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

Amal Jlassi, Khaoula ElBedoui, Walid Barhoumi, Chokri Maktouf

2019

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.

Paper Citation

in Harvard Style

Jlassi A., ElBedoui K., Barhoumi W. and Maktouf C. (2019). Unsupervised Method based on Probabilistic Neural Network for the Segmentation of Corpus Callosum in MRI Scans. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 545-552. DOI: 10.5220/0007400205450552

in Bibtex Style

@conference{visapp19,
author={Amal Jlassi and Khaoula ElBedoui and Walid Barhoumi and Chokri Maktouf},
title={Unsupervised Method based on Probabilistic Neural Network for the Segmentation of Corpus Callosum in MRI Scans},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={545-552},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007400205450552},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Unsupervised Method based on Probabilistic Neural Network for the Segmentation of Corpus Callosum in MRI Scans
SN - 978-989-758-354-4
AU - Jlassi A.
AU - ElBedoui K.
AU - Barhoumi W.
AU - Maktouf C.
PY - 2019
SP - 545
EP - 552
DO - 10.5220/0007400205450552
PB - SciTePress