CN114863029A

CN114863029A - Bone imaging method based on VR technology

Info

Publication number: CN114863029A
Application number: CN202210556528.1A
Authority: CN
Inventors: 叶哲伟; 刘蓬然
Original assignee: Tongji Medical College of Huazhong University of Science and Technology
Current assignee: Tongji Medical College of Huazhong University of Science and Technology
Priority date: 2022-05-19
Filing date: 2022-05-19
Publication date: 2022-08-05

Abstract

The application relates to a bone imaging method based on VR technology, the method comprises the following steps: preprocessing the obtained bone image, and measuring and calculating a gray value to obtain image information; dividing the original image into different regions according to different gray values by adopting a threshold segmentation method, thereby constructing corresponding bone or tissue geometric models in the different regions; and finally, performing color development treatment to obtain a final virtual skeleton three-dimensional color image. The virtual skeleton image constructed by the method is more visual and clear in display, high in simulation and convenient for a doctor to observe.

Description

Bone imaging method based on VR technology

Technical Field

The invention relates to the technical field of medical orthopedic imaging, in particular to a bone imaging method based on a VR (virtual reality) technology.

Background

Virtual Reality (VR), also known as Virtual technology and Virtual environment, is a completely new practical technology developed in the 20 th century, and is a Virtual world that is generated in a three-dimensional space by computer simulation, and provides a simulation of senses such as vision for a user, so that the user feels the experience of the user, and can observe things in the three-dimensional space in real time without limitation. With the development of science and technology, the virtual reality technology has made great progress and gradually becomes a new scientific and technical field.

Bone imaging methods are common means for examining the degree of bone damage, and currently, common methods for bone imaging include: x-ray examination, CT examination, magnetic resonance examination (MRI).

The X-ray examination is the most commonly used examination method for orthopedics department, the structure of the bone and the range and the degree of osteoarthropathy can be clearly and correctly displayed on the whole through the X-ray examination, the examination method is simple, but the defects are obvious, the soft tissue developing cannot be realized, the ghost interference exists, in addition, the X-ray examination is harmful to the human body, and especially for infants and pregnant women.

CT examination can carry out tomography to different aspect of the body, and its speed of scanning is very fast, has improved the resolution ratio of tissue inspection greatly, nevertheless because is tomography, can not see the whole condition.

Magnetic resonance examination (MRI) is an examination technique for detecting signals in the human body by applying a magnetic field to the outside of the human body. The collision resonance examination technology can see that the judgment of the bone tissue is relatively weak under the conditions of soft tissue (such as muscle and ligament), nerve tissue and bone tissue.

The above examination techniques, as bone imaging methods most applied in orthopedics, all have certain limitations, and the photographed images fail to systematically, omnidirectionally and intuitively reflect the damage or lesion of bones.

Disclosure of Invention

The application conception is to provide a bone imaging method, which can be used for displaying bone and tissue images more clearly, intuitively and systematically.

The Virtual Reality (VR) technology is to create a virtual information environment on a multidimensional information space, so that a user can have an immersive immersion sense and have perfect interaction capacity with the environment, and the core is modeling and simulation.

Furthermore, the method and the device have the advantages that a Virtual Reality (VR) technology is applied to skeleton imaging, the obtained skeleton and tissue images are three-dimensional images, the images are virtual images obtained by modeling through scanning key points of the skeleton and the tissues, the virtual images are strong in intuition and good in display effect, and viewers can easily know the state of the skeleton and the tissue comprehensively.

In order to achieve the purpose, the application provides the following technical scheme:

the application provides a bone imaging method based on VR technology, which comprises the following steps:

an image acquisition module acquires a bone image;

the image preprocessing module preprocesses the obtained bone image and transmits image information to the computer module;

the computer module converts the image information into digital information and further divides the image into different areas through the digital information;

the modeling module constructs a virtual skeleton model according to the obtained digital information;

the color matching module carries out color development processing on the image to enable each area to be displayed in different colors, and a three-dimensional color image of the virtual skeleton model is obtained;

the obtained three-dimensional color image is displayed on the VR display module.

Further, the image acquisition module comprises at least one of a camera and a 3D scanning device.

Further, the bone image comprises one or a combination of an X-ray image, a CT image and an MRI image.

Further, the bone image can be directly imported into an image preprocessing module from an X-ray device, a CT device and an MRI device.

Because the original image is easily influenced by the environment and devices in the acquisition process, the image information can be influenced by various kinds of noise, so that the image contains the noise, and the effective information of the image can be influenced when the noise is serious, so that the preprocessing for eliminating the noise of the image is particularly important before the image information is converted into the digital information.

Further, since the color and brightness of an X-ray image, a CT image, or an MRI image are different, each point on a black-and-white image is gray in different degrees. In the computer field, a gray-scale digital image is an image having only one sample color per pixel, and such images are typically displayed as gray scales from darkest black to brightest white, although in theory this sample may be different shades of any color, and even different colors at different brightnesses. The gray image is different from the black and white image, the black and white image only has two colors of black and white in the computer image field, and the gray image has a plurality of levels of color depth between black and white. The logarithmic relationship between white and black is divided into several levels, called "gray scale". The image can be divided into different regions by different gray levels, and the different regions represent different structural parts of bones or tissues, so that the subsequent image segmentation processing in the method can be supported by gray value measurement.

Thus, further, the image preprocessing operation comprises:

eliminating noise; the noise elimination is that the average pixel value around the image pixel points is used for replacing the value of each pixel; and

and (6) measuring and calculating the gray value.

Further, the method for eliminating the noise comprises one or a combination of Gaussian filtering, median filtering, mean filtering and wavelet transformation methods.

Further, the gray value measuring and calculating method comprises a floating point method, an integer method, a shift method, an average value method and a Gamma correction algorithm.

The preferred gray value estimation method is a Gamma correction algorithm.

Image segmentation is based on the principle that discontinuities in gray scale appear at the edges of objects.

Further, the segmentation of the image into different regions is to segment the image into corresponding regions according to different gray values by using a threshold segmentation method.

Further, the process of constructing the virtual bone model is to convert the different segmented regions into corresponding three-dimensional geometric models of bones or tissues.

Further, the method for constructing the virtual skeleton model comprises a direct method and a splicing method.

The application relates to a bone imaging method based on VR technology, compared with the prior art, the application has at least the following beneficial effects:

the method comprises the steps of preprocessing an obtained bone image, measuring and calculating a gray value to obtain image information; dividing the original image into different regions according to different gray values by adopting a threshold segmentation method, thereby constructing corresponding bone or tissue geometric models in the different regions; and finally, obtaining a final virtual skeleton three-dimensional color image through color development treatment. The virtual bone image constructed by the method is more visual and clear in display, high in simulation and convenient for doctors to observe.

Drawings

The present invention will be described in further detail below with reference to the drawings and preferred embodiments, but those skilled in the art will appreciate that the drawings are only drawn for the purpose of illustrating the preferred embodiments and therefore should not be taken as limiting the scope of the invention. Furthermore, unless specifically stated otherwise, the drawings are merely schematic representations based on conceptual representations of elements or structures depicted and may contain exaggerated displays and are not necessarily drawn to scale.

Fig. 1 is a flowchart of bone imaging based on VR technology according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

FIG. 1 is a flow chart of bone imaging based on VR technique, and the bone imaging method based on VR technique according to the present application includes the following specific steps

S1, acquiring image

The bone image acquisition of the embodiment of the application is from at least one of X-ray, CT and MRI examination images;

the image is directly led into the preprocessing module by an image acquisition module or X-ray, CT and MRI examination equipment.

S2, preprocessing the acquired image

The preprocessing module comprises an image noise processing unit, an image acquisition unit and a gray value measuring and calculating unit;

and the noise processing unit performs noise removal processing on the image by adopting a Gaussian filtering method.

Under the image processing concept, the gaussian filter is used as a low-pass filter to filter low-frequency energy (such as noise) and perform image smoothing by linking image frequency domain processing and time domain processing.

The gaussian filtering is a linear smooth filtering, is suitable for eliminating gaussian noise, and is widely applied to a noise reduction process of image processing. Generally speaking, gaussian filtering is a process of performing weighted average on the whole image, and the value of each pixel point is obtained by performing weighted average on the value of each pixel point and other pixel values in the neighborhood. The specific operation of gaussian filtering is: each pixel in the image is scanned using a template (or convolution, mask), and the weighted average gray value of the pixels in the neighborhood determined by the template is used to replace the value of the pixel in the center of the template. The gaussian smoothing filter is very effective for suppressing noise that follows a normal distribution.

The image acquisition unit acquires the image subjected to noise processing, and if the number of the images is multiple, the image acquisition unit generates an image according to image information.

The gray value measuring and calculating unit is used for measuring and calculating the gray value of the acquired image or the combined image.

The color or gray level refers to the difference between brightness and darkness of the displayed pixel points in the black-and-white display, and the difference is expressed as the difference between colors in the color display, and the more gray levels, the clearer and more vivid the image level. The grey scale level depends on the number of bits of the refreshed memory cells per pixel and the performance of the display itself. If the color of each pixel is represented by a 16-bit binary number, we call it a 16-bit map, which can express 2 to the 16 th power, i.e., 65536 colors. If each pixel is represented by a 24-bit binary number, we call it a 24-bit map, which can express 24 powers of 2, i.e. 16777216 colors.

The gray scale is no color, and the RGB color components are all equal. If it is a binary gray image, its pixel value can only be 0 or 1, and we say that its gray level is 2. The RGB values are not simply linear in relation to power but are power functions, the exponent of which is called the Gamma value, typically 2.2, and this scaling process is called Gamma correction.

Therefore, in order to take account of a small storage range (0-255) and a balanced ratio of bright and dark portions, Gamma correction is required for the gray scale values in RGB, instead of directly corresponding to the power values, so that RGB color values cannot be simply and directly added, and the RGB color values must be converted into physical light power by the power of 2.2 to be calculated in the next step.

The calculation method comprises the following steps: any color is composed of three primary colors of red, green and blue, and if the original color of a certain point is RGB (R, G, B), we can convert it into gray scale by the following methods:

1. floating point method: gray ═ R0.3 + G0.59 + B0.11;

2. integer method: gray ═ (R30 + G59 + B11)/100;

3. a shift method: gray ═ (R77 + G151 + B28) > > 8;

4. average value method: (R + G + B)/3;

after Gamma correction, after obtaining Gray, the original RGB (R, G, B) R, G, B are replaced by Gray to form new color RGB (Gray ), and the Gray is the Gray image.

S3, converting image information into digital information

The computer equipment comprises a digital information conversion module which converts the image information into digital information, and the computer equipment identifies the digital information of the image and processes the digital information.

S4, dividing the image into different parts by threshold value division method

The threshold segmentation method is a common processing method for processing image information, the image is segmented into different areas according to different gray values by the threshold segmentation method, and each pixel of the image is divided into a target and a background composition by a selected threshold, so that a binary image is generated; if the image is divided into a plurality of objects and backgrounds, a plurality of thresholds can be selected, i.e. the image can be divided into a plurality of parts, in view of the complex structure of the bone.

The most critical point of the threshold segmentation is the selection of the threshold, and the threshold selection method selected in this embodiment is a genetic algorithm proposed in 1975 by professor j.

(1) Image histogram analysis

For each gray level of the image, the number of pixels of the gray level is counted, and the number is used as a relation graph of the pixels and the gray level, namely a histogram of the image.

The boundary display of the boundary between the skeleton part and the tissue in the skeleton image acquired by the image acquisition module is not obvious enough, a proper boundary point is required to be selected to determine which part is the skeleton and which part is the tissue, and for distinguishing adjacent parts with other boundaries which are not obvious enough, the proper boundary point is required to be selected to distinguish the adjacent parts, and the key of selecting the boundary point is to select a threshold value.

(2) Application of genetic algorithms

And carrying out binarization on the preprocessed image to obtain a binary image, so that the data volume of the subsequently processed image can be greatly reduced.

The process of calculating the threshold value by using a genetic algorithm is as follows:

taking the cross rate as 0.3, the variation rate as 0.1, and the shutdown criterion as follows:

the maximum iteration number G is 20;

the calculation is terminated when the ratio of the average fitness value of the current population and the average fitness value of the previous generation is in the range of 0.995-1.005.

In the image processing, the genetic algorithm can perform maximum inter-class variance calculation again between [ t-0.1 and 1.005] on the basis of the determined threshold value t so as to obtain an optimal threshold value.

The gray value of the background is set to 0, that is, the background is turned to black, and the gray value of the object is set to 1, that is, white is displayed.

And calculating to obtain an optimal threshold value of 0.9358, carrying out binarization processing on the preprocessed image by using the threshold value, and storing in a jpg format for subsequent processing.

S5, three-dimensional modeling, obtaining a virtual skeleton model

After the bone image is preprocessed and segmented, the image is converted into a regular and ordered three-dimensional data field, and the data is used for carrying out three-dimensional modeling again, namely, three-dimensional geometric models of different bone structures or tissues are constructed for the segmented and extracted regions.

The three-dimensional modeling modes mainly comprise two modes:

the direct method comprises the following steps: the data of the three-dimensional data field is directly projected on a display plane, the data field is regarded as a semitransparent substance, a certain color and a certain light resistance degree are given to the data field, and light rays penetrate through the whole data field to perform color synthesis, so that a virtual bone image is displayed.

The direct connection does not need geometric modeling on data field data, so that the method is simple, but the definition of a geometric model (skeleton image) constructed by the direct method is general, and the method does not have a good visual effect, and is not adopted in general.

Splicing method: the surface modeling of the skeleton adopts a mesh drawing method, a mesh is generated through a two-dimensional contour boundary interpolation curve, a quadric surface is generated on the basis of the mesh, and the quadric surface is spliced into a three-dimensional virtual skeleton image.

Firstly, a spline curve of a preprocessed bone image is obtained, a contour line of the preprocessed bone image is cut off by the spline curve, four data points which are in topological rectangle boundary columns are calculated by adopting an insertion node algorithm to be used as four corner points for constructing a spline surface, and other data are made to be common corner points of adjacent curved surfaces.

And secondly, splicing the generated curved surfaces to form a three-dimensional virtual model.

S6, color rendering processing is carried out on the virtual skeleton model

And carrying out color mixing treatment of different colors on the obtained areas with different gray levels in the image to obtain a three-dimensional color image of the virtual skeleton model.

In summary, the present application relates to a bone imaging method based on VR technology, which obtains image information by preprocessing an obtained bone image and measuring a gray value; dividing the original image into different regions according to different gray values by adopting a threshold segmentation method, and constructing corresponding bone or tissue geometric models in the different regions; and finally, obtaining a final virtual skeleton three-dimensional color image through color development treatment. The virtual bone image constructed by the method is more visual and clear in display, high in simulation and convenient for doctors to observe.

The present invention has been described in detail, and the principles and embodiments of the present invention have been described herein using specific examples, which are provided only to assist in understanding the present invention and the core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims

1. A method of bone imaging based on VR technology, comprising:

an image acquisition module acquires a bone image;

2. The method of claim 1, wherein the image acquisition module comprises at least one of a camera, a 3D scanning device.

3. The method of claim 1, wherein the bone image comprises one or a combination of an X-ray image, a CT image, and an MRI image.

4. The method of claim 1, wherein the bone image is imported directly from an X-ray device, a CT device, and an MRI device to an image pre-processing module.

5. The method of claim 1, wherein the image pre-processing operation comprises:

and (6) measuring and calculating the gray value.

6. The method of claim 5 wherein the noise elimination method comprises one or a combination of median filtering, mean filtering, and wavelet transform.

7. The method of claim 5, wherein the gray scale value estimation comprises one of floating point method, integer method, shift method, mean value method and Gamma correction algorithm.

8. The method of gray scale value estimation of claim 5, wherein the method is a Gamma correction algorithm.

9. The method of claim 1, wherein the segmenting the image into different regions is performed by segmenting the image into corresponding regions at different gray values by using a threshold segmentation method.

10. The method of claim 1, wherein said method of constructing a virtual bone model comprises direct method, mosaic method.