An automatic self‐initialized clustering method for brain tissue segmentation and pathology detection from magnetic resonance human head scans with graphics processing unit machine

Author:

Thiruvenkadam Kalaiselvi1ORCID,Nagarajan Kalaichelvi1,Padmanaban Sriramakrishnan2ORCID

Affiliation:

1. Department of Computer Science and Applications The Gandhigram Rural Institute (Deemed to be University) Dindigul India

2. Department of Computer Applications Kalasalingam Academy of Research and Education (Deemed to be University) Krishnankoil India

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Reference20 articles.

1. Automatic brain tissue segmentation based on self‐initializing K‐means clustering technique;Kalaiselvi T;Int J Intell Syst Appl,2017

2. Energy update restricted Chan‐vese model for tumor extraction from MRI of human head scans;Kalaiselvi T;Int J Comput Methods,2017

3. Whole brain myelin mapping using T1‐ and T2‐weighted MR imaging data;Ganzetti M;Front Human Neurosci.,2014

4. Early grey matter changes in structural covariance networks in Huntington's disease

5. RoyS BandyopadhyaySK.A new method of brain tissues segmentation from MRI with accuracy estimation. Paper presented at: International Conference on Computational Modeling and Security (CMS 2016) Procedia Computer Science; 2016; 85:362‐369.

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