Metodologia computacional para detecção e diagnóstico automáticos e planejamento cirúrgico do estrabismo

Abstract

Strabismus is a condition that affects approximately 4% of the population causing aesthetic problems, reversible at any age, and irreversible sensory changes that modify the mechanism of vision. The Hirschberg test is one of the types of existing tests to detect such a condition. Detection Systems and computeraided diagnosis are being used with some success in helping health professionals. However, in spite of the increasing routine use of high-tech technologies, the diagnosis and therapy in ophthalmology is not a reality within the strabismus subspecialty. Thus, this thesis aims to present a methodology to detect and automatically diagnose and propose the plan of strabismus surgery through digital images. To do this, the study is organized in seven steps: (1) face segmentation; (2) eye region detection; (3) eyes location; (4) limbus and brilliance location; (5) detection, (6) diagnosis and (7) surgical planning of strabismus. The effectiveness of the study in the indication of the diagnosis and surgical plan was evaluated by the mean diference between the results provided by the methodology and the original indication of the expert. Patients were evaluated for eye positions: PPO, INFRA, SUPRA, DEXTRO and LEVO. The method was 88% accurate in identifying esotropias (ET), 100% in exotropias (XT), 80.33% in hipertropias (HT) and 83.33% in hipotropias (HoT). The overall average error in diagnosis was 5:6 and 3:83 for horizontal and vertical desviations, respectivelly. In planning surgeries of medial rectus muscles the average error was 0.6 mm for recession, and 0.9 mm for ressection. For lateral rectus muscles, the average error was 0.8 mm for recession, and 1 mm for resection.