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Review of Fuzzy Image Segmentation Techniques

Review of Fuzzy Image Segmentation Techniques
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Author(s): Gour C. Karmakar (Monash University, Australia), Laurence Dooley (Monash University, Australia) and Mahbubhur Rahman Syed (Minnesota State University, Mankato, USA)
Copyright: 2001
Pages: 32
Source title: Design and Management of Multimedia Information Systems: Opportunities and Challenges
Source Author(s)/Editor(s): Mahbubur Rahman Syed (Minnesota State University Mankato, USA)
DOI: 10.4018/978-1-930708-00-6.ch014


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This chapter provides a comprehensive overview of various methods of fuzzy logic-based image segmentation techniques. Fuzzy image segmentation techniques outperform conventional techniques, as they are able to evaluate imprecise data as well as being more robust in noisy environment. Fuzzy clustering methods need to set the number of clusters prior to segmentation and are sensitive to the initialization of cluster centers. Fuzzy rule-based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. It is also capable of drawing partial inference using fuzzy IF-THEN rules. It has been also intensively applied in medical imaging. These rules are, however, application-domain specific and very difficult to define either manually or automatically that can complete the segmentation alone. Fuzzy geometry and thresholding-based image segmentation techniques are suitable only for bimodal images and can be applied in multimodal images, but they don’t produce a good result for the images that contain a significant amount of overlapping pixels between background and foreground regions. A few techniques on image segmentation based on fuzzy integral and soft computing techniques have been published and appear to offer considerable promise.

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