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Brain tumor segmentation from multimodal MR images using rough sets

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Abstract

Automatic segmentation of brain tumors from Magnetic Resonance images is a challenging task due to the wide variation in intensity, size, location of tumors in images. Defining a precise boundary for a tumor is essential for diagnosis and treatment of patients. Rough set theory, an extension of classical set theory, deals with the vagueness of data by determining the boundary region of a set. The aim of this work is to explore the possibility and effectiveness of using a rough set model to represent the tumor regions in MR images accurately, with Quadtree partitioning and simple K-means as precursors to indicate and limit the possible relevant regions. The advantage of using rough sets lie in its ability to represent the impreciseness of set boundaries, which is one of the major challenges faced in tumor segmentation. Experiments are carried out on the BRATS 2013 and 2015 databases and results are comparable to those reported by recent works.

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Saha, R., Phophalia, A., & Mitra, S. K. (2016). Brain tumor segmentation from multimodal MR images using rough sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10481 LNCS, pp. 133–144). Springer Verlag. https://doi.org/10.1007/978-3-319-68124-5_12

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