Lesion Feature Detection of Retinopathy Images

In recent years, Diabetic Retinopathy (DR) is fast growing health problem in the society. Ophthalmologists use exudates, microaneurysms and hemorrhages features to diagnose the DR. Detection of these features plays important role in diagnosis and further treatment. This paper proposes automatic lesion detection and analysis methods so as to identify all three features without human intervention. The algorithm developed includes pre-processing as well as post processing, which is very important in DR images. Extraction of minute details in lesions requires highly enhanced images. Contrast enhancement and Morphological operations were applied on green channel DR images. Further, the segmentation is performed on enhanced DR images to capture required region of interest. The Kirsch algorithm has been applied to capture exudates candidate from DR image. Further Top-hat method on preprocessed image extracts microaneurysms and Thresholding technique has been applied for hemorrhage detection. The performance evaluation of algorithms is carried out through the publicly available database. The proposed algorithms depict better results in detection of three major parameters from digital retinopathy images with respect to toolkit evaluation adapted. This automatic detection method helps in reducing screening DR image time as well. The development of proposed algorithm leads to accurate DR detection by helping Ophthalmologists in the diagnosis process.