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A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region...

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Autores principales: Haleem, Muhammad Salman, Han, Liangxiu, Hemert, Jano van, Li, Baihua, Fleming, Alan, Pasquale, Louis R., Song, Brian J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719827/
https://www.ncbi.nlm.nih.gov/pubmed/29218460
http://dx.doi.org/10.1007/s10916-017-0859-4
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author Haleem, Muhammad Salman
Han, Liangxiu
Hemert, Jano van
Li, Baihua
Fleming, Alan
Pasquale, Louis R.
Song, Brian J.
author_facet Haleem, Muhammad Salman
Han, Liangxiu
Hemert, Jano van
Li, Baihua
Fleming, Alan
Pasquale, Louis R.
Song, Brian J.
author_sort Haleem, Muhammad Salman
collection PubMed
description This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.
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spelling pubmed-57198272017-12-11 A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis Haleem, Muhammad Salman Han, Liangxiu Hemert, Jano van Li, Baihua Fleming, Alan Pasquale, Louis R. Song, Brian J. J Med Syst Image & Signal Processing This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis. Springer US 2017-12-07 2018 /pmc/articles/PMC5719827/ /pubmed/29218460 http://dx.doi.org/10.1007/s10916-017-0859-4 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.
spellingShingle Image & Signal Processing
Haleem, Muhammad Salman
Han, Liangxiu
Hemert, Jano van
Li, Baihua
Fleming, Alan
Pasquale, Louis R.
Song, Brian J.
A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis
title A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis
title_full A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis
title_fullStr A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis
title_full_unstemmed A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis
title_short A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis
title_sort novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis
topic Image & Signal Processing
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719827/
https://www.ncbi.nlm.nih.gov/pubmed/29218460
http://dx.doi.org/10.1007/s10916-017-0859-4
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