A Weighted K-means Algorithm applied to Brain Tissue Classification

Authors
Abras, Guillermo N.; Ballarín, Virginia Laura
Publication Year
2005
Language
English
Format
article
Status
Published version
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.
Facultad de Informática
Subject
Ciencias Informáticas
imagen
PATTERN RECOGNITION
Algorithms
Access level
Open access
License
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
Repository
SEDICI (UNLP)
Institution
Universidad Nacional de La Plata
OAI Identifier
oai:sedici.unlp.edu.ar:10915/9583