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Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM
Abstract Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.
Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM
Abstract Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.
Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM
Jiji, G. Wiselin (author) / Dehmeshki, Jamshid (author)
2014-04-01
9 pages
Article (Journal)
Electronic Resource
English
Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM
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