Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm
This work addresses the challenge of accurately modeling complex curvilinear networks, such as blood vessels or neurons, for applications in medical imaging and computer vision, representing an incremental improvement over existing methods.
The paper tackles the problem of reconstructing tree-like curvilinear structures from images by analyzing local orientations and rankings using supervised learning and graph theory, achieving faithful topological feature extraction with minimal pixels across various datasets.
We propose a novel tree-like curvilinear structure reconstruction algorithm based on supervised learning and graph theory. In this work we analyze image patches to obtain the local major orientations and the rankings that correspond to the curvilinear structure. To extract local curvilinear features, we compute oriented gradient information using steerable filters. We then employ Structured Support Vector Machine for ordinal regression of the input image patches, where the ordering is determined by shape similarity to latent curvilinear structure. Finally, we progressively reconstruct the curvilinear structure by looking for geodesic paths connecting remote vertices in the graph built on the structured output rankings. Experimental results show that the proposed algorithm faithfully provides topological features of the curvilinear structures using minimal pixels for various datasets.