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Label fusion method combining pixel greyscale probability for brain MR segmentation
Multi-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a label fusion method that incorporates pixel greyscale probability information. The propo...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6884484/ https://www.ncbi.nlm.nih.gov/pubmed/31784630 http://dx.doi.org/10.1038/s41598-019-54527-x |
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author | Wang, Monan Li, Pengcheng |
author_facet | Wang, Monan Li, Pengcheng |
author_sort | Wang, Monan |
collection | PubMed |
description | Multi-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a label fusion method that incorporates pixel greyscale probability information. The proposed method combines the advantages of label fusion methods based on sparse representation (SRLF) and weighted voting methods using patch similarity weights (PSWV) and introduces pixel greyscale probability information to improve the segmentation accuracy. We apply the proposed method to the segmentation of deep brain tissues in challenging 3D brain MR images from publicly available IBSR datasets, including images of the thalamus, hippocampus, caudate, putamen, pallidum and amygdala. The experimental results show that the proposed method has higher segmentation accuracy and robustness than the related methods. Compared with the state-of-the-art methods, the proposed method obtains the best putamen, pallidum and amygdala segmentation results and hippocampus and caudate segmentation results that are similar to those of the comparison methods. |
format | Online Article Text |
id | pubmed-6884484 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68844842019-12-06 Label fusion method combining pixel greyscale probability for brain MR segmentation Wang, Monan Li, Pengcheng Sci Rep Article Multi-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a label fusion method that incorporates pixel greyscale probability information. The proposed method combines the advantages of label fusion methods based on sparse representation (SRLF) and weighted voting methods using patch similarity weights (PSWV) and introduces pixel greyscale probability information to improve the segmentation accuracy. We apply the proposed method to the segmentation of deep brain tissues in challenging 3D brain MR images from publicly available IBSR datasets, including images of the thalamus, hippocampus, caudate, putamen, pallidum and amygdala. The experimental results show that the proposed method has higher segmentation accuracy and robustness than the related methods. Compared with the state-of-the-art methods, the proposed method obtains the best putamen, pallidum and amygdala segmentation results and hippocampus and caudate segmentation results that are similar to those of the comparison methods. Nature Publishing Group UK 2019-11-29 /pmc/articles/PMC6884484/ /pubmed/31784630 http://dx.doi.org/10.1038/s41598-019-54527-x Text en © The Author(s) 2019 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wang, Monan Li, Pengcheng Label fusion method combining pixel greyscale probability for brain MR segmentation |
title | Label fusion method combining pixel greyscale probability for brain MR segmentation |
title_full | Label fusion method combining pixel greyscale probability for brain MR segmentation |
title_fullStr | Label fusion method combining pixel greyscale probability for brain MR segmentation |
title_full_unstemmed | Label fusion method combining pixel greyscale probability for brain MR segmentation |
title_short | Label fusion method combining pixel greyscale probability for brain MR segmentation |
title_sort | label fusion method combining pixel greyscale probability for brain mr segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6884484/ https://www.ncbi.nlm.nih.gov/pubmed/31784630 http://dx.doi.org/10.1038/s41598-019-54527-x |
work_keys_str_mv | AT wangmonan labelfusionmethodcombiningpixelgreyscaleprobabilityforbrainmrsegmentation AT lipengcheng labelfusionmethodcombiningpixelgreyscaleprobabilityforbrainmrsegmentation |