An efficient multistage segmentation method for accurate hard exudates and lesion detection in digital retinal images

Abstract

Digital retinal images are commonly used for hard exudates and lesion detection. An efficient segmentation method is needed to detect and discern the lesions from the retinal area. In this paper, a hybrid method is presented for digital retinal image processing for diagnosis and screening purposes. The goal of this research is to suggest a supervised/semi supervised approach for exudates detection in fundus images and it is also to investigate a technique to find the optimum structure. The image is first transformed into fuzzy domain after an initialization. A cellular learning automata model is used to detect any abnormality on the image which is related to a lesion. The automaton is created with an extra term as the rule updating term to increase the flexibility and capability of the cellular automata. The selection and updating of rule are implemented automatically We also performed allocating the score and penalty value for the cells toward the process of segmentation Three main statistical criteria are introduced as the sensitivity, specificity and accuracy. A number of 50 retinal images with visually detection hard exudates and lesions are the experimental dataset for evaluation and validation of the method. For STARE retina image dataset, for a neighborhood of 5 × 5, score of ϑ = 0.01, penalty of ξ = 0.01, ratio of state overall variation in three sequential cycles in cellular automata $\overline{\eta }=0.5$ , updating additive value σ = 0.02 & rule selection threshold value ρ = 0.8 the mean value of statistical criteria averaged over all dataset can reach 99% which is an outstanding assessment result for the proposed method.