Registering Retinal Vessel Images from Local to Global via Multiscale and Multicycle Features
We propose a comprehensive method using multiscale and multicycle features for retinal vessel image registration with a local and global strategy. The multiscale vessel maps generated by multiwavelet kernels and multiscale hierarchical decomposition contain segmentation results at varying image resolutions in different levels of vessel details. Then the multicycle feature composed of various combinations of cycle structures with different numbers of vertices is extracted. The cycle structure consisting of vessel bifurcation points, crossover points of arteries and veins, and the connected vessels can be found by our Angle based Depth First Search (ADFS) algorithm. Local initial registration is implemented by the matched Cycle Vessel feature points and global final registration is completed by the Cycle Vessel Bifurcation feature points using similarity transformation. Finally, our Skeleton Alignment Error Measure (SAEM) is calculated for optimal scale and cycle feature selection, yielding the best registration result intelligently. Experimental results show that our method outperforms state of the art methods on retinal vessel image registration using different features in terms of accuracy and robustness.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)