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Reconnection of Interrupted Curvilinear Structures via Cortically Inspired Completion for Ophthalmologic Images.
- Source :
-
IEEE Transactions on Biomedical Engineering . May2018, Vol. 65 Issue 5, p1151-1165. 15p. - Publication Year :
- 2018
-
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) \equiv \mathbb R^2 \rtimes S^1 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 $7 \times 7$ to $39 \times 39$, 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00189294
- Volume :
- 65
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 129266295
- Full Text :
- https://doi.org/10.1109/TBME.2017.2787025