Multiple sclerosis lesion detection in multimodal MRI using simple clustering-based segmentation and classification. (2020)

• Record Type: Journal Article
• Authors: Cetin, Ozdemir; Seymen, Volkan; Sakoglu, Unal
• Is Part Of: Informatics in medicine unlocked. Volume 20(2020)
• Publication Date: 2020
• Subjects: Multiple sclerosis -- Multimodal MRI -- Segmentation -- Classification -- Clustering; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2020.100409
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Background: Multiple sclerosis (MS) is an immune-mediated inflammatory disease that attacks myelinated axons in the central nervous system, destroying myelin and axons to variable degrees and, resulting in significant physical disability. Magnetic resonance imaging (MRI) is useful in the diagnosis of MS, surpassing all other imaging techniques in terms of prediction accuracy. Depending on the number and location of lesions, however, the success of MR can vary significantly in terms of sensitivity and specificity in the diagnosis of MS. Adverse effects of various intensity and residual artifacts in the MRI data make it challenging to compute MS lesion volume to assess the progression of MS. Therefore, the development of robust and automated MS lesion detection methods has been a challenge. Objectives: This study aims to develop a novel, robust, and simple image segmentation method to perform quantitative analysis of MS lesions from multimodal MRI data. Methods: An algorithm based on a supervised minimum Euclidean distance-based clustering method employing three 2D MRI modalities, T1-weighted (T1w), fluid-attenuated inversion recovery (FLAIR), and T2-weighted (T2w) MRI was developed for classification of significant brain tissues and MS lesions. The developed method was applied to an MRI dataset from six MS patients. Results: The developed method classifies various brain tissues and detects MS lesions with over 90% accuracy and specificity, and 62%–65% sensitivity, on average. Conclusions: Segmentation of different brain tissues using our proposed algorithm results in superior MS lesion-detection accuracy, comparable with the recent deep-learning classification results in the literature.