Detection of Red Lesions and Hard Exudates in Color Fundus Images (International Journal of Engineering and Computer Science, 2014)
Diabetic Retinopathy is the damage caused to the blood vessels in retina due to diabetes. The severe case of diabetic retinopathy leads to vision loss. It is important to diagnose diabetic retinopathy in earlier stage. In this work automatic methods for detection of various lesions of diabetic retinopathy from color fundus images are explained. The retinal structures which include blood vessels, optic disc and fovea are also detected. The prominent lesions present in an abnormal color fundus image include the brighter lesion such as hard exudates and darker lesions such as microaneurysms and haemorrhages. The severity of the disease based on location of the hard exudates in the retina is also explained. Hard exudates are detected by a supervised learning technique on normal color fundus images. The global features of normal color fundus image are captured using a feature extraction technique. Based on this feature the images are classified to be normal or abnormal. The classification of abnormal image as moderate or severe is done by considering the rough rotational symmetry of the macula of a normal color fundus image. The presence of red lesions is detected based on its appearance on the color fundus image. A moat operator is used for the red lesion detection. The algorithms were tested on a small dataset. Hard exudates are detected with an accuracy of 95% and classified with an accuracy of 96%. Red lesions are detected with an accuracy of 90%.