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Mostrar el registro sencillo del ítem 2008-05-23T17:00:29Z 2008-05-23T03:00:00Z 2005-10
dc.description.abstract 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. en
dc.format.extent p. 121-126 es
dc.language en es
dc.title A Weighted K-means Algorithm applied to Brain Tissue Classification en
dc.type Articulo es
sedici.identifier.uri es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Abras, Guillermo N. es
sedici.creator.person Ballarín, Virginia Laura es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.other imagen es
sedici.description.fulltext true es Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000000610 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 5, no. 3 es
sedici.subject.acmcss98 PATTERN RECOGNITION es
sedici.subject.acmcss98 Algorithms es

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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)