Zhang J, Bekkers E, Chen D, Berendschot TTJM, Schouten J, Pluim JPW, Shi Y, Dashtbozorg B, Romeny BMTH. Reconnection of Interrupted Curvilinear Structures via Cortically Inspired Completion for Ophthalmologic Images.
IEEE Trans Biomed Eng 2019;
65:1151-1165. [PMID:
29683430 DOI:
10.1109/tbme.2017.2787025]
[Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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 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 , 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 in reconnecting gaps of sizes from to , 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.
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