Authors:
Amal Jlassi
1
;
Khaoula ElBedoui
2
;
Walid Barhoumi
2
and
Chokri Maktouf
3
Affiliations:
1
Université de Tunis El Manar, Institut Supérieur d’Informatique, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), 2 Rue Bayrouni, 2080 Ariana and Tunisia
;
2
Université de Tunis El Manar, Institut Supérieur d’Informatique, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), 2 Rue Bayrouni, 2080 Ariana, Tunisia, Université de Carthage, Ecole Nationale d’Ingénieurs de Carthage, 45 Rue des Entrepreneurs, 2035 Carthage and Tunisia
;
3
Biophysics and Nuclear Medicine Department, Pasteur Institute of Tunis, 13 Place Pasteur, 1002 Tunis and Tunisia
Keyword(s):
Corpus Callosum, MRI, Unsupervised Classification, Probabilistic Neural Network, Cluster Validity Index.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Applications
Abstract:
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.