A novel combined level set model for automatic MR image segmentation

Medical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The...

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Autores principales:	Li Jianzhang, Nebelung Sven, Rath Björn, Tingart Markus, Eschweiler Jörg
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	level set method mri segmentation intensity inhomogeneity Medicine R
Acceso en línea:	https://doaj.org/article/7e84993e1fd945a09f88c303acc1fb33
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id	oai:doaj.org-article:7e84993e1fd945a09f88c303acc1fb33
record_format	dspace
spelling	oai:doaj.org-article:7e84993e1fd945a09f88c303acc1fb332021-12-05T14:10:42ZA novel combined level set model for automatic MR image segmentation2364-550410.1515/cdbme-2020-3006https://doaj.org/article/7e84993e1fd945a09f88c303acc1fb332020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3006https://doaj.org/toc/2364-5504Medical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The flexible integration of energy terms affords the level set method to deal with variable difficulties. In this paper, we introduce a novel combined level set model that mainly cooperates with an edge detector and a local region intensity descriptor. The noise and intensity inhomogeneities are eliminated by the local region intensity descriptor. The edge detector helps the level set model to locate the object boundaries more precisely. The proposed model was validated on synthesized images and magnetic resonance images of in vivo wrist bones. Comparing with the ground truth, the proposed method reached a Dice similarity coefficient of > 0.99 on all image tests, while the compared segmentation approaches failed the segmentations. The presented combined level set model can be used for the object segmentation in magnetic resonance images.Li JianzhangNebelung SvenRath BjörnTingart MarkusEschweiler JörgDe Gruyterarticlelevel set methodmrisegmentationintensity inhomogeneityMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 20-23 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	level set method mri segmentation intensity inhomogeneity Medicine R
spellingShingle	level set method mri segmentation intensity inhomogeneity Medicine R Li Jianzhang Nebelung Sven Rath Björn Tingart Markus Eschweiler Jörg A novel combined level set model for automatic MR image segmentation
description	Medical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The flexible integration of energy terms affords the level set method to deal with variable difficulties. In this paper, we introduce a novel combined level set model that mainly cooperates with an edge detector and a local region intensity descriptor. The noise and intensity inhomogeneities are eliminated by the local region intensity descriptor. The edge detector helps the level set model to locate the object boundaries more precisely. The proposed model was validated on synthesized images and magnetic resonance images of in vivo wrist bones. Comparing with the ground truth, the proposed method reached a Dice similarity coefficient of > 0.99 on all image tests, while the compared segmentation approaches failed the segmentations. The presented combined level set model can be used for the object segmentation in magnetic resonance images.
format	article
author	Li Jianzhang Nebelung Sven Rath Björn Tingart Markus Eschweiler Jörg
author_facet	Li Jianzhang Nebelung Sven Rath Björn Tingart Markus Eschweiler Jörg
author_sort	Li Jianzhang
title	A novel combined level set model for automatic MR image segmentation
title_short	A novel combined level set model for automatic MR image segmentation
title_full	A novel combined level set model for automatic MR image segmentation
title_fullStr	A novel combined level set model for automatic MR image segmentation
title_full_unstemmed	A novel combined level set model for automatic MR image segmentation
title_sort	novel combined level set model for automatic mr image segmentation
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/7e84993e1fd945a09f88c303acc1fb33
work_keys_str_mv	AT lijianzhang anovelcombinedlevelsetmodelforautomaticmrimagesegmentation AT nebelungsven anovelcombinedlevelsetmodelforautomaticmrimagesegmentation AT rathbjorn anovelcombinedlevelsetmodelforautomaticmrimagesegmentation AT tingartmarkus anovelcombinedlevelsetmodelforautomaticmrimagesegmentation AT eschweilerjorg anovelcombinedlevelsetmodelforautomaticmrimagesegmentation AT lijianzhang novelcombinedlevelsetmodelforautomaticmrimagesegmentation AT nebelungsven novelcombinedlevelsetmodelforautomaticmrimagesegmentation AT rathbjorn novelcombinedlevelsetmodelforautomaticmrimagesegmentation AT tingartmarkus novelcombinedlevelsetmodelforautomaticmrimagesegmentation AT eschweilerjorg novelcombinedlevelsetmodelforautomaticmrimagesegmentation
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A novel combined level set model for automatic MR image segmentation

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