Screening for diabetic retinopathy using computer based image analysis and statistical classification
Authors: Ege, B.M., Hejlesen, O.K., Larsen, O.V., Møller, K., Jennings, B., Kerr, D. and Cavan, D.A.
Journal: Computer Methods and Programs in Biomedicine
Volume: 62
Issue: 3
Pages: 165-175
ISSN: 0169-2607
DOI: 10.1016/S0169-2607(00)00065-1
Abstract: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. (C) 2000 Elsevier Science Ireland Ltd.
Source: Scopus
Screening for diabetic retinopathy using computer based image analysis and statistical classification.
Authors: Ege, B.M., Hejlesen, O.K., Larsen, O.V., Møller, K., Jennings, B., Kerr, D. and Cavan, D.A.
Journal: Comput Methods Programs Biomed
Volume: 62
Issue: 3
Pages: 165-175
ISSN: 0169-2607
DOI: 10.1016/s0169-2607(00)00065-1
Abstract: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.
Source: PubMed
Screening for diabetic retinopathy using computer based image analysis and statistical classification
Authors: Ege, B.M., Hejlesen, O.K., Larsen, O.V., Moller, K., Jennings, B., Kerr, D. and Cavan, D.A.
Journal: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume: 62
Issue: 3
Pages: 165-175
ISSN: 0169-2607
DOI: 10.1016/S0169-2607(00)00065-1
Source: Web of Science (Lite)
Screening for diabetic retinopathy using computer based image analysis and statistical classification
Authors: Ege, B.M., Hejlesen, O.K., Larsen, O.V., Moller, K., Jennings, B., Kerr, D. and Cavan, D.A.
Journal: Computer Methods and Programs in Biomedicine
Volume: 62
Pages: 165-175
ISSN: 0169-2607
DOI: 10.1016/S0169-2607(00)00065-1
Abstract: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.
Source: Manual
Preferred by: David Kerr
Screening for diabetic retinopathy using computer based image analysis and statistical classification.
Authors: Ege, B.M., Hejlesen, O.K., Larsen, O.V., Møller, K., Jennings, B., Kerr, D. and Cavan, D.A.
Journal: Comput. Methods Programs Biomed.
Volume: 62
Pages: 165-175
DOI: 10.1016/S0169-2607(00)00065-1
Source: DBLP
Screening for diabetic retinopathy using computer based image analysis and statistical classification.
Authors: Ege, B.M., Hejlesen, O.K., Larsen, O.V., Møller, K., Jennings, B., Kerr, D. and Cavan, D.A.
Journal: Computer methods and programs in biomedicine
Volume: 62
Issue: 3
Pages: 165-175
eISSN: 1872-7565
ISSN: 0169-2607
DOI: 10.1016/s0169-2607(00)00065-1
Abstract: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.
Source: Europe PubMed Central