GA-based multiresolution fusion of segmented brain images using PD-, T1- and T2-weighted MR modalities

Bhattacharya, Mahua; Das, Arpita; Chandana, M.

doi:10.1007/s00521-011-0730-3

Cited by 7 publications

(1 citation statement)

References 44 publications

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“…Among fuzzy clustering algorithms, Fuzzy C -Mean is a powerful technique that has been extensively used in MR image segmentation [ 17 ] in which voxels are partially classified into various classes using different memberships for each class [ 18 , 19 ]. Fuzzy C -Mean was first conceived by Dunn [ 20 ] and generalizes K -means algorithm to allow soft segmentation [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Brain Segmentation in MR Images

Yazdani

Yusof

Karimian

et al. 2015

Computational and Mathematical Methods in Medicine

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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.

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