A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding

Abstract It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size...

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Autores principales:	Inyoung Bae, Jong-Hee Chae, Yeji Han
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b8a75ca265314cd0b83431c1e29b7bd9
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spelling	oai:doaj.org-article:b8a75ca265314cd0b83431c1e29b7bd92021-12-05T12:14:16ZA brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding10.1038/s41598-021-02722-02045-2322https://doaj.org/article/b8a75ca265314cd0b83431c1e29b7bd92021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02722-0https://doaj.org/toc/2045-2322Abstract It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from infant brain images. In this study, we propose a brain extraction algorithm for infant T2-weighted images. The proposed method utilizes histogram partitioning to separate brain regions from the background image. Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate masks for infant T2-weighted brain images can be generated. For validation, we applied the proposed algorithm and conventional methods to T2 infant images (0–24 months of age) acquired with 2D and 3D sequences at 3T MRI. The Dice coefficients and Precision scores, which were calculated as quantitative measures, showed the highest values for the proposed method as follows: For images acquired with a 2D imaging sequence, the average Dice coefficients were 0.9650 ± 0.006 for the proposed method, 0.9262 ± 0.006 for iBEAT, and 0.9490 ± 0.006 for BET. For the data acquired with a 3D imaging sequence, the average Dice coefficient was 0.9746 ± 0.008 for the proposed method, 0.9448 ± 0.004 for iBEAT, and 0.9622 ± 0.01 for BET. The average Precision was 0.9638 ± 0.009 and 0.9565 ± 0.016 for the proposed method, 0.8981 ± 0.01 and 0.8968 ± 0.008 for iBEAT, and 0.9346 ± 0.014 and 0.9282 ± 0.019 for BET for images acquired with 2D and 3D imaging sequences, respectively, demonstrating that the proposed method could be efficiently used for brain extraction in T2-weighted infant images.Inyoung BaeJong-Hee ChaeYeji HanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Inyoung Bae Jong-Hee Chae Yeji Han A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding
description	Abstract It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from infant brain images. In this study, we propose a brain extraction algorithm for infant T2-weighted images. The proposed method utilizes histogram partitioning to separate brain regions from the background image. Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate masks for infant T2-weighted brain images can be generated. For validation, we applied the proposed algorithm and conventional methods to T2 infant images (0–24 months of age) acquired with 2D and 3D sequences at 3T MRI. The Dice coefficients and Precision scores, which were calculated as quantitative measures, showed the highest values for the proposed method as follows: For images acquired with a 2D imaging sequence, the average Dice coefficients were 0.9650 ± 0.006 for the proposed method, 0.9262 ± 0.006 for iBEAT, and 0.9490 ± 0.006 for BET. For the data acquired with a 3D imaging sequence, the average Dice coefficient was 0.9746 ± 0.008 for the proposed method, 0.9448 ± 0.004 for iBEAT, and 0.9622 ± 0.01 for BET. The average Precision was 0.9638 ± 0.009 and 0.9565 ± 0.016 for the proposed method, 0.8981 ± 0.01 and 0.8968 ± 0.008 for iBEAT, and 0.9346 ± 0.014 and 0.9282 ± 0.019 for BET for images acquired with 2D and 3D imaging sequences, respectively, demonstrating that the proposed method could be efficiently used for brain extraction in T2-weighted infant images.
format	article
author	Inyoung Bae Jong-Hee Chae Yeji Han
author_facet	Inyoung Bae Jong-Hee Chae Yeji Han
author_sort	Inyoung Bae
title	A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding
title_short	A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding
title_full	A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding
title_fullStr	A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding
title_full_unstemmed	A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding
title_sort	brain extraction algorithm for infant t2 weighted magnetic resonance images based on fuzzy c-means thresholding
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b8a75ca265314cd0b83431c1e29b7bd9
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A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding

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