Performance Metrics for Segmentation Algorithms in Brain MRI for Early Detection of Autism
Nagashree N1, Premjyoti Patil2, Shantakumar Patil3, Mallikarjun Kokatanur4
1Nagashree N, VTU Research Scholar, Department of CSE, Nagarjuna College of Engineering & Technology, Bengaluru (Karnataka), India.
2Dr. Premjyoti Patil, Professor, Department of E&C, Nagarjuna College of Engineering & Technology, Bengaluru (Karnataka), India.
3Dr. Shantakumar Patil, Professor, Department of CSE, Nagarjuna College of Engineering & Technology, Bengaluru (Karnataka), India.
4Mr. Mallikarjun Kokatanur, Senior, Department of Software Engineer, Sabre Travel Technologies Pvt. Ltd, Bengaluru (Karnataka), India.
Manuscript received on 06 December 2019 | Revised Manuscript received on 14 December 2019 | Manuscript Published on 31 December 2019 | PP: 561-564 | Volume-9 Issue-2S December 2019 | Retrieval Number: B14011292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1401.1292S19
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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: Autism is an abnormal condition of human brain neurons, which makes individuals attention deficient, unable to speak and several other neurodevelopmental disorders as detected in the children with the age group of 2 to 5 years. However, autism is a neurological irregularity with more than one behavioral problem. Autism would be generally detected by behavioral symptoms, but early detection was not possible with behavioral approach. So, studying the structure of brain by using MRI image of the brain would be an efficient technique in early detection of autism. Various image classification and segmentation methods have been developed by many researchers. This work proposes a new performance metrics to find out efficiency of segmentation algorithms.
Keywords: ASD, Genetic Threshold, K-means, Segmentation.
Scope of the Article: Parallel and Distributed Algorithms