Advances in rough set based hybrid approaches for medical image analysis

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Conference Article

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


Recent advancement in the area of medical imaging produces a huge amount of image data. Automatic extraction of meaningful information from these data has become necessary. In this regard, different image processing techniques provide efficient tools to extract and interpret meaningful information from the medical images, which, in turn, provide valuable directions for medical diagnosis. One of the major problems in real-life medical image data analysis is uncertainty. Among other soft computing techniques, rough sets provide a powerful tool to handle uncertainties, vagueness, and incompleteness associated with data, while fuzzy set and probabilistic paradigm serve as analytical tools for dealing with uncertainty that arises due to the overlapping characteristics and/or randomness in data. Hence, they can be integrated judiciously to develop efficient algorithms for automatic analysis of medical image data. In this regard, the paper presents a brief review on recent advances of rough set based hybrid intelligent approaches for medical image analysis.

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