An Advanced MRI Multi-Modalities Segmentation Methodology Dedicated to Multiple Sclerosis Lesions Exploration and Differentiation

Multiple sclerosis (MS) is one of the most common neurological diseases in young people. This paper dealt with an automatic biomedical aided tool involving volumetric segmentation of multiple sclerosis lesions. To meet this challenge, our proposed methodology requires one preliminary cerebral zones...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on nanobioscience Vol. 16; no. 8; pp. 656 - 665
Main Authors:	Ghribi, Olfa, Sellami, Lamia, Ben Slima, Mohamed, Ben Hamida, Ahmed, Mhiri, Chokri, Ben Mahfoudh, Keireddine
Format:	Magazine Article
Language:	English
Published:	United States IEEE 01-12-2017
Subjects:	3D MRI brain segmentation Algorithms Brain - diagnostic imaging Brain modeling Gaussian mixture modal Gaussian mixture model gray matter lesion Grey matter Humans Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods lesion expansion Lesions Magnetic Resonance Imaging - methods Multiple sclerosis Multiple Sclerosis - diagnostic imaging multiple sclerosis lesions exploration Normal Distribution
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Multiple sclerosis (MS) is one of the most common neurological diseases in young people. This paper dealt with an automatic biomedical aided tool involving volumetric segmentation of multiple sclerosis lesions. To meet this challenge, our proposed methodology requires one preliminary cerebral zones segmentation performed using a new Gaussian mixture model based on various databases atlases. Afterward, lesion segmentation begins with the estimation of a lesion map, which is then subjected to threshold constraints and refined by a new lesion expansion algorithm. The evaluation was carried out on four clinical databases integrating various clinical cases which had different lesion loads and were presented by a set of MRI modalities at several noise levels. The results compared with those of the existing methods proved excellent cerebral segmentation with dice averages close to 0.8 and sensitivity and specificity averages greater than 0.9. In addition, depending on the used database, the lesion segmentation recorded mean values were close to or greater than 0.8 for the different metrics. The detection error and outline error averages were about 0.3. Besides the ability to identify the lesions affecting the different parts of the brain, even those spreading in the gray matter, the proposed methodology identified the lesions cores and their surrounding vasogenic edema. This has been thoroughly tested and validated by highly qualified radiologists and neurologists. The evaluation of the resulting discriminations recorded values close to or greater than 0.9 for dice, sensitivity, and specificity. As a valuable benefit, a computer aided diagnosis tool could be offered to clinicians. It would help efficiently during the MS diagnosis and avoid several confusions. Besides, it could be used for longitudinal survey and henceforth extends to other pathologies that could be explored by MRI modalities, such as glioblastoma or alzheimer's disease.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1536-1241 1558-2639
DOI:	10.1109/TNB.2017.2763246