A Weighted K-means Algorithm applied to Brain Tissue Classification

Tissue classification in Magnetic Resonance (MR) brain images is an important issue in the analysis of several brain dementias. This paper presents a modification of the classical K-means algorithm taking into account the number of times specific features appear in an image, employing, for that purp...

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Detalles Bibliográficos
Autores principales: Abras, Guillermo N., Ballarín, Virginia Laura
Formato: Articulo
Lenguaje:Inglés
Publicado: 2005
Materias:
Ciencias Informáticas
PATTERN RECOGNITION
imagen
Algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9583
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct05-2.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-9583
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
PATTERN RECOGNITION
imagen
Algorithms
spellingShingle Ciencias Informáticas
PATTERN RECOGNITION
imagen
Algorithms
Abras, Guillermo N.
Ballarín, Virginia Laura
A Weighted K-means Algorithm applied to Brain Tissue Classification
topic_facet Ciencias Informáticas
PATTERN RECOGNITION
imagen
Algorithms
description Tissue classification in Magnetic Resonance (MR) brain images is an important issue in the analysis of several brain dementias. This paper presents a modification of the classical K-means algorithm taking into account the number of times specific features appear in an image, employing, for that purpose, a weighted mean to calculate the centroid of every cluster. Pattern Recognition techniques allow grouping pixels based on features similarity. In this paper, multispectral gray-level intensity MR brain images are used. T1, T2 and PD-weighted images provide different and complementary information about the tissues. Segmentation is performed in order to classify each pixel of the resulting image according to four possible classes: cerebro-spinal fluid (CSF), white matter (WM), gray matter (GM) and background. T1, T2 and PD-weighted images are used as patterns. The proposed algorithm weighs the number of pixels corresponding to each set of gray levels in the feature vector. As a consequence, an automatic segmentation of the brain tissue is obtained. The algorithm provides faster results if compared with the traditional K-means, thereby retrieving complementary information from the images.
format Articulo
Articulo
author Abras, Guillermo N.
Ballarín, Virginia Laura
author_facet Abras, Guillermo N.
Ballarín, Virginia Laura
author_sort Abras, Guillermo N.
title A Weighted K-means Algorithm applied to Brain Tissue Classification
title_short A Weighted K-means Algorithm applied to Brain Tissue Classification
title_full A Weighted K-means Algorithm applied to Brain Tissue Classification
title_fullStr A Weighted K-means Algorithm applied to Brain Tissue Classification
title_full_unstemmed A Weighted K-means Algorithm applied to Brain Tissue Classification
title_sort weighted k-means algorithm applied to brain tissue classification
publishDate 2005
url http://sedici.unlp.edu.ar/handle/10915/9583
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct05-2.pdf
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