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MR Image Segmentation of Patients’ Brain Using Disease Specific a priori Knowledge
Hassan Tavakkoli, Ali Sadeqi
Pages - 1 - 10     |    Revised - 15-01-2012     |    Published - 21-02-2012
Volume - 6   Issue - 1    |    Publication Date - February 2012  Table of Contents
Electrodes, Electroencephalography (EEG), Neuro Image Coding, IRIS Patterns & Brain Waves , Signal Processing
Segmentation of high quality brain MR images using a priori knowledge about brain structures enables a more accurate and comprehensive interpretation. Benefits of applying a priori knowledge about the brain structures may also be employed for image segmentation of specific brain and neural patients. Such procedure may be performed to determine the disease stage or monitor its gradual progression over time. However segmenting brain images of patients using general a priori knowledge which corresponds to healthy subjects would result in inaccurate and unreliable interpretation in the regions which are affected by the disease. In this paper, a technique is proposed for extracting a priori knowledge about structural distribution of different brain tissues affected by a specific disease to be applied for accurate segmentation of the patients’ brain images. For this purpose, extracted a priori knowledge is gradually represented as disease specific probability maps throughout an iterative process, and then is utilized in a statistical approach for segmentation of new patients’ images. Experiments conducted on a large set of images acquired from patients with a similar neurodegenerative disease implied success of the proposed technique for representing meaningful a priori knowledge as disease specific probability maps. Promising results obtained also indicated an accurate segmentation of brain MR images of the new patients using the represented a priori knowledge, into three tissue classes of gray matter, white matter, and cerebrospinal fluid. This enables an accurate estimation of tissues’ thickness and volumes and can be counted as a substantial forward step for more reliable monitoring and interpretation of progression in specific brain and neural diseases.
CITED BY (1)  
1 Rajan, R. E., & Prasadh, K. (2012). Feature Selection on Segmented Image using Automatic Subjective Optimality Model. International Journal of Computer Applications, 56(1).
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Dr. Hassan Tavakkoli
Applied Neuroscience Research Center, Baqiyatallah University of Medical Sciences - Iran
Mr. Ali Sadeqi
Applied Neuroscience Research Center - Iran