Automated optic disc detection in retinal images by applying region-based active aontour model in a variational level set formulation

An efficient optic disk localization and segmentation are important tasks in an automated retinal image analysis system. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents a method to automatically locate and boundary detect of the optic disk. The detection procedure comprises two independent methodologies. On one hand, a location methodology obtains a pixel that belongs to the OD using iterative thresholding method followed by Principal Component Analysis techniques (PCA) and, on the other hand, a boundary segmentation methodology estimates the OD boundary by applying region-based active contour model in a variational level set formulation (RSF). The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity The results from the RSF method were compared with conventional optic disk detection using a geometric active contour models (ACM) and later verified with hand-drawn ground truth. Results indicate 89% accuracy for identification and 95.05% average accuracy in localizing the optic disc boundary.