GLCM-based detection and classification of microaneurysm in diabetic retinopathy fundus images

Diabetic retinopathy is a major cause of blindness and it includes the lesions like microaneurysms, haemorrhages, and exudates. Microaneurysms are the first clinical sign of diabetic retinopathy and it is a small red dot on the retinopathy fundus images. The number of micro aneurysms is used to indicate the severity of the disease. The proposed algorithm detects and classifies the micro aneurysm from diabetic retinopathy fundus images in low resolution images also. Initially the image is processed by a median filter and enhanced by contrast limited adaptive histogram equalisation (CLAHE). Micro aneurysms are detected by extended minima method for candidate extraction. The statistical features are extracted by grey level coocurrence matrix (GLCM) and are given to the classifier to classify microaneurysms accurately. These detected MA are validated by comparing with expert ophthalmologists' hand-drawn ground-truth images. The simulation results show the performance of the proposed algorithm.