Abstract
Image processing is extensively considered in medical field for computer-supported disease assessment. Brain tumor is one of the deadliest cancers for the human community and requires image/signal processing approaches to record and analyze the disease-affected regions. In this work, Cuckoo Search Algorithm (CA) assisted approach is proposed to segment tumor from a two-dimensional Magnetic Resonance Image (MRI). Primarily, Tsallis entropy-monitored multilevel thresholding is implemented for the brain MRI dataset based on CA. Afterward, the skull section is detached by means of an image filtering approach. The skull stripped image is then treated using the image morphological function in order to obtain a smooth image exterior. Lastly, the tumor section is mined using the regularized level set technique. The efficiency and the clinical importance of presented method are confirmed based on the image similarity measures and the statistical measures. Experimental results of the proposed approach offer better values of Jaccard, Dice, precision, sensitivity, and accuracy values. Hence the proposed approach is clinically significant and in future, it can be used to diagnose the brain tumor images.
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Rajinikanth, V., Fernandes, S.L., Bhushan, B., Harisha, Sunder, N.R. (2018). Segmentation and Analysis of Brain Tumor Using Tsallis Entropy and Regularised Level Set. In: Satapathy, S., Bhateja, V., Chowdary, P., Chakravarthy, V., Anguera, J. (eds) Proceedings of 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-10-4280-5_33
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DOI: https://doi.org/10.1007/978-981-10-4280-5_33
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