Blood Vessel Analysis on High Resolution Fundus Retinal Images

Image analysis is a relevant tool to improve the healthcare services. Fundus retinal image analysis allows the early detection of ophthalmic diseases such as diabetes and glaucoma. Thus, growing interest is observed on the development of segmentation algorithms for blood vessels in retinal images. For this purpose, Kernel-based approaches with Gaussian matched filters have been successfully used. Nowadays, improved image sensors and computers deliver high resolution images, and different parameter values are required for the efficient operation of such filters. In this work, an optimization system using genetic algorithms is designed to calculate those values. To evaluate our methodology, a segmentation algorithm is proposed and the outcomes are evaluated on the HRF image database. Performance measures are obtained and compared to those obtained using state of the art methods. This analysis represents a first step in the detection and classification of normal and abnormal eye conditions.