Staff View: Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix

Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix

This paper presents an automated segmentation of brain lesion from Diffusion-weighted magnetic resonance images (DW-MRI or DWI) based on region and boundary information in gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and absce...

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Main Authors:	Mohd. Saad, N., Syed Abu Bakar, Syed Abdul Rahman, Muda, S., Musa, Mohd. Mokji, Salahuddin, L.
Format:	Book Section
Published:	IEEE Explorer 2011
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/28922/ http://dx.doi.org/10.1109/IST.2011.5962179
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id	my.utm.28922
record_format	eprints
spelling	my.utm.289222017-07-26T08:06:03Z http://eprints.utm.my/id/eprint/28922/ Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix Mohd. Saad, N. Syed Abu Bakar, Syed Abdul Rahman Muda, S. Musa, Mohd. Mokji Salahuddin, L. TK Electrical engineering. Electronics Nuclear engineering This paper presents an automated segmentation of brain lesion from Diffusion-weighted magnetic resonance images (DW-MRI or DWI) based on region and boundary information in gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, GLCM is computed to segment the lesions. Different peaks from the GLCM cross-section indicate the present of normal brain region, cerebral spinal fluid (CSF), hyperintense or hypointense lesions. Minimum and maximum threshold values are computed from the GLCM cross-section. Region and boundary information from the GLCM are introduced as the statistical features for segmentation of hyperintense and hypointense lesions. The proposed method provides very good segmentation results even in a small brain lesion. IEEE Explorer 2011 Book Section PeerReviewed Mohd. Saad, N. and Syed Abu Bakar, Syed Abdul Rahman and Muda, S. and Musa, Mohd. Mokji and Salahuddin, L. (2011) Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix. In: 2011 IEEE International Conference on Imaging Systems and Techniques, IST 2011 - Proceedings. IEEE Explorer, 284 -289. ISBN 978-161284896-9 http://dx.doi.org/10.1109/IST.2011.5962179 10.1109/IST.2011.5962179
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mohd. Saad, N. Syed Abu Bakar, Syed Abdul Rahman Muda, S. Musa, Mohd. Mokji Salahuddin, L. Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix
description	This paper presents an automated segmentation of brain lesion from Diffusion-weighted magnetic resonance images (DW-MRI or DWI) based on region and boundary information in gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, GLCM is computed to segment the lesions. Different peaks from the GLCM cross-section indicate the present of normal brain region, cerebral spinal fluid (CSF), hyperintense or hypointense lesions. Minimum and maximum threshold values are computed from the GLCM cross-section. Region and boundary information from the GLCM are introduced as the statistical features for segmentation of hyperintense and hypointense lesions. The proposed method provides very good segmentation results even in a small brain lesion.
format	Book Section
author	Mohd. Saad, N. Syed Abu Bakar, Syed Abdul Rahman Muda, S. Musa, Mohd. Mokji Salahuddin, L.
author_facet	Mohd. Saad, N. Syed Abu Bakar, Syed Abdul Rahman Muda, S. Musa, Mohd. Mokji Salahuddin, L.
author_sort	Mohd. Saad, N.
title	Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix
title_short	Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix
title_full	Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix
title_fullStr	Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix
title_full_unstemmed	Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix
title_sort	brain lesion segmentation of diffusion-weighted mri using gray level co-occurrence matrix
publisher	IEEE Explorer
publishDate	2011
url	http://eprints.utm.my/id/eprint/28922/ http://dx.doi.org/10.1109/IST.2011.5962179
_version_	1643648191540756480
score	13.244367

Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix

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