A Hybrid Approach of Detection of Glaucoma with Optic Disk Segmentation and Microaneurysm Detection in Retinal Fundus Images

Abstract

Diabetes is the root cause for the visual problems of human, the medical treatment is highly efficient in protecting the vision loss for the diabetic patients. Vision test are encouraged for the patients on regular basis for early detection of vision problems. Automated diabetic retinopathy evaluation programs have been developed for the identification of patients with vision losing diabetic eye disease. The non diabetic persons are also encouraged to obtain the evaluation of eye for the prevention of blindness. The proposed system is an automatic detection and evaluation of the eye disease. The detection of micro aneurysms and hemorrhages in retinal image are the base for the automated classification of red lesion. It includes the following contributions a) Extraction of a set of Dynamic shape features b) Classification process for the differentiation of lesions and vessel segments. This method is examined with the fundus images in e-ophtha dataset obtained from the freely availed database called ADCIS. The preprocessing of input images was done by mean filter. The Morphological flooding is applied for the extraction of shape features. Finally, the classification was done using Random Forest (RF) classifier. The experimental results on dataset validate the efficiency of the proposed method in the detection of redlesions.