Unsupervised white matter lesion identification in multiple sclerosis (MS) using MRI segmentation and pattern classification: a novel approach with CVIPtools

Hanieh Ajami; Mahsa Kargar Nigjeh; Scott E. Umbaugh

doi:10.1117/12.2688268

4 October 2023 Unsupervised white matter lesion identification in multiple sclerosis (MS) using MRI segmentation and pattern classification: a novel approach with CVIPtools

Hanieh Ajami, Mahsa Kargar Nigjeh, Scott E. Umbaugh

Author Affiliations +

Proceedings Volume 12674, Applications of Digital Image Processing XLVI; 1267410 (2023) https://doi.org/10.1117/12.2688268
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Accurately identifying multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) of the brain and spinal cord is a challenging task due to variations in location, size, and shape, as well as anatomical differences among individuals. The number and volume of these lesions play a crucial role in assessing the severity of MS, monitoring disease progression, and evaluating the effectiveness of new drugs in clinical trials. Manual segmentation, while used previously, is not ideal due to its reliance on expert knowledge, time-consuming nature, and susceptibility to variations among different experts. To address these challenges, several automatic methods for segmenting MS lesions have been proposed. This research presents an innovative unsupervised methodology for the accurate identification and categorization of white matter lesions in MRI scans of patients with Multiple Sclerosis (MS). The methodology combines state-of-the-art computer vision-based image processing techniques, leveraging the powerful capabilities of CVIPtools. Through integration of preprocessing, segmentation, feature extraction, and pattern classification stages, our algorithm achieves over 90% accuracy in lesion detection and classification. Employing the K-Nearest Neighbor algorithm for pattern classification, the algorithm achieves a rate of 90.63% success for lesion classification and 93.33% success for non-lesion classification. This approach holds significant promise for enhancing the accuracy and efficacy of white matter lesion analysis, aiding in the early detection and monitoring of MS.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hanieh Ajami, Mahsa Kargar Nigjeh, and Scott E. Umbaugh "Unsupervised white matter lesion identification in multiple sclerosis (MS) using MRI segmentation and pattern classification: a novel approach with CVIPtools", Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 1267410 (4 October 2023); https://doi.org/10.1117/12.2688268

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Multiple sclerosis

Magnetic resonance imaging

Image segmentation

Image classification

Brain

Feature extraction

White matter

Show All Keywords

Keywords/Phrases

Search In:

Publication Years