CN114364323A

CN114364323A - VRDS AI (virtual reality) based vein image identification method and product

Info

Publication number: CN114364323A
Application number: CN201980099716.2A
Authority: CN
Inventors: 斯图尔特平·李; 戴维伟·李
Original assignee: Weiai Medical Technology Shenzhen Co ltd
Current assignee: Cao Sheng
Priority date: 2019-08-16
Filing date: 2019-08-16
Publication date: 2022-04-15
Also published as: WO2021030994A1

Abstract

A VRDS AI-based vein image recognition method and product (100,300,400) for use in a medical imaging device. The method comprises the following steps: acquiring a scanned image of a target portion including inferior vena cava of a target user (S201); processing the scanned image to obtain a target image of the inferior vena cava (S202); generating a first characteristic data set of the inferior vena cava according to the target image (S203), wherein the first characteristic data set is used for reflecting the physiological characteristics of the inferior vena cava of the target user; comparing the first characteristic data set with a pre-stored original characteristic data set to obtain a comparison result (S204); and performing a preset operation according to the comparison result (S205). The method is beneficial to improving the safety of the medical imaging device in identity recognition.

Description

VRDS AI (virtual reality) based vein image identification method and product

Technical Field

The application relates to the technical field of medical imaging devices, in particular to a VRDS AI-based vein image identification method and a product.

Background

At present, user identification and other identification operations are mainly performed by methods such as fingerprint identification, face identification and the like, and all the methods are easily counterfeited by illegal users, so that the use safety is influenced.

Disclosure of Invention

The embodiment of the application provides a VRDS AI-based vein image identification method and a product, aiming at improving the safety of identity identification of a medical imaging device.

In a first aspect, an embodiment of the present application provides a VRDS AI based vein image recognition method, which is applied to a medical imaging apparatus; the method comprises the following steps:

acquiring a scanning image of a target part containing inferior vena cava of a target user;

processing the scanned image to obtain a target image of the inferior vena cava;

generating a first feature data set of the inferior vena cava according to the target image, wherein the first feature data set is used for reflecting physiological features of the inferior vena cava of the target user;

comparing the first characteristic data set with a pre-stored original characteristic data set to obtain a comparison result;

and executing preset operation according to the comparison result.

In a second aspect, the present application provides a medical imaging apparatus, including a processing unit and a communication unit, wherein,

the processing unit is used for acquiring a scanning image of a target part containing the inferior vena cava of a target user through the communication unit; the scanning image is processed to obtain a target image of the inferior vena cava; and a first feature data set for generating the inferior vena cava from the target imagery, the first feature data set for reflecting physiological features of the inferior vena cava of the target user; the first characteristic data set is used for comparing with a pre-stored original characteristic data set to obtain a comparison result; and the operation module is used for executing preset operation according to the comparison result.

In a third aspect, the present application provides a medical imaging apparatus, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in any of the methods of the first aspect of the present application.

In a fourth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program makes a computer perform part or all of the steps described in any one of the methods of the first aspect of the present application.

In a fifth aspect, the present application provides 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 perform some or all of the steps as described in any one of the methods of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

It can be seen that, in the embodiment of the present application, a medical imaging device first obtains a scanned image of a target portion, including an inferior vena cava, of a target user, processes the scanned image to obtain a target image of the inferior vena cava, and then generates a first feature data set of the inferior vena cava according to the target image, where the first feature data set is used to reflect physiological features of the inferior vena cava of the target user, and then compares the first feature data set with a pre-stored original feature data set to obtain a comparison result, and finally executes a preset operation according to the comparison result. It can be seen that, because the inferior vena cava of the human body are different from each other and have unique identification, the medical imaging device of the application realizes identity recognition to complete preset operation by analyzing the image characteristics of the inferior vena cava of the user and comparing the image characteristics, and is different from a physiological characteristic verification mechanism on the surface of the human body, the image characteristics of the inferior vena cava inside the human body are difficult to be counterfeited by illegal users, so that the medical imaging device is beneficial to improving the safety of identity recognition.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic structural diagram of a VRDS Ai based medical image intelligent analysis processing system according to an embodiment of the present application;

fig. 2 is a schematic flowchart illustrating a VRDS AI vein image-based identification method according to an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of a medical imaging apparatus provided in an embodiment of the present application;

fig. 4 is a block diagram of functional units of a medical imaging apparatus according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The medical imaging apparatus according to the embodiments of the present application refers to various apparatuses that reproduce the internal structure of a human body as an image using various media as information carriers, and the image information corresponds to the actual structure of the human body in terms of spatial and temporal distribution. The "DICOM data" refers to original image file data which reflects internal structural features of a human body and is acquired by medical equipment, and may include information such as computed tomography CT, magnetic resonance MRI, diffusion tensor imaging DTI, positron emission tomography PET-CT, and the "map source" refers to Texture2D/3D image volume data generated by analyzing the original DICOM data. "VRDS" refers to a Virtual Reality medical system (VRDS).

Referring to fig. 1, fig. 1 is a schematic structural diagram of a VRDS Ai-based medical image intelligent analysis processing system 100, where the system 100 includes a medical imaging apparatus 110 and a network database 120, where the medical imaging apparatus 110 may include a local medical imaging apparatus 111 and/or a terminal medical imaging apparatus 112, and the local medical imaging apparatus 111 or the terminal medical imaging apparatus 112 is configured to perform, based on original DICOM data, identification, positioning, four-dimensional volume rendering and identification comparison and identity identification of a vena cava of a human body based on identification of a VRDS Ai-based vein image presented in this application embodiment, so as to achieve a four-dimensional stereoscopic imaging effect (the 4-dimensional medical image specifically refers to a medical image including internal spatial structural features and external spatial structural features of a displayed tissue, the internal spatial structural features refer to that slice data inside the tissue is not lost, that is, the medical imaging device may present the internal structure of the target organ, blood vessel, etc., and the external spatial structural characteristics refer to the environmental characteristics between tissues, including the spatial position characteristics (including intersection, spacing, fusion) between tissues, etc., such as the edge structural characteristics of the intersection position between the kidney and artery, etc.), the local medical imaging device 111 may also be used to edit the image source data with respect to the terminal medical imaging device 112, to form the transfer function result of the four-dimensional human body image, which may include the transfer function result of the internal organ surface of the human body and the tissue structure inside the internal organ of the human body, and the transfer function result of the cubic space, such as the number of sets of the cubic edit box and arc edit required by the transfer function, coordinates, color, transparency, etc. The network database 120 may be, for example, a cloud server, and the like, and the network database 120 is configured to store a map source generated by parsing the raw DICOM data and a transfer function result of the four-dimensional human body image edited by the local medical imaging apparatus 111, where the map source may be from a plurality of local medical imaging apparatuses 111 to implement interactive diagnosis of a plurality of doctors.

When the user performs specific image display through the medical imaging apparatus 110, the user may select a display or a Head Mounted Display (HMDS) of the virtual reality VR to display in combination with an operation action, where the operation action refers to operation control performed on a four-dimensional human body image by the user through an external shooting device of the medical imaging apparatus, such as a mouse, a keyboard, and the like, so as to implement human-computer interaction, where the operation action includes at least one of the following: (1) changing the color and/or transparency of a specific organ/tissue, (2) positioning a zoom view, (3) rotating the view to realize multi-view 360-degree observation of a four-dimensional human body image, (4) entering 'internal observation of a human body organ, real-time shearing effect rendering', and (5) moving the view up and down.

The following describes in detail a VRDS AI-based vein image recognition method according to an embodiment of the present invention.

Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a VRDS AI vein image-based identification method applied to the medical imaging apparatus shown in fig. 1 according to an embodiment of the present disclosure; as shown in the figure, the VRDS AI based vein image recognition method includes:

s201, a medical imaging device acquires a scanning image of a target part containing inferior vena cava of a target user;

wherein the scanned image comprises any one of: CT images, MRI images, DTI images, PET-CT images.

S202, the medical imaging device processes the scanning image to obtain a target image of the inferior vena cava;

s203, the medical imaging device generates a first feature data set of the inferior vena cava according to the target image, wherein the first feature data set is used for reflecting physiological features of the inferior vena cava of the target user;

the physiological characteristics of the inferior vena cava refer to specific type characteristic data which are set based on prior data and can reflect the unique identity of a user, and since the formation of the inferior vena cava of a human body comprises a complex connection process and a degeneration process of a plurality of embryonic veins, the inferior vena cava of the human body is a main pipeline for refluxing the lower limb and abdominal organ veins to the right atrium, research and development personnel can find single or multiple types of characteristic data which can be used for identifying the unique identity of the user from multiple types of physiological characteristic data of the inferior vena cava through big data test analysis. The setting principle is similar to the setting principle of fingerprint features, but is more complex than the setting principle of the fingerprint features, because the fingerprint is the skin surface tissue of a user, the physiological features of the fingerprint are only limited to the correlation features of the fingerprint textures, and the fingerprint is generally two-dimensional, and the inferior vena cava is complex in the internal environment of a human body due to high distribution complexity, so that effective physiological feature data are difficult to extract by the traditional two-dimensional image analysis method, a medical imaging device is required to firstly extract comprehensive and accurate image data of the inferior vena cava, and the image is further analyzed on the basis to obtain the effective physiological feature data.

S204, the medical imaging device compares the first characteristic data set with a pre-stored original characteristic data set to obtain a comparison result;

in a specific implementation, if the original feature data set includes a single type of feature data, the matching degree is obtained only by comparing the single type of feature data, and the comparison is performed based on the matching degree.

In this possible example, the comparing the first feature data set with a pre-stored original feature data set includes: calculating the reference matching degree of the feature data of each category; according to the matching degree weight distribution of the feature data of the multiple types, weighting and summing to obtain a comprehensive matching degree; and obtaining a comparison result according to the comprehensive matching degree and a preset matching degree threshold value.

The matching degree weight distribution of the multiple types of feature data can be an empirical value, and the age group of the target user can also be predicted according to the target image of the inferior vena cava of the current user, and then the weight distribution corresponding to the age group is inquired, so that the influence on the identification result due to the difference of the inferior vena cava of different age groups can be avoided, and the comparison accuracy is improved.

For example, the plurality of types of feature data include feature data a, feature data B, feature data C, and feature data D, and the age groups are divided into three age groups Q1, Q2, and Q3, so that the exclusive matching degree weight assignment of each age group can be obtained based on the statistical analysis of big data, as shown in table 1.

TABLE 1

In the preset weight distribution, the weight of the feature data related to the spatial structure characteristic of the inferior vena cava can be set higher, because the feature data relates to the three-dimensional spatial distribution characteristic of the inferior vena cava, and the three-dimensional spatial distribution characteristic is difficult to acquire and accurately analyze by a common device, the difficulty of acquiring the feature data by an illegal user is very high, and the setting can make the calculation result of the final matching degree difficult to be easily falsified to influence the accuracy and improve the safety.

S205, the medical imaging device executes preset operation according to the comparison result.

In one possible example, the inferior vena cava includes the main vein, which refers to the superior and inferior vena cava and the central vein merging into the right atrium, and the sub-veins, which refer to the inferior vena cava connecting the main vein with the kidney; the medical imaging device generates a first set of feature data of the inferior vena cava from the target imagery, comprising: 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: the number of the sub-veins, the shape of the main vein and/or the sub-veins, the spatial position relationship of the main vein and the sub-veins, and the spatial position relationship of the sub-veins; and generating the first characteristic data set according to the characteristic data of the at least one attribute.

The number of the sub-veins refers to the number of the sub-veins connecting the kidney, the shape refers to the outline characteristics, the radius and the like of the inferior vena cava, and the spatial position relationship refers to the position relationship description information such as the intersection, the convergence, the adjacency, the far distance or the relative distance between the veins.

In specific implementation, for the intersection characteristic of the sub-veins, in the data processing process before the medical imaging device generates the target image, the medical imaging device calls the intersection network model to perform spatial separation processing on the fusion data of the intersection positions of the veins, so that the intersection characteristic between different inferior vena cava can be obtained in the data processing process, and the intersection characteristic can be pre-stored in the first characteristic data set. Therefore, repeated processing is avoided, and the processing efficiency is improved.

In this example, it can be seen that the medical imaging apparatus can perform accurate identification by analyzing the number, shape, spatial position relationship, and other characteristics of the inferior vena cava.

In one possible example, the at least one attribute comprises the number; 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, and the characteristic data comprises the following components: the medical imaging device determining images of the main vein, first kidney and second kidney in the target image; analyzing the image between the main vein and the first kidney in the target image to obtain a first number of the sub-veins; analyzing the image between the main vein and the second kidney in the target image to obtain a second number of the sub-veins; determining a sum of the first number and the second number as the number of feature data.

In specific implementation, for a user with only a single kidney, the second number corresponding to the second kidney may be set to be zero, so that the processing efficiency is improved.

As can be seen, in this example, the medical imaging apparatus can accurately obtain the number of the sub-veins of the current user by analyzing the images of the sub-veins, thereby providing a basis for subsequent identification comparison, and improving accuracy and safety.

In one possible example, the at least one attribute comprises the shape, the shape comprising a shape of a major vein and/or a 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, and the feature data comprises: the medical imaging device determining an image of the main vein and/or the sub-vein in the target image; determining the contour and radius of the main vein and/or the sub-vein according to the image of the main vein and/or the sub-vein; determining the contour and the radius as feature data of the shape.

The feature data of the contour may be specifically described by a trend of the vein, which may describe an angle of each segment with respect to a reference line, or directly describe a curvature distribution of the vein, which is not limited herein, and the feature data of the radius may be specifically described by a radius of one or more reference positions.

In this example, the medical imaging apparatus can accurately acquire feature data of the shape of the main vein and/or 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 subsequent identification comparison, thereby improving accuracy and safety.

In one possible example, the at least one attribute includes a spatial positional relationship of the main vein and the sub-veins; 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, and the feature data comprises: the medical imaging device determining an image of the main vein and each of a plurality of sub-veins in the target image; determining a connection position and a connection angle of the main vein and each sub-vein according to the image of the main vein and the image of each sub-vein; and determining the connection position and the connection angle as the spatial position relation of the main vein and the sub-vein.

As can be seen, in this example, the medical imaging apparatus can accurately acquire the feature 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, thereby providing a basis for subsequent identification comparison, and improving accuracy and safety.

In one possible example, the at least one attribute includes a spatial positional relationship of the sub-veins; 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, and the feature data comprises: the medical imaging device determining images of the main vein, first kidney and second kidney in the target image; analyzing an image between the main vein and the first kidney to obtain a first sub-vein set, and analyzing an image between the main vein and the second kidney to obtain a second sub-vein set; determining a first positional relationship between any two sub-veins in the first set of sub-veins, and determining a second positional relationship between any two sub-veins in the second set of sub-veins; determining the first positional relationship and the second positional relationship as a spatial positional relationship of the sub-vein.

Wherein the first positional relationship and the second positional relationship comprise at least one of: positional relationship descriptors such as bifurcations, intertwining staggers, non-contact, proximity, or relative distance of particular locations.

In this example, the medical imaging device can accurately acquire the feature data of the spatial position relationship of the sub-vein of the current user by analyzing the image of the sub-vein, so that a basis is provided for subsequent identification comparison, and the accuracy and the safety are improved.

In one possible example, the medical imaging device processes the scan image to obtain a target image of the inferior vena cava, comprising: the medical imaging device generates a bitmap BMP data source according to the scanning image; generating first vein image data according to the BMP data source, wherein the first vein image data comprises a raw data set of the inferior vena cava, and the raw data set is a transfer function result of a cubic space of the inferior vena cava surface and a tissue structure inside the inferior vena cava; generating second vein image data according to the first vein image data, wherein the second vein image data comprise a segmentation data set of the inferior vena cava, and the segmentation data set comprises mutually independent image data of the inferior vena cava with a cross sub-relationship; and processing the second vein image data to obtain a target image of the inferior vena cava.

The specific implementation manner for generating the bitmap BMP data source according to the scan image includes: taking the scanned image as medical digital imaging and communication DICOM data of a target user; parsing the DICOM data to generate a map source of a target user, the map source comprising Texture2D/3D image volume data; executing a first preset process to the graph source to obtain the BMP data source, wherein the first preset process comprises at least one of the following operations: VRDS restriction contrast self-adaptive histogram equalization, mixed partial differential denoising and VRDS Ai elastic deformation processing.

The dicom (digital Imaging and Communications in medicine), i.e., digital Imaging and Communications in medicine, is an international standard for medical images and related information. In specific implementation, the medical imaging device acquires a plurality of collected scanning images reflecting the internal structure characteristics of the human body of a target user, screens out at least one scanning image containing a target organ properly through definition, accuracy and the like, and then performs further processing on the scanning image to obtain a bitmap BMP data source. In this example, the medical imaging apparatus may perform screening, analysis and first preset processing based on the acquired scan image to obtain a bitmap BMP data source, so as to improve the accuracy and definition of medical image imaging.

Wherein the VRDS limited contrast adaptive histogram equalization comprises the steps of: region noise contrast amplitude limiting and global contrast amplitude limiting; dividing a local histogram of a graph source into a plurality of partitions, determining the inclination of a transformation function according to the inclination of a cumulative histogram of a neighborhood of each partition aiming at each partition, determining the contrast amplification degree of the periphery of a pixel value of each partition according to the inclination of the transformation function, then carrying out limit cutting according to the contrast amplification degree to generate the distribution of an effective histogram and simultaneously generate a value of the size of the effective available neighborhood, and uniformly distributing the cut partial histograms to other areas of the histogram; the hybrid partial differential denoising comprises the following steps: through VRDS Ai curvature drive and VRDS Ai high-order mixed denoising, the curvature of the image edge is smaller than the preset curvature, and a mixed partial differential denoising model which can protect the image edge and can avoid the step effect in the smoothing process is realized; the VRDS Ai elastic deformation processing comprises the following steps: and superposing positive and negative random distances on the image lattice to form a difference position matrix, then forming a new lattice on the gray level of each difference position, realizing the distortion deformation inside the image, and then performing rotation, distortion and translation operations on the image.

The mixed partial differential denoising can adopt a CDD (complementary direct-division) and a high-order denoising model to process the image source; the CDD model (Curvature drive diffusion) is formed by introducing Curvature drive on the basis of a TV (Total variation) model, and the problem that the TV model cannot repair the visual connectivity of images is solved. The high-order denoising refers to denoising an image based on a Partial Differential Equation (PDE) method. In the specific implementation, the graph source is subjected to noise filtering according to the change of a specified differential equation function, so that noise points in the graph source are filtered, the solution of a partial differential equation is the BMP data source obtained after denoising, and the image denoising method based on the PDE has the characteristic of anisotropic diffusion, so that diffusion effects of different degrees can be performed in different regions of the graph source, and the effect of inhibiting noise and protecting image edge texture information is achieved.

It can be seen that in the present example, the medical imaging apparatus operates by at least one of the following image processing operations: the VRDS limits contrast self-adaptive histogram equalization, mixed partial differential denoising and VRDS Ai elastic deformation processing, improves the execution efficiency of image processing, also improves the image quality and protects the edge texture of the image.

In this possible example, the generating first vein image data from the BMP data source includes: importing the BMP data source into a preset VRDS medical network model, calling each transfer function in a pre-stored transfer function set through the VRDS medical network model, and processing the BMP data source through a plurality of transfer functions in the transfer function set to obtain first vein image data, wherein the transfer function set comprises the transfer functions of the inferior vena cava, and the transfer functions are preset through a reverse editor.

The BMP (full Bitmap) is a standard image file format in the Windows operating system, and can be divided into two types: a Device Dependent Bitmap (DDB) and a Device Independent Bitmap (DIB). The scan image includes any one of: CT images, MRI images, DTI images, PET-CT images.

The VRDS medical network model is a preset network model, and the training method comprises the following three steps: sampling an image and scaling; extracting and scoring the 3D convolutional neural network characteristics; and evaluating the medical imaging device and training the network. In the implementation process, sampling is firstly carried out to obtain N BMP data sources, and then M BMP data sources are extracted from the N BMP data sources according to a preset interval. It should be noted that the preset interval can be flexibly set according to the usage scenario. M BMP data sources are sampled from the N BMP, and then the sampled M BMP data sources are scaled to a fixed size (for example, S pixels long and S pixels wide), and the resulting processing result is used as an input to the 3D convolutional neural network. This takes the M BMP data sources as inputs to the 3D convolutional neural network. Specifically, 3D convolution processing is performed on the BMP data source by using a 3D convolution neural network, and a feature map is obtained.

In this possible example, the generating second vein imaging data from the first vein imaging data includes: importing the first vein image data into a preset cross blood vessel network model, and performing space segmentation processing on the original data of the cross position through the cross blood vessel network model to obtain mutually independent image data of a plurality of inferior vena cava of the cross position; and updating the original data set through the mutually independent image data to obtain second vein image data.

In this possible example, the processing the second vein image data to obtain the target image of the inferior vena cava comprises: performing at least one of the following processing operations on the second vein image data to obtain a target image of the inferior vena cava: 2D boundary optimization processing, 3D boundary optimization processing and data enhancement processing.

Wherein the 2D boundary optimization process comprises the operations of: and acquiring low-resolution information and high-resolution information by multiple sampling, wherein the low-resolution information can provide the contextual semantic information of the segmentation target in the whole image, namely the characteristic reflecting the relation between the target and the environment, and the segmentation target comprises the target vein. The 3D boundary optimization process includes the operations of: respectively putting the second medical image data into a 3D convolution layer for 3D convolution operation to obtain a characteristic diagram; the 3D pooling layer compresses the feature map and carries out nonlinear activation; and carrying out cascade operation on the compressed feature map to obtain a prediction result image output by the model. The data enhancement processing includes at least one of: data enhancement based on arbitrary angle rotation, data enhancement based on histogram equalization, data enhancement based on white balance, data enhancement based on mirroring operation, data enhancement based on random clipping, and data enhancement based on simulating different illumination variations.

In one possible example, before the obtaining the scan image of the target site of the target user containing the inferior vena cava, the method further comprises: and inputting the original characteristic data set.

In accordance with the embodiment shown in fig. 2, please refer to fig. 3, fig. 3 is a schematic structural diagram of a medical imaging apparatus 300 according to an embodiment of the present application, and as shown, the medical imaging apparatus 300 includes a processor 310, a memory 320, a communication interface 330, and one or more programs 321, where the one or more programs 321 are stored in the memory 320 and configured to be executed by the processor 310, and the one or more programs 321 include instructions for performing the following steps;

acquiring a scanning image of a target part containing inferior vena cava of a target user; the scanning image is processed to obtain a target image of the inferior vena cava; and a first feature data set for generating the inferior vena cava from the target imagery, the first feature data set for reflecting physiological features of the inferior vena cava of the target user; the first characteristic data set is used for comparing with a pre-stored original characteristic data set to obtain a comparison result; and the operation module is used for executing preset operation according to the comparison result.

It can be seen that, in the embodiment of the present application, a medical imaging device first obtains a scanned image of a target portion, including an inferior vena cava, of a target user, processes the scanned image to obtain a target image of the inferior vena cava, and then generates a first feature data set of the inferior vena cava according to the target image, where the first feature data set is used to reflect physiological features of the inferior vena cava of the target user, and then compares the first feature data set with a pre-stored original feature data set to obtain a comparison result, and finally performs a preset operation according to the comparison result. It can be seen that, because the inferior vena cava of the human body are different from each other and have unique identification, the medical imaging device of the application realizes identity recognition to complete preset operation by analyzing the image characteristics of the inferior vena cava of the user and comparing the image characteristics, and is different from a physiological characteristic verification mechanism on the surface of the human body, the image characteristics of the inferior vena cava inside the human body are difficult to be counterfeited by illegal users, so that the medical imaging device is beneficial to improving the safety of identity recognition.

In one possible example, the inferior vena cava includes the main vein, which refers to the superior and inferior vena cava and the central vein merging into the right atrium, and the sub-veins, which refer to the inferior vena cava connecting the main vein with the kidney; in connection with the generating of the first set of feature data of the inferior vena cava from the target image, the instructions in the program are specifically configured to perform the following: generating feature data of at least one of the following attributes of the inferior vena cava from the target image: the number of the sub-veins, the shape of the main vein and/or the sub-veins, the spatial position relationship of the main vein and the sub-veins, and the spatial position relationship of the sub-veins; and for generating the first set of feature data from feature data of the at least one attribute.

In one possible example, the at least one attribute comprises the number; in the aspect of generating feature data of at least one of the following attributes of the inferior vena cava from the target image, the instructions in the program are specifically configured to: determining images of the main vein, first kidney and second kidney in the target image; and analyzing the image between the main vein and the first kidney in the target image to obtain a first number of the sub-veins; and analyzing the image between the main vein and the second kidney in the target image to obtain a second number of the sub-veins; and feature data for determining a sum of the first number and the second number as the number.

In one possible example, the at least one attribute comprises the shape, the shape comprising a shape of a major vein and/or a sub-vein; in the aspect of generating feature data of at least one of the following attributes of the inferior vena cava from the target image, the instructions in the program are specifically configured to: determining an image of the main vein and/or the sub-vein in the target image; and for determining the contour and radius of the main vein and/or the sub-vein from the image of the main vein and/or the sub-vein; and feature data for determining the contour and the radius as the shape.

In one possible example, the at least one attribute includes a spatial positional relationship of the main vein and the sub-veins; in the aspect of generating feature data of at least one of the following attributes of the inferior vena cava from the target image, the instructions in the program are specifically configured to: determining an image of the main vein and each of a plurality of sub-veins in the target image; the main vein image and each sub-vein image are used for determining the connection position and connection angle of the main vein and each sub-vein; and the connecting position and the connecting angle are determined as the spatial position relation of the main vein and the sub-vein.

In one possible example, the at least one attribute includes a spatial positional relationship of the sub-veins; in the aspect of generating feature data of at least one of the following attributes of the inferior vena cava from the target image, the instructions in the program are specifically configured to: determining images of the main vein, first kidney and second kidney in the target image; the image analysis device is used for analyzing the image between the main vein and the first kidney to obtain a first sub-vein set, and analyzing the image between the main vein and the second kidney to obtain a second sub-vein set; and for determining a first positional relationship between any two of the first set of sub-veins and determining a second positional relationship between any two of the second set of sub-veins; and means for determining the first positional relationship and the second positional relationship as a spatial positional relationship of the sub-vein.

In one possible example, in the processing the scan image to obtain the target image of the inferior vena cava, the instructions in the program are specifically configured to: generating a bitmap BMP data source according to the scanning image; and generating first vein image data from the BMP data source, the first vein image data comprising a raw data set of the inferior vena cava, the raw data set being a result of a transfer function of a cubic space of the inferior vena cava surface and a tissue structure inside the inferior vena cava; and generating second vein image data from the first vein image data, the second vein image data comprising a segmented data set of the inferior vena cava, the segmented data set having mutually independent image data of the inferior vena cava in a cross-over sub-relationship; and the second vein image data are processed to obtain a target image of the inferior vena cava.

The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the medical imaging apparatus includes hardware structures and/or software modules for performing the respective functions in order to realize the functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The embodiment of the present application may perform the division of the functional units for the medical imaging apparatus according to the above method examples, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.

Fig. 4 is a block diagram of functional units of a medical imaging apparatus 400 according to an embodiment of the present application. Comprising a processing unit 401 and a communication unit 402, wherein,

the processing unit 401 is configured to acquire a scanned image of a target portion of a target user including an inferior vena cava through the communication unit 402; the scanning image is processed to obtain a target image of the inferior vena cava; and a first feature data set for generating the inferior vena cava from the target imagery, the first feature data set for reflecting physiological features of the inferior vena cava of the target user; the first characteristic data set is used for comparing with a pre-stored original characteristic data set to obtain a comparison result; and the operation module is used for executing preset operation according to the comparison result.

Therein, the medical imaging apparatus 400 may further comprise 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 display screen or a transceiver, and the storage unit 403 may be a memory.

In one possible example, the inferior vena cava includes the main vein, which refers to the superior and inferior vena cava and the central vein merging into the right atrium, and the sub-veins, which refer to the inferior vena cava connecting the main vein with the kidney; in respect of the generating of the first set of feature data of the inferior vena cava from the target image, the processing unit 401 is specifically configured to: generating feature data of at least one of the following attributes of the inferior vena cava from the target image: the number of the sub-veins, the shape of the main vein and/or the sub-veins, the spatial position relationship of the main vein and the sub-veins, and the spatial position relationship of the sub-veins; and for generating the first set of feature data from feature data of the at least one attribute.

In one possible example, the at least one attribute comprises the number; in respect of the generating of the feature data of at least one of the following attributes of the inferior vena cava from the target image, the processing unit 401 is specifically configured to: determining images of the main vein, first kidney and second kidney in the target image; and analyzing the image between the main vein and the first kidney in the target image to obtain a first number of the sub-veins; and analyzing the image between the main vein and the second kidney in the target image to obtain a second number of the sub-veins; and feature data for determining a sum of the first number and the second number as the number.

In one possible example, the at least one attribute comprises the shape, the shape comprising a shape of a major vein and/or a sub-vein; in respect of the generating of the feature data of at least one of the following attributes of the inferior vena cava from the target image, the processing unit 401 is specifically configured to: determining an image of the main vein and/or the sub-vein in the target image; and for determining the contour and radius of the main vein and/or the sub-vein from the image of the main vein and/or the sub-vein; and feature data for determining the contour and the radius as the shape.

In one possible example, the at least one attribute includes a spatial positional relationship of the main vein and the sub-veins; in respect of the generating of the feature data of at least one of the following attributes of the inferior vena cava from the target image, the processing unit 401 is specifically configured to: determining an image of the main vein and each of a plurality of sub-veins in the target image; the main vein image and each sub-vein image are used for determining the connection position and connection angle of the main vein and each sub-vein; and the connecting position and the connecting angle are determined as the spatial position relation of the main vein and the sub-vein.

In one possible example, the at least one attribute includes a spatial positional relationship of the sub-veins; in respect of the generating of the feature data of at least one of the following attributes of the inferior vena cava from the target image, the processing unit 401 is specifically configured to: determining images of the main vein, first kidney and second kidney in the target image; the image analysis device is used for analyzing the image between the main vein and the first kidney to obtain a first sub-vein set, and analyzing the image between the main vein and the second kidney to obtain a second sub-vein set; and for determining a first positional relationship between any two of the first set of sub-veins and determining a second positional relationship between any two of the second set of sub-veins; and means for determining the first positional relationship and the second positional relationship as a spatial positional relationship of the sub-vein.

In one possible example, in terms of the processing the scan image to obtain the target image of the inferior vena cava, the processing unit 401 is specifically configured to: generating a bitmap BMP data source according to the scanning image; and generating first vein image data from the BMP data source, the first vein image data comprising a raw data set of the inferior vena cava, the raw data set being a result of a transfer function of a cubic space of the inferior vena cava surface and a tissue structure inside the inferior vena cava; and generating second vein image data from the first vein image data, the second vein image data comprising a segmented data set of the inferior vena cava, the segmented data set having mutually independent image data of the inferior vena cava in a cross-over sub-relationship; and the second vein image data are processed to obtain a target image of the inferior vena cava.

Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to perform part or all of the steps of any one of the methods as set forth in the above method embodiments, the computer including a medical imaging apparatus.

Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package, said computer comprising the medical imaging apparatus.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

A virtual reality medical system artificial intelligence (VRDS) AI vein image recognition method is applied to a medical imaging device and comprises the following steps:

acquiring a scanning image of a target part containing inferior vena cava of a target user;

processing the scanned image to obtain a target image of the inferior vena cava;

generating a first feature data set of the inferior vena cava according to the target image, wherein the first feature data set is used for reflecting physiological features of the inferior vena cava of the target user;

comparing the first characteristic data set with a pre-stored original characteristic data set to obtain a comparison result;

and executing preset operation according to the comparison result.
The method of claim 1, wherein the raw feature data set comprises a plurality of types of feature data, and wherein comparing the first feature data set with a pre-stored raw feature data set comprises:

calculating the reference matching degree of the feature data of each category;

according to preset weight distribution, weighting and summing to obtain a comprehensive matching degree;

and obtaining a comparison result according to the comprehensive matching degree and a preset matching degree threshold value.
The method of claim 1, wherein the inferior vena cava comprises the superior and inferior vena cava and the central vein into the right atrium, and the sub-veins are the inferior vena cava connecting the superior vena cava with the kidney; the generating a first set of feature data of the inferior vena cava from the target image comprises:

generating feature data of at least one of the following attributes of the inferior vena cava from the target image: the number of the sub-veins, the shape of the main vein and/or the sub-veins, the spatial position relationship of the main vein and the sub-veins, and the spatial position relationship of the sub-veins;

and generating the first characteristic data set according to the characteristic data of the at least one attribute.
The method of claim 3, wherein the number of the sub-veins is the number of the sub-veins connecting the kidney, the shape is the contour characteristic and the radius of the inferior vena cava, and the spatial position relationship is the position relationship description information of the intersection, convergence, adjacency, distancing or relative distance between the veins.
The method of claim 3, wherein the at least one attribute comprises the number; generating characteristic data of at least one of the following attributes of the inferior vena cava according to the target image, wherein the characteristic data comprises:

determining images of the main vein, first kidney and second kidney in the target image;

analyzing the image between the main vein and the first kidney in the target image to obtain a first number of the sub-veins;

analyzing the image between the main vein and the second kidney in the target image to obtain a second number of the sub-veins;

determining a sum of the first number and the second number as the number of feature data.
The method of claim 3, wherein the at least one attribute comprises the shape, the shape comprising a shape of a major vein and/or a sub-vein; generating characteristic data of at least one of the following attributes of the inferior vena cava according to the target image, wherein the characteristic data comprises:

determining an image of the main vein and/or the sub-vein in the target image;

determining the contour and radius of the main vein and/or the sub-vein according to the image of the main vein and/or the sub-vein;

determining the contour and the radius as feature data of the shape.
The method of claim 3, wherein the at least one attribute comprises a spatial positional relationship of the main vein and the sub-veins; generating characteristic data of at least one of the following attributes of the inferior vena cava according to the target image, wherein the characteristic data comprises:

determining an image of the main vein and each of a plurality of sub-veins in the target image;

determining a connection position and a connection angle of the main vein and each sub-vein according to the image of the main vein and the image of each sub-vein;

and determining the connection position and the connection angle as the spatial position relation of the main vein and the sub-vein.
The method of claim 3, wherein the at least one attribute includes a spatial positional relationship of the sub-veins; generating characteristic data of at least one of the following attributes of the inferior vena cava according to the target image, wherein the characteristic data comprises:

determining images of the main vein, first kidney and second kidney in the target image;

analyzing an image between the main vein and the first kidney to obtain a first sub-vein set, and analyzing an image between the main vein and the second kidney to obtain a second sub-vein set;

determining a first positional relationship between any two sub-veins in the first set of sub-veins, and determining a second positional relationship between any two sub-veins in the second set of sub-veins;

determining the first positional relationship and the second positional relationship as a spatial positional relationship of the sub-vein.
The method of any one of claims 1-8, wherein said processing said scan image to obtain a target image of said inferior vena cava comprises:

generating a bitmap BMP data source according to the scanning image;

generating first vein image data according to the BMP data source, wherein the first vein image data comprises a raw data set of the inferior vena cava, and the raw data set is a transfer function result of a cubic space of the inferior vena cava surface and a tissue structure inside the inferior vena cava;

generating second vein image data according to the first vein image data, wherein the second vein image data comprise a segmentation data set of the inferior vena cava, and the segmentation data set comprises mutually independent image data of the inferior vena cava with a cross sub-relationship;

and processing the second vein image data to obtain a target image of the inferior vena cava.
A medical imaging apparatus, comprising a processing unit and a communication unit, wherein,

the processing unit is used for acquiring a scanning image of a target part containing the inferior vena cava of a target user through the communication unit; the scanning image is processed to obtain a target image of the inferior vena cava; and a first feature data set for generating the inferior vena cava from the target imagery, the first feature data set for reflecting physiological features of the inferior vena cava of the target user; the first characteristic data set is used for comparing with a pre-stored original characteristic data set to obtain a comparison result; and the operation module is used for executing preset operation according to the comparison result.
The apparatus according to claim 10, wherein the raw feature data set comprises a plurality of types of feature data, and in terms of the raw feature data set comprising a plurality of types of feature data, the processing unit is specifically configured to: calculating the reference matching degree of the feature data of each category; according to preset weight distribution, weighting and summing to obtain comprehensive matching degree; and obtaining a comparison result according to the comprehensive matching degree and a preset matching degree threshold value.
The device of claim 10, wherein the inferior vena cava comprises a main vein and a sub-vein, the main vein being the superior and inferior vena cava and the central vein merging into the right atrium, the sub-vein being the inferior vena cava connecting the main vein with the kidney; in respect of the generating of the first set of feature data of the inferior vena cava from the target image, the processing unit is specifically configured to: generating feature data of at least one of the following attributes of the inferior vena cava from the target image: the number of the sub-veins, the shape of the main vein and/or the sub-veins, the spatial position relationship of the main vein and the sub-veins, and the spatial position relationship of the sub-veins; and generating the first feature data set according to the feature data of the at least one attribute.
The apparatus of claim 12, wherein the number of the sub-veins is the number of the sub-veins connecting the kidney, the shape is the contour characteristic and radius of the inferior vena cava, and the spatial position relationship is the position relationship description information of the intersection, convergence, adjacency, distancing or relative distance between the veins.
The apparatus of claim 12, wherein the at least one attribute comprises the number; in respect of the generating of the feature data of at least one of the following properties of the inferior vena cava from the target image, the processing unit is specifically configured to: determining images of the main vein, first kidney and second kidney in the target image; analyzing the image between the main vein and the first kidney in the target image to obtain a first number of the sub-veins; analyzing the image between the main vein and the second kidney in the target image to obtain a second number of the sub-veins; and determining the sum of the first number and the second number as the number of feature data.
The apparatus of claim 12, wherein the at least one attribute comprises the shape, the shape comprising a shape of a major vein and/or a sub-vein; in respect of the generating of the feature data of at least one of the following properties of the inferior vena cava from the target image, the processing unit is specifically configured to: determining an image of the main vein and/or the sub-vein in the target image; determining the contour and radius of the main vein and/or the sub-vein according to the image of the main vein and/or the sub-vein; and determining the contour and the radius as feature data of the shape.
The apparatus of claim 12, wherein the at least one attribute comprises a spatial positional relationship of the main vein and the sub-veins; in respect of the generating of the feature data of at least one of the following properties of the inferior vena cava from the target image, the processing unit is specifically configured to: determining an image of the main vein and each of a plurality of sub-veins in the target image; determining a connection position and a connection angle of the main vein and each sub-vein according to the image of the main vein and the image of each sub-vein; and determining the connection position and the connection angle as the spatial position relationship of the main vein and the sub-vein.
The apparatus of claim 12, wherein the at least one attribute comprises a spatial positional relationship of the sub-veins; in respect of the generating of the feature data of at least one of the following properties of the inferior vena cava from the target image, the processing unit is specifically configured to: determining images of the main vein, first kidney and second kidney in the target image; analyzing the image between the main vein and the first kidney to obtain a first sub-vein set, and analyzing the image between the main vein and the second kidney to obtain a second sub-vein set; determining a first position relation between any two sub-veins in the first sub-vein set, and determining a second position relation between any two sub-veins in the second sub-vein set; and determining the first positional relationship and the second positional relationship as a spatial positional relationship of the sub-vein.
The apparatus according to any one of claims 10-17, wherein in said processing the scan image to obtain the target image of the inferior vena cava, the processing unit is specifically configured to: generating a bitmap BMP data source according to the scanning image; generating first vein image data according to the BMP data source, wherein the first vein image data comprise an original data set of the inferior vena cava, and the original data set is a transfer function result of a cubic space of the inferior vena cava surface and a tissue structure inside the inferior vena cava; generating second vein image data according to the first vein image data, wherein the second vein image data comprise a segmentation data set of the inferior vena cava, and the segmentation data set comprises mutually independent image data of the inferior vena cava with a cross sub-relationship; and processing the second vein image data to obtain a target image of the inferior vena cava.
A medical imaging apparatus comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-9.
A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-9.