Image Segmentation Using Rough Set Theory: A Review

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Author(s): Payel Roy (JIS College of Engineering, India), Srijan Goswami (IGMGS, India), Sayan Chakraborty (JIS College of Engineering, India), Ahmad Taher Azar (Benha University, Egypt)and Nilanjan Dey (Jadavpur University, India)
Copyright: 2017
Pages: 13
Source title: Medical Imaging: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0571-6.ch059

Keywords: Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

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Image Segmentation Using Rough Set Theory: A Review

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