Cargando…

Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification

This paper introduces a novel methodology for the segmentation of brain MS lesions in MRI volumes using a new clustering algorithm named SCPFCM. SCPFCM uses membership, typicality and spatial information to cluster each voxel. The proposed method relies on an initial segmentation of MS lesions in T1...

Descripción completa

Detalles Bibliográficos
Autores principales: Khotanlou, Hassan, Afrasiabi, Mahlagha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3347225/
https://www.ncbi.nlm.nih.gov/pubmed/22606670
_version_ 1782232270931755008
author Khotanlou, Hassan
Afrasiabi, Mahlagha
author_facet Khotanlou, Hassan
Afrasiabi, Mahlagha
author_sort Khotanlou, Hassan
collection PubMed
description This paper introduces a novel methodology for the segmentation of brain MS lesions in MRI volumes using a new clustering algorithm named SCPFCM. SCPFCM uses membership, typicality and spatial information to cluster each voxel. The proposed method relies on an initial segmentation of MS lesions in T1-w and T2-w images by applying SCPFCM algorithm, and the T1 image is then used as a mask and is compared with T2 image. The proposed method was applied to 10 clinical MRI datasets. The results obtained on different types of lesions have been evaluated by comparison with manual segmentations.
format Online
Article
Text
id pubmed-3347225
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher Medknow Publications & Media Pvt Ltd
record_format MEDLINE/PubMed
spelling pubmed-33472252012-05-09 Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification Khotanlou, Hassan Afrasiabi, Mahlagha J Med Signals Sens Original Article This paper introduces a novel methodology for the segmentation of brain MS lesions in MRI volumes using a new clustering algorithm named SCPFCM. SCPFCM uses membership, typicality and spatial information to cluster each voxel. The proposed method relies on an initial segmentation of MS lesions in T1-w and T2-w images by applying SCPFCM algorithm, and the T1 image is then used as a mask and is compared with T2 image. The proposed method was applied to 10 clinical MRI datasets. The results obtained on different types of lesions have been evaluated by comparison with manual segmentations. Medknow Publications & Media Pvt Ltd 2011 /pmc/articles/PMC3347225/ /pubmed/22606670 Text en Copyright: © Journal of Medical Signals and Sensors http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Khotanlou, Hassan
Afrasiabi, Mahlagha
Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification
title Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification
title_full Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification
title_fullStr Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification
title_full_unstemmed Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification
title_short Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification
title_sort segmentation of multiple sclerosis lesions in brain mr images using spatially constrained possibilistic fuzzy c-means classification
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3347225/
https://www.ncbi.nlm.nih.gov/pubmed/22606670
work_keys_str_mv AT khotanlouhassan segmentationofmultiplesclerosislesionsinbrainmrimagesusingspatiallyconstrainedpossibilisticfuzzycmeansclassification
AT afrasiabimahlagha segmentationofmultiplesclerosislesionsinbrainmrimagesusingspatiallyconstrainedpossibilisticfuzzycmeansclassification