NDLI: Review of advanced computational approaches on multiple sclerosis segmentation and classification

Content Provider	IET Digital Library
Author	Shanmuganathan, Manimurugan Almutairi, Saad Aborokbah, Majed Mohammed Ganesan, Subramaniam Ramachandran, Varatharajan
Abstract	In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely; therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shapes and sizes which can be very difficult to identify based on anatomical positions in various subjects. This can be determined by precise segmentation; manual segmentation would be very difficult to perform as it requires high level knowledge which takes additional time. Inter- and intra-expert variability need to be determined in order to perform the automated segmentation of lesions. The principal aim of this survey effort is to provide an analysis of the different categorization and segmentation methods and their techniques. This survey work will be valuable for researchers working in MS by considering and carefully evaluating the past work. The benefits and drawbacks of existing techniques are reviewed and the issue of MS lesion segmentation and classification is elucidated.
Starting Page	333
Ending Page	341
Page Count	9
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 6, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0543
Journal	IET Signal Processing
Publisher Date	2020-03-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Segmentation Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Brain Classification Method Computer Vision And Image Processing Technique Image Segmentation Lesion Development Level Magnetic Resonance Image Magnetic Resonance Imaging Manual Segmentation Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy MS Lesion Segmentation MS Lesions Multiple Sclerosis Classification Multiple Sclerosis Segmentation Neurophysiology Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Precise Segmentation Sample Lesions Segmentation Process Spectroscopy Survey Work
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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