An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification.
The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease....
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 8; pp. 1379 - 1391 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2018
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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=130773114&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130773114 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2018 vid: 56 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 130773114 130773114 NLM29318442 10.1007/s11517-017-1771-2 NLM29318442 130773114 ppf: 1379 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification. aug: au: Marin, D. Gegundez-Arias, M. E. Ponte, B. Alvarez, F. Garrido, J. Ortega, C. Vasallo, M. J. Bravo, J. M. affil: Department of Electronic, Computer Science and Automatic Engineering, University of Huelva, Huelva, Spain sug: subj: Macular Degeneration Diagnosis Image Processing, Computer Assisted Algorithms Diabetic Retinopathy Diagnosis Exudates and Transudates ROC Curve Sensitivity and Specificity Automation Risk Factors Arthritis Impact Measurement Scales Scales ab: The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme. Graphical Abstract Diagnosis system of risk of diabetic macular edema (DME) based on exudate (Ex) detection in fundus images. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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