A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

[EN] Edge detection in medical imaging is a significant task for object recognition of human organs and is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an efficient approach based on generalized Hill entropy to find a good solution for dete...

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Detalles Bibliográficos
Autores: Elaraby, A, Moratal, David|||0000-0002-2825-3646
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/108351
Acceso en línea:https://riunet.upv.es/handle/10251/108351
Access Level:acceso abierto
Palabra clave:Image edge detection
Hill entropy
Thresholding
Canny edge detection
Medical imaging
Image analysis
TECNOLOGIA ELECTRONICA
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spelling A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detectionElaraby, AMoratal, David|||0000-0002-2825-3646Image edge detectionHill entropyThresholdingCanny edge detectionMedical imagingImage analysisTECNOLOGIA ELECTRONICA[EN] Edge detection in medical imaging is a significant task for object recognition of human organs and is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an efficient approach based on generalized Hill entropy to find a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: firstly, a global threshold calculated by means of generalized Hill entropy is used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The final edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared to Canny and Tsallis entropy using sets of medical images corrupted by various types of noise. We used Pratt's Figure Of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicated that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four different types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images. (c) 2017 Sharif University of Technology. All rights reserved.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and by FEDER funds under Grant BFU2015-64380-C2-2-R.ElsevierDepartamento de Ingeniería ElectrónicaCentro de Biomateriales e Ingeniería TisularEscuela Técnica Superior de Ingeniería IndustrialMinisterio de Economía, Industria y CompetitividadRepositorio Institucional de la Universitat Politècnica de València Riunet20172017-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/108351reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 BFU2015-64380-C2-2-R ANALISIS DE TEXTURAS EN IMAGEN CEREBRAL MULTIMODAL POR RESONANCIA MAGNETICA PARA UNA DETECCION TEMPRANA DE ALTERACIONES EN LA RED Y BIOMARCADORES DE ENFERMEDADopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1083512026-06-13T07:49:27Z
dc.title.none.fl_str_mv A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
title A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
spellingShingle A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
Elaraby, A
Image edge detection
Hill entropy
Thresholding
Canny edge detection
Medical imaging
Image analysis
TECNOLOGIA ELECTRONICA
title_short A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
title_full A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
title_fullStr A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
title_full_unstemmed A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
title_sort A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
dc.creator.none.fl_str_mv Elaraby, A
Moratal, David|||0000-0002-2825-3646
author Elaraby, A
author_facet Elaraby, A
Moratal, David|||0000-0002-2825-3646
author_role author
author2 Moratal, David|||0000-0002-2825-3646
author2_role author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Electrónica
Centro de Biomateriales e Ingeniería Tisular
Escuela Técnica Superior de Ingeniería Industrial
Ministerio de Economía, Industria y Competitividad
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Image edge detection
Hill entropy
Thresholding
Canny edge detection
Medical imaging
Image analysis
TECNOLOGIA ELECTRONICA
topic Image edge detection
Hill entropy
Thresholding
Canny edge detection
Medical imaging
Image analysis
TECNOLOGIA ELECTRONICA
description [EN] Edge detection in medical imaging is a significant task for object recognition of human organs and is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an efficient approach based on generalized Hill entropy to find a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: firstly, a global threshold calculated by means of generalized Hill entropy is used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The final edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared to Canny and Tsallis entropy using sets of medical images corrupted by various types of noise. We used Pratt's Figure Of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicated that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four different types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images. (c) 2017 Sharif University of Technology. All rights reserved.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/108351
url https://riunet.upv.es/handle/10251/108351
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 BFU2015-64380-C2-2-R ANALISIS DE TEXTURAS EN IMAGEN CEREBRAL MULTIMODAL POR RESONANCIA MAGNETICA PARA UNA DETECCION TEMPRANA DE ALTERACIONES EN LA RED Y BIOMARCADORES DE ENFERMEDAD
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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