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Robust FCM image segmentation method

An image segmentation, robust technology, applied in the field of robust FCM image segmentation, can solve the problem of inability to achieve clustering and so on

Active Publication Date: 2016-06-08
EAST CHINA NORMAL UNIVERSITY
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Problems solved by technology

However, in the case of a large noise ratio, the FLICM algorithm cannot achieve good clustering

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  • Robust FCM image segmentation method

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Embodiment Construction

[0047] The present invention will be further described in detail in conjunction with the following specific embodiments and accompanying drawings. The process, conditions, experimental methods, etc. for implementing the present invention, except for the content specifically mentioned below, are common knowledge and common knowledge in this field, and the present invention has no special limitation content.

[0048] refer to figure 1 , the robust FCM image segmentation method of the present invention comprises the following steps:

[0049] Step a: Input a noise-containing image to be processed;

[0050] Step b: Set the fuzzy index m, the iteration stop threshold ε and the maximum number of iterations maxIter, and initialize the number of clusters c and the neighborhood window W;

[0051] Step c: Initialize the degree of membership U with a random number between 0 and 1 0 , and use the initial degree of membership U 0 Calculate the cluster center V of the 0th iteration 0 ;C...

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Abstract

The invention discloses a robust FCM image segmentation method comprising the following steps: setting a fuzzy index, an iteration stop threshold and a maximum number of iterations, and initializing the clustering number and a neighboring window; initializing the membership degree, and calculating the clustering center of a 0th iteration; calculating a fuzzy factor according to the gray value difference between a sample point and an initial clustering center; using the fuzzy factor to calculate the membership degree of iteration; recalculating the clustering center of iteration; if judging that the difference between the membership degree before iteration and the membership degree after iteration is smaller than the iteration stop threshold or the number of iterations exceeds the maximum number of iterations, completing image segmentation and outputting an image after segmentation; or repeating next iteration until the condition is satisfied. According to the invention, a new fuzzy factor is built, the local space information and gray value information are fully utilized and directly act on an original image, the details of the original image are preserved as many as possible, and the robustness to noise is enhanced. The advantage of the robust FCM image segmentation method is more prominent especially in the case of high noise.

Description

technical field [0001] The invention belongs to the technical field of image segmentation and is a robust FCM image segmentation method. Background technique [0002] Image segmentation is an important process in image processing and computer vision. Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. The existing image segmentation is mainly divided into the following categories: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods and cluster-based segmentation methods. Fuzzy clustering or FCM algorithm is a kind of clustering segmentation method. It was proposed by Dunn and promoted by Bezdek. It has been successfully used in medical image processing, artificial intelligence, pattern recognition and other aspects. [0003] The traditional FCM adds a membership function on the basis of hard classification, so that e...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
Inventor 文颖侯丽丽张乐
Owner EAST CHINA NORMAL UNIVERSITY
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