MRI Human Brain Segmentation/Classification Using Bayesian Technique - Safa, Soodabeh
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Automatic three-dimensional (3-D) segmentation of the magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of attention lately. Quantitative analysis of signal intensity on MRI changes and their correlation with clinical finding provide important information to diagnose of many neurodegenerative and psychiatric diseases. After the enhancement of images and brain extraction, brain segmentation is the next level. As a result, brain segmentation is of great interest to many researches. Expectation maximization algorithm has been extensively used in a variety…mehr

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Produktbeschreibung
Automatic three-dimensional (3-D) segmentation of the magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of attention lately. Quantitative analysis of signal intensity on MRI changes and their correlation with clinical finding provide important information to diagnose of many neurodegenerative and psychiatric diseases. After the enhancement of images and brain extraction, brain segmentation is the next level. As a result, brain segmentation is of great interest to many researches. Expectation maximization algorithm has been extensively used in a variety of medical image processing applications, especially for detecting human brain disease.
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Autorenporträt
Soodabeh Safashe received the B.Eng degree in software from the Mazandaran University of science and technology in 2004 and Master degree in Computer Science from the Multimedia University in 2010 and Ph.D. degree in Computer Science from the University of Putra Malaysia. Her current research interests include Content Based Image Retrieval methods