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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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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924181/
https://www.ncbi.nlm.nih.gov/pubmed/35292654
http://dx.doi.org/10.1038/s41598-022-07843-8
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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
collection PubMed
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.
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spelling pubmed-89241812022-03-17 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 Sci Rep Article 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 Publishing Group UK 2022-03-15 /pmc/articles/PMC8924181/ /pubmed/35292654 http://dx.doi.org/10.1038/s41598-022-07843-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
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
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924181/
https://www.ncbi.nlm.nih.gov/pubmed/35292654
http://dx.doi.org/10.1038/s41598-022-07843-8
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