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Brain MRI (magnetic resonance image) segmentation method based on improved fuzzy C-means clustering algorithm

An image segmentation and mean value clustering technology, which is applied in image analysis, image enhancement, image data processing, etc., can solve problems such as inability to segment brain MRI images well, achieve enhanced anti-noise, broad market prospects, overcome The effect of limitations

Active Publication Date: 2017-02-15
BEIHANG UNIV
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Problems solved by technology

Therefore, the traditional fuzzy C-means algorithm cannot segment brain MRI images well.

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  • Brain MRI (magnetic resonance image) segmentation method based on improved fuzzy C-means clustering algorithm
  • Brain MRI (magnetic resonance image) segmentation method based on improved fuzzy C-means clustering algorithm
  • Brain MRI (magnetic resonance image) segmentation method based on improved fuzzy C-means clustering algorithm

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Embodiment Construction

[0051] In order to better understand the technical solutions of the present invention, the implementation manners of the present invention will be further described below in conjunction with the accompanying drawings.

[0052] The objective function form of the improved fuzzy C-means algorithm proposed by the present invention is as follows:

[0053]

[0054] Indicates the degree of intuitionistic fuzzy membership, ρ ki Indicates the similarity of pixels to classes. The meanings of other parameters in the formula are given in Table 1.

[0055] The specific implementation steps of the improved fuzzy C-means algorithm are as follows:

[0056] Step 1: Use the fuzzy C-means algorithm for initial classification, and obtain the membership degree matrix U 0 As the initialization result of the membership degree matrix U in the improved algorithm, and set the number of iterations t=1;

[0057] Step 2: Given the number of clusters c, the fuzzy factor m, the threshold ε for stop...

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Abstract

The invention relates to a brain MRI (magnetic resonance image) segmentation method based on an improved fuzzy C-means clustering algorithm. The method comprises steps that 1, initial classification is carried through utilizing the fuzzy C-means clustering algorithm; 2, the clustering quantity c, a fuzzy factor m, an algorithm iteration stop threshold Epsilon, the maximum iteration times max, a neighborhood window size and other artificial setting parameters are given; 3, a similarity matrix W of two pixels is calculated; 4, similarity rhoki of pixel pair types is calculated; 5, a membership matrix U is updated; 6, if ||U(t+1)-U(t)||<Epsilon, or t=max, iteration stops, U(t+1) is outputted, otherwise t=t+1, and the process turns to the step 4; and 7, for U(t+1), the maximum membership algorithm is employed to carry out deblurring operation, and label distribution is carried out to accomplish image segmentation. Through the method, three-portion optimization including improving a clustering center mode, introducing the partial space information and utilizing the intuitionistic fuzzy set information is accomplished, effects of noise resistance enhancement and segmentation precision improvement are realized, and an actual problem of high-precision segmentation for a brain MRI is solved.

Description

[0001] (1) Technical field [0002] The invention relates to an image segmentation method based on an improved fuzzy C-means clustering algorithm, belongs to the field of digital image processing, and mainly relates to fuzzy set theory and image clustering segmentation technology. It has broad application prospects in medical image processing systems. [0003] (2) Background technology [0004] Divide the image into several non-overlapping areas according to the features such as grayscale, color, texture, and shape of the image, and make these features appear similar in the same area and show obvious differences in different areas. This process is for image segmentation. Image segmentation is the basis of image recognition and understanding, and the quality of the segmentation result will directly affect the accuracy of the subsequent process. Therefore, it is of great significance to design and realize an effective and high-precision image segmentation algorithm. Due to the ...

Claims

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Application Information

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IPC IPC(8): G06T7/10G06T7/136
CPCG06T2207/10088G06T2207/30016
Inventor 白相志刘浩楠
Owner BEIHANG UNIV
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