Method for segmenting multi-dimensional texture image on basis of fuzzy C-means clustering and spatial information

Abstract

The invention discloses a method for segmenting a multi-dimensional texture image on the basis of fuzzy C-means FCM clustering and spatial information and mainly solves the problem of poor quality of image segmentation. The realizing process comprises the following steps of: inputting the texture image to be segmented, carrying out two-dimensional discrete wavelet transformation to the image, and calculating the characteristic vector corresponding to each wavelet coefficient; segmenting the coarsest scale of wavelet transformation; calculating spatial coordinate factors corresponding to the coefficients of the coarsest scale, adding the spatial coordinate factors into an objective function of a traditional FCM clustering algorithm and obtaining the segmenting result marker mapping and the marking field of the scale; obtaining the segmenting result marker mapping of the next scale by adopting the multiple dimensional segmenting method determined by an adaptive scale until the obtained segmenting result marker mapping is at the finest scale; and outputting the segmenting result of the finest scale as the final segmenting result. The method has the advantages of accurate segmenting edge and good consistency of segmenting regions and can be used for segmenting texture images, SAR images including texture information, remote sensing images and medical images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a texture image segmentation method, which can be applied to texture images and images containing texture information, such as the segmentation of synthetic aperture radar SAR, remote sensing images and medical images. Background technique [0002] In image processing and computer vision research, texture image segmentation is the most classic method. It plays a key role in many problems such as image classification, image retrieval, image understanding, object recognition, etc. Fuzzy C-means FCM algorithm is one of the most popular methods in clustering segmentation methods. By introducing FCM clustering, many improved algorithms have emerged. [0003] The traditional FCM clustering method obtains the optimal clustering by optimizing the similarity of the objective function between image pixels and C-class centers to obtain local maxima, because the image ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the inaccurate edge segmentation and insufficient regional consistency of the prior art, and propose a multi-scale texture image segmentation method based on fuzzy C-means clustering and spatial information to improve the quality of image segmentation

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