Brain Tumour Segmentation Based on SFCM using Back Propagation Neural Network
Swetha P1, Mohanram S2
1Swetha P, PG Scholar, Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai.
2Mohanram S, Assistant Professor, Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai
Manuscript received on 02 July 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 August 2019 | PP: 177-179 | Volume-8 Issue-10, August 2019 | Retrieval Number: H6841068819/2019©BEIESP | DOI: 10.35940/ijitee.H6841.0881019
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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: Magnetic Resonance image (MRI) is predominant in clinical application. MRI used in diagnostic and therapeutic applications and it is pain free treatment. Blur boundaries in high resolution medical resonance image, the tumour segmentation and classification is very hard. In identification method brain tumour is used to upgrade the accuracy and reduce the analysis time. The tumour tissues classified into four they are normal, begin, premalignant and malignant. In MR images, the amount of data is high to explain and analysis. In current years, segmentation of tumour in magnetic resonance image has essential in research field of clinical imaging. Exact shape, size and location of tumour can diagnose. The diagnostic method contain four stages, pre-processing, feature extraction, classification and segmentation.
Keywords: Digital image processing, Magnetic resonance image, Spatial fuzzy c-means clustering, Back propagation neural network, Raspberry pi.
Scope of the Article: Clustering