Retinal Image Quality Assessment Using Morphological Operations

Abstract

Retinopathy of Prematurity (ROP) disease affects newborn babies born preterm. The disease has five stages, with stage IV and V being critical where if the disease is not diagnosed at stage III when the vessels begin to grow abnormally, the reversing it is not possible. Diagnosis and treatment are possible between stage I-III. Hospitals without eye specialists, a doctor can be instructed on how to capture retina image which is transmitted online to an ophthalmologist for disease diagnosis. Different devices produce images of varying qualities and during transmission, some image features could be lost. Some images are captured under poor lighting conditions resulting to poor quality images being generated. This study proposes an algorithm which performs quality assessment of retina images before being used to diagnose ROP Stage II or III disease. The algorithm was developed and tested using Retinopathy of prematurity disease data of 91 images available at the Kaggle database and the objective was to separate images of quality from non-quality ones. The algorithm was able to separate quality from non-quality retina images with 92.82% sensitivity, 96.98% specificity and 97.31% accuracy. Performance evaluation was conducted by means of estimating the similarity measure of DSC and Jaccard index (JI), producing agreeable indices of 94.81% DSC and 88.42% JI.