PURPOSE: Diabetic retinopathy is a leading cause of vision loss in developed countries. Regular diabetic retinal eye screenings are needed to detect early signs of retinopathy, so that appropriate treatments can be rendered to prevent blindness. Digital imaging is becoming available as a means of screening for diabetic retinopathy. However, with the large number of patients undergoing screenings, medical professionals require a tremendous amount of time and effort in order to analyse and diagnose the fundus photographs. Our aim is to develop an automatic algorithm using digital image analysis for detecting these early lesions from retinal images. METHODS: An automatic method to detect hard exudates, a lesion associated with diabetic retinopathy, is proposed. The algorithm is based on their colour, using a statistical classification, and their sharp edges, applying an edge detector, to localise them. RESULTS: A sensitivity of 79.62% with a mean number of 3 false positives per image is obtained in a database of 20 retinal images with variable colour, brightness and quality. It can also be seen that the number of the false negative cases increases when the hard exudates were very close to the vessel tree. CONCLUSION: The long term goal of the project is to automate the screening for diabetic retinopathy with retinal images. We have described the preliminary development of a tool to provide automatic analysis of digital fundus photographs to localise hard exudates. Future work will address the issue of improving the obtained results and also will focus on detecting other lesions.
PURPOSE:Diabetic retinopathy is a leading cause of vision loss in developed countries. Regular diabetic retinal eye screenings are needed to detect early signs of retinopathy, so that appropriate treatments can be rendered to prevent blindness. Digital imaging is becoming available as a means of screening for diabetic retinopathy. However, with the large number of patients undergoing screenings, medical professionals require a tremendous amount of time and effort in order to analyse and diagnose the fundus photographs. Our aim is to develop an automatic algorithm using digital image analysis for detecting these early lesions from retinal images. METHODS: An automatic method to detect hard exudates, a lesion associated with diabetic retinopathy, is proposed. The algorithm is based on their colour, using a statistical classification, and their sharp edges, applying an edge detector, to localise them. RESULTS: A sensitivity of 79.62% with a mean number of 3 false positives per image is obtained in a database of 20 retinal images with variable colour, brightness and quality. It can also be seen that the number of the false negative cases increases when the hard exudates were very close to the vessel tree. CONCLUSION: The long term goal of the project is to automate the screening for diabetic retinopathy with retinal images. We have described the preliminary development of a tool to provide automatic analysis of digital fundus photographs to localise hard exudates. Future work will address the issue of improving the obtained results and also will focus on detecting other lesions.