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Semi-automatic active contour-based segmentation to remove eyes, meninges, and skull from MRI

Semi-automatic active contour-based segmentation to remove eyes, meninges, and skull from MRI PurposeBrain imaging acquisition can present different issues, such as noisy images which can result in a problematic diagnosis. Image preparation such as skull stripping and region segmentation is a fundamental step in order to support a better medical diagnosis outcome. Therefore, this study presented a segmentation technique based on the active contour model to perform skull stripping.MethodsThe method is applied on the neuroimaging database available by the OASIS neuroimaging dataset. The method proposed here uses active contour model followed by k-means clustering technique in order to converge to a locally minimal energy value which can be equivalent to the brain tissue area aiming to avoid loss of image quality and brain structures. Statistical analysis was also performed in order to determine how image texture characteristics were affected.ResultsThe active contour method achieved results within the ones presented on the state-of-art values segmentation methods with 96.4% of sensitivity and 96% of specificity using only 4 k-means clusters. Image texture characteristics such as entropy and correlation presented values of 1.8804 and 0.96, respectively.ConclusionThe high entropy value accentuated the gray-level contrast and highlighted anatomical structures for brain visualization. These high evaluation scores demonstrate that the semi-automatic contour-based segmentation algorithm is a powerful tool for segmentation and skull-stripping decreasing loss of image quality and brain structures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Semi-automatic active contour-based segmentation to remove eyes, meninges, and skull from MRI

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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2020
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-020-00066-8
Publisher site
See Article on Publisher Site

Abstract

PurposeBrain imaging acquisition can present different issues, such as noisy images which can result in a problematic diagnosis. Image preparation such as skull stripping and region segmentation is a fundamental step in order to support a better medical diagnosis outcome. Therefore, this study presented a segmentation technique based on the active contour model to perform skull stripping.MethodsThe method is applied on the neuroimaging database available by the OASIS neuroimaging dataset. The method proposed here uses active contour model followed by k-means clustering technique in order to converge to a locally minimal energy value which can be equivalent to the brain tissue area aiming to avoid loss of image quality and brain structures. Statistical analysis was also performed in order to determine how image texture characteristics were affected.ResultsThe active contour method achieved results within the ones presented on the state-of-art values segmentation methods with 96.4% of sensitivity and 96% of specificity using only 4 k-means clusters. Image texture characteristics such as entropy and correlation presented values of 1.8804 and 0.96, respectively.ConclusionThe high entropy value accentuated the gray-level contrast and highlighted anatomical structures for brain visualization. These high evaluation scores demonstrate that the semi-automatic contour-based segmentation algorithm is a powerful tool for segmentation and skull-stripping decreasing loss of image quality and brain structures.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Sep 4, 2020

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