Research And Application Of Image Segmentation Based On Fuzzy Theory And Its Extension

Image segmentation is the basis and key technology of image processing, pattern recognition. The results of segmentation can directly affect the other steps. Image bears fuzziness in nature, which make the segmentation more difficult. Since the uncertainties are non-randomness, we cannot use classical mathematics or probability theory to handle it.Fuzzy set theory is presented by Zadeh in1965which is an extension of classic set. Fuzzy logic shows great superiority in describing, dealing with fuzzy events and imprecise knowledge. Thus, fuzzy image segmentation has been an important and hot research topic in image segmentation. In order to handle the uncertainty and improve the accuracy of image segmentation, study of fuzzy image segmentation has theoretical significance and application value.This dissertation focuses on fuzzy image segmentation. First, design the algorithm based on classical fuzzy theory and segment the fuzzy image, especially the fuzzy edge. Then, I improve the fuzzy segmentation algorithm in order to meet the high precision of the practical application of medical ultrasound image segmentation. I take the lead in using a new extended fuzzy theory--neutrosophy in segmentation field, define a new three-domain image representation, and setup a novel image segmentation framework. It can solve complex image segmentation problems with higher ability of uncertainty representation. It also solves the problem of information lacking by using varied image information under this segmentation framework. My research provides new idea and method to solve the bottleneck of segmentation, and can be further extended to image enhancement, image de-noising, and other image processing areas, which has theoretical significance and wide application prospects.The main contributions of the dissertation are as followings:1) A fuzzy watershed method (FWST) based on classical fuzzy theory is proposed. The proposed fuzzy method works well for the image with blurred edges and noise, overcomes the over-segmentation problem of traditional watershed algorithm and has fast running speed. It also can be used to breast ultrasound (BUS) segmentation and get good results.Traditional watershed algorithm can get closed,exact edge, thus be widely used in image segmentation. But it is sensitive to noise and often generates over-segmentation. After analyzing the uncertainty of image, the dissertation combines the classical fuzzy theory and watershed algorithm, transforms the image into fuzzy domain by using histogram and maximum fuzzy entropy principle, and then enhanced by the fuzzy intensifying function. The watershed method is applied in the fuzzy domain to get closed edge. Experiments show that the proposed fuzzy method can get more accurate result than other edge detection algorithms.2) A new three-domain image representation is defined which based on extended fuzzy theory--neutrosophy. Neutrosophic image can represent the image uncertainty better by introducing a new domain I; through the correlation and relative independence of T,I,F, it can use varied image information under this segmentation framework, thus improve the segmentation results. Neutrosophic image has generality as a new image representation and improved way of fuzzy segmentation algorithms.Since the limitation of fuzzy theory, fuzzy segmentation has the deficiency of uncertainty expression of image, it also use limited information and cannot handle spatial information. It will obtain more reasonable results by using variety information in image segmentation. The dissertation overviews the exist extensions of fuzzy theory, introduces a new extended fuzzy logic theory-neutrosophy. Neutrosophy is the generalization of classical fuzzy theory and variety existing extended fuzzy theory. At present, its related research mainly focuses on theory. The dissertation introduces the neutrosophy in image processing, and specified it by using interval neutrosophic set, defines neutrosophic image and interval neutrosophic image, discusses neutrosophic domainn,neutrosophic components, and establishes theoretical foundation for applying neutrosophic theory to image segmentation.3) New gray and color image region merge segmentation algorithms based on neutrosophic image is proposed. The gray segmentation algorithm uses varied image information such as edge information spatial information based on three-domain representation, enhances the expression ability of image uncertainty, improve the accuracy of segmentation. The proposed color segmentation algonthm can integrate color and other information in two color spaces, overcome the problem of selecting color space in color segmentation, the ability of using varied information is more obvious. Experiments proved the superiority and important theoretical significance of neutrosophic image as improved approach of fuzzy segmentation algorithms.According to image features and segmentation rules, the proposed algorithm defines image homogeneity, combines global information local information edge information, utilize global and local segmentation strategy. The algorithm calculates the initial cluster center by using histogram, measures the pixel indeterminacy by I, defines region merge rule based on I and region adjacency relationship. Experimental results of gray segmentation prove that the proposed approach is effective than fuzzy logic methods. It can avoid the wrong segmentation phenomenon and get more homogeneous regions. In color segmentation, the independence of T,I,F and the comprehensive ability of using varied information are more obviously. How to select a suitable color space is a problem in color segmentation, different color space has its advantages and disadvantages and cannot replace each other. However, most existing color segmentation algorithms only utilize one color space. Utilizing neutrosophic image and the relationship of neutrosophic components, the proposed algorithm can integrate color and spatial information, as well as global and local information in two color spaces. Using more information can improve the segmentation results and the algorithm's universality. The proposed color segmentation method has noise-tolerant ability. It reduces the over-segmentation phenomenon, and the segmentation results have good consistency with the subjective visual perception. The experimental results demonstrate that the proposed approaches are more effective and powerful than fuzzy and non-fuzzy methods.4) A new fully automatic BUS tumor segmentation algorithm based on neutrosophic image is proposed. The proposed algorithm based on neutrosophic image framework, uses texture,spatial information of BUS image and improve the algorithm FWST which based on classical fuzzy theory. It combines medical knowledge and designs mass selection rules. The new algorithm can get accurate tumor boundary with less time complexity. Experiments proved the important actual application value of neutrosophic image as improved approach of fuzzy segmentation algorithms.Due to the low contrast of BUS image and blurry tumor boundary, most existing BUS image segmentation methods need region of interests to be manually selected and cannot obtain ideally results. The dissertation applies the neutrosophic image to automatic BUS image tumor segmentation and design a new algorithm NWST. Statistical results of experiment demonstrate that the proposed algorithm can segment tumors more accurately than other automatic BUS segmentation methods and has the best similarity to the manual delineation of tumor region. NWST can handle more blurry edge than fuzzy watershed method with less time complexity, and meet the requirements of real-time in practical applications.