干旱区生态环境知识资源中心

In this paper, a comparison of two different approaches is given for brain tissue segmentation using various sources of techniques, the level-set thresholding segmentation with sparse model (LTSSM) and the segmentation with the k-means clustering (SKMC), in magnetic resonance imaging (MRI). In the LTSSM approach, the system searches for level-set thresholding in the working subsets recursively for segmentation. Unlike the LTSSM approach, the SKMC approach applies the k-means clustering to group the brain tissue objects into three classes (grey matter, white matter and cerebrospinal fluid), and then segment the three groups in the different components in the RGB color space. At the validation stage, both approaches of the LTSSM and the SKMC are implemented using the real-time OASIS data for comparison purpose. The experimental results demonstrate the robustness of both approaches for brain tissue segmentation with comparison, in terms of the Dice similarity and sensitivity in MRI.