A Professional Estimate on the Segmentation of Brain Cancer in MR Images using M-FCM
Tessy George1, T. Ramakrishnan2
1Tessy George*, PG Scholar, Department of Electronics and Instrumentation Engineering, National Engineering College, Kovilpatti, Tamilnadu, India.
2Dr. T. Ramakrishnan, Assistant Professor (Senior Grade), Department of Electronics and Instrumentation Engineering, National Engineering College, Kovilpatti, Tamilnadu, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2425-2430 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2920059120/2020©BEIESP | DOI: 10.35940/ijrte.A2920.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Brain imaging innovations have been forever made for a significant part in analyzing and focusing the unused sees of the brain life systems and functions. A computer software code is designed for the detection of cancer in brain magnetic resonance images. Image segmentation, morphological operations and feature extraction are some of the image processing methods developed for the brain cancer detection in MR images concerning the cancer influenced sufferers. In the proposed research, a Modified morphological-based Fuzzy-C-Means (M- FCM) algorithm is proposed to segment the cancer region in the brain MR images. M-FCM algorithm is used to perform the segmentation process significantly through the idealize choice of a cluster, based on the updated membership function. Quantitative analysis between ground truth and segmented cancer is presented in terms of segmentation accuracy and segmentation sensitivity.
Keywords: Brain cancer, M-FCM, Segmentation Accuracy and Segmentation Sensitivity.
Scope of the Article: Instrumentation Engineering