A new computer vision-based approach to aid the diagnosis of Parkinson's disease
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , |
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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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 |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128339289833472 |