Segmentation of Brain MRI with Tumor by Image Overlapping using Cellular Automata
Jasmeena Tariq1, A.Kumaravel2, Fasel Qadir3
1Jasmeena Tariq, research scholar at, Computer Applications, Bharath Institute of Higher Education and Research, Chennai.
2Dr Kumaravel, Professor and Dean, School of Computing, Bharath University, Chennai.
3Dr. Fasel Qadir, Assistant Professor, Department of Computer Science‟s North Campus, Delina University of Kashmir, J&K.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 8487-8490 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9939118419/2019©BEIESP | DOI: 10.35940/ijrte.D9939.118419
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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: In order to improve the health care reach, we need efficient and fast computer aided simulation processes or algorithms. When some change is found in pathological reports and biomedical quantities, the person is susceptible to diseases. If the diseases are detected earlier then there can be increase in the rate of mortality. Tumor is one such disease which has been seen to be one of the most fatal for human beings. Detecting and removing tumor is big challenge for medical practitioners. Medical image processing can be used through cellular automata has proven to be one of the fast and reliable method for detection of tumor cells. To study the capabilities of medical science CA’s are being used extensively, as they are useful in studying the self-reproducing biological systems. Purpose: This paper presents an algorithm for segmentation of MRI image through cellular automata, using Conway’s Game of Life. A new approach is being used in this paper, first the image is converted into gray level image. Then edge detection is done for this image using Game of Life. This edge detected image is overlapped with the gray scale image to get the resulted segmented image as an output. Materials and Methods: In order to run the proposed algorithm MATLAB2019b is used and the images are obtained. Results: Algorithm was used on different MRI’s and the results were taken.
Keywords: Tumor, Segmentation, Cellular Automata, MRI, Edge, Grid.
Scope of the Article: Healthcare Informatics.