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Open AccessFeature PaperArticle
Information 2017, 8(4), 138;

MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques

Department of Computer Science, University of Bari Aldo Moro, 70125 Bari, Italy
Department of Electrical and Information Engineering, Polytechnic of Bari, 70125 Bari, Italy
Current Address: Via Orabona, 4-70125 Bari, Italy.
Author to whom correspondence should be addressed.
Received: 8 October 2017 / Revised: 1 November 2017 / Accepted: 1 November 2017 / Published: 4 November 2017
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate on specific regions of the brain in order to analyze them. However, segmentation of brain images is a difficult task due to high similarities and correlations of intensity among different regions of the brain image. Among various methods proposed in the literature, clustering algorithms prove to be successful tools for image segmentation. In this paper, we present a framework for image segmentation that is devoted to support the expert in identifying different brain regions for further analysis. The framework includes different clustering methods to perform segmentation of MR images. Furthermore, it enables easy comparison of different segmentation results by providing a quantitative evaluation using an entropy-based measure as well as other measures commonly used to evaluate segmentation results. To show the potential of the framework, the implemented clustering methods are compared on simulated T1-weighted MR brain images from the Internet Brain Segmentation Repository (IBSR database) provided with ground truth segmentation. View Full-Text
Keywords: MR imaging; image segmentation; clustering MR imaging; image segmentation; clustering

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Caponetti, L.; Castellano, G.; Corsini, V. MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques. Information 2017, 8, 138.

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