WO2020173054A1 - Vrds 4d医学影像处理方法及产品 - Google Patents

Vrds 4d医学影像处理方法及产品 Download PDF

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WO2020173054A1
WO2020173054A1 PCT/CN2019/101161 CN2019101161W WO2020173054A1 WO 2020173054 A1 WO2020173054 A1 WO 2020173054A1 CN 2019101161 W CN2019101161 W CN 2019101161W WO 2020173054 A1 WO2020173054 A1 WO 2020173054A1
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image data
image
data sets
medical
data
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PCT/CN2019/101161
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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 AU2019431568A priority Critical patent/AU2019431568B2/en
Priority to US17/433,238 priority patent/US20220139054A1/en
Priority to EP19917170.3A priority patent/EP3933848A4/en
Publication of WO2020173054A1 publication Critical patent/WO2020173054A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
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    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
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    • G06T2200/00—Indexing scheme for image data processing or generation, in general
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    • G06V2201/03—Recognition of patterns in medical or anatomical images

Definitions

  • This application relates to the technical field of medical imaging devices, in particular to a VRDS 4D medical image processing method and product.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • DTI Diffusion Tensor Imaging
  • Positron Emission Computed Tomography Computed Tomography
  • PET Magnetic Resonance Imaging
  • PET Positron Emission Computed Tomography
  • PET PET
  • doctors still watch and read continuous two-dimensional slice scan images to judge and analyze the patient's diseased tissues such as tumors.
  • two-dimensional slice scan images cannot show the spatial structure characteristics of the diseased tissue, which affects the doctor's diagnosis of the disease.
  • the embodiments of the present application provide a VRDS 4D medical image processing method and product, in order to improve the refinement and accuracy of medical image display performed by the medical imaging device.
  • an embodiment of the present application provides a VRDS 4D medical image processing method, which is applied to a medical imaging device; the method includes:
  • the image source includes texture 2D/3D image volume data, and the multiple scanned images are collected by medical equipment 2D medical images of
  • N is a positive integer greater than 1;
  • the N first image data sets and the N second image data sets have a one-to-one correspondence, and each The second image data set adds spatial location information relative to the corresponding first image data set, and the spatial location information is used to reflect the spatial location attribute of the image data;
  • the embodiments of the present application provide a VRDS 4D medical image processing device, which is applied to a medical imaging device;
  • the VRDS 4D medical image processing device includes a processing unit and a communication unit, wherein:
  • the processing unit is configured to perform initial data analysis and processing on multiple scanned images to obtain an image source containing image features of the target part of the target user, and the image source includes texture 2D/3D image volume data.
  • the scanned image is a two-dimensional medical image collected by medical equipment; and is used to perform post-data processing on the image source to obtain N first image data sets, and the data volume of each first image data set is the same as the image source.
  • the data in any two first image data sets are independent of each other, and N is a positive integer greater than 1; and used to perform preset processing on the N first image data sets to obtain N second image data sets.
  • Image data sets, the N first image data sets and the N second image data sets have a one-to-one correspondence, and each second image data set adds spatial location information relative to the corresponding first image data set,
  • the spatial position information is used to reflect the spatial position attribute of the image data; and used to perform VRDS 4D medical image display according to the N second image data sets through the communication unit.
  • 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 performs initial data analysis processing on multiple scanned images to obtain an image source containing the image characteristics of the target part of the target user, and secondly, performs post-data processing on the image source to obtain N first image data sets, where the data amount of each first image data set is the same as the data amount of the image source, the data in any two first image data sets are independent of each other, and N is a positive integer greater than 1, Third, the N first image data sets are pre-processed to obtain N second image data sets.
  • the N first image data sets and N second image data sets are in one-to-one correspondence, and each second image data set Compared with the corresponding first image data collection, the collection adds spatial location information, which is used to reflect the spatial location attributes of the image data.
  • VRDS 4D medical image display is performed according to N second image data sets. It can be seen that the medical imaging device in this application can optimize the original scanned image in the data volume dimension and the spatial dimension, thereby realizing VRDS 4D medical image display, that is, presenting 3D images outside and inside the tissue with real spatial structural characteristics. Improve the refinement and accuracy of medical image display by medical imaging devices.
  • FIG. 1 is a schematic structural diagram of a VRDS-based 4D medical image intelligent analysis and processing system provided by an embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a VRDS 4D medical image processing method provided by 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 VRDS 4D medical image processing 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-based 4D medical image intelligent analysis and processing system 100 according to 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 be based on the original DICOM data, based on the VRDS 4D medical image processing algorithm presented in the embodiment of this application, Perform four-dimensional 4D volume rendering of the internal tissues and organs of the human body to realize the 4D stereo imaging effect (the four-dimensional medical image specifically refers to the medical image including the internal spatial structural features and external spatial structural features of the displayed tissue.
  • the internal spatial structural features It means that the slice data inside the tissue is not lost, that is, the medical imaging device can present the internal structure of the target organ, blood vessel and other tissues.
  • the external spatial structure characteristics refer to the environmental characteristics between the tissue and the tissue, including the spatial position between the tissue and the tissue Characteristics (including intersection, interval, fusion), etc., such as the edge structure characteristics of the intersection position between the kidney and the artery, etc.), the local medical imaging device 111 can also be used to edit the image source data relative to the terminal medical imaging device 112.
  • the transfer function result of forming a four-dimensional human body image.
  • the transfer function result can include the transfer function result of the surface of the internal organs and the tissue structure of the internal organs, and the transfer function result of the cube space, such as the cube edit box and The number, coordinates, color, transparency and other information of the array of arc editing.
  • 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.
  • the operation action refers to the user’s medical imaging
  • the external intake equipment of the device such as a mouse, keyboard, etc., controls the operation of the four-dimensional human body image to realize human-computer interaction.
  • the operation action includes at least one of the following: (1) Change the color of a specific organ/tissue and / Or 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) Move the view up and down.
  • FIG. 2 is a schematic flowchart of a VRDS 4D medical image processing method according to an embodiment of the present application, which is applied to the medical imaging device described in FIG. 1; as shown in the figure, the VRDS 4D medical image processing method include:
  • the medical imaging device performs initial data analysis and processing on multiple scanned images to obtain an image source containing image features of the target part of the target user, the image source includes texture 2D/3D image volume data, the multiple scanned images It is a two-dimensional medical image collected by medical equipment;
  • the target site may include organs such as kidneys, arteries, veins, and the like, for example.
  • the multiple scan images include any one of the following: CT images, MRI images, DTI images, and PET-CT images.
  • the medical imaging device acquires multiple scanned images that reflect the internal structural features of the target user's human body collected by medical equipment; at least one scanned image containing the target part is selected from the multiple scanned images, and the The at least one scanned image is used as the medical digital imaging and communication DICOM data of the target user; the DICOM data is analyzed to generate the image source of the target user, and the image source includes texture 2D/3D image volume data.
  • the medical imaging device performs post-data processing on the image source to obtain N first image data sets.
  • the data amount of each first image data set is the same as the data amount of the image source, and any two second image data sets are The data in an image data set are mutually independent, and N is a positive integer greater than 1;
  • the medical imaging device performs data enhancement processing on the image source
  • the data enhancement processing includes at least one of the following: data enhancement based on arbitrary angle rotation, data enhancement based on zoom, data enhancement based on translation, and data enhancement based on mirroring. Operational data enhancement, shear-based data enhancement, and elastic deformation-based data enhancement.
  • the image rotation refers to the geometric transformation of the image source by a certain angle
  • the scaling refers to the process of adjusting the size of the digital image in computer image processing, that is, the processing efficiency and the smoothness of the result
  • the translation is to add all the pixel coordinates of the image to the specified horizontal offset and vertical offset respectively
  • the clipping is divided into two types: regular Rectangle Subset, Pdygon Subset, regular framing means that the boundary of the cropped image is a rectangle, which can be determined by the coordinates of the upper left corner and the lower right corner
  • the cropping position of the image, the entire cropping process is relatively simple, irregular framing cutting means that the boundary range of the cropped image is an arbitrary polygon
  • the mirroring is divided into two types: horizontal mirroring and vertical mirroring.
  • Horizontal mirroring uses the vertical center line of the image as the axis to swap the pixels of the image, that is, swapping the left half and right half of the image.
  • the vertical mirroring uses the horizontal center line of the image as the axis, and the upper half of the image is Swap after get off work.
  • the medical imaging device performs preset processing on the N first image data sets to obtain N second image data sets, the N first image data sets and the N second image data sets One-to-one correspondence, and each second image data set adds spatial position information relative to the corresponding first image data set, and the spatial position information is used to reflect the spatial position attribute of the image data;
  • performing preset processing on the N first image data sets includes data separation and integration.
  • the spatial location information includes spatial direction information, spatial topology information, spatial metric information, and the like.
  • the medical imaging device performs VRDS 4D medical image display according to the N second image data sets.
  • the VRDS 4D medical image display refers to the presentation of VRDS four-dimensional medical images.
  • the medical imaging device filters out target image data from the N second image data sets as VRDS 4D imaging data.
  • the medical imaging device first performs initial data analysis processing on multiple scanned images to obtain an image source containing the image characteristics of the target part of the target user, and secondly, performs post-data processing on the image source to obtain N first image data sets, where the data amount of each first image data set is the same as the data amount of the image source, the data in any two first image data sets are independent of each other, and N is a positive integer greater than 1, Third, the N first image data sets are pre-processed to obtain N second image data sets.
  • the N first image data sets and N second image data sets are in one-to-one correspondence, and each second image data set Compared with the corresponding first image data collection, the collection adds spatial location information, which is used to reflect the spatial location attributes of the image data.
  • VRDS 4D medical image display is performed according to N second image data sets. It can be seen that the medical imaging device in this application can optimize the original scanned image in the data volume dimension and the spatial dimension, thereby realizing VRDS 4D medical image display, that is, presenting 3D images outside and inside the tissue with real spatial structural characteristics. Improve the refinement and accuracy of medical image display by medical imaging devices.
  • the performing post-data processing on the image source to obtain N first image data sets includes: performing data enhancement processing on the image source to obtain N first image data sets, so
  • the data enhancement processing includes at least one of the following: rotation, zooming, translation, shearing, mirroring, and elastic deformation.
  • the enhancement process for all data in the image source includes at least one: rotation, zooming, translation, shearing, mirroring, and elastic deformation; after this data enhancement process
  • the data of is used as a first image data set; in the N times of data enhancement processing, N first image data sets can be obtained according to the image source data and multiple data enhancement processing methods.
  • the medical imaging device can perform data enhancement processing on the image source based on image data processing methods such as rotation, zooming, translation, shearing, mirroring, and elastic deformation, to obtain N first image data sets, the said The image quality in the first image data set ensures the clarity and accuracy of the image.
  • the data enhancement processing includes elastic deformation; the elastic deformation of the image source to obtain N first image data sets includes: on the original pixel lattice of the image source, Random distances in N directions are superimposed to form N difference position matrices, the N directions include at least positive and negative directions; the gray value of the corresponding pixel is calculated on each difference position matrix to obtain N enhanced pixels Dot matrix; generating N first image data sets according to the N enhanced pixel dot matrix, and the image data in each first image data set includes superimposed information of pixels.
  • the image dot matrix of the image source is superimposed on the positive and negative random distances to form a difference position matrix, and then the gray value of the corresponding pixel is calculated at each difference position to form a new dot matrix. It can realize the distortion inside the image, and then rotate, distort, and translate the image.
  • the medical imaging device can perform elastic deformation processing on the image source, the execution efficiency of image processing is improved, and the degree of refinement of medical image display by the medical imaging device is improved.
  • the performing preset processing on the N first image data sets to obtain N second image data sets includes: performing data separation on the N first image data sets to obtain A separate data set, the separate data set includes a plurality of separate data subsets, the image data in each separate data subset includes organization identification information, the organization identification information is used to identify the organization to which the image data belongs, the organization Including at least one of the following: organs, tumors, and blood vessels; performing data integration on the N first image data sets to obtain an integrated data set, and the integrated data set includes multiple types of tissues with preset occupancy patterns Image data; import the separated data set and the integrated data set into the VRDS Ai 4D imaging data analyzer, through the Ai matching of the gray value of the medical image and the conversion calculation of the superimposed information of the pixel point, the gray matching is obtained Spatial location information of each image data of the condition; generating N second image data sets according to the spatial location information and the N first image data sets.
  • the preset occupancy mode refers to a preset combination of different tissues and organs, such as a combination of kidney and artery, a combination of kidney and tumor, and the like.
  • the N first image data sets can be separated according to the tissue identification information.
  • the N first image data sets can be separated into organ data subsets, tumor data subsets, and blood vessel data. Subset etc. Then perform data integration on the N first image data sets, and use the pre-stored medical image template matching models of the multiple tissues and the conversion calculation of the superimposed information of the pixels to obtain the gray-scale matching data from the integrated data sets.
  • the image data of the degree matching condition and the spatial position information of each image data thereby generating N second image data sets.
  • the medical imaging device can generate N second image data sets based on the separation and integration of the N first image data sets, the Ai matching of the medical image gray value, and the conversion calculation of the superimposed information of the pixels.
  • the image data collection realizes the merging, consolidation and enhancement of image data.
  • the Ai matching of the gray value of the medical image is achieved by the following steps: calling the pre-stored medical image template matching models of the multiple tissues; importing the image data in the integrated data set into the The medical imaging template matching model filters out the image data that meet the gray-scale matching conditions.
  • gray level matching can determine the corresponding relationship of the image by using a certain similarity measure, such as correlation function, covariance function, sum of squares of difference, sum of absolute difference and other measurement extremes.
  • a certain similarity measure such as correlation function, covariance function, sum of squares of difference, sum of absolute difference and other measurement extremes.
  • the image matching can be divided into several steps such as image input, image preprocessing, matching feature extraction, image matching, and output result. Firstly, the image is preprocessed to extract its high-level feature values, and then according to the medical image template matching model, the image data that meets the gray-level matching conditions are selected.
  • the medical image template matching model can be constructed using various algorithms such as commonly used convolutional neural networks, and is trained in advance through medical sample data to improve the accuracy of model prediction.
  • the medical imaging device can obtain image data that meets the gray-scale matching conditions based on the pre-stored medical image template matching models of the multiple tissues and the image data in the integrated data set, the medical image quality is improved.
  • the quality ensures the refinement and accuracy of the image.
  • the conversion calculation of the superimposed information of the pixel is realized by the following operations: obtain the superimposed information of the currently processed pixel, the superimposed information includes direction information and random distance parameters; Information and the random distance parameter calculation space coordinate as the spatial position information of the pixel.
  • the conversion calculation of the superposition information of the pixel points is to calculate the space coordinates according to the direction information of the pixel points and the random distance parameter.
  • the superimposition information data of the currently processed pixel is obtained, and the obtained superimposition information data is used.
  • the direction information and the random distance parameter in the superimposition information are all obtained by superimposing at least two directions or at least two random distance parameters, Finally, a space coordinate is calculated according to the direction information and the random distance parameter, and the space coordinate is the space position information of the pixel.
  • the medical imaging device can calculate the spatial coordinates of the pixel based on the direction information of the pixel and the random distance parameter as the spatial position information of the pixel, it reflects the spatial position attribute of the image data and improves the data of the image data. The perfection of information and the accuracy and clarity of medical imaging.
  • the performing VRDS 4D medical image display according to the N second image data sets includes: according to the tissues associated with the preset occupancy mode, from the N second image data sets Select the target image data associated with the tissue as VRDS 4D imaging data; and output the VRDS 4D imaging data on a display device.
  • the distribution positions of the image data of the same organ of the human body in the N second image data sets are often related to each other. In order to reduce the time for screening image data, Improve the efficiency of data screening.
  • the medical imaging device When screening the first second image data set, the medical imaging device traverses the entire data set in order from front to back to filter out the first target image data, and then for the second second image data
  • the position of the first target image data can be used as the starting detection position of the current collection, and the screening range can be reduced to the maximum range of the tissue currently being processed (tissues include organs such as kidneys, etc.) Determine the maximum range)
  • the processing operation from the third second image data set to the Nth second image data set is similar to the processing procedure of the second second image data set, and will not be repeated here. This can significantly improve the efficiency of data screening and the efficiency of image display.
  • the target image data associated with the heart and blood vessels can be filtered from the N second image data according to the tissue identification information of the image data , Using the target image data as VRDS 4D imaging data to perform VRDS 4D imaging to show the images of the heart and blood vessels.
  • the N second image data may include at least one of organs, tumors, and blood vessels.
  • the VRDS 4D image is output on the display device, when a partial position selection for a certain tissue is detected, the image of the selected partial position of the tissue is retrieved, and operations such as zooming, rotating, and observing the inner side can be performed.
  • the medical imaging device can be based on the tissues associated with the preset occupancy mode
  • Shuai selects the target data corresponding to the tissues associated with the preset occupancy mode to perform VRDS 4D imaging data and display it, which improves the quality of medical images , And the degree of refinement and accuracy of medical image display performed by medical imaging devices.
  • 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 image source includes texture 2D/3D image volume data, and the multiple scanned images are collected by medical equipment 2D medical images of
  • N is a positive integer greater than 1;
  • the N first image data sets and the N second image data sets have a one-to-one correspondence, and each The second image data set adds spatial location information relative to the corresponding first image data set, and the spatial location information is used to reflect the spatial location attribute of the image data;
  • the medical imaging device first performs initial data analysis processing on multiple scanned images to obtain an image source containing the image characteristics of the target part of the target user, and secondly, performs post-data processing on the image source to obtain N first image data sets, where the data amount of each first image data set is the same as the data amount of the image source, the data in any two first image data sets are independent of each other, and N is a positive integer greater than 1, Third, the N first image data sets are pre-processed to obtain N second image data sets.
  • the N first image data sets and N second image data sets are in one-to-one correspondence, and each second image data set Compared with the corresponding first image data collection, the collection adds spatial location information, which is used to reflect the spatial location attributes of the image data.
  • VRDS 4D medical image display is performed according to N second image data sets. It can be seen that the medical imaging device in this application can optimize the original scanned image in the data volume dimension and the spatial dimension, thereby realizing VRDS 4D medical image display, that is, presenting 3D images outside and inside the tissue with real spatial structural characteristics. Improve the refinement and accuracy of medical image display by medical imaging devices.
  • the instructions in the program are specifically used to perform the following operations:
  • the enhancement processing obtains N first image data sets, and the data enhancement processing includes at least one of the following: rotation, scaling, translation, shearing, mirroring, and elastic deformation.
  • the data enhancement processing includes elastic deformation; in the aspect of performing elastic deformation on the image source to obtain N first image data sets, the instructions in the program are specifically used to perform the following operations : On the original pixel matrix of the image source, superimpose random distances in N directions to form N difference position matrices, the N directions include at least positive and negative directions; calculate on each difference position matrix Corresponding to the gray values of the pixels, N enhanced pixel dot matrixes are obtained; N first image data sets are generated according to the N enhanced pixel dot matrixes, and the image data in each first image data set includes the superposition of pixels information.
  • the instructions in the program are specifically used to perform the following operations: Perform data separation on the N first image data sets to obtain a separated data set.
  • the separated data set includes a plurality of separated data subsets.
  • the image data in each separated data subset includes organization identification information, and the organization identification information is used To identify the tissue to which the image data belongs, the tissue includes at least one of the following: organs, tumors, and blood vessels; data integration is performed on the N first image data sets to obtain an integrated data set, in the integrated data set Multi-type image data of multiple tissues including preset occupancy patterns; import the separated data set and the integrated data set into the VRDS Ai 4D imaging data analyzer, through the Ai matching of the medical image gray value and the pixel The conversion calculation of the superposition information of the points obtains the spatial position information of each image data that meets the gray-scale matching condition; and the N second image data sets are generated according to the spatial position information and the N first image data sets.
  • the instructions in the program are specifically used to perform the following operations: call pre-stored medical image template matching models of the multiple tissues;
  • the image data in the integrated data set is imported into the medical image template matching model, and the image data that meets the gray-level matching conditions are screened out.
  • the instructions in the program are specifically used to perform the following operations: obtain the superimposed information of the currently processed pixel, and the superimposed information includes Direction information and random distance parameters; calculating space coordinates according to the direction information and the random distance parameters as the spatial position information of the pixel.
  • the instructions in the program are specifically used to perform the following operations: associate according to the preset occupancy mode
  • the target image data associated with the tissue is selected from the N second image data sets as VRDS 4D imaging data; the VRDS 4D imaging data is output on a display device.
  • 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 units of the VRDS 4D medical image processing device 400 involved in an embodiment of the present application.
  • the VRDS 4D medical image processing device 400 is applied to a medical imaging device.
  • the VRDS 4D medical image processing device 400 includes a processing unit 401 and a communication unit 402. Among them,
  • the processing unit 401 is configured to perform initial data analysis processing on multiple scanned images to obtain an image source containing the image features of the target part of the target user.
  • the image source includes texture 2D/3D image volume data.
  • the scanned images are two-dimensional medical images collected by medical equipment; and are used to perform post-data processing on the image source to obtain N first image data sets, and the data volume of each first image data set corresponds to the figure
  • the amount of data of the source is the same, the data in any two first image data sets are independent of each other, and N is a positive integer greater than 1; and used to perform preset processing on the N first image data sets to obtain the Nth Two image data sets, the N first image data sets and the N second image data sets have a one-to-one correspondence, and each second image data set adds spatial location information relative to the corresponding first image data set
  • the spatial position information is used to reflect the spatial position attribute of the image data; and used to perform VRDS 4D medical image display according to the N second image data sets through the communication unit 4
  • the VRDS 4D medical image processing device 400 further includes a storage unit 403, the processing unit 401 may be a processor, the communication unit 402 may be a transceiver, and the storage unit may be a memory.
  • the medical imaging device first performs initial data analysis processing on multiple scanned images to obtain an image source containing the image characteristics of the target part of the target user, and secondly, performs post-data processing on the image source to obtain N first image data sets, where the data amount of each first image data set is the same as the data amount of the image source, the data in any two first image data sets are independent of each other, and N is a positive integer greater than 1, Third, the N first image data sets are pre-processed to obtain N second image data sets.
  • the N first image data sets and N second image data sets are in one-to-one correspondence, and each second image data set Compared with the corresponding first image data collection, the collection adds spatial location information, which is used to reflect the spatial location attributes of the image data.
  • VRDS 4D medical image display is performed according to N second image data sets. It can be seen that the medical imaging device in this application can optimize the original scanned image in the data volume dimension and the spatial dimension, thereby realizing VRDS 4D medical image display, that is, presenting 3D images outside and inside the tissue with real spatial structural characteristics. Improve the refinement and accuracy of medical image display by medical imaging devices.
  • the processing unit 401 is specifically configured to: perform data enhancement processing on the image source to obtain The N first image data sets, and the data enhancement processing includes at least one of the following: rotation, scaling, translation, shearing, mirroring, and elastic deformation.
  • the data enhancement processing includes elastic deformation; in terms of performing elastic deformation on the image source to obtain N first image data sets, the processing unit 401 is specifically configured to: On the original pixel matrix of the image source, random distances in N directions are superimposed to form N difference position matrices, and the N directions include at least positive and negative directions; the corresponding pixel points are calculated on each difference position matrix
  • the gray value is obtained by obtaining N enhanced pixel dot matrices; N first image data sets are generated according to the N enhanced pixel dot matrices, and the image data in each first image data set includes superimposed information of pixels.
  • the processing unit 401 is specifically configured to: Perform data separation on an image data set to obtain a separated data set.
  • the separated data set includes a plurality of separated data subsets.
  • the image data in each separated data subset includes organization identification information, and the organization identification information is used to identify the
  • the tissue to which the image data belongs the tissue includes at least one of the following: organs, tumors, blood vessels; data integration is performed on the N first image data sets to obtain an integrated data set, and the integrated data set includes a preset account Multi-type image data of multiple tissues in a bit pattern; import the separated data set and the integrated data set into the VRDS Ai 4D imaging data analyzer, through the Ai matching of the gray value of the medical image and the superimposed information of the pixel point According to the conversion calculation of, obtain the spatial position information of each image data that meets the gray-level matching condition; generate N second image data sets according to the spatial position information and the N first image data sets.
  • the processing unit 401 is specifically configured to: call the pre-stored medical image template matching models of the multiple tissues; and collect the integrated data The image data in is imported into the medical image template matching model, and the image data that meets the gray-level matching conditions are screened out.
  • the processing unit 401 is specifically configured to: obtain the superimposed information of the currently processed pixel, and the superimposed information includes direction information and random information. Distance parameter; calculating spatial coordinates according to the direction information and the random distance parameter as the spatial position information of the pixel.
  • the processing unit 401 is specifically configured to: according to the tissue associated with the preset occupancy pattern, from Select the target image data associated with the tissue from the N second image data sets as VRDS 4D imaging data; output the VRDS 4D imaging data on a display device.
  • 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. 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.
  • the functional units in the various embodiments 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 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

本申请实施例公开了一种VRDS 4D医学影像处理方法及产品，应用于医学成像装置；方法包括：对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源；对图源进行后数据处理，得到N个第一图像数据集合；对N个第一图像数据集合进行预设处理，得到N个第二图像数据集合；根据N个第二图像数据集合进行VRDS 4D医学影像显示。本申请实施例有利于提高医学成像装置进行医学影像显示的精细化程度和准确度。

Description

VRDS 4D医学影像处理方法及产品 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VRDS 4D医学影像处理方法及产品。
背景技术
目前，医生通过电子计算机断层扫描(Computed Tomography，CT)、磁共振成像(Magnetic Resonance Imaging，MRI)、弥散张量成像(Diffusion Tensor Imaging，DTI)、正电子发射型计算机断层显像(Positron Emission Computed Tomography，PET)等技术获取病变组织的形态、位置、拓扑结构等信息。医生仍然采用观看阅读连续的二维切片扫描图像，以此对患者的病变组织如肿瘤进行判断分析。然而，两维切片扫描图像无法呈现出病变组织的空间结构特性，影响到医生对疾病的诊断。随着医学成像技术的飞速发展，人们对医学成像提出了新的需求。
发明内容
本申请实施例提供了一种VRDS 4D医学影像处理方法及产品，以期提高医学成像装置进行医学影像显示的精细化程度和准确度。
第一方面，本申请实施例提供一种VRDS 4D医学影像处理方法，应用于医学成像装置；所述方法包括：
对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，所述图源包括纹理Texture 2D/3D图像体数据，所述多张扫描图像是通过医疗设备采集的二维医学图像；
对所述图源进行后数据处理，得到N个第一图像数据集合，每个第一图像数据集合的数据量与所述图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数；
对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，所述N个第一图像数据集合和所述N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，所述空间位置信息用于反映图像数据的空间位置属性；
根据所述N个第二图像数据集合进行VRDS 4D医学影像显示。
第二方面，本申请实施例提供一种VRDS 4D医学影像处理装置，应用于医学成像装置；所述VRDS 4D医学影像处理装置包括处理单元和通信单元，其中，
所述处理单元，用于对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，所述图源包括纹理Texture 2D/3D图像体数据，所述多张扫描图像是通过医疗设备采集的二维医学图像；以及用于对所述图源进行后数据处理，得到N个第一图像数据集合，每个第一图像数据集合的数据量与所述图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数；以及用于对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，所述N个第一图像数据集合和所述N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，所述空间位置信息用于反映图像数据的空间位置属性；以及用于通过所述通信单元根据所述N个第二图像数据集合进行VRDS 4D医学影像显示。
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，医学成像装置首先对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，其次，对图源进行后数据处理，得到N个第一图像数据集合，其中，每个第一图像数据集合的数据量与图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数，再次，对N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，N个第一图像数据集合和N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，空间位置信息用于反映图像数据的空间位置属性，最后，根据N个第二图像数据集合进行VRDS 4D医学影像显示。可见，本申请中的医学成像装置能够将原始的扫描图像在数据量维度和空间维度进行数据优化，从而实现VRDS 4D医学影像显示，即呈现具备真实空间结构特性的组织外部和内部的3D影像，提高医学成像装置进行医学影像显示的精细化程度和准确度。
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 4D医学影像智能分析处理系统的结构示意图；
图2是本申请实施例提供的一种VRDS 4D医学影像处理方法的流程示意图；
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VRDS 4D医学影像处理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、系统、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”是指虚拟现实医用系统(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 4D医学影像智能分析处理系统100的结构示意图，该系统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 4D医学影像处理算法为基础，进行人体内部组织器官的占位的四维4D体绘制，实现4D立体成像效果(该四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，可以选择显示器和/或虚拟现实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 4D医学影像的肿瘤识别算法进行详细介绍。
请参阅图2，图2是本申请实施例提供了一种VRDS 4D医学影像处理方法的流程示意图，应用于如图1所述的医学成像装置；如图所示，本VRDS 4D医学影像处理方法包括：
S201，医学成像装置对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，所述图源包括纹理Texture 2D/3D图像体数据，所述多张扫描图像是通过医疗设备采集的二维医学图像；
其中，所述目标部位例如可以包括肾脏等器官以及动脉、静脉等。所述多张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。
具体实现中，所述医学成像装置获取通过医疗设备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；从所述多张扫描图像中筛选出包含目标部位的至少一张扫描图像，将所述至少一张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。
S202，所述医学成像装置对所述图源进行后数据处理，得到N个第一图像数据集合，每个第一图像数据集合的数据量与所述图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数；
其中，所述医学成像装置对所述图源进行数据增强处理，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、基于缩放的数据增强、基于平移的数据增强、基于镜像操作的数据增强、基于剪切的数据增强和基于弹性变形的数据增强。
其中，所述图像旋转是指将所述图源进行旋转一定角度的几何变换；所述缩放是指计算机图像处理中，对数字图像的大小进行调整的过程，即在处理效率以及结果的平滑度(smoothness)和清晰度(sharpness)上做一个权衡；所述平移就是将图像所有的像素坐标分别加上指定的水平偏移量和垂直偏移量；所述剪切分为两种类型：规则分幅剪切(Rectangle Subset)，不规则分幅剪切(Pdygon Subset)，规则分幅剪切是指裁剪图像的边界范围是一个矩形，通过左上角和右下角两点的坐标，就可以确定图像的裁剪位置，整个裁剪过程比较简单，不规则分幅剪切是指裁剪图像的边界范围是任意多边形；所述镜像分为两种：水平镜像和垂直镜像。水平镜像以图像垂直中线为轴，将图像的像素进行对换，也就是将图像的左半部和右半部对调，垂直镜像则是以图像的水平中线为轴，将图像的上半部分和下班部分对调。
S203，所述医学成像装置对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，所述N个第一图像数据集合和所述N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，所述空间位置信息用于反映图像数据的空间位置属性；
其中，对所述N个第一图像数据集合进行预设处理包括数据的分离、整合等。所述空间位置信息包括空间方向信息，空间拓扑信息和空间度量信息等。
S204，所述医学成像装置根据所述N个第二图像数据集合进行VRDS 4D医学影像显示。
其中，所述VRDS 4D医学影像显示是指呈现VRDS四维医学影像。
其中，所述医学成像装置从所述N个第二图像数据集合中筛选出目标图像数据作为VRDS 4D成像数据。
可以看出，本申请实施例中，医学成像装置首先对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，其次，对图源进行后数据处理，得到N个第一图像数据集合，其中，每个第一图像数据集合的数据量与图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数，再次，对N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，N个第一图像数据集合和N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，空间位置信息用于反映图像数据的空间位置属性，最后，根据N个第二图像数据集合进行VRDS 4D医学影像显示。可见，本申请中的医学成像装置能够将原始的扫描图像在数据量维度和空间维度进行数据优化，从而实现VRDS 4D医学影像显示，即呈现具备真实空间结构特性的组织外部和内部的3D影像，提高医学成像装置进行医学影像显示的精细化程度和准确度。
在一个可能的示例中，所述对所述图源进行后数据处理，得到N个第一图像数据集合，包括：对所述图源进行数据增强处理，得到N个第一图像数据集合，所述数据增强处理包括以下至少一种：旋转、缩放、平移、剪切、镜像、弹性变形。
具体实现中，在一次数据增强处理中，对所述图源中的所有数据的增强处理方式包括 至少一种：旋转、缩放、平移、剪切、镜像、弹性变形；将该次数据增强处理后的数据作为1个第一图像数据集合；N次数据增强处理中，根据所述图源数据和多种数据增强处理方式可得到N个第一图像数据集合。
可见，本示例中，由于医学成像装置能够基于旋转、缩放、平移、剪切、镜像、弹性变形等图像数据处理方式对图源进行数据增强处理得到N个第一图像数据集合，提高了所述第一图像数据集合中图像质量，确保图像的清晰度和准确度。
在一个可能的示例中，所述数据增强处理包括弹性变形；所述对所述图源进行弹性变形，得到N个第一图像数据集合，包括：在所述图源的原始像素点阵上，叠加N个方向的随机距离形成N个差值位置矩阵，所述N个方向至少包括正向和负向；在每个差值位置矩阵上计算对应像素点的灰度值，得到N个增强像素点阵；根据所述N个增强像素点阵生成N个第一图像数据集合，每个第一图像数据集合中的图像数据包括像素点的叠加信息。
具体实现中，将所述图源的图像点阵上，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计算对应像素点的灰度值，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、平移等操作。
可见，本示例中，由于医学成像装置能够对所述图源进行弹性变形处理，提高了图像处理的执行效率，提高医学成像装置进行医学影像显示的精细化程度。
在一个可能的示例中，所述对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，包括：对所述N个第一图像数据集合进行数据分离，得到分离数据集合，所述分离数据集合包括多个分离数据子集，每个分离数据子集中的图像数据包括组织标识信息，所述组织标识信息用于标识所述图像数据所属的组织，所述组织包括以下至少一种：器官、肿瘤、血管；对所述N个第一图像数据集合进行数据整合，得到整合数据集合，所述整合数据集合中包括预设占位模式的多个组织的多类图像数据；将所述分离数据集合和所述整合数据集合导入VRDS Ai 4D成像数据分析器，通过医学影像灰度值的Ai匹配以及所述像素点的叠加信息的转换计算，得到符合灰度匹配条件的每个图像数据的空间位置信息；根据所述空间位置信息和所述N个第一图像数据集合生成N个第二图像数据集合。
其中，所述预设占位模式是指预设的不同组织器官的组合，例如肾脏和动脉的组合、肾脏和肿瘤的组合等。
具体实现中，可以根据组织标识信息对所述N个第一图像数据集合进行数据分离，例如，可以将N个第一图像数据集合进行数据分离为器官数据子集、肿瘤数据子集、血管数据子集等。再对所述N个第一图像数据集合进行数据整合，利用预存的所述多个组织的医学影像模板匹配模型和所述像素点的叠加信息的转换计算，从所述整合数据集合得到符合灰度匹配条件的图像数据和每个图像数据的空间位置信息，从而生成N个第二图像数据集合。
可见，本示例中，由于医学成像装置能够基于对N个第一图像数据集合的分离、整合、医学影像灰度值的Ai匹配以及所述像素点的叠加信息的转换计算，生成N个第二图像数据集合，实现了对图像数据的合并、巩固和增强。
在一个可能的示例中，所述医学影像灰度值的Ai匹配通过如下步骤实现：调用预存的所述多个组织的医学影像模板匹配模型；将所述整合数据集合中的图像数据导入所述医学影像模板匹配模型，筛选出的符合灰度匹配条件的图像数据。
其中，灰度匹配可以通过利用某种相似性度量，如相关函数、协方差函数、差平方和、差绝对值和等测度极值，判定图像的对应关系。
具体实现中，所述图像匹配可以分为图像输入、图像预处理、匹配特征提取、图像匹配、输出结果等几个步骤。首先对图像进行预处理来提取其高层次的特征值，然后根据所 述医学影像模板匹配模型，筛选出的符合灰度匹配条件的图像数据。该医学影像模板匹配模型可以采用常用的卷积神经网络等各类算法来构建，并预先通过医疗样本数据进行训练，提高模型预测的准确度。
可见，本示例中，由于医学成像装置能够基于预存的所述多个组织的医学影像模板匹配模型和所述整合数据集合中的图像数据得到符合灰度匹配条件的图像数据，提高了医学影像的质量，确保了影像的精细化程度和准确性。
在一个可能的示例中，所述像素点的叠加信息的转换计算通过如下操作实现：获取当前处理的像素点的所述叠加信息，所述叠加信息包括方向信息和随机距离参数；根据所述方向信息和所述随机距离参数计算空间坐标作为所述像素点的空间位置信息。
其中，像素点的叠加信息的转换计算是根据所述像素点的方向信息和随机距离参数计算空间坐标。
具体实现中，获取当前处理的像素点的叠加信息数据，使用获得的叠加信息数据，所述叠加信息中的方向信息和随机距离参数都通过至少两个方向或至少两个随机距离参数叠加得到，最后根据所述方向信息和所述随机距离参数计算空间坐标，空间坐标为所述像素点的空间位置信息。
可见，本示例中，由于医学成像装置能够基于像素点的方向信息和所述随机距离参数计算空间坐标作为所述像素点的空间位置信息，反映图像数据的空间位置属性，提高了图像数据的数据信息的完善和医学影像成像的准确度和清晰度。
在一个可能的示例中，所述根据所述N个第二图像数据集合进行VRDS 4D医学影像显示，包括：根据所述预设占位模式关联的组织，从所述N个第二图像数据集合中筛选与所述组织关联的目标图像数据作为VRDS 4D成像数据；在显示设备上输出所述VRDS 4D成像数据。
具体实现中，由于器官、血管等组织之间的空间关联性，人体的相同器官的图像数据在该N个第二图像数据集合的分布位置往往是相互关联的，为缩减图像数据的筛选时长，提高数据筛选效率，医学成像装置在针对第一个第二图像数据集合进行筛选时，按照从前往后等顺序遍历整个数据集合从而筛选出第一目标图像数据，然后针对第二个第二图像数据集合进行筛选时，可以根据第一目标图像数据的位置为当前集合的起始检测位置，并将筛选范围缩减至当前处理的组织的最大范围(组织包括肾脏等器官，可以根据该器官的体积等确定最大范围)，第三个第二图像数据集合直至第N个第二图像数据集合的处理操作与第二个第二图像数据集合的处理过程类似，此处不再赘述。如此可以显著提高数据筛选效率，提高影像显示效率。
举例来说，所述预设占位模式关联的组织为心脏和血管时，可以根据图像数据的组织标识信息，从所述N个第二图像数据中筛选出与心脏、血管关联的目标图像数据，将目标图像数据作为VRDS 4D成像数据进行VRDS 4D成像以展示所述心脏和血管的影像。其中，所述N个第二图像数据可以包含器官、肿瘤、血管中的至少一种。
此外，在显示设备上输出所述VRDS 4D影像后，当检测到针对某组织的部分位置选择时，调取选择的组织部分位置的影像，可执行放大，旋转，观察内侧等操作。
可见，本示例中，由于医学成像装置能够基于预设占位模式关联的组织，帅选出与预设占位模式关联的组织对应目标数据进行VRDS 4D成像数据并显示，提高了医学影像的质量，以及医学成像装置进行医学影像显示的精细化程度和准确度。
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；
对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，所述图源包括纹理Texture 2D/3D图像体数据，所述多张扫描图像是通过医疗设备采集的二维医学图像；
对所述图源进行后数据处理，得到N个第一图像数据集合，每个第一图像数据集合的数据量与所述图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数；
对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，所述N个第一图像数据集合和所述N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，所述空间位置信息用于反映图像数据的空间位置属性；
根据所述N个第二图像数据集合进行VRDS 4D医学影像显示。
可以看出，本申请实施例中，医学成像装置首先对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，其次，对图源进行后数据处理，得到N个第一图像数据集合，其中，每个第一图像数据集合的数据量与图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数，再次，对N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，N个第一图像数据集合和N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，空间位置信息用于反映图像数据的空间位置属性，最后，根据N个第二图像数据集合进行VRDS 4D医学影像显示。可见，本申请中的医学成像装置能够将原始的扫描图像在数据量维度和空间维度进行数据优化，从而实现VRDS 4D医学影像显示，即呈现具备真实空间结构特性的组织外部和内部的3D影像，提高医学成像装置进行医学影像显示的精细化程度和准确度。
在一个可能的示例中，在所述对所述图源进行后数据处理，得到N个第一图像数据集合方面，所述程序中的指令具体用于执行以下操作：对所述图源进行数据增强处理，得到N个第一图像数据集合，所述数据增强处理包括以下至少一种：旋转、缩放、平移、剪切、镜像、弹性变形。
在一个可能的示例中，在所述数据增强处理包括弹性变形；所述对所述图源进行弹性变形，得到N个第一图像数据集合方面，所述程序中的指令具体用于执行以下操作：在所述图源的原始像素点阵上，叠加N个方向的随机距离形成N个差值位置矩阵，所述N个方向至少包括正向和负向；在每个差值位置矩阵上计算对应像素点的灰度值，得到N个增强像素点阵；根据所述N个增强像素点阵生成N个第一图像数据集合，每个第一图像数据集合中的图像数据包括像素点的叠加信息。
在一个可能的示例中，在所述对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合方面，所述程序中的指令具体用于执行以下操作：对所述N个第一图像数据集合进行数据分离，得到分离数据集合，所述分离数据集合包括多个分离数据子集，每个分离数据子集中的图像数据包括组织标识信息，所述组织标识信息用于标识所述图像数据所属的组织，所述组织包括以下至少一种：器官、肿瘤、血管；对所述N个第一图像数据集合进行数据整合，得到整合数据集合，所述整合数据集合中包括预设占位模式的多个组织的多类图像数据；将所述分离数据集合和所述整合数据集合导入VRDS Ai 4D成像数据分析器，通过医学影像灰度值的Ai匹配以及所述像素点的叠加信息的转换计算，得到符 合灰度匹配条件的每个图像数据的空间位置信息；根据所述空间位置信息和所述N个第一图像数据集合生成N个第二图像数据集合。
在一个可能的示例中，在所述医学影像灰度值的Ai匹配方面，所述程序中的指令具体用于执行以下操作：调用预存的所述多个组织的医学影像模板匹配模型；将所述整合数据集合中的图像数据导入所述医学影像模板匹配模型，筛选出的符合灰度匹配条件的图像数据。
在一个可能的示例中，在所述像素点的叠加信息的转换计算方面，所述程序中的指令具体用于执行以下操作：获取当前处理的像素点的所述叠加信息，所述叠加信息包括方向信息和随机距离参数；根据所述方向信息和所述随机距离参数计算空间坐标作为所述像素点的空间位置信息。
在一个可能的示例中，在所述根据所述N个第二图像数据集合进行VRDS 4D医学影像显示方面，所述程序中的指令具体用于执行以下操作：根据所述预设占位模式关联的组织，从所述N个第二图像数据集合中筛选与所述组织关联的目标图像数据作为VRDS 4D成像数据；在显示设备上输出所述VRDS 4D成像数据。
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VRDS 4D医学影像处理装置400的功能单元组成框图。该VRDS 4D医学影像处理装置400应用于医学成像装置，该VRDS 4D医学影像处理装置400包括处理单元401和通信单元402，其中，
所述处理单元401，用于对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，所述图源包括纹理Texture 2D/3D图像体数据，所述多张扫描图像是通过医疗设备采集的二维医学图像；以及用于对所述图源进行后数据处理，得到N个第一图像数据集合，每个第一图像数据集合的数据量与所述图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数；以及用于对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，所述N个第一图像数据集合和所述N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，所述空间位置信息用于反映图像数据的空间位置属性；以及用于通过所述通信单元402根据所述N个第二图像数据集合进行VRDS 4D医学影像显示。
所述VRDS 4D医学影像处理装置400还包括存储单元403，所述处理单元401可以是处理器，所述通信单元402可以是收发器，所述存储单元可以是存储器。
可以看出，本申请实施例中，医学成像装置首先对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，其次，对图源进行后数据处理，得到N个第一图像数据集合，其中，每个第一图像数据集合的数据量与图源的数据量相同， 任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数，再次，对N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，N个第一图像数据集合和N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，空间位置信息用于反映图像数据的空间位置属性，最后，根据N个第二图像数据集合进行VRDS 4D医学影像显示。可见，本申请中的医学成像装置能够将原始的扫描图像在数据量维度和空间维度进行数据优化，从而实现VRDS 4D医学影像显示，即呈现具备真实空间结构特性的组织外部和内部的3D影像，提高医学成像装置进行医学影像显示的精细化程度和准确度。
在一个可能的示例中，在所述对所述图源进行后数据处理，得到N个第一图像数据集合方面，所述处理单元401具体用于：对所述图源进行数据增强处理，得到N个第一图像数据集合，所述数据增强处理包括以下至少一种：旋转、缩放、平移、剪切、镜像、弹性变形。
在一个可能的示例中，在所述数据增强处理包括弹性变形；所述对所述图源进行弹性变形，得到N个第一图像数据集合方面，所述处理单元401具体用于：在所述图源的原始像素点阵上，叠加N个方向的随机距离形成N个差值位置矩阵，所述N个方向至少包括正向和负向；在每个差值位置矩阵上计算对应像素点的灰度值，得到N个增强像素点阵；根据所述N个增强像素点阵生成N个第一图像数据集合，每个第一图像数据集合中的图像数据包括像素点的叠加信息。
在一个可能的示例中，在所述对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合方面，所述处理单元401具体用于：对所述N个第一图像数据集合进行数据分离，得到分离数据集合，所述分离数据集合包括多个分离数据子集，每个分离数据子集中的图像数据包括组织标识信息，所述组织标识信息用于标识所述图像数据所属的组织，所述组织包括以下至少一种：器官、肿瘤、血管；对所述N个第一图像数据集合进行数据整合，得到整合数据集合，所述整合数据集合中包括预设占位模式的多个组织的多类图像数据；将所述分离数据集合和所述整合数据集合导入VRDS Ai 4D成像数据分析器，通过医学影像灰度值的Ai匹配以及所述像素点的叠加信息的转换计算，得到符合灰度匹配条件的每个图像数据的空间位置信息；根据所述空间位置信息和所述N个第一图像数据集合生成N个第二图像数据集合。
在一个可能的示例中，在所述医学影像灰度值的Ai匹配方面，所述处理单元401具体用于：调用预存的所述多个组织的医学影像模板匹配模型；将所述整合数据集合中的图像数据导入所述医学影像模板匹配模型，筛选出的符合灰度匹配条件的图像数据。
在一个可能的示例中，在所述像素点的叠加信息的转换计算方面，所述处理单元401具体用于：获取当前处理的像素点的所述叠加信息，所述叠加信息包括方向信息和随机距离参数；根据所述方向信息和所述随机距离参数计算空间坐标作为所述像素点的空间位置信息。
在一个可能的示例中，在所述根据所述N个第二图像数据集合进行VRDS 4D医学影像显示方面，所述处理单元401具体用于：根据所述预设占位模式关联的组织，从所述N个第二图像数据集合中筛选与所述组织关联的目标图像数据作为VRDS 4D成像数据；在显示设备上输出所述VRDS 4D成像数据。
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系统，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VRDS 4D医学影像处理方法，其特征在于，应用于医学成像装置；所述方法包括：
    对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，所述图源包括纹理Texture 2D/3D图像体数据，所述多张扫描图像是通过医疗设备采集的二维医学图像；
    对所述图源进行后数据处理，得到N个第一图像数据集合，每个第一图像数据集合的数据量与所述图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数；
    对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，所述N个第一图像数据集合和所述N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，所述空间位置信息用于反映图像数据的空间位置属性；
    根据所述N个第二图像数据集合进行VRDS 4D医学影像显示。
  2. 根据权利要求1所述的方法，其特征在于，所述对所述图源进行后数据处理，得到N个第一图像数据集合，包括：
    对所述图源进行数据增强处理，得到N个第一图像数据集合，所述数据增强处理包括以下至少一种：旋转、缩放、平移、剪切、镜像、弹性变形。
  3. 根据权利要求1所述的方法，其特征在于，所述数据增强处理包括弹性变形；所述对所述图源进行弹性变形，得到N个第一图像数据集合，包括：
    在所述图源的原始像素点阵上，叠加N个方向的随机距离形成N个差值位置矩阵，所述N个方向至少包括正向和负向；
    在每个差值位置矩阵上计算对应像素点的灰度值，得到N个增强像素点阵；
    根据所述N个增强像素点阵生成N个第一图像数据集合，每个第一图像数据集合中的图像数据包括像素点的叠加信息。
  4. 根据权利要求3所述的方法，其特征在于，所述对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，包括：
    对所述N个第一图像数据集合进行数据分离，得到分离数据集合，所述分离数据集合包括多个分离数据子集，每个分离数据子集中的图像数据包括组织标识信息，所述组织标识信息用于标识所述图像数据所属的组织，所述组织包括以下至少一种：器官、肿瘤、血管；
    对所述N个第一图像数据集合进行数据整合，得到整合数据集合，所述整合数据集合中包括预设占位模式的多个组织的多类图像数据；
    将所述分离数据集合和所述整合数据集合导入VRDS Ai 4D成像数据分析器，通过医学影像灰度值的Ai匹配以及所述像素点的叠加信息的转换计算，得到符合灰度匹配条件的每个图像数据的空间位置信息；
    根据所述空间位置信息和所述N个第一图像数据集合生成N个第二图像数据集合。
  5. 根据权利要求4所述的方法，其特征在于，所述医学影像灰度值的Ai匹配通过如下步骤实现：调用预存的所述多个组织的医学影像模板匹配模型；将所述整合数据集合中的图像数据导入所述医学影像模板匹配模型，筛选出的符合灰度匹配条件的图像数据。
  6. 根据权利要求4或5所述的方法，其特征在于，所述像素点的叠加信息的转换计算通过如下操作实现：获取当前处理的像素点的所述叠加信息，所述叠加信息包括方向信息和随机距离参数；根据所述方向信息和所述随机距离参数计算空间坐标作为所述像素点的 空间位置信息。
  7. 根据权利要求6所述的方法，其特征在于，所述像素点的叠加信息的转换计算是根据所述像素点的方向信息和随机距离参数计算空间坐标。
  8. 根据权利要求1-7任一项所述的方法，其特征在于，所述根据所述N个第二图像数据集合进行VRDS 4D医学影像显示，包括：
    根据所述预设占位模式关联的组织，从所述N个第二图像数据集合中筛选与所述组织关联的目标图像数据作为VRDS 4D成像数据；
    在显示设备上输出所述VRDS 4D成像数据。
  9. 根据权利要求1所述的方法，其特征在于，所述根据所述N个第二图像数据集合进行VRDS 4D医学影像显示之后，所述方法还包括：
    检测到针对目标组织的目标位置选择指令；
    根据所述选择指令调取选择的目标位置的影像，并将所述影像执行预设操作，所述预设操作至少包括以下一种：放大、旋转或观察内侧。
  10. 一种VRDS 4D医学影像处理装置，其特征在于，应用于医学成像装置；所述VRDS4D医学影像处理装置包括处理单元和通信单元，其中，
    所述处理单元，用于对多张扫描图像进行初始数据分析处理，得到包含目标用户的目标部位的图像特征的图源，所述图源包括纹理Texture 2D/3D图像体数据，所述多张扫描图像是通过医疗设备采集的二维医学图像；以及用于对所述图源进行后数据处理，得到N个第一图像数据集合，每个第一图像数据集合的数据量与所述图源的数据量相同，任意两个第一图像数据集合中的数据相互独立，N为大于1的正整数；以及用于对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合，所述N个第一图像数据集合和所述N个第二图像数据集合一一对应，且每个第二图像数据集合相对于对应的第一图像数据集合增加了空间位置信息，所述空间位置信息用于反映图像数据的空间位置属性；以及用于通过所述通信单元根据所述N个第二图像数据集合进行VRDS 4D医学影像显示。
  11. 根据权利要求10所述的装置，其特征在于，在所述对所述图源进行后数据处理，得到N个第一图像数据集合方面，所述处理单元具体用于：对所述图源进行数据增强处理，得到N个第一图像数据集合，所述数据增强处理包括以下至少一种：旋转、缩放、平移、剪切、镜像、弹性变形。
  12. 根据权利要求10所述的装置，其特征在于，所述数据增强处理包括弹性变形；在所述对所述图源进行弹性变形，得到N个第一图像数据集合方面，所述处理单元具体用于：在所述图源的原始像素点阵上，叠加N个方向的随机距离形成N个差值位置矩阵，所述N个方向至少包括正向和负向；在每个差值位置矩阵上计算对应像素点的灰度值，得到N个增强像素点阵；根据所述N个增强像素点阵生成N个第一图像数据集合，每个第一图像数据集合中的图像数据包括像素点的叠加信息。
  13. 根据权利要求12所述的装置，其特征在于，在所述对所述N个第一图像数据集合进行预设处理，得到N个第二图像数据集合方面，所述处理单元具体用于：对所述N个第一图像数据集合进行数据分离，得到分离数据集合，所述分离数据集合包括多个分离数据子集，每个分离数据子集中的图像数据包括组织标识信息，所述组织标识信息用于标识所述图像数据所属的组织，所述组织包括以下至少一种：器官、肿瘤、血管；对所述N个第一图像数据集合进行数据整合，得到整合数据集合，所述整合数据集合中包括预设占位模式的多个组织的多类图像数据；将所述分离数据集合和所述整合数据集合导入VRDS Ai 4D成像数据分析器，通过医学影像灰度值的Ai匹配以及所述像素点的叠加信息的转换计算，得到符合灰度匹配条件的每个图像数据的空间位置信息；根据所述空间位置信息和所述N个 第一图像数据集合生成N个第二图像数据集合。
  14. 根据权利要求13所述的装置，其特征在于，所述医学影像灰度值的Ai匹配通过如下步骤实现：调用预存的所述多个组织的医学影像模板匹配模型；将所述整合数据集合中的图像数据导入所述医学影像模板匹配模型，筛选出的符合灰度匹配条件的图像数据。
  15. 根据权利要求13或14所述的装置，其特征在于，所述像素点的叠加信息的转换计算通过如下操作实现：获取当前处理的像素点的所述叠加信息，所述叠加信息包括方向信息和随机距离参数；根据所述方向信息和所述随机距离参数计算空间坐标作为所述像素点的空间位置信息。
  16. 根据权利要求15所述的装置，其特征在于，所述像素点的叠加信息的转换计算是根据所述像素点的方向信息和随机距离参数计算空间坐标。
  17. 根据权利要求10-16任一项所述的装置，其特征在于，在所述根据所述N个第二图像数据集合进行VRDS 4D医学影像显示方面，所述通信单元具体用于：根据所述预设占位模式关联的组织，从所述N个第二图像数据集合中筛选与所述组织关联的目标图像数据作为VRDS 4D成像数据；在显示设备上输出所述VRDS 4D成像数据。
  18. 根据权利要求10所述的装置，其特征在于，所述根据所述N个第二图像数据集合进行VRDS 4D医学影像显示之后，所述处理单元还具体用于：检测到针对目标组织的目标位置选择指令；根据所述选择指令调取选择的目标位置的影像，并将所述影像执行预设操作，所述预设操作至少包括以下一种：放大、旋转或观察内侧。
  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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