Abstract
Image examination is widely considered in medical field for computer-supported disease evaluation. Brain cancer is the deadliest cancer and requires image/signal processing approaches to record and analyze the illness sections. In this work, Bat algorithm (BA)-supported practice is executed to extract cancer region from magnetic resonance imaging (MRI). Initially, Kapur’s entropy-based multi-thresholding is applied on the brain MRI database. Subsequently, the tumor region is segmented using the Chan–Vese active contour (CVAC) approach. The competence and the importance of employed method are confirmed by means of the picture likeliness values and the arithmetical measures. Experimental result of this study verifies that present approach offers superior values of picture likeliness and arithmetical measures on the considered database.
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Suresh Manic, K., Hasoon, F.N., Shibli, N.A., Satapathy, S.C., Rajinikanth, V. (2019). An Approach to Examine Brain Tumor Based on Kapur’s Entropy and Chan–Vese Algorithm. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 797. Springer, Singapore. https://doi.org/10.1007/978-981-13-1165-9_81
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DOI: https://doi.org/10.1007/978-981-13-1165-9_81
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