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A fully automated approach is presented to extract brain areas efficiently from FDG-PET head scans. A threshold value is automatically calculated from the histogram graph of the brain images, followed by region growing and morphological operations, to segment brain areas from these images. Next, the midsagittal lines on axial slices are detected to separate the brain into two hemispheres. The proposed...
This paper introduces an interactive and intelligent approach for accurate brain segmentation. A high resolution 3-Tesla magnetic resonance (MR) dataset was tested by state of the art automated algorithms as well as segmented by making use of the proposed interactive tools. The results show that the automated algorithms gave an incomplete or anatomically incorrect brain surface. About 4% false positive...
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