K-means clustering approach for segmentation of corpus callosum from brain magnetic resonance images

Gaurav Vivek Bhalerao; Niranjana Sampathila

doi:10.1109/CIMCA.2014.7057839

K-means clustering approach for segmentation of corpus callosum from brain magnetic resonance images

Gaurav Vivek Bhalerao, Niranjana Sampathila

Department of Biomedical Engineering, Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

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 mid-sagittal 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.

Original language	English
Title of host publication	Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	434-437
Number of pages	4
ISBN (Electronic)	9781479965465
DOIs	https://doi.org/10.1109/CIMCA.2014.7057839
Publication status	Published - 10-03-2014
Event	2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014 - Bangalore, India Duration: 21-11-2014 → 22-11-2014

Conference

Conference	2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014
Country/Territory	India
City	Bangalore
Period	21-11-14 → 22-11-14

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Electrical and Electronic Engineering

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10.1109/CIMCA.2014.7057839

Cite this

Bhalerao, G. V., & Sampathila, N. (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, I4C 2014 (pp. 434-437). [7057839] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIMCA.2014.7057839

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title = "K-means clustering approach for segmentation of corpus callosum from brain magnetic resonance images",

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 mid-sagittal 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.",

author = "Bhalerao, {Gaurav Vivek} and Niranjana Sampathila",

year = "2014",

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doi = "10.1109/CIMCA.2014.7057839",

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booktitle = "Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014",

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note = "2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014 ; Conference date: 21-11-2014 Through 22-11-2014",

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Bhalerao, GV & Sampathila, N 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, I4C 2014., 7057839, Institute of Electrical and Electronics Engineers Inc., pp. 434-437, 2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014, Bangalore, India, 21-11-14. https://doi.org/10.1109/CIMCA.2014.7057839

K-means clustering approach for segmentation of corpus callosum from brain magnetic resonance images. / Bhalerao, Gaurav Vivek; Sampathila, Niranjana.
Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 434-437 7057839.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - K-means clustering approach for segmentation of corpus callosum from brain magnetic resonance images

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N2 - 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 mid-sagittal 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.

AB - 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 mid-sagittal 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.

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BT - Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014

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ER -

K-means clustering approach for segmentation of corpus callosum from brain magnetic resonance images

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