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Rough set based image segmentation method for quickly inhibiting fuzzy clustering

A technology of fuzzy clustering and image segmentation, applied in the field of image processing, can solve problems such as poor noise robustness, low accuracy of segmentation, and slow running speed, so as to enhance robustness, avoid classification errors, and improve accuracy Effect

Active Publication Date: 2016-07-06
XIDIAN UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the above-mentioned deficiencies in the prior art, and proposes an image segmentation method based on rough sets to quickly suppress fuzzy clustering, which is used to solve the problems of slow running speed and correct segmentation in existing image segmentation methods. Technical issues of low rate and poor robustness to noise

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  • Rough set based image segmentation method for quickly inhibiting fuzzy clustering
  • Rough set based image segmentation method for quickly inhibiting fuzzy clustering
  • Rough set based image segmentation method for quickly inhibiting fuzzy clustering

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Embodiment Construction

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] refer to figure 1 , an image segmentation method based on rough sets to quickly suppress fuzzy clustering, including the following steps:

[0028] Step 1, input an image I to be segmented 1 ;

[0029] The image may be an artificially synthesized image, a medical image or a natural image of any size. In this embodiment, an artificially synthesized image with a size of 244×244 pixels is used.

[0030] Step 2, by image I 1 Get the weighted mean x' of the local information of the pixels in the image i and the mean of the non-local information

[0031] Using the local information of the image to be segmented, that is, the image neighborhood similarity of the neighborhood information, can reduce the influence of noise on the segmentation results, and better retain the detailed information of the image;

[0032] When the centr...

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Abstract

The invention proposes a rough set based image segmentation method for quickly inhibiting fuzzy clustering. The method is used for solving the technical problems of low running speed, low segmentation accuracy and poor noise robustness of an existing image segmentation method. The method is implemented by the steps of 1, inputting a to-be-segmented image I1; 2, calculating a weighted mean of local information and a mean of non local information of pixel points xi in the image I1; 3, obtaining a reconstructed image; 4, clustering a grey level histogram of the reconstructed image; 5, judging whether a current iterative frequency is greater than a maximum iterative frequency T or not, and if yes, performing the step 6, otherwise, adding 1 to the iterative frequency and performing the step 6; 6, outputting a membership matrix and a clustering center of the obtained reconstructed image; and 7, obtaining segmented images. According to the method, the running speed of image segmentation is increased, the accuracy of segmentation is improved, and the noise robustness is enhanced; and the method can be used for feature extraction and target identification of artificially synthesized images, medical images and natural images.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a grayscale image segmentation method, in particular to an image segmentation method based on rough sets to quickly suppress fuzzy clustering, which can be used for feature extraction and extraction of artificially synthesized images, medical images, and natural images. Target Recognition. Background technique [0002] With the continuous advancement of image processing technology, the application and demand for image processing are constantly increasing, and image segmentation is an important step in the process of image analysis and processing, so it is of great significance to study image segmentation methods. Image segmentation is to assign and cluster the pixels in the image according to the similarity criterion of certain features of the image, so that the pixels with the same class label have similar properties, and then extract the region or feature of interest from the inpu...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T2207/20112G06T2207/10004G06F18/2321G06F18/22
Inventor 尚荣华焦李成文爱玲田平平刘芳马文萍王爽侯彪刘红英
Owner XIDIAN UNIV
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