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Medical image segmentation method used for liver model in robot virtual training system

A virtual training and medical image technology, applied in the field of medical image segmentation of liver models, can solve the problem of lack of specific training for patient liver cases

Active Publication Date: 2017-07-07
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At this time, when doctors conduct surgical training, there is a lack of targeted training for different liver cases of patients

Method used

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  • Medical image segmentation method used for liver model in robot virtual training system
  • Medical image segmentation method used for liver model in robot virtual training system
  • Medical image segmentation method used for liver model in robot virtual training system

Examples

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specific Embodiment approach 1

[0041]Specific Embodiment 1: A medical image segmentation method for a liver model in a robot virtual training system according to this embodiment is specifically prepared according to the following steps:

[0042] Step 1. In order to better eliminate the impulse noise of the liver medical scan image, the improved Gaussian weighted median filter is used to smooth the liver medical scan image to obtain the filtered rendering; where the improved filter is Gaussian weighted integration to size M 1 × M 1 The square median filter; the effect figure after filtering is the gray value matrix I of x row y column; the pixel label in the effect figure after setting filter is l; M 1 is the side length of the filter window;

[0043] Step 2. Select the kernel function graph cut method to pre-segment the filtered effect image to obtain the pre-segmented image as shown in Figure 2 (a) to (c);

[0044] Step 3. Use the improved semi-automatic gradient vector flow GVF_Snake (the English full ...

specific Embodiment approach 2

[0061] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in step 1, in order to better eliminate the impulse noise of the liver medical scan image, the improved Gaussian weighted median filter is used to smooth the liver medical scan image The specific process of obtaining the filtered renderings:

[0062] A Gaussian weighted median filter method is used to filter liver medical scan images; medical images obtained by computerized tomography (CT) imaging equipment are sources for establishing virtual organ models in the robot virtual training system. Due to the interference of strong electromagnetic fields or the occurrence of bit errors during image transmission, the organ images acquired by CT imaging equipment usually contain impulse noise, which leads to the loss of useful data information in the images and affects the segmentation and extraction of organ tissues. Therefore, before establishing the virtual liver model in the rob...

specific Embodiment approach 3

[0075] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: in step 2, the kernel function graph cut method is selected to pre-segment the filtered effect map to obtain the pre-segmented image. The specific process is:

[0076] Step 21, use the graph cut method to segment the filtered effect map, that is, divide the gray value matrix I into N domains; consider the visual functions of the filtered effect map in step 1, such as color, intensity, etc., after step 1 filtering The effect picture can be expanded to multiple pixel labels;

[0077] Step 22. In N domains Each pixel p in is assigned a pixel label l; get the field S of the pixel label l l ;Set N domains The different pixel labels of each vertex are γ, and for each image pixel point p, satisfy γ(p)∈S; p∈M;

[0078] Step two and three. Due to the complexity of the image data, the Gaussian model cannot separate nonlinear data well. Therefore, the nonlinear mapping fu...

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Abstract

The invention relates to a medical image segmentation method, in particular to a medical image segmentation method used for a liver model in a robot virtual training system. The medical image segmentation method used for the liver model in the robot virtual training system is disclosed to solve the problem that no corresponding virtual liver models established according to patient liver features exists at present. The medical image segmentation method is implemented by the steps of: step 1, acquiring an effect image after filtering; step 2, selecting a kernel function image segmentation method for pre-segmenting the effect image after filtering to obtain a pre-segmented image; step 3, and utilizing an improved semi-automatic gradient vector flow GVF_Snake method to realize segmentation of the pre-segmented image. The medical image segmentation method is applied to the field of medical image segmentation of the liver model in the robot virtual training system.

Description

technical field [0001] The invention relates to a medical image segmentation method, in particular to a medical image segmentation method used for a liver model in a robot virtual training system. Background technique [0002] Medical robotics has become the most cutting-edge technology for current medical applications. Compared with traditional surgery, doctors use robots to perform surgery on patients, which has the advantages of higher precision and smaller wound surface. Before doctors are proficient in operating medical robots, robotic surgery training is required, and traditional surgical training methods can no longer satisfy the recurring training mode. With the development of computer technology and virtual reality technology, the developed virtual surgery training system has become an important way to train doctors to operate robots. [0003] Liver cancer is one of the most common cancers in the world, and its mortality rate is much higher than other cancers. Th...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T5/00
CPCG06T2207/20032G06T2207/20081G06T2207/30056G06T5/70
Inventor 吴冬梅鲍义东
Owner HARBIN INST OF TECH
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