Optimizing and evaluating a graph-based segmentation of MRI wrist bones

In this paper, a quantitative evaluation of the graph-based segmentation method presented in a previous work is performed. The algorithm, starting from a single source element belonging to a region of interest, aims at finding the optimal path minimizing a new cost function for all elements of a digital volume. The method is an adaptive, unsupervised, and semi-automatic approach. For the assessment, a training phase and a testing phase are considered. The system is able to learn and adapt to the ground truth. The performance of the method is estimated by computing classical indices from the confusion matrix, similarity measures, and distance measures. Our work is based on the segmentation and 3D reconstructions of carpal bones derived from Magnetic Resonance Imaging (MRI) volumetric data of patients affected by rheumatic diseases.