Automatic segmentation of corneal dystrophy on photographic images based on texture analysis

Purpose: To develop an automatic algorithm to analyze dystrophic lesions on photographic images of corneal dystrophy. Methods: The dataset included 32 images of corneal dystrophy. The dystrophic area was manually segmented twice. Manually labeled dystrophy areas were compared with automatically segmented images. First, we manually removed the light reflex from the image of the cornea. Using an automatic approach, we extracted the brown color of the iris. Then, the program detected the circular region of the pupil and the corneal surface. A whitish dystrophy area was defined based on the image intensity on the iris and the pupil. The sliding square kernel was applied to clearly define the dystrophic region. Results: For the manual analysis and the twice automatic approach, the Dice similarity was 0.804 and 0.801, respectively. The Pearson correlation coefficient was 0.807 and 0.806, respectively. The total number of distinct dystrophic areas showed no significant difference between the manual and automatic approaches according to the Wilcoxon signed-rank test (p < 0.0001, both). Conclusions: We proposed an automatic algorithm for detecting the dystrophy areas on photographic images with an accuracy of approximately 0.80. This system can be applied to detect and predict the progression of corneal dystrophy.