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Entire brain consists of several tissues specifically gray matter (GM), white matter (WM) and cerebrospinal fluid CSF. From brain image it is troublesome to delineate these tissue regions exclusively since these regions are not well defined by sharp boundaries. In present paper a combination of approaches namely bias-field corrected fuzzy C-means and level set segmentation are presented for brain...
In the past decades, image processing technologies have been applied to process medical images. Usually, image segmentation is an important strategy. Fuzzy c-means clustering algorithm has been wildly used for segmentation of brain magnetic resonance image. In the paper, we implement a genetic Fuzzy c-means clustering algorithm based on embedded graphic process units system, NVIDIA TK1, to accelerate...
Morphometric measurements such as volume, thickness and sulcal depth are used to provide valuable information about cortical characteristics in both healthy and diseased conditions of the brain. Relevantly, the focus of this paper is to illustrate the morphometric method of assessing the volume changes in the brain caused by aging and/or pathological condition. Using the T1-weighted magnetic resonance...
A standard segmentation problem within Magnetic Resonance Imaging (MRI) is the task of labelling voxels according to their tissue type that are White Matter (WM), Gray Matter (GM), and Cerebrospinal fluid (CSF).Image segmentation provides volumetric quantification of cortical atrophy and thus helps in the diagnosis of degenerative diseases such as Epilepsy, Schizophrenia, Alzheimer's disease, Dementia...
An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features...
Accurate segmentation of magnetic resonance images (MRI) corrupted by intensity inhomogeneity is a challenging problem and has received an enormous amount of attention lately. On the basis of the local image model, we propose a different segmentation method for MR brain images without estimation and correction for intensity heterogeneity. Firstly, we obtain clustering context based on the distributing...
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