MR Image Segmentation Based on Contrast Enhancement with Collaborative Learning - Chang, Yuchou (European Journal of Applied Sciences, 2018)
Magnetic resonance imaging (MRI) has been widely used on clinical applications. Due to different weighting strategies MRI, different weighting images have different contrast even for the same anatomy structure of the same subject. Since some MR brain images have low contrast, different types of tissues such as white matter, grey matter, and cerebrospinal fluid (CSF) are difficult to be divided and segmented. Image contrast needs to be enhanced for better post-processing and image analysis. In this paper, a two-step MR brain image segmentation technique is proposed to solve low contrast MR image segmentation problem. A collaborative learning based image enhancement is firstly applied on low contrast MR brain image. Then, spectral clustering algorithm is used for segmenting enhanced image. Experimental results illustrate that the proposed 2-step segmentation method is able to identify boundaries between tissues well, so that MR image segmentation accuracy is improved in compared to image segmentation without contrast enhancement and exact histogram equalization enhanced image segmentation.