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Self-parameterized active contours based on regional edge structure for medical image segmentation

This work introduces a novel framework for unsupervised parameterization of region-based active contour regularization and data fidelity terms, which is applied for medical image segmentation. The work aims to relieve MDs from the laborious, time-consuming task of empirical parameterization and bols...

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Detalles Bibliográficos
Autores principales: Mylona, Eleftheria A, Savelonas, Michalis A, Maroulis, Dimitris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4141071/
https://www.ncbi.nlm.nih.gov/pubmed/25152851
http://dx.doi.org/10.1186/2193-1801-3-424
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author Mylona, Eleftheria A
Savelonas, Michalis A
Maroulis, Dimitris
author_facet Mylona, Eleftheria A
Savelonas, Michalis A
Maroulis, Dimitris
author_sort Mylona, Eleftheria A
collection PubMed
description This work introduces a novel framework for unsupervised parameterization of region-based active contour regularization and data fidelity terms, which is applied for medical image segmentation. The work aims to relieve MDs from the laborious, time-consuming task of empirical parameterization and bolster the objectivity of the segmentation results. The proposed framework is inspired by an observed isomorphism between the eigenvalues of structure tensors and active contour parameters. Both may act as descriptors of the orientation coherence in regions containing edges. The experimental results demonstrate that the proposed framework maintains a high segmentation quality without the need of trial-and-error parameter adjustment.
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spelling pubmed-41410712014-08-22 Self-parameterized active contours based on regional edge structure for medical image segmentation Mylona, Eleftheria A Savelonas, Michalis A Maroulis, Dimitris Springerplus Research This work introduces a novel framework for unsupervised parameterization of region-based active contour regularization and data fidelity terms, which is applied for medical image segmentation. The work aims to relieve MDs from the laborious, time-consuming task of empirical parameterization and bolster the objectivity of the segmentation results. The proposed framework is inspired by an observed isomorphism between the eigenvalues of structure tensors and active contour parameters. Both may act as descriptors of the orientation coherence in regions containing edges. The experimental results demonstrate that the proposed framework maintains a high segmentation quality without the need of trial-and-error parameter adjustment. Springer International Publishing 2014-08-11 /pmc/articles/PMC4141071/ /pubmed/25152851 http://dx.doi.org/10.1186/2193-1801-3-424 Text en © Mylona et al.; licensee Springer. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
spellingShingle Research
Mylona, Eleftheria A
Savelonas, Michalis A
Maroulis, Dimitris
Self-parameterized active contours based on regional edge structure for medical image segmentation
title Self-parameterized active contours based on regional edge structure for medical image segmentation
title_full Self-parameterized active contours based on regional edge structure for medical image segmentation
title_fullStr Self-parameterized active contours based on regional edge structure for medical image segmentation
title_full_unstemmed Self-parameterized active contours based on regional edge structure for medical image segmentation
title_short Self-parameterized active contours based on regional edge structure for medical image segmentation
title_sort self-parameterized active contours based on regional edge structure for medical image segmentation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4141071/
https://www.ncbi.nlm.nih.gov/pubmed/25152851
http://dx.doi.org/10.1186/2193-1801-3-424
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