A new computer vision-based approach to aid the diagnosis of Parkinson's disease

Detalhes bibliográficos
Autor(a) principal: Pereira, Clayton R.
Data de Publicação: 2016
Outros Autores: Pereira, Danilo R., Silva, Francisco A., Masieiro, Joao P., Weber, Silke A. T. [UNESP], Hook, Christian, Papa, Joao P. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.cmpb.2016.08.005
http://hdl.handle.net/11449/165337
Resumo: Background and Objective: Even today, pointing out an exam that can diagnose a patient with Parkinson's disease (PD) accurately enough is not an easy task. Although a number of techniques have been used in search for a more precise method, detecting such illness and measuring its level of severity early enough to postpone its side effects are not straightforward. In this work, after reviewing a considerable number of works, we conclude that only a few techniques address the problem of PD recognition by means of micrography using computer vision techniques. Therefore, we consider the problem of aiding automatic PD diagnosis by means of spirals and meanders filled out in forms, which are then compared with the template for feature extraction. Methods: In our work, both the template and the drawings are identified and separated automatically using image processing techniques, thus needing no user intervention. Since we have no registered images, the idea is to obtain a suitable representation of both template and drawings using the very same approach for all images in a fast and accurate approach. Results: The results have shown that we can obtain very reasonable recognition rates (around approximate to 67%), with the most accurate class being the one represented by the patients, which outnumbered the control individuals in the proposed dataset. Conclusions: The proposed approach seemed to be suitable for aiding in automatic PD diagnosis by means of computer vision and machine learning techniques. Also, meander images play an important role, leading to higher accuracies than spiral images. We also observed that the main problem in detecting PD is the patients in the early stages, who can draw near-perfect objects, which are very similar to the ones made by control patients. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
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spelling A new computer vision-based approach to aid the diagnosis of Parkinson's diseaseParkinson's diseasePattern recognitionMicrographyBackground and Objective: Even today, pointing out an exam that can diagnose a patient with Parkinson's disease (PD) accurately enough is not an easy task. Although a number of techniques have been used in search for a more precise method, detecting such illness and measuring its level of severity early enough to postpone its side effects are not straightforward. In this work, after reviewing a considerable number of works, we conclude that only a few techniques address the problem of PD recognition by means of micrography using computer vision techniques. Therefore, we consider the problem of aiding automatic PD diagnosis by means of spirals and meanders filled out in forms, which are then compared with the template for feature extraction. Methods: In our work, both the template and the drawings are identified and separated automatically using image processing techniques, thus needing no user intervention. Since we have no registered images, the idea is to obtain a suitable representation of both template and drawings using the very same approach for all images in a fast and accurate approach. Results: The results have shown that we can obtain very reasonable recognition rates (around approximate to 67%), with the most accurate class being the one represented by the patients, which outnumbered the control individuals in the proposed dataset. Conclusions: The proposed approach seemed to be suitable for aiding in automatic PD diagnosis by means of computer vision and machine learning techniques. Also, meander images play an important role, leading to higher accuracies than spiral images. We also observed that the main problem in detecting PD is the patients in the early stages, who can draw near-perfect objects, which are very similar to the ones made by control patients. (C) 2016 Elsevier Ireland Ltd. All rights reserved.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed Sao Carlos, Dept Comp, BR-13560 Sao Carlos, SP, BrazilUniv Western Sao Paulo, Sao Paulo, SP, BrazilSao Paulo State Univ, Dept Ophthalmol & Otorhinolaryngol, Sao Paulo, SP, BrazilOstbayer Tech Hsch, Regensburg, GermanySao Paulo State Univ, Dept Comp, Sao Paulo, SP, BrazilSao Paulo State Univ, Dept Ophthalmol & Otorhinolaryngol, Sao Paulo, SP, BrazilSao Paulo State Univ, Dept Comp, Sao Paulo, SP, BrazilCAPES: PROCAD 2966/2014FAPESP: 2009/16206-1FAPESP: 2013/20387-7FAPESP: 2014/2014/16250-9CNPq: 303182/2011-3CNPq: 70571/2013-6CNPq: 306166/2014-3Elsevier B.V.Universidade Federal de São Carlos (UFSCar)Univ Western Sao PauloUniversidade Estadual Paulista (Unesp)Ostbayer Tech HschPereira, Clayton R.Pereira, Danilo R.Silva, Francisco A.Masieiro, Joao P.Weber, Silke A. T. [UNESP]Hook, ChristianPapa, Joao P. [UNESP]2018-11-27T21:47:45Z2018-11-27T21:47:45Z2016-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article79-+application/pdfhttp://dx.doi.org/10.1016/j.cmpb.2016.08.005Computer Methods And Programs In Biomedicine. Clare: Elsevier Ireland Ltd, v. 136, p. 79-+, 2016.0169-2607http://hdl.handle.net/11449/16533710.1016/j.cmpb.2016.08.005WOS:000385330400009WOS000385330400009.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputer Methods And Programs In Biomedicine0,786info:eu-repo/semantics/openAccess2024-04-23T16:10:42Zoai:repositorio.unesp.br:11449/165337Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:16:57.393247Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A new computer vision-based approach to aid the diagnosis of Parkinson's disease
title A new computer vision-based approach to aid the diagnosis of Parkinson's disease
spellingShingle A new computer vision-based approach to aid the diagnosis of Parkinson's disease
Pereira, Clayton R.
Parkinson's disease
Pattern recognition
Micrography
title_short A new computer vision-based approach to aid the diagnosis of Parkinson's disease
title_full A new computer vision-based approach to aid the diagnosis of Parkinson's disease
title_fullStr A new computer vision-based approach to aid the diagnosis of Parkinson's disease
title_full_unstemmed A new computer vision-based approach to aid the diagnosis of Parkinson's disease
title_sort A new computer vision-based approach to aid the diagnosis of Parkinson's disease
author Pereira, Clayton R.
author_facet Pereira, Clayton R.
Pereira, Danilo R.
Silva, Francisco A.
Masieiro, Joao P.
Weber, Silke A. T. [UNESP]
Hook, Christian
Papa, Joao P. [UNESP]
author_role author
author2 Pereira, Danilo R.
Silva, Francisco A.
Masieiro, Joao P.
Weber, Silke A. T. [UNESP]
Hook, Christian
Papa, Joao P. [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de São Carlos (UFSCar)
Univ Western Sao Paulo
Universidade Estadual Paulista (Unesp)
Ostbayer Tech Hsch
dc.contributor.author.fl_str_mv Pereira, Clayton R.
Pereira, Danilo R.
Silva, Francisco A.
Masieiro, Joao P.
Weber, Silke A. T. [UNESP]
Hook, Christian
Papa, Joao P. [UNESP]
dc.subject.por.fl_str_mv Parkinson's disease
Pattern recognition
Micrography
topic Parkinson's disease
Pattern recognition
Micrography
description Background and Objective: Even today, pointing out an exam that can diagnose a patient with Parkinson's disease (PD) accurately enough is not an easy task. Although a number of techniques have been used in search for a more precise method, detecting such illness and measuring its level of severity early enough to postpone its side effects are not straightforward. In this work, after reviewing a considerable number of works, we conclude that only a few techniques address the problem of PD recognition by means of micrography using computer vision techniques. Therefore, we consider the problem of aiding automatic PD diagnosis by means of spirals and meanders filled out in forms, which are then compared with the template for feature extraction. Methods: In our work, both the template and the drawings are identified and separated automatically using image processing techniques, thus needing no user intervention. Since we have no registered images, the idea is to obtain a suitable representation of both template and drawings using the very same approach for all images in a fast and accurate approach. Results: The results have shown that we can obtain very reasonable recognition rates (around approximate to 67%), with the most accurate class being the one represented by the patients, which outnumbered the control individuals in the proposed dataset. Conclusions: The proposed approach seemed to be suitable for aiding in automatic PD diagnosis by means of computer vision and machine learning techniques. Also, meander images play an important role, leading to higher accuracies than spiral images. We also observed that the main problem in detecting PD is the patients in the early stages, who can draw near-perfect objects, which are very similar to the ones made by control patients. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
publishDate 2016
dc.date.none.fl_str_mv 2016-11-01
2018-11-27T21:47:45Z
2018-11-27T21:47:45Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.cmpb.2016.08.005
Computer Methods And Programs In Biomedicine. Clare: Elsevier Ireland Ltd, v. 136, p. 79-+, 2016.
0169-2607
http://hdl.handle.net/11449/165337
10.1016/j.cmpb.2016.08.005
WOS:000385330400009
WOS000385330400009.pdf
url http://dx.doi.org/10.1016/j.cmpb.2016.08.005
http://hdl.handle.net/11449/165337
identifier_str_mv Computer Methods And Programs In Biomedicine. Clare: Elsevier Ireland Ltd, v. 136, p. 79-+, 2016.
0169-2607
10.1016/j.cmpb.2016.08.005
WOS:000385330400009
WOS000385330400009.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Computer Methods And Programs In Biomedicine
0,786
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 79-+
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
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repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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