Automated drusen detection in retinal images using analytical modelling algorithms.
Background: Drusen are common features in the ageing macula associated with exudative Age-Related Macular Degeneration (ARMD). They are visible in retinal images and their quantitative analysis is important in the follow up of the ARMD. However, their evaluation is fastidious and difficult to reprod...
| Publicado en: | BioMedical Engineering OnLine Vol. 10; no. 1; pp. 59 - 75 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=63884111&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 63884111 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 2011 vid: 10 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 63884111 63884111 NLM21749717 63884111 10.1186/1475-925X-10-59 NLM21749717 63884111 ppf: 59 ppct: 16 formats: tig: atl: Automated drusen detection in retinal images using analytical modelling algorithms. aug: au: Mora, André D. Vieira, Pedro M. Manivannan, Ayyakkannu Fonseca, José M. Mora, André D Fonseca, José M affil: Center of Technologies and Systems, Uninova, Campus da FCT-UNL, 2829-516 Caparica, Portugal sug: subj: Retinal Diseases Diagnosis Macular Degeneration Pathology Algorithms Image Processing, Computer Assisted Methods Macular Degeneration Diagnosis Models, Biological Reproducibility of Results Retina Pathology Diagnosis, Eye Retina Human Sensitivity and Specificity Comparative Studies Multicenter Studies Evaluation Research Validation Studies Clinical Assessment Tools Scales ab: Background: Drusen are common features in the ageing macula associated with exudative Age-Related Macular Degeneration (ARMD). They are visible in retinal images and their quantitative analysis is important in the follow up of the ARMD. However, their evaluation is fastidious and difficult to reproduce when performed manually.Methods: This article proposes a methodology for Automatic Drusen Deposits Detection and quantification in Retinal Images (AD3RI) by using digital image processing techniques. It includes an image pre-processing method to correct the uneven illumination and to normalize the intensity contrast with smoothing splines. The drusen detection uses a gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. The detected drusen are then fitted by Modified Gaussian functions, producing a model of the image that is used to evaluate the affected area.Twenty two images were graded by eight experts, with the aid of a custom made software and compared with AD3RI. This comparison was based both on the total area and on the pixel-to-pixel analysis. The coefficient of variation, the intraclass correlation coefficient, the sensitivity, the specificity and the kappa coefficient were calculated.Results: The ground truth used in this study was the experts' average grading. In order to evaluate the proposed methodology three indicators were defined: AD3RI compared to the ground truth (A2G); each expert compared to the other experts (E2E) and a standard Global Threshold method compared to the ground truth (T2G).The results obtained for the three indicators, A2G, E2E and T2G, were: coefficient of variation 28.8 %, 22.5 % and 41.1 %, intraclass correlation coefficient 0.92, 0.88 and 0.67, sensitivity 0.68, 0.67 and 0.74, specificity 0.96, 0.97 and 0.94, and kappa coefficient 0.58, 0.60 and 0.49, respectively.Conclusions: The gradings produced by AD3RI obtained an agreement with the ground truth similar to the experts (with a higher reproducibility) and significantly better than the Threshold Method. Despite the higher sensitivity of the Threshold method, explained by its over segmentation bias, it has lower specificity and lower kappa coefficient. Therefore, it can be concluded that AD3RI accurately quantifies drusen, using a reproducible method with benefits for ARMD evaluation and follow-up. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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