Cargando…

Modified distance regularized level set evolution for brain ventricles segmentation

Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in...

Descripción completa

Detalles Bibliográficos
Autores principales: Jayaraman, Thirumagal, Reddy M., Sravan, Mahadevappa, Manjunatha, Sadhu, Anup, Dutta, Pranab Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719594/
https://www.ncbi.nlm.nih.gov/pubmed/33283254
http://dx.doi.org/10.1186/s42492-020-00064-8
_version_ 1783619710553686016
author Jayaraman, Thirumagal
Reddy M., Sravan
Mahadevappa, Manjunatha
Sadhu, Anup
Dutta, Pranab Kumar
author_facet Jayaraman, Thirumagal
Reddy M., Sravan
Mahadevappa, Manjunatha
Sadhu, Anup
Dutta, Pranab Kumar
author_sort Jayaraman, Thirumagal
collection PubMed
description Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%, specificity in the range of 98%–99%, and accuracy in the range of 95%–98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.
format Online
Article
Text
id pubmed-7719594
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Springer Singapore
record_format MEDLINE/PubMed
spelling pubmed-77195942020-12-11 Modified distance regularized level set evolution for brain ventricles segmentation Jayaraman, Thirumagal Reddy M., Sravan Mahadevappa, Manjunatha Sadhu, Anup Dutta, Pranab Kumar Vis Comput Ind Biomed Art Original Article Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%, specificity in the range of 98%–99%, and accuracy in the range of 95%–98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal. Springer Singapore 2020-12-07 /pmc/articles/PMC7719594/ /pubmed/33283254 http://dx.doi.org/10.1186/s42492-020-00064-8 Text en © The Author(s) 2020 Open AccessThis 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/.
spellingShingle Original Article
Jayaraman, Thirumagal
Reddy M., Sravan
Mahadevappa, Manjunatha
Sadhu, Anup
Dutta, Pranab Kumar
Modified distance regularized level set evolution for brain ventricles segmentation
title Modified distance regularized level set evolution for brain ventricles segmentation
title_full Modified distance regularized level set evolution for brain ventricles segmentation
title_fullStr Modified distance regularized level set evolution for brain ventricles segmentation
title_full_unstemmed Modified distance regularized level set evolution for brain ventricles segmentation
title_short Modified distance regularized level set evolution for brain ventricles segmentation
title_sort modified distance regularized level set evolution for brain ventricles segmentation
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719594/
https://www.ncbi.nlm.nih.gov/pubmed/33283254
http://dx.doi.org/10.1186/s42492-020-00064-8
work_keys_str_mv AT jayaramanthirumagal modifieddistanceregularizedlevelsetevolutionforbrainventriclessegmentation
AT reddymsravan modifieddistanceregularizedlevelsetevolutionforbrainventriclessegmentation
AT mahadevappamanjunatha modifieddistanceregularizedlevelsetevolutionforbrainventriclessegmentation
AT sadhuanup modifieddistanceregularizedlevelsetevolutionforbrainventriclessegmentation
AT duttapranabkumar modifieddistanceregularizedlevelsetevolutionforbrainventriclessegmentation