Publication
Title
Semi-supervised clustering for MR brain image segmentation
Author
Abstract
Magnetic resonance (MR) brain image segmentation of different anatomical structures or tissue types has become a critical requirement in the diagnosis of neurological diseases. Depending on the availability of the training samples, image segmentation can be either supervised or unsupervised. While supervised learning requires a sufficient amount of labelled training data, which is expensive and time-consuming, unsupervised learning techniques suffer from the problem of local traps. Semi-supervised algorithms that includes prior knowledge into the unsupervised learning can enhance the segmentation process without the need of labelled training data. This paper proposes a method to improve the quality of MR brain tissue segmentation and to accelerate the convergence process. The proposed method is a clustering based semi-supervised classifier that does not need a set of labelled training data and uses less human expert analysis than a supervised approach. The proposed classifier labels the voxels clusters of an image slice and then uses statistics and class labels information of the resultant clusters to classify the remaining image slices by applying Gaussian Mixture Model (GMM). The experimental results show that the proposed semi-supervised approach accelerates the convergence and improves the results accuracy when comparing with the classical GMM approach. (C) 2013 Elsevier Ltd. All rights reserved.
Language
English
Source (journal)
Expert systems with applications. - New York
Publication
New York : 2014
ISSN
0957-4174
Volume/pages
41:41(2014), p. 1492-1497
ISI
000330158700049
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 17.11.2015
Last edited 19.09.2017
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