High-Performance Algorithms Application for Retinal Image Segmentation Based on Texture Features

Diabetic retinopathy is a dangerous disease of the eye fundus. If the treatment is untimely or inadequate, people affected by the disease may loose their eyesight for a variety of reasons. Laser photocoagulation is an advanced technique for treating diabetic retinopathy, with an eye surgeon extracting certain retinal areas to be exposed to laser pulses based on his expertise. Laser light parameters and pulse repetition rate are also chosen based on the previous experience of surgical interventions. An automated mapping of a preliminary coagulation pattern enables a number of challenges associated with the surgical procedure on the retina to be addressed. The manual mapping of the coagulation pattern is a highly demanding job that requires high-level concentration. It would be much more convenient if a doctor was able slightly to adjust an automatically mapped preliminary coagulation pattern rather than mapping it themselves. In this way, both the possibility of human error and the preparatory phase the surgical procedure are essentially reduced. Of great interest is an algorithm for extracting a laser coagulation zone, which is based on an algorithm for retinal image segmentation. The algorithm performs segmentation using texture features but takes long to run. Because of this, here, we propose a high-performance algorithm for retinal image segmentation, which enables a consecutive version to be made essentially faster, while outperforming a parallel algorithm.