a unified framework for brain segmentation in mr images

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ID: 228639
2015
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Ranked #187 of 191 articles by views in advanced functional materials

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Abstract
Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.
Reference Key
yazdani2015computationala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;S. Yazdani;R. Yusof;A. Karimian;A. H. Riazi;M. Bennamoun
Journal advanced functional materials
Year 2015
DOI
10.1155/2015/829893
URL
http://dx.doi.org/10.1155/2015/829893
Keywords
medical informatics computer applications to medicine

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