Staff View: Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy

Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy

White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurat...

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Main Authors:	Ong, Kokhaur, Young, David M., Sulaiman, Sarina, Shamsuddin, Siti Mariyam, Mohd. Zain, Norzaini Rose, Hashim, Hilwati, Yuen, Kahhay, Sanders, Stephan J., Yu, Weimiao, Hang, Seepheng
Format:	Article
Language:	English
Published:	Nature Research 2022
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/103972/1/HangSeePheng2022_DetectionofSubtleWhiteMatter.pdf http://eprints.utm.my/103972/ http://dx.doi.org/10.1038/s41598-022-07843-8
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spelling	my.utm.1039722023-12-11T01:46:31Z http://eprints.utm.my/103972/ Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy Ong, Kokhaur Young, David M. Sulaiman, Sarina Shamsuddin, Siti Mariyam Mohd. Zain, Norzaini Rose Hashim, Hilwati Yuen, Kahhay Sanders, Stephan J. Yu, Weimiao Hang, Seepheng QA Mathematics White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation (r = 0.895, p-value < 0.001), respectively, and outperforming three leading algorithms (Trimmed Mean Outlier Detection, Lesion Prediction Algorithm, and SALEM-LS) in five of the six established key metrics defined in the MICCAI Grand Challenge. By facilitating more accurate segmentation of subtle WML, this approach may enable earlier diagnosis and intervention. Nature Research 2022-12 Article PeerReviewed application/pdf en http://eprints.utm.my/103972/1/HangSeePheng2022_DetectionofSubtleWhiteMatter.pdf Ong, Kokhaur and Young, David M. and Sulaiman, Sarina and Shamsuddin, Siti Mariyam and Mohd. Zain, Norzaini Rose and Hashim, Hilwati and Yuen, Kahhay and Sanders, Stephan J. and Yu, Weimiao and Hang, Seepheng (2022) Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy. Scientific Reports, 12 (1). pp. 1-16. ISSN 2045-2322 http://dx.doi.org/10.1038/s41598-022-07843-8 DOI:10.1038/s41598-022-07843-8
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Ong, Kokhaur Young, David M. Sulaiman, Sarina Shamsuddin, Siti Mariyam Mohd. Zain, Norzaini Rose Hashim, Hilwati Yuen, Kahhay Sanders, Stephan J. Yu, Weimiao Hang, Seepheng Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy
description	White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation (r = 0.895, p-value < 0.001), respectively, and outperforming three leading algorithms (Trimmed Mean Outlier Detection, Lesion Prediction Algorithm, and SALEM-LS) in five of the six established key metrics defined in the MICCAI Grand Challenge. By facilitating more accurate segmentation of subtle WML, this approach may enable earlier diagnosis and intervention.
format	Article
author	Ong, Kokhaur Young, David M. Sulaiman, Sarina Shamsuddin, Siti Mariyam Mohd. Zain, Norzaini Rose Hashim, Hilwati Yuen, Kahhay Sanders, Stephan J. Yu, Weimiao Hang, Seepheng
author_facet	Ong, Kokhaur Young, David M. Sulaiman, Sarina Shamsuddin, Siti Mariyam Mohd. Zain, Norzaini Rose Hashim, Hilwati Yuen, Kahhay Sanders, Stephan J. Yu, Weimiao Hang, Seepheng
author_sort	Ong, Kokhaur
title	Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy
title_short	Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy
title_full	Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy
title_fullStr	Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy
title_full_unstemmed	Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy
title_sort	detection of subtle white matter lesions in mri through texture feature extraction and boundary delineation using an embedded clustering strategy
publisher	Nature Research
publishDate	2022
url	http://eprints.utm.my/103972/1/HangSeePheng2022_DetectionofSubtleWhiteMatter.pdf http://eprints.utm.my/103972/ http://dx.doi.org/10.1038/s41598-022-07843-8
_version_	1787132167109738496
score	13.23648

Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy

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