MU Digital Repository
Logo

K-means Clustering Approach for Segmentation of Corpus Callosum from Brain Magnetic Resonance Images

Bhalerao, Gaurav Vivek and Niranjana , S (2014) K-means Clustering Approach for Segmentation of Corpus Callosum from Brain Magnetic Resonance Images. In: Proceedings of International Conference on Circuits, Communication,Control and Computing, 21-22 November 2014.

[img] PDF
ieeeconference2014_i4c_.pdf - Published Version
Restricted to Registered users only

Download (567kB) | Request a copy

Abstract

The corpus callosum is one of the most important structures in human brain. Most of the neurological disorders reflect directly or indirectly on the morphological features of Corpus Callosum. The midsagittal brain Magnetic Resonance images fully describe the anatomical structure of corpus callosum. Often considered challenging task of segmenting Corpus Callosum from Magnetic Resonance images has proved the importance of studies on Corpus Callosum segmentation. In this paper, a K-means clustering algorithm is proposed for segmentation of the region of Corpus Callosum. The results of segmentation can be used further for feature extraction and classification for medical diagnosis

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Corpus Callosum, K-means clustering, Segmentation and Magnetic Resonance Image
Subjects: Engineering > MIT Manipal > Biomedical
Depositing User: MIT Library
Date Deposited: 09 Apr 2015 09:37
Last Modified: 14 Apr 2015 06:54
URI: http://eprints.manipal.edu/id/eprint/142391

Actions (login required)

View Item View Item