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Automated detection of bright lesions from contrast normalized fundus images

Exudates are one of the abnormalities present in the eye which can lead to vision loss. Fundus images may consist of artifacts which occur during image acquisition and hamper the accuracy of detection of exudates. There is a need to develop an image processing based techniques for automated and correct segmentation of exudates from fundus images. This paper demonstrates an automatic computer vision algorithm for efficient identification of the exudates from fundus images by strategic fusion of techniques i.e. contrast normalization, top-hat transformation and average filtering. The proposed technique correctly detects exudates from the fundus images and rejects the artifacts and reflections. The average computation time for exudates segmentation from fundus images is 11 seconds. The proposed method is computationally efficient and robust and can be used for real time applications.