A semiautomatic segmentation approach to corneal lesions. (June 2020) - Lima, Pablo V.C.; Veras, Rodrigo de M.S.; Vogado, Luis H.S.; Portela, Helano M.B.F.; Almeida, João D.S. de; Aires, Kelson R.T.; Leite, Daniel
Abstract: The cornea is an essential structure for the proper functioning of human vision. It can suffer injuries like tumors, areas of epithelial removal, infections, and post-surgical injuries that need prompt and effective treatment. The affected region's area evolution monitoring enables the physician to evaluate the treatment effectiveness. This paper presents a semi-automatic method that can assist the physician in monitoring the evolution of corneal lesions. Our approach uses some specialist-marked regions to train a random forest classifier, and then to classify the other image areas as lesion or non-lesion. We extract color information, and, after classification, we apply an active contour operation to the most significant connected component. Our tests show that by marking 5% of the pixels, our method achieves an accuracy of 99.08% and a Dice of 0.85 on average. According to the literature, the segmentation in more than 90% of the images was considered excellent.