Performance Analysis of Fusion Based Brain Tumour Detection Using Chan-Vese and Level Set Segmentation Algorithms
K. Rajesh Babu1, P. V. Naganjaneyulu2, K. Satya Prasad3

1K. Rajesh Babu, Department of Electronics and Communication Engineering, KLEF, Guntur, (Andhra Pradesh), India.
2P. V. Naganjaneyulu, Sri Mittapalli College of Engineering, Guntur, (Andhra Pradesh), India.
3K. Satya Prasad, Vignan’s Foundation for Science, Technology & Research, Guntur, (Andhra Pradesh), India.

Manuscript received on 18 March 2019 | Revised Manuscript received on 25 March 2019 | Manuscript published on 30 March 2019 | PP: 2089-2096 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2707037619/19©BEIESP
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Abstract: Brain tumour shortens the life expectancy of the diseased if not identified at early stages. Accompanied by variety of segmentation algorithms, MRI has been widely used as one of the identification procedures. But no single technique is commonly accepted for accurate segmentation that correlates with pathological studies. This paper highlights the effectiveness of CNN fusion followed by Chan-Vese active contour based segmentation intended for the detection of brain tumour and compares it performance with other contemporary approaches using various metrics.
Keywords: Fuzzy C-Means, K-Means, CNN, CT, NSCT, MWGF, GFF, Chan-Vese, Level Set.

Scope of the Article: Fuzzy Logics