Unsupervised Method Based on
Superpixel Segmentation for Corpus
Callosum Parcellation in MRI Scans
Amal Jlassi
1(B) , Khaoula ElBedoui
1,2 , Walid Barhoumi
1,2 ,
and Chokri Maktouf
3
1 Institut Sup´ erieur d’Informatique, Research Team on Intelligent Systems
in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de recherche en
Informatique, Mod´ elisation et Traitement de l’Information et de la Connaissance
(LIMTIC), Universit´ e de Tunis El Manar, Tunis, Tunisia
amal.jlassi1991@hotmail.com
2 Universit´ e de Carthage, Ecole Nationale d’Ing´ enieurs de Carthage, Tunis, Tunisia
{khaoula.elbedoui,walid.barhoumi}@enicarthage.rnu.tn
3 Nuclear Medicine Department, Pasteur Institute of Tunis, Tunis, Tunisia
Abstract. In this paper, we introduce an unsupervised method for
the parcellation of the Corpus Callosum (CC) from MRI images. Since
there are no visible landmarks within the structure that explicit its
parcels, non-geometric CC parcellation is a challenging task especially
that almost of proposed methods are geometric or data-based. In fact,
in order to subdivide the CC from brain sagittal MRI scans, we adopt
the probabilistic neural network as a clustering technique. Then, we use
a cluster validity measure based on the maximum entropy (Vmep) to
obtain the optimal number of classes. After that, we obtain the isolated
CC that we parcel automatically using SLIC (Simple Linear Iterative
Clustering) as superpixel segmentation technique. The obtained results
on two challenging public datasets prove the performance of the proposed
method against geometric methods from the state of the art. Indeed, as
best as we know, it is the first work that investigates the validation of a
CC parcellation method on ground-truth datasets using many objective
metrics.
Keywords: Corpus callosum · MRI · Parcellation · Superpixel
1 Introduction
Thanks to advances in magnetic resonance imaging, neuroscientists and clinicians can study in depth the Corpus Callosum (CC) and mainly the correlation
between the CC’s dimensions and some neurological diseases. The CC, which is
the largest white matter structure and the biggest fiber tract connecting corresponding regions of the cerebral cortex in the two cerebral hemispheres, integrates motor, sensory, and cognitive functions of the brain [1]. Anatomically,
c
The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 114–125, 2020.
https://doi.org/10.1007/978-3-030-51517-1_10
Superpixel Segmentation for Corpus
Callosum Parcellation in MRI Scans
Amal Jlassi
1(B) , Khaoula ElBedoui
1,2 , Walid Barhoumi
1,2 ,
and Chokri Maktouf
3
1 Institut Sup´ erieur d’Informatique, Research Team on Intelligent Systems
in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de recherche en
Informatique, Mod´ elisation et Traitement de l’Information et de la Connaissance
(LIMTIC), Universit´ e de Tunis El Manar, Tunis, Tunisia
amal.jlassi1991@hotmail.com
2 Universit´ e de Carthage, Ecole Nationale d’Ing´ enieurs de Carthage, Tunis, Tunisia
{khaoula.elbedoui,walid.barhoumi}@enicarthage.rnu.tn
3 Nuclear Medicine Department, Pasteur Institute of Tunis, Tunis, Tunisia
Abstract. In this paper, we introduce an unsupervised method for
the parcellation of the Corpus Callosum (CC) from MRI images. Since
there are no visible landmarks within the structure that explicit its
parcels, non-geometric CC parcellation is a challenging task especially
that almost of proposed methods are geometric or data-based. In fact,
in order to subdivide the CC from brain sagittal MRI scans, we adopt
the probabilistic neural network as a clustering technique. Then, we use
a cluster validity measure based on the maximum entropy (Vmep) to
obtain the optimal number of classes. After that, we obtain the isolated
CC that we parcel automatically using SLIC (Simple Linear Iterative
Clustering) as superpixel segmentation technique. The obtained results
on two challenging public datasets prove the performance of the proposed
method against geometric methods from the state of the art. Indeed, as
best as we know, it is the first work that investigates the validation of a
CC parcellation method on ground-truth datasets using many objective
metrics.
Keywords: Corpus callosum · MRI · Parcellation · Superpixel
1 Introduction
Thanks to advances in magnetic resonance imaging, neuroscientists and clinicians can study in depth the Corpus Callosum (CC) and mainly the correlation
between the CC’s dimensions and some neurological diseases. The CC, which is
the largest white matter structure and the biggest fiber tract connecting corresponding regions of the cerebral cortex in the two cerebral hemispheres, integrates motor, sensory, and cognitive functions of the brain [1]. Anatomically,
c
The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 114–125, 2020.
https://doi.org/10.1007/978-3-030-51517-1_10