the age groups are divided into three age groups: Q1, Q2, and Q3, and then each can be obtained based on big data statistical analysis.
  • the distribution of exclusive matching weights for age groups is shown in Table 1.
  • the weight of the feature data associated with the spatial structure characteristics of the inferior vena cava can be set higher. This is because such feature data involves the three-dimensional spatial distribution characteristics of the inferior vena cava.
  • the three-dimensional spatial distribution characteristics are difficult to collect and accurately analyze by general devices, so it is very difficult for illegal users to obtain this type of characteristic data.
  • This setting can make the final matching degree calculation result difficult to be easily tampered with and affect the accuracy. Improve safety.
  • S205 The medical imaging device performs a preset operation according to the comparison result.
  • the medical imaging device first obtains a scanned image of the target user's target part including the inferior vena cava, and secondly, processes the scanned image to obtain a target image of the inferior vena cava, and again, generates the inferior vena cava based on the target image
  • the first feature data set of veins the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user, and then the first feature data set is compared with the pre-stored original feature data set to obtain the comparison result, Finally, perform preset operations based on the comparison results.
  • the medical imaging device of the present application analyzes the image features of the user's inferior vena cava and compares the image features to achieve identity recognition to complete the preset operation. Due to the physiological feature verification mechanism on the surface of the human body, the image features of the inferior vena cava inside the human body are difficult to be forged by illegal users, which is beneficial to improve the security of identity recognition.
  • the inferior vena cava includes a main vein and a sub-vene
  • the main vein in question refers to the superior and inferior vena cava and the central vein that enters the right atrium
  • the sub-vein refers to the connection between the main vein and The inferior vena cava of the kidney
  • the medical imaging device generates a first feature data set of the inferior vena cava according to the target image, including: the medical imaging device generates at least the following of the inferior vena cava according to the target image Characteristic data of an attribute: the number of the sub-vein, the shape of the main vein and/or the sub-vein, the spatial position relationship between the main vein and the sub-vein, and the spatial position relationship of the sub-vein Generating the first feature data set according to the feature data of the at least one attribute.
  • the number of sub veins refers to the number of sub veins connected to the kidney
  • the shape refers to the contour characteristics, radius, etc. of the inferior vena cava
  • the spatial position relationship refers to the intersection, convergence, and neighboring between veins. , Far away, or relative distance and other positional relationship description information.
  • the medical imaging device will call the cross network model to perform spatial separation processing on the fusion data of the cross positions of the veins during the data processing process before generating the target image, so the data processing process Then the cross characteristics between different inferior vena cava can be obtained, and can be pre-stored in the first feature data set. This avoids repeated processing and improves processing efficiency.
  • the medical imaging device can perform accurate identity recognition by analyzing characteristics such as the number, shape, and spatial position relationship of the inferior vena cava.
  • the at least one attribute includes the quantity; the medical imaging device generates characteristic data of at least one of the following attributes of the inferior vena cava according to the target image, including: the medical imaging The device determines the images of the main vein, the first kidney, and the second kidney in the target image; analyzes the image between the main vein and the first kidney in the target image to obtain the image of the child vein The first number; analyze the image between the main vein and the second kidney in the target image to obtain the second number of the sub-veins; determine the sum of the first number and the second number Is the characteristic data of the stated quantity.
  • the second number corresponding to the second kidney can be set to zero, which improves processing efficiency.
  • the medical imaging device can accurately obtain the number of sub-veins of the current user by analyzing the images of the sub-veins, thereby providing a basis for subsequent identification and comparison and improving accuracy and safety.
  • the at least one attribute includes the shape, and the shape includes the shape of a main vein and/or a sub-vein; the medical imaging device generates the following information of the inferior vena cava according to the target image.
  • the characteristic data of at least one attribute includes: the medical imaging device determines the image of the main vein and/or the sub-vein in the target image; according to the image of the main vein and/or the sub-vein Determine the outline and radius of the main vein and/or the sub-vein; determine the outline and the radius as characteristic data of the shape.
  • the characteristic data of the contour can be specifically described by the trend of the vein, etc.
  • the trend can describe the angle of each segment relative to the reference line, or directly describe the curvature distribution of the vein, etc., which is not uniquely limited here.
  • the characteristic data of the radius can be specifically described by the radius of one or more reference positions.
  • the medical imaging device can accurately obtain the characteristic data of the shape of the main vein and/or sub-vein of the current user by analyzing the images of the main vein and the sub-vein, so as to provide a basis for the subsequent identification and comparison and improve the accuracy. And security.
  • the at least one attribute includes the spatial position relationship between the main vein and the sub vein; the medical imaging device generates at least one of the following attributes of the inferior vena cava according to the target image
  • the characteristic data includes: the medical imaging device determines the main vein and the image of each sub-vein in the target image; and determines the main vein and the image of each sub-vein in the target image
  • the connection position and connection angle of the main vein and each sub-vein; the connection position and the connection angle are determined as the spatial position relationship between the main vein and the sub-vein.
  • the medical imaging device can accurately obtain the characteristic data of the spatial position relationship between the main vein and the sub-vein of the current user by analyzing the images of the main vein and the sub-vein, so as to provide a basis for the subsequent identification and comparison and improve the accuracy and safety.
  • the at least one attribute includes the spatial position relationship of the sub-vein; the medical imaging device generates feature data of at least one of the following attributes of the inferior vena cava according to the target image, including : The medical imaging device determines the images of the main vein, the first kidney, and the second kidney in the target image; analyzes the image between the main vein and the first kidney to obtain a first sub-vein set Analyzing the image between the main vein and the second kidney to obtain a second sub-vein set; determining the first positional relationship between any two sub-veins in the first sub-vein set, and determining the first The second positional relationship between any two sub-veins in the two sub-vein sets; the first positional relationship and the second positional relationship are determined as the spatial positional relationship of the sub-veins.
  • first positional relationship and the second positional relationship include at least one of the following: positional relationship description information such as bifurcation, winding staggered, non-contact, contact, adjacent, or relative distance of a specific position.
  • the medical imaging device can accurately obtain the characteristic data of the spatial position relationship of the current user's sub-vein by analyzing the image of the sub-vein, so as to provide a basis for subsequent identification comparison and improve accuracy and safety.
  • processing the scanned image by the medical imaging device to obtain the target image of the inferior vena cava includes: the medical imaging device generates a bitmap BMP data source according to the scanned image; and according to the BMP The data source generates first venous image data, the first venous image data includes an original data set of the inferior vena cava, and the original data set is an image of the surface of the inferior vena cava and the tissue structure inside the inferior vena cava A transfer function result in a cube space; generating second vein image data according to the first vein image data, the second vein image data including a segmentation data set of the inferior vena cava, the segmentation data set having a cross position relationship Mutually independent image data of the inferior vena cava; processing the second venous image data to obtain a target image of the inferior vena cava.
  • the specific implementation of generating a bitmap BMP data source according to the scanned image includes: using the scanned image as the medical digital imaging and communication DICOM data of the target user; parsing the DICOM data to generate the image source of the target user, the The image source includes texture 2D/3D image volume data; the BMP data source is obtained by performing first preset processing on the image source, and the first preset processing includes at least one of the following operations: VRDS restricted contrast adaptive histogram Image equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing.
  • the DICOM Digital Imaging and Communications in Medicine
  • the medical imaging device first acquires multiple scanned images that reflect the internal structural characteristics of the target user's human body, and can screen out at least one suitable scanned image that contains the target organ through sharpness and accuracy. Further processing is performed on the scanned image to obtain a bitmap BMP data source. It can be seen that, in this example, the medical imaging device can obtain a bitmap BMP data source after filtering, parsing, and first preset processing based on the acquired scanned image, which improves the accuracy and clarity of medical image imaging.
  • the VRDS limited contrast adaptive histogram equalization includes the following steps: regional noise ratio limiting, global contrast limiting; dividing the local histogram of the image source into multiple partitions, and for each partition, according to the neighbors of the partition.
  • the slope of the cumulative histogram of the domain determines the slope of the transformation function, and the degree of contrast amplification around the pixel value of the partition is determined according to the slope of the transformation function, and then the limit cropping process is performed according to the degree of contrast amplification to generate the effective histogram.
  • the hybrid partial differential denoising includes the following steps: driving by VRDS Ai curvature and The VRDS Ai high-order hybrid denoising makes the curvature of the image edge less than the preset curvature, and realizes a hybrid partial differential denoising model that can protect the image edge and avoid the step effect in the smoothing process;
  • the VRDS Ai elastic deformation processing includes The following steps: On the image dot matrix, superimpose the positive and negative random distances to form a difference position matrix, and then form a new dot matrix with the grayscale at each difference position, which can realize the distortion and deformation of the image, and then the image Perform rotation, twist, and translation operations.
  • the hybrid partial differential denoising can use CDD and high-order denoising models to process the image source;
  • the CDD model (Curvature Driven Diffusions) model is based on the TV (Total Variation) model with the introduction of curvature drive and It solves the problem that the TV model cannot repair the visual connectivity of the image.
  • high-order denoising refers to denoising the image based on the partial differential equation (PDE) method.
  • the image source perform noise filtering according to the specified differential equation function change, thereby filtering out the noise in the image source, and the solution of the partial differential equation is the BMP data source obtained after denoising
  • the PDE-based image denoising method has the characteristics of anisotropic diffusion, so it can perform different degrees of diffusion in different regions of the image source, thereby achieving the effect of suppressing noise while protecting the edge texture information of the image.
  • the medical imaging device uses at least one of the following image processing operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing, which improves the execution efficiency of image processing, and Improve the image quality and protect the edge texture of the image.
  • the generating the first vein image data according to the BMP data source includes: importing the BMP data source into a preset VRDS medical network model, and invoking the pre-stored delivery through the VRDS medical network model For each transfer function in the function set, the BMP data source is processed by multiple transfer functions in the transfer function set to obtain the first venous image data, and the transfer function set includes all presets set by a reverse editor. Describe the transfer function of the inferior vena cava.
  • BMP full name Bitmap
  • DDB device-dependent bitmap
  • DIB device-independent bitmap
  • the VRDS medical network model is a preset network model, and its training method includes the following three steps: image sampling and scale scaling; 3D convolutional neural network feature extraction and scoring; medical imaging device evaluation and network training.
  • sampling is required to obtain N BMP data sources, and then M BMP data sources are extracted from N BMP data sources at a preset interval. It needs to be explained that the preset interval can be flexibly set according to the usage scenario.
  • Sample M from N then scale the sampled M BMP data sources to a fixed size (for example, the length is S pixels and the width is S pixels), and the resulting processing result is used as the input of the 3D convolutional neural network .
  • M BMP data sources are used as the input of the 3D convolutional neural network.
  • a 3D convolutional neural network is used to perform 3D convolution processing on the BMP data source to obtain a feature map.
  • the generating second vein image data according to the first vein image data includes: importing the first vein image data into a preset cross blood vessel network model, and pass the cross blood vessel network model Perform spatial segmentation processing on the original data at the intersection to obtain mutually independent image data of multiple inferior vena cava at the intersection; update the original data set through the independent image data to obtain a second vein Image data.
  • the processing the second vein image data to obtain the target image of the inferior vena cava includes: performing at least one of the following processing operations on the second vein image data to obtain the inferior vena cava
  • the target image 2D boundary optimization processing, 3D boundary optimization processing, data enhancement processing.
  • the 2D boundary optimization processing includes the following operations: multiple sampling to obtain low-resolution information and high-resolution information, where the low-resolution information can provide contextual semantic information of the segmented target in the entire image, that is, reflect the relationship between the target and the environment. Characteristics of the inter-relationship, the segmentation target includes the target vein.
  • the 3D boundary optimization processing includes the following operations: putting the second medical image data into a 3D convolution layer to perform a 3D convolution operation to obtain a feature map; the 3D pooling layer compresses the feature map and performs Non-linear activation; cascade operation is performed on the compressed feature maps to obtain the prediction result image output by the model.
  • the data enhancement processing includes at least one of the following: data enhancement based on arbitrary angle rotation, data enhancement based on histogram equalization, data enhancement based on white balance, data enhancement based on mirroring operations, data enhancement based on random cut, and data enhancement based on Data enhancement to simulate different lighting changes.
  • the method before acquiring the scanned image of the target part of the target user including the inferior vena cava, the method further includes: entering the original feature data set.
  • FIG. 3 is a schematic structural diagram of a medical imaging apparatus 300 provided by an embodiment of the present application.
  • the medical imaging apparatus 300 includes a processor 310, a memory 320, a communication interface 330, and one or more programs 321, wherein the one or more programs 321 are stored in the above-mentioned memory 320 and are configured to be executed by the above-mentioned processor 310, and the one or more The program 321 includes instructions for performing the following steps;
  • the medical imaging device first obtains a scanned image of the target user's target part including the inferior vena cava, and secondly, processes the scanned image to obtain a target image of the inferior vena cava, and again, generates the inferior vena cava based on the target image
  • the first feature data set of veins the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user, and then the first feature data set is compared with the pre-stored original feature data set to obtain the comparison result, Finally, perform preset operations based on the comparison results.
  • the medical imaging device of the present application analyzes the image features of the user's inferior vena cava and compares the image features to achieve identity recognition to complete the preset operation. Due to the physiological feature verification mechanism on the surface of the human body, the image features of the inferior vena cava inside the human body are difficult to be forged by illegal users, which is beneficial to improve the security of identity recognition.
  • the inferior vena cava includes a main vein and a sub-vene
  • the main vein in question refers to the superior and inferior vena cava and the central vein that enters the right atrium
  • the sub-vein refers to the connection between the main vein and The inferior vena cava of the kidney
  • the instructions in the program are specifically used to perform the following operations: generating the inferior vena cava based on the target image Characteristic data of at least one of the following attributes of the vena cava: the number of the sub-vein, the shape of the main vein and/or the sub-vein, the spatial position relationship between the main vein and the sub-vein, the sub-vein The spatial position relationship of the veins; and for generating the first feature data set according to the feature data of the at least one attribute.
  • the at least one attribute includes the number; in terms of generating characteristic data of at least one of the following attributes of the inferior vena cava according to the target image, the instructions in the program specifically Used to perform the following operations: determine the images of the main vein, the first kidney, and the second kidney in the target image; and analyze the difference between the main vein and the first kidney in the target image Image to obtain the first number of the sub-veins; and used to analyze the image between the main vein and the second kidney in the target image to obtain the second number of the sub-veins; and The sum of the first quantity and the second quantity is determined as the characteristic data of the quantity.
  • the at least one attribute includes the shape, and the shape includes the shape of a main vein and/or a sub-vein; and at least one of the following of the inferior vena cava is generated according to the target image
  • the instructions in the program are specifically used to perform the following operations: determine the image of the main vein and/or the sub-vein in the target image; /Or the image of the sub-vein determines the outline and radius of the main vein and/or the sub-vein; and feature data for determining the outline and the radius as the shape.
  • the at least one attribute includes the spatial position relationship between the main vein and the sub-vein; and the characteristics of the following at least one attribute of the inferior vena cava are generated according to the target image
  • the instructions in the program are specifically used to perform the following operations: determine the image of the main vein and each sub-vein in the target image; Images of each sub-vein to determine the connection position and connection angle of the main vein and each sub-vein; and used to determine the connection position and the connection angle as the spatial position of the main vein and the sub-vein relationship.
  • the at least one attribute includes the spatial position relationship of the sub-vein; in the aspect of generating feature data of at least one of the following attributes of the inferior vena cava according to the target image, the The instructions in the program are specifically used to perform the following operations: determine the images of the main vein, the first kidney, and the second kidney in the target image; and analyze the difference between the main vein and the first kidney.
  • Image to obtain a first sub-vein set analyze the image between the main vein and the second kidney to obtain a second sub-vein set; and used to determine the distance between any two sub-veins in the first sub-vein set
  • the first positional relationship of the second sub-vein set, and the second positional relationship between any two sub-veins in the second sub-vein set; and the first positional relationship and the second positional relationship are used to determine the sub-vein The spatial relationship of veins.
  • the instructions in the program are specifically used to perform the following operations: generating a bitmap BMP data source according to the scanned image And used to generate first venous image data according to the BMP data source, the first venous image data including the original data set of the inferior vena cava, the original data set being the surface of the inferior vena cava and the The transfer function result of the cube space of the tissue structure inside the inferior vena cava; and for generating second vein image data according to the first vein image data, the second vein image data including the segmentation data set of the inferior vena cava
  • the segmentation data set has mutually independent image data of the inferior vena cava in a cross position relationship; and is used to process the second venous image data to obtain a target image of the inferior vena cava.
  • the medical imaging apparatus includes hardware structures and/or software modules corresponding to each function.
  • this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software-driven hardware depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.
  • the embodiment of the present application may divide the medical imaging device into functional units according to the foregoing method examples.
  • each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and there may be other division methods in actual implementation.
  • FIG. 4 is a block diagram of the functional unit composition of the medical imaging device 400 involved in an embodiment of the present application. It includes a processing unit 401 and a communication unit 402, wherein,
  • the processing unit 401 is configured to obtain a scanned image of a target part of the target user including the inferior vena cava through the communication unit 402; and to process the scanned image to obtain a target image of the inferior vena cava; and Generate a first feature data set of the inferior vena cava according to the target image, the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user; and used to compare the first The feature data set is compared with the pre-stored original feature data set to obtain a comparison result; and used to perform a preset operation according to the comparison result.
  • the medical imaging apparatus 400 may further include a storage unit 403 for storing program codes and data of the electronic device.
  • the processing unit 401 may be a processor
  • the communication unit 402 may be a touch screen or a transceiver
  • the storage unit 403 may be a memory.
  • the medical imaging device first obtains a scanned image of the target user's target part including the inferior vena cava, and secondly, processes the scanned image to obtain a target image of the inferior vena cava, and again, generates the inferior vena cava based on the target image
  • the first feature data set of veins the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user, and then the first feature data set is compared with the pre-stored original feature data set to obtain the comparison result, Finally, perform preset operations based on the comparison results.
  • the medical imaging device of the present application analyzes the image features of the user's inferior vena cava and compares the image features to achieve identity recognition to complete the preset operation. Due to the physiological feature verification mechanism on the surface of the human body, the image features of the inferior vena cava inside the human body are difficult to be forged by illegal users, which is beneficial to improve the security of identity recognition.
  • the inferior vena cava includes a main vein and a sub-vene
  • the main vein in question refers to the superior and inferior vena cava and the central vein that enters the right atrium
  • the sub-vein refers to the connection between the main vein and The inferior vena cava of the kidney
  • the processing unit 401 is specifically configured to: generate the following information of the inferior vena cava according to the target image Characteristic data of at least one attribute: the number of the sub veins, the shape of the main vein and/or the sub vein, the spatial position relationship between the main vein and the sub vein, the spatial position of the sub vein Relationship; and for generating the first feature data set according to feature data of the at least one attribute.
  • the at least one attribute includes the quantity; in terms of generating characteristic data of the following at least one attribute of the inferior vena cava according to the target image, the processing unit 401 specifically uses To: determine the images of the main vein, the first kidney and the second kidney in the target image; and to analyze the images between the main vein and the first kidney in the target image to obtain all The first number of the sub-veins; and used to analyze the image between the main vein and the second kidney in the target image to obtain the second number of the sub-veins; and used to compare the first The sum of the quantity and the second quantity is determined as the characteristic data of the quantity.
  • the at least one attribute includes the shape, and the shape includes the shape of a main vein and/or a sub-vein; and at least one of the following of the inferior vena cava is generated according to the target image
  • the processing unit 401 is specifically configured to: determine the image of the main vein and/or the sub-vein in the target image; The image of the sub-vein determines the outline and radius of the main vein and/or the sub-vein; and feature data for determining the outline and the radius as the shape.
  • the at least one attribute includes the spatial position relationship between the main vein and the sub-vein; and the characteristics of the following at least one attribute of the inferior vena cava are generated according to the target image
  • the processing unit 401 is specifically configured to: determine the image of the main vein and each sub-vein in the target image; and to determine the image of the main vein and the image of each sub-vein , Determining the connection position and connection angle of the main vein and each of the sub veins; and determining the connection position and the connection angle as the spatial position relationship between the main vein and the sub vein.
  • the at least one attribute includes the spatial position relationship of the sub-vein; in the aspect of generating feature data of at least one of the following attributes of the inferior vena cava according to the target image, the The processing unit 401 is specifically configured to: determine the images of the main vein, the first kidney and the second kidney in the target image; and to analyze the images between the main vein and the first kidney to obtain the first A sub-vein set, which analyzes the image between the main vein and the second kidney to obtain a second sub-vein set; and is used to determine the first position between any two sub-veins in the first sub-vein set Relationship, and determining a second positional relationship between any two sub-veins in the second sub-vein set; and used to determine the first positional relationship and the second positional relationship as the spatial position of the sub-vein relationship.
  • the processing unit 401 is specifically configured to: generate a bitmap BMP data source according to the scanned image; and First venous image data is generated according to the BMP data source, the first venous image data includes the original data set of the inferior vena cava, the original data set is the surface of the inferior vena cava and the interior of the inferior vena cava The result of the transfer function in the cube space of the tissue structure; and for generating second vein image data according to the first vein image data, the second vein image data including the segmentation data set of the inferior vena cava, the segmentation The data set has mutually independent image data of the inferior vena cava in a cross position relationship; and is used to process the second venous image data to obtain a target image of the inferior vena cava.
  • An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method as recorded in the above method embodiment ,
  • the aforementioned computer includes a medical imaging device.
  • the embodiments of the present application also provide a computer program product.
  • the above-mentioned computer program product includes a non-transitory computer-readable storage medium storing a computer program.
  • the above-mentioned computer program is operable to cause a computer to execute any of the methods described in the above-mentioned method embodiments. Part or all of the steps of the method.
  • the computer program product may be a software installation package, and the computer includes a medical imaging device.
  • the disclosed device may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the above-mentioned units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or integrated. To another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
  • the units described above as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • each unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable memory.
  • the technical solution of the present application essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, A number of instructions are included to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the foregoing methods of the various embodiments of the present application.
  • the aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other various media that can store program codes.
  • the program can be stored in a computer-readable memory, and the memory can include: flash disk , Read-only memory (English: Read-Only Memory, abbreviation: ROM), random access device (English: Random Access Memory, abbreviation: RAM), magnetic disk or optical disc, etc.

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Abstract

一种基于VRDS AI静脉影像的识别方法及产品（100,300,400），应用于医学成像装置。该方法包括：获取目标用户的包含下腔静脉的目标部位的扫描图像（S201）；处理扫描图像得到下腔静脉的目标影像（S202）；根据目标影像生成下腔静脉的第一特征数据集合（S203），第一特征数据集合用于反映目标用户的下腔静脉的生理特征；将第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果（S204）；根据比对结果执行预设操作（S205）。该方法有利于提高医学成像装置进行身份识别的安全性。

Description

基于VRDS AI静脉影像的识别方法及产品 技术领域
本申请涉及医学成像装置技术领域，具体涉及一种基于VRDS AI静脉影像的识别方法及产品。
背景技术
目前，用户身份识别等身份识别操作主要通过指纹识别、人脸识别等方法，此类方法均容易被非法用户造假，影响使用安全性。
发明内容
本申请实施例提供了一种基于VRDS AI静脉影像的识别方法及产品，以期提高医学成像装置进行身份识别的安全性。
第一方面，本申请实施例提供一种基于VRDS AI静脉影像的识别方法，应用于医学成像装置；所述方法包括：
获取目标用户的包含下腔静脉的目标部位的扫描图像；
处理所述扫描图像得到所述下腔静脉的目标影像；
根据所述目标影像生成所述下腔静脉的第一特征数据集合，所述第一特征数据集合用于反映所述目标用户的所述下腔静脉的生理特征；
将所述第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果；
根据比对结果执行预设操作。
第二方面，本申请实施例提供一种医学成像装置，包括处理单元和通信单元，其中，
所述处理单元，用于通过所述通信单元获取目标用户的包含下腔静脉的目标部位的扫描图像；以及用于处理所述扫描图像得到所述下腔静脉的目标影像；以及用于根据所述目标影像生成所述下腔静脉的第一特征数据集合，所述第一特征数据集合用于反映所述目标用户的所述下腔静脉的生理特征；以及用于将所述第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果；以及用于根据比对结果执行预设操作。
第三方面，本申请实施例提供一种医学成像装置，包括处理器、存储器、通信接口以及一个或多个程序，其中，上述一个或多个程序被存储在上述存储器中，并且被配置由上述处理器执行，上述程序包括用于执行本申请实施例第一方面任一方法中的步骤的指令。
第四方面，本申请实施例提供了一种计算机可读存储介质，其中，上述计算机可读存储介质存储用于电子数据交换的计算机程序，其中，上述计算机程序使得计算机执行如本申请实施例第一方面任一方法中所描述的部分或全部步骤。
第五方面，本申请实施例提供了一种计算机程序产品，其中，上述计算机程序产品包括存储了计算机程序的非瞬时性计算机可读存储介质，上述计算机程序可操作来使计算机执行如本申请实施例第一方面任一方法中所描述的部分或全部步骤。该计算机程序产品可以为一个软件安装包。
可以看出，本申请实施例中，医学成像装置首先获取目标用户的包含下腔静脉的目标部位的扫描图像，其次，处理扫描图像得到下腔静脉的目标影像，再次，根据目标影像生成下腔静脉的第一特征数据集合，第一特征数据集合用于反映目标用户的下腔静脉的生理特征，然后，将第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果，最后，根据比对结果执行预设操作。可见，由于人体下腔静脉互不相同，具有唯一标识性， 本申请医学成像装置通过分析用户的下腔静脉的影像特征，并通过比对该影像特征从而实现身份识别以完成预设操作，不同于人体表面的生理特征验证机制，人体内部的下腔静脉的影像特征难以被非法用户造假，故而有利于提高身份识别的安全性。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种基于VRDS Ai医学影像智能分析处理***的结构示意图；
图2是本申请实施例提供的一种基于VRDS AI静脉影像的识别方法的流程示意图；
图3是本申请实施例提供的一种医学成像装置的结构示意图；
图4是本申请实施例提供的一种医学成像装置的功能单元组成框图。
具体实施方式
为了使本技术领域的人员更好地理解本申请方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别不同对象，而不是用于描述特定顺序。此外，术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、***、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其他步骤或单元。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
本申请实施例所涉及到的医学成像装置是指利用各种不同媒介作为信息载体，将人体内部的结构重现为影像的各种仪器，其影像信息与人体实际结构有着空间和时间分布上的对应关系。“DICOM数据”是指通过医疗设备采集的反映人体内部结构特征的原始图像文件数据，可以包括电子计算机断层扫描CT、核磁共振MRI、弥散张量成像DTI、正电子发射型计算机断层显像PET-CT等信息，“图源”是指解析原始DICOM数据生成的Texture2D/3D图像体数据。“VRDS”是指虚拟现实医用***(Virtual Reality Doctor system，简称为VRDS)。
请参阅图1，图1是本申请实施例提供了一种基于VRDS Ai医学影像智能分析处理***100的结构示意图，该***100包括医学成像装置110和网络数据库120，其中医学成像装置110可以包括本地医学成像装置111和/或终端医学成像装置112，本地医学成像装置111或终端医学成像装置112用于基于原始DICOM数据，以本申请实施例所呈现的基于VRDS AI静脉影像的身份识别为基础，进行人体下腔静脉的识别、定位、四维体绘制和识别比对以及身份识别，实现四维立体成像效果(该4维医学影像具体是指医学影像包括所显示组织的内部空间结构特征及外部空间结构特征，所述内部空间结构特征是指组织内部的切片数据未丢失，即医学成像装置可以呈现目标器官、血管等组织的内部构造，外部空间结构特性是指 组织与组织之间的环境特征，包括组织与组织之间的空间位置特性(包括交叉、间隔、融合)等，如肾脏与动脉之间的交叉位置的边缘结构特性等)，本地医学成像装置111相对于终端医学成像装置112还可以用于对图源数据进行编辑，形成四维人体图像的传递函数结果，该传递函数结果可以包括人体内脏器官表面和人体内脏器官内的组织结构的传递函数结果，以及立方体空间的传递函数结果，如传递函数所需的立方编辑框与弧线编辑的数组数量、坐标、颜色、透明度等信息。网络数据库120例如可以是云服务器等，该网络数据库120用于存储解析原始DICOM数据生成的图源，以及本地医学成像装置111编辑得到的四维人体图像的传递函数结果，图源可以是来自于多个本地医学成像装置111以实现多个医生的交互诊断。
用户通过上述医学成像装置110进行具体的图像显示时，可以选择显示器或者虚拟现实VR的头戴式显示器(Head mounted Displays Set，HMDS)结合操作动作进行显示，操作动作是指用户通过医学成像装置的外部摄入设备，如鼠标、键盘等，对四维人体图像进行的操作控制，以实现人机交互，该操作动作包括以下至少一种：(1)改变某个具体器官/组织的颜色和/或透明度，(2)定位缩放视图，(3)旋转视图，实现四维人体图像的多视角360度观察，(4)“进入”人体器官内部观察内部构造，实时剪切效果渲染，(5)上下移动视图。
下面对本申请实施例涉及到的基于VRDS AI静脉影像的识别方法进行详细介绍。
请参阅图2，图2是本申请实施例提供了一种基于VRDS AI静脉影像的识别方法的流程示意图，应用于如图1所述的医学成像装置；如图所示，本基于VRDS AI静脉影像的识别方法包括：
S201，医学成像装置获取目标用户的包含下腔静脉的目标部位的扫描图像；
其中，所述扫描图像包括以下任意一种：CT图像、MRI图像、DTI图像、PET-CT图像。
S202，所述医学成像装置处理所述扫描图像得到所述下腔静脉的目标影像；
S203，所述医学成像装置根据所述目标影像生成所述下腔静脉的第一特征数据集合，所述第一特征数据集合用于反映所述目标用户的所述下腔静脉的生理特征；
其中，所述下腔静脉的生理特征是指基于先验数据设置的能够反映用户唯一身份标识的特定类型的特征数据，由于人体的下腔静脉的形成包括有复杂的连接过程和多种胚胎期静脉的退化过程，是下肢和腹部脏器静脉回流到右心房的主要管道，故而研发人员能够通过大数据测试分析，从下腔静脉的多类生理特征数据中找到能够用于标识用户身份唯一性的单类或多类特征数据。此设置原理类似于指纹特征的设置原理，但又比指纹特征的设置原理复杂，因为指纹为用户皮肤表面组织，其生理特征仅局限于指纹纹理的关联特征，且一般是二维的，而下腔静脉由于其分布复杂程度高、所处人体内部环境复杂，故而传统的二维图像分析方法难以提取出有效的生理特征数据，需要医学成像装置首先能够提取出全面、准确的下腔静脉的影像数据，在此基础上在进一步分析图像获取到有效的生理特征数据。
S204，所述医学成像装置将所述第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果；
具体实现中，若原始特征数据集合包括单个类型的特征数据，则仅需要比对该单个类型的特征数据得到匹配度，并基于匹配度比对即可。
在本可能的示例中，所述原始特征数据集合包括多个类型的特征数据，所述将所述第一特征数据集合与预存的原始特征数据集合进行比对，包括：计算出每个类别的特征数据的参考匹配度；根据所述多个类型的特征数据的匹配度权值分配，加权求和得到综合匹配度；根据所述综合匹配度与预设的匹配度阈值得到比对结果。
其中，所述多个类型的特征数据的匹配度权值分配可以是经验值，也可以根据当前用户的下腔静脉的目标影像预测目标用户的年龄段，然后查询出该年龄段对应的权值分布，如此可以避免因不同年龄段的下腔静脉的差异性而对识别结果造成影响，提高比对准确度。
举例来说，多个类型的特征数据包括特征数据A、特征数据B、特征数据C、特征数据D，年龄段划分为Q1、Q2、Q3三个年龄段，则可以基于大数据统计分析得到各个年龄段的专属匹配度权值分配，如表1所示。
表1
Figure PCTCN2019101164-appb-000001
其中，预设的权值分配中，与下腔静脉的空间结构特性关联的特征数据的权值可以设置的高一些，这是由于此类特征数据由于涉及到下腔静脉的三维空间分布特性，而该三维空间分布特性一般装置很难采集并准确分析出来，从而非法用户获取该类特征数据的难度就会很大，如此设置可以使得最终匹配度的计算结果难以被轻易篡改而影响准确度，提高安全性。
S205，所述医学成像装置根据比对结果执行预设操作。
可以看出，本申请实施例中，医学成像装置首先获取目标用户的包含下腔静脉的目标部位的扫描图像，其次，处理扫描图像得到下腔静脉的目标影像，再次，根据目标影像生成下腔静脉的第一特征数据集合，第一特征数据集合用于反映目标用户的下腔静脉的生理特征，然后，将第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果，最后，根据比对结果执行预设操作。可见，由于人体下腔静脉互不相同，具有唯一标识性，本申请医学成像装置通过分析用户的下腔静脉的影像特征，并通过比对该影像特征从而实现身份识别以完成预设操作，不同于人体表面的生理特征验证机制，人体内部的下腔静脉的影像特征难以被非法用户造假，故而有利于提高身份识别的安全性。
在一个可能的示例中，所述下腔静脉包括主静脉和子静脉，所诉主静脉是指上、下腔静脉及汇入右心房的中心静脉，所述子静脉是指连接所述主静脉与肾脏的下腔静脉；所述医学成像装置根据所述目标影像生成所述下腔静脉的第一特征数据集合，包括：所述医学成像装置根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据：所述子静脉的数量、所述主静脉和/或所述子静脉的形状、所述主静脉与所述子静脉的空间位置关系，所述子静脉的空间位置关系；根据所述至少一种属性的特征数据生成所述第一特征数据集合。
其中，所述子静脉的数量是指连接肾脏的子静脉的数量，所述形状是指下腔静脉的轮廓特性、半径等，所述空间位置关系是指静脉之间的交叉、汇聚、相邻、远离、或者相对距离等位置关系描述信息。
具体实现中，针对子静脉的交叉特性，由于医学成像装置在生成目标影像之前的数据处理过程中，会调用交叉网络模型对静脉的交叉位置的融合数据进行空间分隔处理，故而该数据处理过程中即可得到不同下腔静脉之间的交叉特性，并可以预存在第一特征数据集合中。如此避免重复处理，提高处理效率。
可见，本示例中，医学成像装置能够通过分析下腔静脉的数量、形状、空间位置关系等特性，进行准确的身份识别。
在一个可能的示例中，所述至少一种属性包括所述数量；所述医学成像装置根据所述 目标影像生成所述下腔静脉的以下至少一种属性的特征数据，包括：所述医学成像装置确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；分析所述目标影像中所述主静脉和所述第一肾脏之间的影像，得到所述子静脉的第一数量；分析所述目标影像中所述主静脉和所述第二肾脏之间的影像，得到所述子静脉的第二数量；将所述第一数量和所述第二数量的和确定为所述数量的特征数据。
具体实现中，针对仅有单个肾脏的用户，可以设置第二肾脏对应的第二数量为零，提高处理效率。
可见，本示例中，医学成像装置能够通过分析子静脉的影像，准确拿到当前用户的子静脉的数量，从而为后续身份识别比对提供依据，提高准确度和安全性。
在一个可能的示例中，所述至少一种属性包括所述形状，所述形状包括主静脉和/或子静脉的形状；所述医学成像装置根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据，包括：所述医学成像装置确定所述目标影像中的所述主静脉和/或所述子静脉的影像；根据所述主静脉和/或所述子静脉的影像确定所述主静脉和/或所述子静脉的轮廓和半径；将所述轮廓和所述半径确定为所述形状的特征数据。
其中，所述轮廓的特征数据具体可以通过静脉的走势等描述，该走势可以相对参照线来描述各个分段的角度等，或者直接描述静脉的曲率分布等，此处不做唯一限定，所述半径的特征数据具体可以通过一个或多个参考位置的半径来描述。
可见，本示例中，医学成像装置能够通过分析主静脉和子静脉的影像，准确获取当前用户的主静脉和/或子静脉的形状的特征数据，从而为后续身份识别比对提供依据，提高准确度和安全性。
在一个可能的示例中，所述至少一种属性包括所述主静脉与所述子静脉的空间位置关系；所述医学成像装置根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据，包括：所述医学成像装置确定所述目标影像中的所述主静脉和多个子静脉中每个子静脉的影像；根据所述主静脉的影像和每个子静脉的影像，确定所述主静脉与所述每个子静脉的连接位置、连接角度；将所述连接位置和所述连接角度确定为所述主静脉与所述子静脉的空间位置关系。
可见，本示例中，医学成像装置能够通过分析主静脉和子静脉的影像，准确获取当前用户的主静脉和子静脉的空间位置关系的特征数据，从而为后续身份识别比对提供依据，提高准确度和安全性。
在一个可能的示例中，所述至少一种属性包括所述子静脉的空间位置关系；所述医学成像装置根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据，包括：所述医学成像装置确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；分析所述主静脉和所述第一肾脏之间的影像，得到第一子静脉集合，分析所述主静脉和所述第二肾脏之间的影像，得到第二子静脉集合；确定所述第一子静脉集合中任意两个子静脉之间的第一位置关系，以及确定所述第二子静脉集合中任意两个子静脉之间的第二位置关系；将所述第一位置关系和所述第二位置关系确定为所述子静脉的空间位置关系。
其中，所述第一位置关系和所述第二位置关系包括以下至少一种：分叉、缠绕交错、非接触、接触、相邻、或者特定位置的相对距离等位置关系描述信息。
可见，本示例中，医学成像装置能够通过分析子静脉的影像，准确获取当前用户的子静脉的空间位置关系的特征数据，从而为后续身份识别比对提供依据，提高准确度和安全性。
在一个可能的示例中，所述医学成像装置处理所述扫描图像得到所述下腔静脉的目标影像，包括：所述医学成像装置根据所述扫描图像生成位图BMP数据源；根据所述BMP 数据源生成第一静脉影像数据，所述第一静脉影像数据包括所述下腔静脉的原始数据集合，所述原始数据集合为所述下腔静脉表面和所述下腔静脉内部的组织结构的立方体空间的传递函数结果；根据所述第一静脉影像数据生成第二静脉影像数据，所述第二静脉影像数据包括所述下腔静脉的分割数据集合，所述分割数据集合有交叉位子关系的下腔静脉的相互独立的影像数据；处理所述第二静脉影像数据得到所述下腔静脉的目标影像。
其中，根据所述扫描图像生成位图BMP数据源的具体实现方式包括：将所述扫描图像作为目标用户的医学数字成像和通信DICOM数据；解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；针对所述图源执行第一预设处理得到所述BMP数据源，所述第一预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
其中，所述DICOM(Digital Imaging and Communications in Medicine)即医学数字成像和通信，是医学图像和相关信息的国际标准。具体实现中，所述医学成像装置先获取已经采集的反映目标用户的人体内部结构特征的多张扫描图像，可以通过清晰度、准确度等筛选出合适的包含目标器官的至少一张扫描图像，再对所述扫描图像执行进一步处理，得到位图BMP数据源。可见，本示例中，所述医学成像装置可以基于获取的扫描图像，进行筛选、解析和第一预设处理处理后得到位图BMP数据源，提高了医学影像成像的准确度和清晰度。
其中，所述VRDS限制对比度自适应直方图均衡包括以下步骤：区域噪音比度限幅、全局对比度限幅；将图源的局部直方图划分多个分区，针对每个分区，根据该分区的邻域的累积直方图的斜度确定变换函数的斜度，根据该变换函数的斜度确定该分区的像素值周边的对比度放大程度，然后根据该对比度放大程度进行限度裁剪处理，产生有效直方图的分布，同时也产生有效可用的邻域大小的取值，将这些裁剪掉的部分直方图均匀的分布到直方图的其他区域；所述混合偏微分去噪包括以下步骤：通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪，使得图像边缘的曲率小于预设曲率，实现即可保护图像边缘、又可以避免平滑过程中出现阶梯效应的混合偏微分去噪模型；所述VRDS Ai弹性变形处理包括以下步骤：在图像点阵上，叠加正负向随机距离形成差值位置矩阵，然后在每个差值位置上的灰度，形成新的点阵，可以实现图像内部的扭曲变形，再对图像进行旋转、扭曲、平移操作。
其中，所述混合偏微分去噪可以采用CDD和高阶去噪模型对所述图源进行处理；CDD模型(Curvature Driven Diffusions)模型是在TV(Total Variation)模型的基础上引进了曲率驱动而形成的，解决了TV模型不能修复图像视觉连通性的问题。其中，高阶去噪是指基于偏微分方程(PDE)方法对图像进行去噪处理。具体实现中，让所述图源按照指定的微分方程函数变化进行滤噪作用，从而滤除所述图源中的噪点，而偏微分方程的解就是去噪后的得到的所述BMP数据源，基于PDE的图像去噪方法具有各向异性扩散的特点，因此能够在所述图源的不同区域进行不同程度的扩散作用，从而取得抑制噪声的同时保护图像边缘纹理信息的效果。
可见，本示例中，所述医学成像装置通过以下至少一种图像处理操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理，提高了图像处理的执行效率，还提高了图像质量，保护图像边缘纹理。
在本可能的示例中，所述根据所述BMP数据源生成第一静脉影像数据，包括：将所述BMP数据源导入预设的VRDS医学网络模型，通过所述VRDS医学网络模型调用预存的传递函数集合中的每个传递函数，通过所述传递函数集合中的多个传递函数处理所述BMP数据源，得到第一静脉影像数据，所述传递函数集合包括通过反向编辑器预先设置的所述下 腔静脉的传递函数。
其中，BMP(全称Bitmap)是Windows操作***中的标准图像文件格式，可以分成两类：设备相关位图(DDB)和设备无关位图(DIB)。所述扫描图像包括以下任意一种：CT图像、MRI图像、DTI图像、PET-CT图像。
其中，所述VRDS医学网络模型为预设网络模型，其训练方法包含如下三个步骤：图像采样及尺度缩放；3D卷积神经网络特征提取及打分；医学成像装置评价与网络训练。在实施过程中，先将需要进行采样，获取N个BMP数据源，再按照预设的间隔从N个BMP数据源中提取出M个BMP数据源。需要进行说明的是，预设的间隔可根据使用场景进行灵活设定。从N个中采样出M个，然后，将采样出来的M个BMP数据源缩放到固定尺寸(例如，长为S像素，宽为S像素)，得到的处理结果作为3D卷积神经网络的输入。这样将M个BMP数据源作为3D卷积神经网络的输入。具体的，利用3D卷积神经网络对所述BMP数据源进行3D卷积处理，获得特征图。
在本可能的示例中，所述根据所述第一静脉影像数据生成第二静脉影像数据，包括：将所述第一静脉影像数据导入预设的交叉血管网络模型，通过所述交叉血管网络模型对所述交叉位置的原始数据进行空间分割处理，得到所述交叉位置的多个下腔静脉的相互独立的影像数据；通过所述相互独立的影像数据更新所述原始数据集合，得到第二静脉影像数据。
在本可能的示例中，所述处理所述第二静脉影像数据得到所述下腔静脉的目标影像，包括：针对所述第二静脉影像数据执行以下至少一种处理操作得到所述下腔静脉的目标影像：2D边界优化处理、3D边界优化处理、数据增强处理。
其中，所述2D边界优化处理包括以下操作：多次采样获取低分辨率信息和高分辨率信息，其中，低分辨率信息能够提供分割目标在整个图像中上下文语义信息，即反映目标与环境之间关系的特征，所述分割目标包括所述目标静脉。所述3D边界优化处理包括以下操作：将所述第二医学影像数据分别放入3D卷积层中进行3D卷积操作，获取特征图；3D池化层对所述特征图进行压缩，并进行非线性激活；对压缩后的所述特征图进行级联操作，获取模型输出的预测结果图像。所述数据增强处理包括以下至少一种：基于任意角度旋转的数据增强、基于直方图均衡的数据增强、基于白平衡的数据增强、基于镜像操作的数据增强、基于随机剪切的数据增强和基于模拟不同光照变化的数据增强。
在一个可能的示例中，所述获取目标用户的包含下腔静脉的目标部位的扫描图像之前，所述方法还包括：录入所述原始特征数据集合。
与上述图2所示的实施例一致的，请参阅图3，图3是本申请实施例提供的一种医学成像装置300的结构示意图，如图所示，所述医学成像装置300包括处理器310、存储器320、通信接口330以及一个或多个程序321，其中，所述一个或多个程序321被存储在上述存储器320中，并且被配置由上述处理器310执行，所述一个或多个程序321包括用于执行以下步骤的指令；
获取目标用户的包含下腔静脉的目标部位的扫描图像；以及用于处理所述扫描图像得到所述下腔静脉的目标影像；以及用于根据所述目标影像生成所述下腔静脉的第一特征数据集合，所述第一特征数据集合用于反映所述目标用户的所述下腔静脉的生理特征；以及用于将所述第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果；以及用于根据比对结果执行预设操作。
可以看出，本申请实施例中，医学成像装置首先获取目标用户的包含下腔静脉的目标部位的扫描图像，其次，处理扫描图像得到下腔静脉的目标影像，再次，根据目标影像生 成下腔静脉的第一特征数据集合，第一特征数据集合用于反映目标用户的下腔静脉的生理特征，然后，将第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果，最后，根据比对结果执行预设操作。可见，由于人体下腔静脉互不相同，具有唯一标识性，本申请医学成像装置通过分析用户的下腔静脉的影像特征，并通过比对该影像特征从而实现身份识别以完成预设操作，不同于人体表面的生理特征验证机制，人体内部的下腔静脉的影像特征难以被非法用户造假，故而有利于提高身份识别的安全性。
在一个可能的示例中，所述下腔静脉包括主静脉和子静脉，所诉主静脉是指上、下腔静脉及汇入右心房的中心静脉，所述子静脉是指连接所述主静脉与肾脏的下腔静脉；在所述根据所述目标影像生成所述下腔静脉的第一特征数据集合方面，所述程序中的指令具体用于执行以下操作：根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据：所述子静脉的数量、所述主静脉和/或所述子静脉的形状、所述主静脉与所述子静脉的空间位置关系，所述子静脉的空间位置关系；以及用于根据所述至少一种属性的特征数据生成所述第一特征数据集合。
在一个可能的示例中，所述至少一种属性包括所述数量；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述程序中的指令具体用于执行以下操作：确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；以及用于分析所述目标影像中所述主静脉和所述第一肾脏之间的影像，得到所述子静脉的第一数量；以及用于分析所述目标影像中所述主静脉和所述第二肾脏之间的影像，得到所述子静脉的第二数量；以及用于将所述第一数量和所述第二数量的和确定为所述数量的特征数据。
在一个可能的示例中，所述至少一种属性包括所述形状，所述形状包括主静脉和/或子静脉的形状；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述程序中的指令具体用于执行以下操作：确定所述目标影像中的所述主静脉和/或所述子静脉的影像；以及用于根据所述主静脉和/或所述子静脉的影像确定所述主静脉和/或所述子静脉的轮廓和半径；以及用于将所述轮廓和所述半径确定为所述形状的特征数据。
在一个可能的示例中，所述至少一种属性包括所述主静脉与所述子静脉的空间位置关系；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述程序中的指令具体用于执行以下操作：确定所述目标影像中的所述主静脉和多个子静脉中每个子静脉的影像；以及用于根据所述主静脉的影像和每个子静脉的影像，确定所述主静脉与所述每个子静脉的连接位置、连接角度；以及用于将所述连接位置和所述连接角度确定为所述主静脉与所述子静脉的空间位置关系。
在一个可能的示例中，所述至少一种属性包括所述子静脉的空间位置关系；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述程序中的指令具体用于执行以下操作：确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；以及用于分析所述主静脉和所述第一肾脏之间的影像，得到第一子静脉集合，分析所述主静脉和所述第二肾脏之间的影像，得到第二子静脉集合；以及用于确定所述第一子静脉集合中任意两个子静脉之间的第一位置关系，以及确定所述第二子静脉集合中任意两个子静脉之间的第二位置关系；以及用于将所述第一位置关系和所述第二位置关系确定为所述子静脉的空间位置关系。
在一个可能的示例中，在所述处理所述扫描图像得到所述下腔静脉的目标影像方面，所述程序中的指令具体用于执行以下操作：根据所述扫描图像生成位图BMP数据源；以及用于根据所述BMP数据源生成第一静脉影像数据，所述第一静脉影像数据包括所述下腔静脉的原始数据集合，所述原始数据集合为所述下腔静脉表面和所述下腔静脉内部的组织结构的立方体空间的传递函数结果；以及用于根据所述第一静脉影像数据生成第二静脉影像 数据，所述第二静脉影像数据包括所述下腔静脉的分割数据集合，所述分割数据集合有交叉位子关系的下腔静脉的相互独立的影像数据；以及用于处理所述第二静脉影像数据得到所述下腔静脉的目标影像。
上述主要从方法侧执行过程的角度对本申请实施例的方案进行了介绍。可以理解的是，医学成像装置为了实现上述功能，其包含了执行各个功能相应的硬件结构和/或软件模块。本领域技术人员应该很容易意识到，结合本文中所提供的实施例描述的各示例的单元及算法步骤，本申请能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
本申请实施例可以根据上述方法示例对医学成像装置进行功能单元的划分，例如，可以对应各个功能划分各个功能单元，也可以将两个或两个以上的功能集成在一个处理单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。需要说明的是，本申请实施例中对单元的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
图4是本申请实施例中所涉及的医学成像装置400的功能单元组成框图。包括处理单元401和通信单元402，其中，
所述处理单元401，用于通过所述通信单元402获取目标用户的包含下腔静脉的目标部位的扫描图像；以及用于处理所述扫描图像得到所述下腔静脉的目标影像；以及用于根据所述目标影像生成所述下腔静脉的第一特征数据集合，所述第一特征数据集合用于反映所述目标用户的所述下腔静脉的生理特征；以及用于将所述第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果；以及用于根据比对结果执行预设操作。
其中，所述医学成像装置400还可以包括存储单元403，用于存储电子设备的程序代码和数据。所述处理单元401可以是处理器，所述通信单元402可以是触控显示屏或者收发器，存储单元403可以是存储器。
可以看出，本申请实施例中，医学成像装置首先获取目标用户的包含下腔静脉的目标部位的扫描图像，其次，处理扫描图像得到下腔静脉的目标影像，再次，根据目标影像生成下腔静脉的第一特征数据集合，第一特征数据集合用于反映目标用户的下腔静脉的生理特征，然后，将第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果，最后，根据比对结果执行预设操作。可见，由于人体下腔静脉互不相同，具有唯一标识性，本申请医学成像装置通过分析用户的下腔静脉的影像特征，并通过比对该影像特征从而实现身份识别以完成预设操作，不同于人体表面的生理特征验证机制，人体内部的下腔静脉的影像特征难以被非法用户造假，故而有利于提高身份识别的安全性。
在一个可能的示例中，所述下腔静脉包括主静脉和子静脉，所诉主静脉是指上、下腔静脉及汇入右心房的中心静脉，所述子静脉是指连接所述主静脉与肾脏的下腔静脉；在所述根据所述目标影像生成所述下腔静脉的第一特征数据集合方面，所述处理单元401具体用于：根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据：所述子静脉的数量、所述主静脉和/或所述子静脉的形状、所述主静脉与所述子静脉的空间位置关系，所述子静脉的空间位置关系；以及用于根据所述至少一种属性的特征数据生成所述第一特征数据集合。
在一个可能的示例中，所述至少一种属性包括所述数量；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述处理单元401具体用于：确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；以及用于分析所述目标影像 中所述主静脉和所述第一肾脏之间的影像，得到所述子静脉的第一数量；以及用于分析所述目标影像中所述主静脉和所述第二肾脏之间的影像，得到所述子静脉的第二数量；以及用于将所述第一数量和所述第二数量的和确定为所述数量的特征数据。
在一个可能的示例中，所述至少一种属性包括所述形状，所述形状包括主静脉和/或子静脉的形状；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述处理单元401具体用于：确定所述目标影像中的所述主静脉和/或所述子静脉的影像；以及用于根据所述主静脉和/或所述子静脉的影像确定所述主静脉和/或所述子静脉的轮廓和半径；以及用于将所述轮廓和所述半径确定为所述形状的特征数据。
在一个可能的示例中，所述至少一种属性包括所述主静脉与所述子静脉的空间位置关系；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述处理单元401具体用于：确定所述目标影像中的所述主静脉和多个子静脉中每个子静脉的影像；以及用于根据所述主静脉的影像和每个子静脉的影像，确定所述主静脉与所述每个子静脉的连接位置、连接角度；以及用于将所述连接位置和所述连接角度确定为所述主静脉与所述子静脉的空间位置关系。
在一个可能的示例中，所述至少一种属性包括所述子静脉的空间位置关系；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述处理单元401具体用于：确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；以及用于分析所述主静脉和所述第一肾脏之间的影像，得到第一子静脉集合，分析所述主静脉和所述第二肾脏之间的影像，得到第二子静脉集合；以及用于确定所述第一子静脉集合中任意两个子静脉之间的第一位置关系，以及确定所述第二子静脉集合中任意两个子静脉之间的第二位置关系；以及用于将所述第一位置关系和所述第二位置关系确定为所述子静脉的空间位置关系。
在一个可能的示例中，在所述处理所述扫描图像得到所述下腔静脉的目标影像方面，所述处理单元401具体用于：根据所述扫描图像生成位图BMP数据源；以及用于根据所述BMP数据源生成第一静脉影像数据，所述第一静脉影像数据包括所述下腔静脉的原始数据集合，所述原始数据集合为所述下腔静脉表面和所述下腔静脉内部的组织结构的立方体空间的传递函数结果；以及用于根据所述第一静脉影像数据生成第二静脉影像数据，所述第二静脉影像数据包括所述下腔静脉的分割数据集合，所述分割数据集合有交叉位子关系的下腔静脉的相互独立的影像数据；以及用于处理所述第二静脉影像数据得到所述下腔静脉的目标影像。
本申请实施例还提供一种计算机存储介质，其中，该计算机存储介质存储用于电子数据交换的计算机程序，该计算机程序使得计算机执行如上述方法实施例中记载的任一方法的部分或全部步骤，上述计算机包括医学成像装置。
本申请实施例还提供一种计算机程序产品，上述计算机程序产品包括存储了计算机程序的非瞬时性计算机可读存储介质，上述计算机程序可操作来使计算机执行如上述方法实施例中记载的任一方法的部分或全部步骤。该计算机程序产品可以为一个软件安装包，上述计算机包括医学成像装置。
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本申请并不受所描述的动作顺序的限制，因为依据本申请，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本申请所必须的。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分， 可以参见其他实施例的相关描述。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置，可通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如上述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个***，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性或其它的形式。
上述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
上述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储器中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储器中，包括若干指令用以使得一台计算机设备(可为个人计算机、服务器或者网络设备等)执行本申请各个实施例上述方法的全部或部分步骤。而前述的存储器包括：U盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、移动硬盘、磁碟或者光盘等各种可以存储程序代码的介质。
本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储器中，存储器可以包括：闪存盘、只读存储器(英文：Read-Only Memory，简称：ROM)、随机存取器(英文：Random Access Memory，简称：RAM)、磁盘或光盘等。
以上对本申请实施例进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的一般技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。

Claims (20)

  1. 一种基于虚拟现实医疗***人工智能VRDS AI静脉影像的识别方法，其特征在于，应用于医学成像装置，所述方法包括：
    获取目标用户的包含下腔静脉的目标部位的扫描图像；
    处理所述扫描图像得到所述下腔静脉的目标影像；
    根据所述目标影像生成所述下腔静脉的第一特征数据集合，所述第一特征数据集合用于反映所述目标用户的所述下腔静脉的生理特征；
    将所述第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果；
    根据比对结果执行预设操作。
  2. 根据权利要求1所述的方法，其特征在于，所述原始特征数据集合包括多个类型的特征数据，所述将所述第一特征数据集合与预存的原始特征数据集合进行比对，包括：
    计算出每个类别的特征数据的参考匹配度；
    根据预设的权值分配，加权求和得到综合匹配度；
    根据所述综合匹配度与预设的匹配度阈值得到比对结果。
  3. 根据权利要求1所述的方法，其特征在于，所述下腔静脉包括主静脉和子静脉，所诉主静脉是指上、下腔静脉及汇入右心房的中心静脉，所述子静脉是指连接所述主静脉与肾脏的下腔静脉；所述根据所述目标影像生成所述下腔静脉的第一特征数据集合，包括：
    根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据：所述子静脉的数量、所述主静脉和/或所述子静脉的形状、所述主静脉与所述子静脉的空间位置关系，所述子静脉的空间位置关系；
    根据所述至少一种属性的特征数据生成所述第一特征数据集合。
  4. 根据权利要求3所述的方法，其特征在于，所述子静脉的数量是指连接肾脏的子静脉的数量，所述形状是指所述下腔静脉的轮廓特性、半径，所述空间位置关系是指静脉之间的交叉、汇聚、相邻、远离、或者相对距离的位置关系描述信息。
  5. 根据权利要求3所述的方法，其特征在于，所述至少一种属性包括所述数量；所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据，包括：
    确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；
    分析所述目标影像中所述主静脉和所述第一肾脏之间的影像，得到所述子静脉的第一数量；
    分析所述目标影像中所述主静脉和所述第二肾脏之间的影像，得到所述子静脉的第二数量；
    将所述第一数量和所述第二数量的和确定为所述数量的特征数据。
  6. 根据权利要求3所述的方法，其特征在于，所述至少一种属性包括所述形状，所述形状包括主静脉和/或子静脉的形状；所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据，包括：
    确定所述目标影像中的所述主静脉和/或所述子静脉的影像；
    根据所述主静脉和/或所述子静脉的影像确定所述主静脉和/或所述子静脉的轮廓和半径；
    将所述轮廓和所述半径确定为所述形状的特征数据。
  7. 根据权利要求3所述的方法，其特征在于，所述至少一种属性包括所述主静脉与所述子静脉的空间位置关系；所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据，包括：
    确定所述目标影像中的所述主静脉和多个子静脉中每个子静脉的影像；
    根据所述主静脉的影像和每个子静脉的影像，确定所述主静脉与所述每个子静脉的连接位置、连接角度；
    将所述连接位置和所述连接角度确定为所述主静脉与所述子静脉的空间位置关系。
  8. 根据权利要求3所述的方法，其特征在于，所述至少一种属性包括所述子静脉的空间位置关系；所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据，包括：
    确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；
    分析所述主静脉和所述第一肾脏之间的影像，得到第一子静脉集合，分析所述主静脉和所述第二肾脏之间的影像，得到第二子静脉集合；
    确定所述第一子静脉集合中任意两个子静脉之间的第一位置关系，以及确定所述第二子静脉集合中任意两个子静脉之间的第二位置关系；
    将所述第一位置关系和所述第二位置关系确定为所述子静脉的空间位置关系。
  9. 根据权利要求1-8任一项所述的方法，其特征在于，所述处理所述扫描图像得到所述下腔静脉的目标影像，包括：
    根据所述扫描图像生成位图BMP数据源；
    根据所述BMP数据源生成第一静脉影像数据，所述第一静脉影像数据包括所述下腔静脉的原始数据集合，所述原始数据集合为所述下腔静脉表面和所述下腔静脉内部的组织结构的立方体空间的传递函数结果；
    根据所述第一静脉影像数据生成第二静脉影像数据，所述第二静脉影像数据包括所述下腔静脉的分割数据集合，所述分割数据集合有交叉位子关系的下腔静脉的相互独立的影像数据；
    处理所述第二静脉影像数据得到所述下腔静脉的目标影像。
  10. 一种医学成像装置，其特征在于，包括处理单元和通信单元，其中，
    所述处理单元，用于通过所述通信单元获取目标用户的包含下腔静脉的目标部位的扫描图像；以及用于处理所述扫描图像得到所述下腔静脉的目标影像；以及用于根据所述目标影像生成所述下腔静脉的第一特征数据集合，所述第一特征数据集合用于反映所述目标用户的所述下腔静脉的生理特征；以及用于将所述第一特征数据集合与预存的原始特征数据集合进行比对，得到比对结果；以及用于根据比对结果执行预设操作。
  11. 根据权利要求10所述的装置，其特征在于，所述原始特征数据集合包括多个类型的特征数据，在所述所述原始特征数据集合包括多个类型的特征数据方面，所述处理单元具体用于：计算出每个类别的特征数据的参考匹配度；以及根据预设的权值分配，加权求和得到综合匹配度；以及根据所述综合匹配度与预设的匹配度阈值得到比对结果。
  12. 根据权利要求10所述的装置，其特征在于，所述下腔静脉包括主静脉和子静脉，所诉主静脉是指上、下腔静脉及汇入右心房的中心静脉，所述子静脉是指连接所述主静脉与肾脏的下腔静脉；在所述根据所述目标影像生成所述下腔静脉的第一特征数据集合方面，所述处理单元具体用于：根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据：所述子静脉的数量、所述主静脉和/或所述子静脉的形状、所述主静脉与所述子静脉的空间位置关系，所述子静脉的空间位置关系；以及根据所述至少一种属性的特征数据生成所述第一特征数据集合。
  13. 根据权利要求12所述的装置，其特征在于，所述子静脉的数量是指连接肾脏的子静脉的数量，所述形状是指所述下腔静脉的轮廓特性、半径，所述空间位置关系是指静脉之间的交叉、汇聚、相邻、远离、或者相对距离的位置关系描述信息。
  14. 根据权利要求12所述的装置，其特征在于，所述至少一种属性包括所述数量；在 所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述处理单元具体用于：确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；以及分析所述目标影像中所述主静脉和所述第一肾脏之间的影像，得到所述子静脉的第一数量；以及分析所述目标影像中所述主静脉和所述第二肾脏之间的影像，得到所述子静脉的第二数量；以及将所述第一数量和所述第二数量的和确定为所述数量的特征数据。
  15. 根据权利要求12所述的装置，其特征在于，所述至少一种属性包括所述形状，所述形状包括主静脉和/或子静脉的形状；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述处理单元具体用于：确定所述目标影像中的所述主静脉和/或所述子静脉的影像；以及根据所述主静脉和/或所述子静脉的影像确定所述主静脉和/或所述子静脉的轮廓和半径；以及将所述轮廓和所述半径确定为所述形状的特征数据。
  16. 根据权利要求12所述的装置，其特征在于，所述至少一种属性包括所述主静脉与所述子静脉的空间位置关系；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述处理单元具体用于：确定所述目标影像中的所述主静脉和多个子静脉中每个子静脉的影像；以及根据所述主静脉的影像和每个子静脉的影像，确定所述主静脉与所述每个子静脉的连接位置、连接角度；以及将所述连接位置和所述连接角度确定为所述主静脉与所述子静脉的空间位置关系。
  17. 根据权利要求12所述的装置，其特征在于，所述至少一种属性包括所述子静脉的空间位置关系；在所述根据所述目标影像生成所述下腔静脉的以下至少一种属性的特征数据方面，所述处理单元具体用于：确定所述目标影像中的所述主静脉、第一肾脏和第二肾脏的影像；以及分析所述主静脉和所述第一肾脏之间的影像，得到第一子静脉集合，分析所述主静脉和所述第二肾脏之间的影像，得到第二子静脉集合；以及确定所述第一子静脉集合中任意两个子静脉之间的第一位置关系，以及确定所述第二子静脉集合中任意两个子静脉之间的第二位置关系；以及将所述第一位置关系和所述第二位置关系确定为所述子静脉的空间位置关系。
  18. 根据权利要求10-17任一项所述的装置，其特征在于，在所述处理所述扫描图像得到所述下腔静脉的目标影像方面，所述处理单元具体用于：根据所述扫描图像生成位图BMP数据源；以及根据所述BMP数据源生成第一静脉影像数据，所述第一静脉影像数据包括所述下腔静脉的原始数据集合，所述原始数据集合为所述下腔静脉表面和所述下腔静脉内部的组织结构的立方体空间的传递函数结果；以及根据所述第一静脉影像数据生成第二静脉影像数据，所述第二静脉影像数据包括所述下腔静脉的分割数据集合，所述分割数据集合有交叉位子关系的下腔静脉的相互独立的影像数据；以及处理所述第二静脉影像数据得到所述下腔静脉的目标影像。
  19. 一种医学成像装置，其特征在于，包括处理器、存储器、通信接口，以及一个或多个程序，所述一个或多个程序被存储在所述存储器中，并且被配置由所述处理器执行，所述程序包括用于执行如权利要求1-9任一项所述的方法中的步骤的指令。
  20. 一种计算机可读存储介质，其特征在于，存储用于电子数据交换的计算机程序，其中，所述计算机程序使得计算机执行如权利要求1-9任一项所述的方法。
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