Screening for diabetic retinopathy using computer based image analysis and statistical classification
| Autor: | David Kerr, Bernhard Mogens Ege, David A. Cavan, Ole K. Hejlesen, Karina Torp Møller, Ole Vilhelm Larsen, Barry Jennings |
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| Jazyk: | angličtina |
| Rok vydání: | 2000 |
| Předmět: |
Mahalanobis distance
Biometry Diabetic Retinopathy business.industry Digital imaging Health Informatics Image processing Diabetic retinopathy medicine.disease Blindness Computer Science Applications Cotton wool spots Digital image Statistical classification Evaluation Studies as Topic Image Processing Computer-Assisted Medicine Optometry Humans Mass Screening Diagnosis Computer-Assisted medicine.symptom business Classifier (UML) Software |
| Zdroj: | Ege, B M, Hejlesen, O K, Larsen, O V, Møller, K, Jennings, B, Kerr, D & Cavan, D A 2000, ' Screening for diabetic retinopathy using computer based image analysis and statistical classification ', Computer Methods and Programs in Biomedicine, vol. 62, no. 3, pp. 165-175 . https://doi.org/10.1016/S0169-2607(00)00065-1 |
| Popis: | Diabetic retinopathy is one of the most common causes of blindness in Europe. However, efficient therapies do exist. An accurate and early diagnosis and correct application of treatment can prevent blindness in more than 50% of all cases. Digital imaging is becoming available as a means of screening for diabetic retinopathy. As well as providing a high quality permanent record of the retinal appearance, which can be used for monitoring of progression or response to treatment, and which can be reviewed by an ophthalmologist, digital images have the potential to be processed by automatic analysis systems. We have described the preliminary development of a tool to provide automatic analysis of digital images taken as part of routine monitoring of diabetic retinopathy in our clinic. Various statistical classifiers, a Bayesian, a Mahalanobis, and a KNN classifier were tested. The system was tested on 134 retinal images. The Mahalanobis classifier had the best results: microaneurysms, haemorrhages, exudates, and cotton wool spots were detected with a sensitivity of 69, 83, 99, and 80%, respectively. |
| Databáze: | OpenAIRE |
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