Lesion identification using unified segmentation-normalisation models and fuzzy clustering

In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used...

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Main Authors:	Seghier, Mohamed L., Ramlackhansingh, Anil, Crinion, Jenny, Leff, Alexander P., Price, Cathy J.
Format:	Online
Language:	English
Published:	Academic Press 2008
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724121/

id	pubmed-2724121
recordtype	oai_dc
spelling	pubmed-27241212009-08-18 Lesion identification using unified segmentation-normalisation models and fuzzy clustering Seghier, Mohamed L. Ramlackhansingh, Anil Crinion, Jenny Leff, Alexander P. Price, Cathy J. Article In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes. Academic Press 2008-07-15 /pmc/articles/PMC2724121/ /pubmed/18482850 http://dx.doi.org/10.1016/j.neuroimage.2008.03.028 Text en © 2008 Elsevier Inc. https://creativecommons.org/licenses/by/3.0/ Open Access under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/) license
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Seghier, Mohamed L. Ramlackhansingh, Anil Crinion, Jenny Leff, Alexander P. Price, Cathy J.
spellingShingle	Seghier, Mohamed L. Ramlackhansingh, Anil Crinion, Jenny Leff, Alexander P. Price, Cathy J. Lesion identification using unified segmentation-normalisation models and fuzzy clustering
author_facet	Seghier, Mohamed L. Ramlackhansingh, Anil Crinion, Jenny Leff, Alexander P. Price, Cathy J.
author_sort	Seghier, Mohamed L.
title	Lesion identification using unified segmentation-normalisation models and fuzzy clustering
title_short	Lesion identification using unified segmentation-normalisation models and fuzzy clustering
title_full	Lesion identification using unified segmentation-normalisation models and fuzzy clustering
title_fullStr	Lesion identification using unified segmentation-normalisation models and fuzzy clustering
title_full_unstemmed	Lesion identification using unified segmentation-normalisation models and fuzzy clustering
title_sort	lesion identification using unified segmentation-normalisation models and fuzzy clustering
description	In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes.
publisher	Academic Press
publishDate	2008
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724121/
_version_	1611441272690573312

Lesion identification using unified segmentation-normalisation models and fuzzy clustering

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