Gradient-based edge detection with skeletonization (GES) segmentation for magnetic resonance optic nerve images

This study proposes a new segmentation method called gradient-based edge detection with skeletonization (GES) for the cross-sectional optic nerve on magnetic resonance (MR) images acquired with T1-weighted fast spoiled gradient-echo (FSPGR) without fat saturation. The raw optic nerve images have very poor resolution with un-clear edges. Therefore, the images were first pre-processed with bicubic interpolation to improve the spatial resolution. Then, the proposed GES segmentation was applied to produce a distinct optic nerve image. The edges of the optic nerve were identified by finding the largest gradient changes in signal intensity between the optic nerve region and its surrounding cerebrospinal fluid (CSF). Particle swarm optimization (PSO) and level set method (LSM) segmentations were applied for comparison. Manual segmentation performed by a certified radiologist was used as the ground truth for the evaluation of the computerized segmentation. GES produced a higher mean Dice similarity coefficient (DSC) index of 0.81 +/- 0.04 compared to the LSM with a mean DSC index of 0.67 +/- 0.17. The bicubic-GES processed optic nerve images were used for the quantitative measurement on ten normal datasets. This study has reported the quantitative values of the longest length of the optic nerve up to the chiasm (37.2 mm) using MR images. The proposed GES segmentation method for the optic nerve will be useful for investigating any optic nerve-related disease that affects the area or volume of the optic nerve.