Interval type-2 fuzzy approach for retinopathy detection in fundus images

Abstract

In the manuscript, an automatic approach for analysis and detection of various stages of retinopathy defects in human eyes has been proposed. The approach consists of a robust preprocessing technique of the retina fundus image to mitigate the effects of noise and poor lightening in the image. To realize a compressive analysis of the defects, methods for extracting blood vessels and optic disc in the fundus image has also been developed. Adaptive Histogram Equalization (AHE), median filtering and Connected Component Analysis techniques were used in separating blood vessels and optic disc from each fundus image. The pre-processing utilizes canny edge detection and Morphological Closing on the fundus image. An interval type-2 fuzzy (IT2F) clustering is applied to segments an input image into four clusters. These four clusters from the fuzzy segmentation are further analyzed to extract various stages of retinopathy abnormalities (e.g., Hemorrhage, hard exudates etc.). The extracted blood vessels and optic disc are removed from the analysis to enhance the defects detection process. Experiments were conducted on DIARETDB1 database. The experimental results obtained are validated using the ground-truth images contained in DIARETDB1 database. Impressive results are recorded throughout the experiment. Hard-Exudates and Hemorrhage were detected from the fundus images and results from similarity indexes such as, accuracy (94.11%) sensitivity (93.03%) and specificity (98.45%) were recorded.