A Study on Pupil and Iris Segmentation of the Anterior Segment of the Eye / 대한의료정보학회지

ABSTRACT

OBJECTIVE: The goal of this study was to develop a novel pupil and iris segmentation algorithm. We evaluated segmentation performance based on a fractal model. Two methods were compared Daugman's and our new proposed method. METHODS: We received 200 anterior segment images with 3,872x2,592 pixels. Here we present an active contour model that accurately detects pupil boundaries in order to improve the performance of segmentation systems. We propose a method that uses iris segmentation based on a fractal model. We compared the performance of Daugman's method and the proposed new method and statistically analyzed the results. RESULTS: We manually compared segmentation with the Daugman's method and the new proposed method. The findings showed that the proposed segmentation accuracy was about 2.5 percent higher than Daugman's method. There was a significant difference (p<0.05) between the under and over data between the two methods. CONCLUSION: The results of this study show that the new proposed method was more accurate than the conventional method for the measurement of segmentation of the eye by CAD (Computer-aided Diagnosis).