Bioinspired Inference System for MR Image Segmentation and Multiple Sclerosis Detection

View Sample PDF

Author(s): Hakima Zouaoui (Ferhat Abbas University, Algeria)and Abdelouahab Moussaoui (Ferhat Abbas University, Sétif, Algeria)
Copyright: 2023
Pages: 22
Source title: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch032

Keywords: Knowledge Management and Library Science / Medical Diagnosis and Practice / Medical Imaging / Medical Information Science Reference

Purchase

View Bioinspired Inference System for MR Image Segmentation and Multiple Sclerosis Detection on the publisher's website for pricing and purchasing information.

Abstract

Multiple sclerosis (MS) is a chronic autoimmune and inflammatory disease affecting the central nervous system (CNS). Magnetic resonance imaging (MRI) provides sufficient imaging contrast to visualize and detect MS lesions, particularly those in the white matter (WM). A robust and precise segmentation of WM lesions from MRI provide essential information about the disease status and evolution. The proposed FPSOPCM segmentation algorithm included an initial segmentation step using fuzzy particle swarm optimization (FPSO). After extraction of WM, atypical data (outliers) is eliminated using possibilistic C-means (PCM) algorithm, and finally, a Mamdani-type fuzzy model was applied to identify MS. The objective of the work presented in this paper is to obtain an improved accuracy in segmentation of MR images for MS detection.

The IRMA Community

Research IRM

Bioinspired Inference System for MR Image Segmentation and Multiple Sclerosis Detection

Purchase

Abstract

Related Content

IRMA Sponsors