Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images

dc.creatorPires R.
dc.creatorJelinek H.F.
dc.creatorWainer J.
dc.creatorValle E.
dc.creatorRocha A.
dc.date2014
dc.date2015-06-25T17:55:11Z
dc.date2015-11-26T14:37:20Z
dc.date2015-06-25T17:55:11Z
dc.date2015-11-26T14:37:20Z
dc.date.accessioned2018-03-28T21:41:42Z
dc.date.available2018-03-28T21:41:42Z
dc.identifier
dc.identifierPlos One. Public Library Of Science, v. 9, n. 6, p. - , 2014.
dc.identifier19326203
dc.identifier10.1371/journal.pone.0096814
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84902336222&partnerID=40&md5=8702adb6e8178aaca68dbe0293a8108b
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/86779
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/86779
dc.identifier2-s2.0-84902336222
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1249116
dc.descriptionDiabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semisoft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2±2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors. © 2014 Pires et al.
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dc.languageen
dc.publisherPublic Library of Science
dc.relationPLoS ONE
dc.rightsaberto
dc.sourceScopus
dc.titleAdvancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images
dc.typeArtículos de revistas


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