Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images
Descripción del Articulo
The authors would like to thank to SEDIMED (a medical diagnosis center company in Peru) who supported with a medical images database. This work was partially funded by the Fondos para la Innovacion, Ciencia y Tecnologia (FINCyT-Peru) under contract 142-10-PITEA-FINCyT and CONCYTEC-Peru with STIC-AmS...
| Autores: | , , |
|---|---|
| Formato: | objeto de conferencia |
| Fecha de Publicación: | 2014 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/1222 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/1222 https://doi.org/10.1109/CBMS.2014.110 |
| Nivel de acceso: | acceso abierto |
| Materia: | svm cbir pattern recognition computer aided diagnosis https://purl.org/pe-repo/ocde/ford#1.02.01 |
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Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images |
| title |
Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images |
| spellingShingle |
Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images Gutierrez-Caceres, J svm cbir pattern recognition computer aided diagnosis https://purl.org/pe-repo/ocde/ford#1.02.01 |
| title_short |
Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images |
| title_full |
Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images |
| title_fullStr |
Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images |
| title_full_unstemmed |
Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images |
| title_sort |
Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images |
| author |
Gutierrez-Caceres, J |
| author_facet |
Gutierrez-Caceres, J Portugal-Zambrano, C Beltran-Castanon, C |
| author_role |
author |
| author2 |
Portugal-Zambrano, C Beltran-Castanon, C |
| author2_role |
author author |
| dc.contributor.author.fl_str_mv |
Gutierrez-Caceres, J Portugal-Zambrano, C Beltran-Castanon, C |
| dc.subject.none.fl_str_mv |
svm |
| topic |
svm cbir pattern recognition computer aided diagnosis https://purl.org/pe-repo/ocde/ford#1.02.01 |
| dc.subject.es_PE.fl_str_mv |
cbir pattern recognition computer aided diagnosis |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#1.02.01 |
| description |
The authors would like to thank to SEDIMED (a medical diagnosis center company in Peru) who supported with a medical images database. This work was partially funded by the Fondos para la Innovacion, Ciencia y Tecnologia (FINCyT-Peru) under contract 142-10-PITEA-FINCyT and CONCYTEC-Peru with STIC-AmSud 2013 under the FERMI project. |
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2014 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2014 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
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conferenceObject |
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https://hdl.handle.net/20.500.12390/1222 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1109/CBMS.2014.110 |
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345222200110 |
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https://hdl.handle.net/20.500.12390/1222 https://doi.org/10.1109/CBMS.2014.110 |
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345222200110 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
| dc.publisher.none.fl_str_mv |
IEEE Xplore |
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IEEE Xplore |
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reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
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Consejo Nacional de Ciencia Tecnología e Innovación |
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CONCYTEC |
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CONCYTEC |
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CONCYTEC-Institucional |
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CONCYTEC-Institucional |
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Repositorio Institucional CONCYTEC |
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repositorio@concytec.gob.pe |
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1844882993557012480 |
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Publicationrp03485600rp01694500rp00689500Gutierrez-Caceres, JPortugal-Zambrano, CBeltran-Castanon, C2024-05-30T23:13:38Z2024-05-30T23:13:38Z2014https://hdl.handle.net/20.500.12390/1222https://doi.org/10.1109/CBMS.2014.110345222200110The authors would like to thank to SEDIMED (a medical diagnosis center company in Peru) who supported with a medical images database. This work was partially funded by the Fondos para la Innovacion, Ciencia y Tecnologia (FINCyT-Peru) under contract 142-10-PITEA-FINCyT and CONCYTEC-Peru with STIC-AmSud 2013 under the FERMI project.This work presents a model to support medical diagnosis through the classification of abnormality normality in medical brain images, in order to help to specialist as a previous step in the brain pathology diagnosis. Our proposal was incorporated into a content-based image retrieval system, thus we developed a useful tool for radiologists. The first step produces the features vector of MR image using Gabor Filter for the data train and test, then as second step features vector of training data are indexed into CBIR module. The third step makes the training of SVM and as four step the test dataset is classified with the SVM trained. Finally, the result of classification are presented with a set of similar images product of a KNN query. This model was implemented as a software tool with graphical interface. We obtained 94.12% of correct classification. Our medical image dataset is composed of 187 MRI images collected from a medical diagnosis company and selected by medical specialist. The result shows that the proposed model is robust and effective as a software tool to aid support to medical diagnostic.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengIEEE Xploreinfo:eu-repo/semantics/openAccesssvmcbir-1pattern recognition-1computer aided diagnosis-1https://purl.org/pe-repo/ocde/ford#1.02.01-1Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Imagesinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/1222oai:repositorio.concytec.gob.pe:20.500.12390/12222024-05-30 15:45:57.036http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="84423681-92c9-4cff-a8b7-7442276ddfa5"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2014</PublicationDate> <DOI>https://doi.org/10.1109/CBMS.2014.110</DOI> <ISI-Number>345222200110</ISI-Number> <Authors> <Author> <DisplayName>Gutierrez-Caceres, J</DisplayName> <Person id="rp03485" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Portugal-Zambrano, C</DisplayName> <Person id="rp01694" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Beltran-Castanon, C</DisplayName> <Person id="rp00689" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>IEEE Xplore</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>svm</Keyword> <Keyword>cbir</Keyword> <Keyword>pattern recognition</Keyword> <Keyword>computer aided diagnosis</Keyword> <Abstract>This work presents a model to support medical diagnosis through the classification of abnormality normality in medical brain images, in order to help to specialist as a previous step in the brain pathology diagnosis. Our proposal was incorporated into a content-based image retrieval system, thus we developed a useful tool for radiologists. The first step produces the features vector of MR image using Gabor Filter for the data train and test, then as second step features vector of training data are indexed into CBIR module. The third step makes the training of SVM and as four step the test dataset is classified with the SVM trained. Finally, the result of classification are presented with a set of similar images product of a KNN query. This model was implemented as a software tool with graphical interface. We obtained 94.12% of correct classification. Our medical image dataset is composed of 187 MRI images collected from a medical diagnosis company and selected by medical specialist. The result shows that the proposed model is robust and effective as a software tool to aid support to medical diagnostic.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
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13.394457 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
