Ophthalmic diseases can impair vision if they are not detected in time. Fundus images can be used for facilitating retinal disease diagnosis. This research presents a novel approach to enhance retinal fundus images by effectively mitigating uncertainties in image quality stemming from factors such as inadequate illumination and faulty imaging instrumentation. The fuzzy clustering technique sets it apart from conventional methods, demonstrating promise in improving artificial intelligence (AI) applications for accurate disease detection and severity assessment. The proposed work successfully manages to remove major imaging artifacts, while improving the contrast and brightness of the image. An important feature of the approach is its capacity for maintaining the natural features on anatomical retinal fundus images. This improves the overall information content delivered by a retinal image for effective disease diagnosis. The method has been practically evaluated by ophthalmologists, and...

Retinal imaging can effectively diagnose diseases that manifest changes in the retinal anatomy. However, manual diagnosis paradigms are both error-prone and cost-intensive. Therefore, computer-aided technologies were developed for an exhaustive examination of retinal pathology and anatomy. In this article, a new retinal image enhancement method based on fuzzy c-means is proposed to enhance low quality retinal blood vessel images while preserving its brightness. Fuzzy c-means clustering groups the intensity levels into multiple clusters and assigns a cluster membership value to each intensity level. These values are subsequently modified and are then mapped to their corresponding initial values. The green channel of a modified image obtained above is equalized using the adaptive histogram equalization to yield the enhanced image. The results for the proposed algorithm were established using standard datasets consisting of 1000 fundus images with 39 categories. The proposed technique pre...