Image segmentation method based on immune clone algorithm and fuzzy kernel-clustering algorithm

A technology of fuzzy kernel clustering and immune cloning, which is applied in the field of image processing, can solve the problems of low segmentation accuracy, low robustness, and the clustering center update process is easy to fall into local optimum, so as to improve segmentation accuracy and enhance robustness. sticky effect

Active Publication Date: 2015-09-02
XIDIAN UNIV
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Problems solved by technology

[0003] At present, many image segmentation methods based on fuzzy C-means clustering (FCM) have been proposed. FCM is suitable for the characteristics of uncertainty and fuzziness in images. It is an unsupervised classification method, and the segmentation process does not require any manual intervention. , suitable for the field of automatic segmentation, so the FCM segmentation method has the characteristics of strong adaptability and fast convergence speed. The disadvantage of FCM is that the FCM algorithm is sensitive to the initial clustering center value, and it is easy to converge to the local optimal value. Does not consider the spatial domain information of pixels, is sensitive to noise, and has low robustness
Compared with the traditional fuzzy C-means clustering algorithm, this method introduces a weighted fuzzy factor into the objective function, which contains the domain information of the image, so that more details of the im

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  • Image segmentation method based on immune clone algorithm and fuzzy kernel-clustering algorithm
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  • Image segmentation method based on immune clone algorithm and fuzzy kernel-clustering algorithm

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Embodiment 1

[0068] The present invention is an image segmentation method based on immune cloning and fuzzy kernel clustering, see figure 1 , the process of image segmentation specifically includes the following steps:

[0069] (1) Read in an image, measure the size of the image, convert it into a grayscale image, and obtain a matrix I={x of the pixels of the image1 ,x 2 ,...,x n}, measure the size of the read-in image as a*b, convert it into a grayscale image and get a matrix I={x 1 ,x 2 ,...,x n};

[0070] (2) Parameter setting of fuzzy clustering method:

[0071] Set the total number of cluster centers c=4 of the fuzzy clustering method, the termination condition e=0.01, the fuzzy parameter m=2 and the maximum number of iterations T=500, the control parameter α=5.5 of the image item after filtering; the input image is segmented Obtaining the initial classification number of the image by visual inspection before, i.e. the total number of cluster centers c, obtained the parameters o...

Embodiment 2

[0085] The image segmentation method based on immune cloning and fuzzy kernel clustering is the same as in embodiment 1, wherein the non-local mean value filtering method utilizing automatic adjustment filter parameters in step 3 is to improve the filter parameters of the non-local mean value filtering method, using adjustable The filtering parameters replace the fixed parameter values ​​of the original filtering method. The filtering parameter h in the non-local mean filtering algorithm has a great influence on the effectiveness of filtering. When the filtering parameter h is set too small or too large, the ideal filtering effect cannot be obtained. Moreover, the filter parameter h should be adjusted according to the degree of noise of the image. The specific steps include:

[0086] (3a) Input a grayscale image, take each pixel of the grayscale image as the central pixel x i , get the pixel as x i As a non-local search window with a radius of r as the center, calculate the p...

Embodiment 3

[0104] The image segmentation method based on immune cloning and fuzzy kernel clustering is the same as embodiment 1-2, and the process of calculating the membership matrix of the image with the membership matrix calculation formula with filtering in step (4b) includes:

[0105] (4b1) Use the weighted fuzzy factor formula to calculate all pixel points x of the grayscale image of the input image i The ambiguity G belonging to the kth class ki , the weighted blur factor G ki The calculation formula is

[0106] G ki = Σ p ∈ N i , i ≠ p 1 1 + d ip ( 1 - u ...

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Abstract

The invention discloses an image segmentation method based on an immune clone algorithm and a fuzzy kernel-clustering algorithm. The image segmentation method aims to solve the technical fields of sensitive initial clustering center, easy local optimum falling, and low segmentation correct rate of a fuzzy C-means clustering method. The image segmentation method includes the steps of reading an image to test the image size and convert the image to a grey-scale image; setting the parameters of a fuzzy kernel-clustering method; filtering the grey-scale image by a non-local means filtering method capable of automatically adjusting parameters to obtain the filtered image; optimizing the grey-scale image of the input image by means of the immune clone algorithm to obtain an initial clustering center; obtaining a final membership grade matrix uki by means of membership grade matrix formula with filtering and obtaining the final clustering center value v2 by means of a clustering center formula with filtering; and outputting the segmented image by means of fuzzification. The image segmentation method has the advantages of local optimum falling free, high segmentation accuracy, and good robustness, and can be applied to the segmentation of artificial synthesis images, medical images natural images or other images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to image segmentation, in particular to an image segmentation method based on immune cloning and fuzzy kernel clustering, which can be used for segmentation of various images such as artificially synthesized images, medical images, and natural images. Background technique [0002] Image segmentation is one of the important contents in the field of computer vision, the primary problem of pattern recognition, and a classic problem of image processing. Objects can be extracted and recognized only on the basis of image segmentation, and the quality of image segmentation directly affects higher-level image analysis and understanding. Therefore, the research on image segmentation is of great significance. [0003] At present, many image segmentation methods based on fuzzy C-means clustering (FCM) have been proposed. FCM is suitable for the characteristics of uncertainty and...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/00
Inventor 尚荣华焦李成田平平刘芳马文萍王爽侯彪刘红英屈嵘
Owner XIDIAN UNIV
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