A semiautomatic segmentation approach to corneal lesions. (June 2020)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Computers & electrical engineering. Volume 84(2020)
• Publication Date: 2020-06
• Subjects: Computer-aided diagnosis -- Image segmentation -- Anterior eye segment -- Corneal lesion -- Eye health,; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106625
• Languages: English
• ISSNs: 0045-7906

Abstract

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.