“School of Cognitive%20Sciences”
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| Paper IPM / Cognitive%20Sciences / 11347 |
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| Abstract: | |||||||
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This paper presents an automatic method for segmentation of brain structures using their symmetry and tissue type information. The proposed method generates segmented structures that have homogenous tissues. It benefits from general symmetry of the brain structures in the two hemispheres. It also benefits from the tissue regions generated by fuzzy c-means clustering. All in all, the proposed method can be described as a dynamic knowledge-based method that eliminates the need for statistical shape models of the structures while generating accurate segmentation results. The proposed approach is implemented in MATLAB and tested on the Internet Brain Segmentation Repository (IBSR) datasets. To this end, it is applied to
the segmentation of caudate and ventricles three-dimensionally in
magnetic resonance images (MRI) of the brain. Impacts of each of the
steps of the proposed approach are demonstrated through experiments.
It is shown that the proposed method generates accurate segmentation
results that are insensitive to initialization and parameter selection. The
proposed method is compared to four previous methods illustrating
advantages and limitations of each method.
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