title: A Unified Framework for Brain Segmentation in MR Images.
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.
authors.affiliation: Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Jalan Semarak, Kuala Lumpur, Malaysia.
authors.forename: S
authors.initials: S
authors.lastname: Yazdani
authors.affiliation: Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Jalan Semarak, Kuala Lumpur, Malaysia.
authors.forename: R
authors.initials: R
authors.lastname: Yusof
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan 81745, Iran.
authors.forename: A
authors.initials: A
authors.lastname: Karimian
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran 14174, Iran.
authors.forename: A H
authors.initials: AH
authors.lastname: Riazi
authors.affiliation: School of Computer Science and Software Engineering, The University of Western Australia, Perth, WA 6907, Australia.
authors.forename: M
authors.initials: M
authors.lastname: Bennamoun
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data; D060388:Support Vector Machine