A Segmentation Approaches to Detect Autism and Dementia from Brain MRI
B.J. Bipin Nair1, T.R. Pruthvi2
1B.J. Bipin Nair, Department of Computer Science, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Coimbatore (T.N), India.
2T.R. Pruthvi, Department of Computer Science, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Coimbatore (T.N), India.
Manuscript received on 09 February 2019 | Revised Manuscript received on 05 April 2019 | Manuscript Published on 28 April 2019 | PP: 141-144 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10340275C19/19©BEIESP
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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 Imaging (MRI) and functional MRI (fMRI) studies involving allows to working human brain to be imaged at high resolution within only in a particular time. In our studies fMRI helps in find out the small changes in the brain image and using segmentation algorithm, using segmentation we locate the region in brain MRI and apply any three-efficient segmentation technique checks, and finally predicting which algorithm is an efficient way of doing segmentation the. Basically, so many researches are happened in the disorder like brain tumor, but the present literature says that very less work happened in the disorder like mental and neurodevelopmental disorders. In our proposed work, we are segmenting autism and dementia disorder using MRI image analysis.
Keywords: Fuzzy C-Means (FCM), K-Means, GT.
Scope of the Article: Reflection and Metadata Approaches