CN118314226A - Medical image processing system and method - Google Patents
Medical image processing system and method Download PDFInfo
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- CN118314226A CN118314226A CN202410214629.XA CN202410214629A CN118314226A CN 118314226 A CN118314226 A CN 118314226A CN 202410214629 A CN202410214629 A CN 202410214629A CN 118314226 A CN118314226 A CN 118314226A
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/001—Texturing; Colouring; Generation of texture or colour
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- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
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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
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
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Abstract
The invention discloses a medical image processing system and a medical image processing method, wherein the method comprises the steps of analyzing an input original medical image to obtain a pixel matrix; performing image enhancement on the pixel matrix, and adding image processing of a shader; and adding bit depth expansion bits to the image after image processing to expand each pixel to at least 10 bits, rendering, adding texture information, and finally displaying the image after image rendering through a user interface to provide more accurate customized image processing.
Description
Technical Field
The invention provides a medical image processing system and a medical image processing method, in particular relates to a medical image processing system and a medical image processing method based on supporting 10 bit depth or higher bit depth, and belongs to the technical field of image processing.
Background
With the development of medical technology, the precision requirement of medical image processing technology is higher and higher, and the medical image processing technology is particularly in the fields of radiology and pathology. Conventional medical image processing systems typically use 8bit depths to represent pixel values. However, such bit depths limit the representation and processing power of fine structure and detail in the image. In order to improve the accuracy and reliability of medical image processing, for certain applications, it is desirable to use higher bit depths. Thus, a medical image processing system and method supporting a 10 bit depth or greater would better meet these needs, and it would be of great significance to provide a medical image processing method supporting a 10 bit depth or greater.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a medical image processing system and a medical image processing method, which are convenient and more accurate for representing and processing detailed information in medical images and improve medical diagnosis effects.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
In a first aspect, the present invention provides a medical image processing method, comprising:
analyzing the input original medical image to obtain a pixel matrix;
performing image enhancement on the pixel matrix, and adding image processing of a shader;
Adding bit depth expansion bits to the image after image processing to expand each pixel to at least 10 bits;
rendering the image added with the bit depth extension bit, and increasing texture information;
And displaying the rendered image through a user interface.
With reference to the first aspect, further, the pixel matrix includes a width, a height, and a pixel value RGB of a single pixel point of the image.
Further, the image enhancement of the pixel matrix and the image processing of adding a shader include:
Initializing a pixel matrix and creating a frame buffer object;
Creating a rendering buffer zone with a depth texture not lower than 10 bits according to the frame buffer object as a depth attachment point, and storing depth information in the rendering process;
adding a vertex shader and a fragment shader to a rendering buffer after the depth texture rendering buffer is created.
Further, adding a bit depth extension bit to the image after image processing includes:
bit depth conversion: performing matrix weighted calculation on the processed image row by row, and converting original data from integer into floating point data;
Bit depth adjustment: and rounding calculation of the decimal part is carried out on floating point type data after the bit depth conversion according to the target bit depth.
Further, the image added with the bit depth expansion bit is rendered, which comprises two off-screen rendering steps,
The first off-screen rendering includes rendering floating point data inside software;
The second off-screen rendering comprises rendering the data rendered by the software on a display, setting main frame data and shortening the rendering speed.
In a second aspect, the present invention provides a medical image processing system comprising
And a data input module: analyzing the input original medical image to obtain a pixel matrix;
An image processing module: performing image enhancement on the pixel matrix, and adding image processing of a shader;
Bit depth processing module: adding bit depth expansion bits to the image after image processing to expand each pixel to at least 10 bits;
and an image rendering module: adding texture information, and rendering an image with added bit depth extension bits;
A user interface module: and displaying the image after image rendering through a user interface.
With reference to the second aspect, further, the user interface module is provided with an option of configuring the image enhancement mode, the texture information and the bit depth information in real time.
Further, the system also includes storing and managing the rendered image.
Compared with the prior art, the invention has the beneficial effects that:
Adding bit depth expansion bits to the image after image processing to expand each pixel to at least 10 bits;
and rendering the image added with the bit depth extension bit by using OpenGL to create a frame buffer object as a target of off-screen rendering, so as to ensure that subsequent rendering operation is performed in the frame buffer object.
Drawings
Fig. 1 is a block diagram of a medical image processing system according to an embodiment of the present invention.
Detailed Description
The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
The invention provides a medical image processing system and a medical image processing method, wherein the medical image processing system is shown in fig. 1 and comprises a data input module, an image processing module, a bit depth processing module, an image rendering module, a user interface module and a storage and management module.
The data input module comprises data receiving and data analyzing, and the specific implementation steps are as follows:
1.1 data reception: for receiving medical images, the medical image formats include, but are not limited to DCM, JPG, BMP, JIFF, and preliminary verification of image information, including valid information, pixel information, and the like.
1.2 Data analysis: and analyzing the received pixel data according to different data formats to form a pixel matrix, wherein the pixel matrix comprises the width and the height of an image and pixel values RGB of single pixel points, R represents the red component value of the pixel points, G represents the green component value of the pixel points, and the blue component value of the pixel points of the B code is stored in a mode of 1 byte for each component.
The specific implementation steps of the image processing module are as follows:
2.1 initializing: further processing is performed on the image data output by the data input module, and OpenGL contexts are created for each image so as to perform rendering operations.
2.2 Creating a frame buffer object: and (3) creating a frame buffer object for the image data initialized in the step 2.1 by using OpenGL as a target of off-screen rendering.
2.3 Creating depth texture: for the frame buffer object created in 2.2, a rendering buffer with 10-bit or higher depth texture is created as a depth attachment point, and depth information in the rendering process is stored.
2.4 Adding a shader: to add a depth texture frame buffer object, vertex shaders, fragment shaders are added to enable rendering and visualization of images of 10 or more bit depth.
2.5 Binding frame buffer object: the frame buffer object of step 2.4 is bound to the current render target to ensure that subsequent rendering operations will take place in the frame buffer object.
The specific implementation steps of the advanced treatment module are as follows:
3.1 bit depth conversion: the image data in the frame buffer object output by the image processing module is subjected to bit depth expansion, and the image data is expanded from the original 8-bit depth to a format of 10-bit depth or higher.
The manner of expansion is as follows:
First, a spatial domain transformation is performed on a matrix of image pixels. The conversion mode is that a window of N multiplied by N (N is a preset value, N is more than or equal to 3 and N is an odd number) is used for scanning the image line by line, and the pixel value RGB of the central point of the window is converted into a floating point pixel R, G, B through weighted calculation.
Design of n×n window:
Setting the weight of each point in the N x N window as w (i, j), wherein I=0 and j=0 is the weight of the window center point, i.e. w (0, 0).
The weights of points in the nxn window except for the weight w (0, 0) of the window center point are calculated as follows:
according to the weights of points except the center point of the window in the N multiplied by N window, the weight w (0, 0) of the center point can be calculated, and the calculation formula is as follows:
Taking a 5×5 window as an example, the weight values of the points in the window are as follows:
| 1/6 | 1/4 | 1/3 | 1/4 | 1/6 |
| 1/4 | 1/3 | 1/2 | 1/3 | 1/4 |
| 1/3 | 1/2 | -22/3 | 1/2 | 1/3 |
| 1/4 | 1/3 | 1/2 | 1/3 | 1/4 |
| 1/6 | 1/4 | 1/3 | 1/4 | 1/6 |
and (3) expanding the bit depth of the image pixel points:
The red component value of any pixel point of an image with the width W and the height H is expressed as f R (x, y), the green component value is expressed as f G (x, y), and the blue component value is expressed as f B (x, y), wherein x is more than or equal to 1 and less than or equal to W, and y is more than or equal to 1 and less than or equal to H.
Spatial domain transforms of n×n windows are performed on f R(x,y)、fG(x,y)、fB (x, y), respectively:
wherein x is equal to or less than 1 and equal to or less than W, y is equal to or less than 1 and equal to or less than H, g R (x, y) represents a floating point type red component value of a pixel point after bit depth expansion, g G (x, y) represents a floating point type green component value of the pixel point after bit depth expansion, and g B (x, y) represents a floating point type blue component value of the pixel point after bit depth expansion.
The floating point data set of the whole image can be obtained through the bit depth expansion:
{gR(x,y)|1≤x≤W,1≤y≤H}、{gG(x,y)|1≤x≤W,1≤y≤H}、{gB(x，y)|1≤x≤W，1≤y≤H}。
3.2 bit depth adjustment: the floating point data set of the image with the bit depth expanded is subjected to bit depth adjustment to adapt to specific bit depths, such as 10 bit depths, 11 bit depths, 12 bit depths and the like. That is, each value of g R(x,y)、gG(x,y)、gB (x, y) in { g R(x,y)|1≤x≤W,1≤y≤H}、{gG(x,y)|1≤x≤W,1≤y≤H}、{gB (x, y) |1. Ltoreq.x.ltoreq.W, 1. Ltoreq.y.ltoreq.H } is bit depth adjusted for a specific bit depth.
The processing mode is as follows:
1) G R(x,y)、gG(x,y)、gB (x, y) is divided into integer and fractional parts:
gR(x,y)＝gRI(x,y)+gRF(x，y)
gG(x,y)＝gGI(x,y)+gGF(x，y)
gB(x,y)＝gBI(x,y)+gBF(x，y)
Wherein x.ltoreq.x.ltoreq.w, y.ltoreq.h, g R (x, y) has an integer part of g RI (x, y), the integer part of g RF(x,y);gG (x, y) has an integer part of g GI (x, y), the integer part of g GF(x,y);gB (x, y) has an integer part of g BI (x, y), and the fractional part has a integer part of g BF (x, y).
2) The fraction obtained in 1) is cut or rounded in accordance with the bit depth requirement, the rounding being dependent on the bit depth, and 0 to 1 is M-aliquoted.
The mode of calculation of M is as follows:
M＝2D-8
where D is the target bit depth.
The distance of g RF (x, y) from each step in the M-score is t RF (i):
When t RF (i) is at the minimum, where i=i R, g RF (x, y) is calculated to obtain the fractional part of the pixel as g RF (x, y)':
The distance of g GF (x, y) from each step in the M-score is t GF (j):
when t GF (j) is at the minimum, where j=j G, g GF (x, y) is calculated to obtain the fractional part of the pixel as g GF (x, y)':
The distance of g BF (x, y) from each step in the M-score is t BF (k):
when t BF (k) is at a minimum, where k=k B, g BF (x, y) is calculated to give a fractional portion of the pixel of g BF (x, y)':
the integer part and the processed decimal part are recombined to obtain a floating point pixel value adjusted according to a specific bit depth:
gR(x,y)′＝gRI(x,y)+gRF(x，y)′
gG(x,y)′＝gGI(x,y)+gGF(x，y)′
gB(x,y)′＝gBI(x,y)+gBF(x，y)′
Wherein x is equal to or less than 1 and equal to or less than W, y is equal to or less than 1 and equal to or less than H, g R (x, y) ' represents a floating point type red component value of a pixel point adjusted according to a specific bit depth, g G (x, y) ' represents a floating point type green component value of the pixel point adjusted according to the specific bit depth, and g B (x, y) ' represents a floating point type blue component value of the pixel point adjusted according to the specific bit depth.
The floating point data set of the whole image can be obtained through the bit depth adjustment:
{gR(x,y)′|1≤x≤W,1≤y≤H}、{gG(x,y)′|1≤x≤W,1≤y≤H}、{gB(x，y)′|1≤x≤W，1≤y≤H}。
Through the above embodiments, the bit depth processing module can convert image data in a frame buffer object into a format supporting a bit depth of 10 bits or more and perform bit depth adjustment. In this way, the medical image data can be ensured to have higher accuracy and quality in representation and storage, and an accurate data basis is provided for subsequent image rendering.
The specific implementation mode of the image rendering module comprises the following aspects:
4.1 performing off-screen rendering: and processing the vertex of the frame buffer object output by the depth processing module through a vertex shader, performing geometric operation on the geometric shader in an OpenGL rendering pipeline, and finally processing the color output of each pixel through a fragment shader.
The specific operation is that the rendering pipeline of the OpenGL executes the rendering, the texturing of the bit depth number is added in the rendering parameters, the specific measure is that the adjusted floating point data is adopted and bound to the texture shader of the OpenGL rendering object, and the frame buffer object is bound to the current rendering target, so that the subsequent rendering operation is ensured to be carried out in the frame buffer object.
And using a fragment shader of the OpenGL rendering pipeline to assign each floating point number in the image data in the frame buffer object to the OpenGL fragment shader, pushing the assigned floating point number texture to a rendering engine by the fragment shader, and pushing pixels of the carrier floating point number texture to a screen to execute rendering.
4.2 Performing off-screen rendering: and sending the frame buffer object in 4.1 into a display window of the OpenGL for rendering in an off-screen rendering mode of the OpenGL.
The specific implementation mode is as follows:
1) Filling red, green and blue channels in a color buffer zone fed by a frame buffer object, binding the depth buffer zone of the frame buffer zone by using the depth buffer zone of a template buffer zone, and pushing the bound frame buffer object into a display window;
2) After the display window is successfully pushed in, the frame buffer object is set as a main rendering object so as to shorten the rendering time when rendering is performed next time.
Through the above operations, the image data is converted into texture data of 10 bits or more bit depth available to OpenGL, and applied to a corresponding shader program in the rendering process. This allows for 10 or higher bit depth rendering of the medical image and texture mapping onto the image object.
Specific embodiments of the user interface module include:
5.1 interface design: the intuitively friendly user interface is designed so that the user can easily use the medical image processing system. The interface should have a good layout and navigation so that the user can quickly find the desired functions and options.
5.2 User interaction: providing functionality to interact with the user, such as menus, buttons, sliders, etc. Through these controls, the user can select processing algorithms, adjust parameters, import and save image data, and the like. User interactions may also include operation undo, redo, and image scaling, panning, and rotating, among others.
5.3 Parameter settings: allowing the user to set parameters for image processing and rendering. Through the user interface, the user can select the bit depth conversion algorithm, adjust the parameters of image brightness, contrast, color space and the like so as to meet the personalized requirements.
5.4 Real-time preview: the real-time preview function is provided, so that a user can instantly see the effect of the image when adjusting parameters or selecting a processing algorithm. This may help the user to quickly adjust parameters to achieve a desired image rendering effect.
Through the embodiment, the user interface module can provide an intuitively friendly interface and interaction function, so that a user can conveniently use the medical image processing system. The user can set parameters, import and save images through the interface, and preview the rendering effect in real time. Thus, the experience and satisfaction degree of the user can be improved, and the medical image processing and analysis can be performed by using the system better.
The storage and management module comprises the following functions in a specific implementation manner:
6.1 data storage: a database or file system is designed for storing medical image data and related information. Structured or unstructured data storage may be employed to accommodate different types and sizes of data. Security, integrity and accessibility of data are ensured.
6.2 Data indexing and retrieval: and establishing an indexing mechanism to index and retrieve the stored medical image data. The method can quickly locate and access the data according to the keywords, metadata, time stamps and the like, and is convenient for users to search and manage the stored image data.
6.3 Data backup and recovery: and (3) formulating a data backup and recovery strategy to ensure the reliability and the recoverability of the medical image data. Data backup may be performed periodically and data redundancy and fault tolerance mechanisms implemented to prevent data loss or corruption. The data can be quickly restored to the original state when needed.
6.4 Rights management: a rights management mechanism is implemented that restricts access to and manipulation of medical image data. And controlling operations such as reading, editing, deleting and the like of the data by the user according to the user roles and the authority level. Ensuring confidentiality and privacy of data.
6.5 Data sharing and collaboration: providing data sharing and collaboration functionality allows sharing and accessing medical image data between multiple users or systems. Sharing and collaboration of data may be performed through a network or cloud storage platform to facilitate inter-institution or inter-team collaboration and research.
By the above embodiment, the storage and management module can effectively manage the storage, indexing and access of medical image data. The safety and the integrity of the data are ensured, and the user can conveniently search, backup and restore the data. Rights management and data sharing functions can control access to and collaboration with data, providing a suitable data management environment. In this way, the reliability, availability and sustainability of medical image data can be ensured, and subsequent image processing and application requirements are supported.
The foregoing is merely an alternative embodiment of the present invention, and it should be noted that it will be apparent to those skilled in the art that modifications and variations can be made without departing from the technical principles of the present invention, and these modifications and variations should also be regarded as the scope of the invention.
Claims (8)
1. A medical image processing method, comprising:
analyzing the input original medical image to obtain a pixel matrix;
performing image enhancement on the pixel matrix, and adding image processing of a shader;
Adding bit depth expansion bits to the image after image processing to expand each pixel to at least 10 bits;
rendering the image added with the bit depth extension bit, and increasing texture information;
And displaying the rendered image through a user interface.
2. The method of claim 1, wherein the pixel matrix comprises a width, a height, and a pixel value RGB of a single pixel point of the image.
3. The method of claim 1, wherein the image processing of the pixel matrix for image enhancement, adding a shader, comprises:
Initializing a pixel matrix and creating a frame buffer object;
Creating a rendering buffer zone with a depth texture not lower than 10 bits according to the frame buffer object as a depth attachment point, and storing depth information in the rendering process;
adding a vertex shader and a fragment shader to a rendering buffer after the depth texture rendering buffer is created.
4. The method of claim 1, wherein adding the bit depth extension bits to the image after image processing comprises:
bit depth conversion: performing matrix weighted calculation on the processed image row by row, and converting original data from integer into floating point data;
Bit depth adjustment: and rounding calculation of the decimal part is carried out on floating point type data after the bit depth conversion according to the target bit depth.
5. The method of claim 4, wherein rendering the image with the added bit depth extension bits comprises performing two off-screen renderings,
The first off-screen rendering includes rendering floating point data inside software;
The second off-screen rendering comprises rendering the data rendered by the software on a display, setting main frame data and shortening the rendering speed.
6. A medical image processing system, comprising
And a data input module: analyzing the input original medical image to obtain a pixel matrix;
An image processing module: performing image enhancement on the pixel matrix, and adding image processing of a shader;
Bit depth processing module: adding bit depth expansion bits to the image after image processing to expand each pixel to at least 10 bits;
and an image rendering module: adding texture information, and rendering an image with added bit depth extension bits;
A user interface module: and displaying the image after image rendering through a user interface.
7. The system of claim 6, wherein the user interface module is provided with options for configuring image enhancement mode, texture information, bit depth information in real time.
8. The system of claim 6, further comprising storing and managing the rendered image.
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