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Fuzzy clustering image segmenting method

A technology of image segmentation and fuzzy clustering, applied in image analysis, image data processing, instrumentation, etc., can solve problems such as unknown position and characteristics of class centers, sensitivity to noise, high computational overhead, etc.

Active Publication Date: 2013-06-12
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, the FCM algorithm also has some disadvantages, such as: it is sensitive to noise; the position and characteristics of the class center are unknown, and initial assumptions must be made; the calculation cost is large, etc.
These shortcomings, especially the sensitivity to noise and the high computational overhead make it difficult to popularize the fuzzy C-means clustering algorithm in practical applications.

Method used

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Embodiment

[0050] The image segmentation method of the present invention is used to segment the early diabetic retinopathy (hard exudation and cotton wool spots) in the noise-free color fundus image with a resolution of 640×480 pixels.

[0051] It can be seen from Fig. 2(a), Fig. 2(b), Fig. 2(c) that the two algorithms are effective for early diabetic retinopathy (hard exudates and cotton wool spots) in color fundus images without noise pollution. The segmentation results are almost the same. As can be seen from Table 1 (table 1 is the segmentation result statistics of FCM and the present invention to early stage diabetic retinopathy (hard exudation and velvet spots) in 55 color fundus images without noise pollution), the number of iterations of the present invention and Clustering time is significantly less than FCM. Therefore, it can be concluded that the present invention can increase the segmentation speed of the traditional FCM algorithm by about 2.3 times without affecting the seg...

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Abstract

The invention discloses a fuzzy clustering image segmenting method which comprises the steps of: clustering a primary image by using a K-means algorithm to obtain K clustering centers; and clustering the image by using the obtained K clustering centers as a primary clustering center of a fuzzy C-means clustering algorithm for segmenting the image. According to the fuzzy clustering image segmenting method, the problem of high calculating complexity because a primary clustering center is randomly selected in the conventional fuzzy C-means clustering algorithm is solved, and the segmenting precision is improved.

Description

technical field [0001] The invention relates to an image segmentation method, in particular to a noise-resistant fuzzy clustering image segmentation method. Background technique [0002] Clustering is to divide a set of samples with a given unknown class label into multiple inherent categories, so that samples in the same class have a high degree of similarity, and samples in different classes are very different. Clustering has no training samples, no prior knowledge, and only uses certain experience or characteristics of things to classify, which belongs to unsupervised (unsupervised) statistical methods. Fuzzy clustering is one of the main techniques of unsupervised pattern recognition. Among various clustering algorithms, the fuzzy C-means clustering (FCM) algorithm is most widely used. When this algorithm is used for image segmentation, it is a marking process after unsupervised fuzzy clustering, which can reduce human intervention when applied, and is very suitable for...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 沈建新高玮玮
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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