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

Gaurav Vivek Bhalerao, Niranjana Sampathila

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 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 languageEnglish
Title of host publicationProceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages434-437
Number of pages4
ISBN (Electronic)9781479965465
DOIs
Publication statusPublished - 10-03-2014
Event2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014 - Bangalore, India
Duration: 21-11-201422-11-2014

Conference

Conference2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014
CountryIndia
CityBangalore
Period21-11-1422-11-14

Fingerprint

Magnetic resonance
Brain
Clustering algorithms
Feature extraction

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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

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Bhalerao GV, Sampathila N. 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. Institute of Electrical and Electronics Engineers Inc. 2014. p. 434-437. 7057839 https://doi.org/10.1109/CIMCA.2014.7057839