Abstract :
Optic disc (OD) segmentation is an important step in automating eye screening for pathological conditions. In this paper, we propose an intensity-based approach to detect the OD boundary, given OD center. OD center is utilized to crop the sub-image that encloses the OD, within which candidate contour points are obtained. Points of maximum intensity variation, both horizontally and vertically, are chosen as candidate contour points. Iterative curve fitting is carried out, incorporating smoothness constraints. The area within the contour is checked for values of mean intensity, variance and compactness. The algorithm is applied on 152 images taken from two public datasets, DIARETDB1 and MESSIDOR. The validation criteria used are common area overlapping between automated segmentation and true OD region (score), sensitivity and Mean Distance to Closest Point (MDCP). The algorithm renders, on an average, a score value of 90%, sensitivity of 93% and MDCP of 8.3 pixels.
Keywords :
curve fitting; eye; image segmentation; iterative methods; medical image processing; patient diagnosis; automated segmentation; automatic optic disc segmentation; common area overlapping; contour point; eye screening; intensity based approach; iterative curve fitting; maximum intensity variation; mean distance to closest point; mean intensity; pathological condition; smoothness constraint; subimage cropping; Biomedical optical imaging; Databases; Image color analysis; Image segmentation; Optical imaging; Optical sensors; Sensitivity;
