publication

Reconnection of Interrupted Curvilinear Structures via Cortically Inspired Completion for Ophthalmologic Images

Zhang, Jiong, Bekkers, Erik, Chen, Da, Berendschot, Tos T.J.M., Schouten, Jan, Pluim, Josien P.W., Shi, Yonggang, Dashtbozorg, Behdad, Romeny, Bart M.Ter Haar

DOI: http://dx.doi.org/10.1109/TBME.2017.2787025

IEEE Transactions on Biomedical Engineering 65 (5), p. 1151-1165

Abstract

Objective: In this paper, we propose a robust, efficient, and automatic reconnection algorithm for bridging interrupted curvilinear skeletons in ophthalmologic images. Methods: This method employs the contour completion process, i.e., mathematical modeling of the direction process in the roto-translation group SE(2)R2r× S1 to achieve line propagation/completion. The completion process can be used to reconstruct interrupted curves by considering their local consistency. An explicit scheme with finite-difference approximation is used to construct the three-dimensional (3-D) completion kernel, where we choose the Gamma distribution for time integration. To process structures in SE(2), the orientation score framework is exploited to lift the 2-D curvilinear segments into the 3-D space. The propagation and reconnection of interrupted segments are achieved by convolving the completion kernel with orientation scores via iterative group convolutions. To overcome the problem of incorrect skeletonization of 2-D structures at junctions, a 3-D segment-wise thinning technique is proposed to process each segment separately in orientation scores. Results: Validations on 4 datasets with different image modalities show that our method achieves an average success rate of 95.24%$ in reconnecting 40,457 gaps of sizes from 7 × 7 to 39 × 39, including challenging junction structures. Conclusion: The reconnection approach can be a useful and reliable technique for bridging complex curvilinear interruptions. Significance: The presented method is a critical work to obtain more complete curvilinear structures in ophthalmologic images. It provides better topological and geometric connectivities for further analysis.