Automated Optic Disc Segmentation Using Basis Splines-Based Active Contour

We develop a fully automated method for the segmentation of optic disc in retinal fundus images using basis-spline-based active contour. The segmentation is achieved by performing scaling, translation, and rotation of the active contour, thereby giving rise to five free parameters. The energy of the active contour is defined by the local contrast and is optimized with respect to five free parameters to get the best fit on the optic disc using gradient descent technique and Green’s theorem. The use of gradient descent technique and Green’s theorem reduces the computational cost and speeds up the segmentation task. The detection of optic disc is achieved using multiresolution-based normalized cross-correlation technique. The detection point is used for the initialization of the active contour. Subsequent optimized evolution of the basis-spline-based active contour provides an accurate segmentation of the optic disc. We present validations on the databases such as Drishti-GS, MESSIDOR, RIGA, and a local database containing 101, 1200, 750, and 942 retinal fundus images, respectively, amounting to a total of 2993 fundus images. Their corresponding Dice index scores are 0.9182, 0.8912, 0.9331, and 0.9343. Basic data exploration is done on the results obtained to visualize the trends and distribution of the performance parameters throughout the databases. This also helps us evaluate the algorithm’s overall performance more accurately.