Multiple sclerosis exploration based on automatic MRI modalities segmentation approach with advanced volumetric evaluations for essential feature extraction

• Published in: Biomedical signal processing and control Vol. 40; pp. 473 - 487
• Main Authors: Ghribi, Olfa, Sellami, Lamia, Slima, Mohamed Ben, Mhiri, Chokri, Dammak, Mariem, Hamida, Ahmed Ben
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2018
• Subjects: Lesion segmentation; Multiple Sclerosis MS; Optimized volumetric feature; Volumetric GLCM; Volumetric GLRM; Volumetric MRI wavelet fusion; Multiple Sclerosis MS; MRI; CHU-HB; Lesion segmentation; MS; Optimized volumetric feature; Volumetric GLCM; CAD; T1BravoGado; T1Gado; SVM; UNC; TLL (ml); GLCM; Volumetric MRI wavelet fusion; Volumetric GLRM; T2w; FLAIR; GA; T1w; CHB; GLRL
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2017.07.008

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•Automatic method for MS lesion exploration based on MRI sequences fusion.•Advanced volumetric feature extraction techniques.•Advanced volumetric feature selection procedure.•It is validated on four databases and demonstrates good accuracy and reproducibility.•It compares positively with other publicly accessible MS lesion segmentation methods.
Multiple Sclerosis (MS) could be considered as one of the most serious neurological diseases that can cause damage to the central nervous system. Such pathology has increased dramatically during the past few years. Hence, MS exploration has captivated the interest of various research studies in clinical as well as technological fields such as medical imaging.
In this context, this paper introduced a new MS exploration approach based on cerebral segmentation and MS lesion identification using the fusion of magnetic resonance (MRI) modalities sequences.
The proposed segmentation approach is based on extracted volumetric features that could be deduced from the gray-level co-occurrence matrix (GLCM) and the gray-level run length (GLRLM) matrix. Volumetric features extraction would be performed by using new voxel wise techniques while preserving connectivity, spatial and shape information.
In addition, our segmentation approach includes an optimized feature selection process combining the genetic algorithm (GA) and the support vector machine (SVM) tool in order to preserve only the essential features that could distinguish the main brain tissues and the MS lesions within both white matter and gray matter.
The evaluation was carried out on four clinical databases. The results revealed an acceptable conformity with the ground truths compared to those of the usual methods Besides, our approach has proved its ability to select the most discriminative features, ensuring an acceptable cerebral segmentation (averages: Dice=0.62±0.11, true positive rate ‘TPR’=0.64±0.12 and positive predictive value ‘PPV’=0.64±0.14) and MS lesions identification with an acceptable accuracy rate (averages: Dice=0.66±0.07, TPR=0.70±0.12 and PPV=0.67±0.03).
Based on these promising results, a computer aided diagnosis (CAD) system was henceforth conceived and could be useful for clinicians in order to carefully facilitate MS exploration. Such a helpful CAD system was really highly needed for clinical explorations and could be extended to other neurological pathologies such as Alzheimer’s and Parkinson’s diseases.