A level set-based framework for 3D kidney segmentation from diffusion MR images
Developing any non-invasive computer-aided diagnostic (CAD) system for the diagnosis of kidney diseases essentially requires the extraction of the kidney from medical images. We propose a shape based level-set framework for 3D kidney segmentation from diffusion-weighted magnetic resonance imaging (DW-MRI). A stochastic speed relationship is used to control the deformable model evolutions. This speed relationship is based on an adaptive shape prior guided by the first- and second-order visual appearance features of the DW-MRI data. These pre-mentioned image features are integrated into a joint Markov-Gibbs random field (MGRF) model of the kidney and its background. DW-MRI data sets from eight subjects acquired at different b-values ranging from 0 to 1000 s/mm2 are tested using a leave-one-subject-out method to evaluate the proposed segmentation approach, and to compare its performance with other segmentation methods using three evaluation metrics: the Dice similarity coefficient (DSC), the 95-percentile modified Hausdorff distance, and the absolute kidney volume difference. Robustness and accuracy of the proposed approach are confirmed through the experimental results' evaluation between manually drawn and automatically segmented contours.