Unsupervised Method based on Probabilistic Neural Network for the Segmentation of Corpus Callosum in MRI Scans

In this paper, we introduce an unsupervised method for the segmentation of the Corpus Callosum (CC) from Magnetic Resonance Imaging (MRI) scans. In fact, in order to extract the CC from sagittal scans in brain MRI, we adopted the Probabilistic Neural Network (PNN) as a clustering technique. Then, we used k-means to obtain the target classes. After that, we introduced a cluster validity measure based on the maximum entropy principle (Vmep), which aims to define dynamically the optimal number of classes. The later criterion was applied in the hidden layer output of the PNN, while varying the number of classes. Finally, we isolated the CC using a spatial-based process. We validated the performance of the proposed method on two challenging datasets using objective metrics (accuracy, sensitivity, Dice coefficient, specificity and Jaccard similarity), and the obtained results proved the superiority of this method against relevant methods from the state of the art.