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Level set medical image segmentation method based on heredity kernel fuzzy clustering

A medical image and fuzzy clustering technology, applied in the field of image processing, can solve the problems of initial contour sensitivity and low anti-noise performance, and achieve the effect of high segmentation efficiency and accuracy.

Active Publication Date: 2013-10-23
JIANGSU SINOWAYS (ZHONGHUI) MEDICAL TECH CO LTD +1
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Problems solved by technology

[0006] The purpose of the present invention is to address the shortcomings of the above-mentioned LBF model, and propose a medical image segmentation method based on genetic kernel fuzzy clustering to solve the problem that the LBF model is sensitive to the initial contour and has low anti-noise performance, thereby improving the quality of medical images. segmentation quality

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  • Level set medical image segmentation method based on heredity kernel fuzzy clustering
  • Level set medical image segmentation method based on heredity kernel fuzzy clustering
  • Level set medical image segmentation method based on heredity kernel fuzzy clustering

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Embodiment Construction

[0062] Effect of the present invention is further illustrated by following simulation experiments:

[0063] The experimental environment is Matlab7.1, CORE i3 CPU, memory 4GB. The experimental data are angiography images and MR images.

[0064] The specific implementation process is as follows:

[0065] Step 1: Get clustered images of medical images.

[0066] Suppose the original space sample It is nonlinearly mapped to a feature space by the kernel, and it can be obtained:

[0067]

[0068] Then the dot product of the original sample space can be expressed as the Mercer kernel in the feature space:

[0069]

[0070] In this paper, the kernel function takes the Gaussian kernel function .

[0071] 1-1 Set the number of initial clustering centers , and cluster the gray value of the medical image according to the number of clusters, each cluster center The 8-bit binary number is encoded into a string to form a clustering group.

[0072] 1-2 Set the maximum evol...

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Abstract

The invention provides a level set medical image segmentation method based on heredity kernel fuzzy clustering and relates to the application of medical image segmentation. According to the level set medical image segmentation method disclosed by the invention, a heredity kernel fuzzy clustering algorithm is utilized to obtain an optimal clustering result of a medical image to be treated and then the clustering result is applied to an initial outline of an LBF (Local Binary Fitting) model to carry out the segmentation on the image, so that a blood vessel image has high segmentation efficiency and accuracy.

Description

technical field [0001] The invention belongs to the field of image processing and relates to the application of medical image segmentation. It specifically involves the application of genetic algorithm (GA), kernel fuzzy C-means clustering algorithm (KFCM) and level set method in the field of image segmentation. Background technique [0002] As an important part in the field of image segmentation, medical image segmentation has always been valued by the international academic community, and a large number of scholars have made remarkable achievements in this field. However, for the same clinical image, the tissues of interest are different due to different application purposes. Therefore, how to choose an appropriate segmentation algorithm according to the needs is a difficult problem in the field of medical image segmentation. For example, for the same MR image of the brain, according to different needs, it can be divided into: extraction of brain tissue, classi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/12
Inventor 朱家明张天平盛朗居小平王涛高飞
Owner JIANGSU SINOWAYS (ZHONGHUI) MEDICAL TECH CO LTD
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