Multiscale Centerline Detection by Learning a Scale-Space Distance Transform

• Published in: 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 2697 - 2704
• Main Authors: Sironi, Amos, Lepetit, Vincent, Fua, Pascal
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Classification; Computer vision; Conferences; Feature extraction; Image segmentation; Pattern recognition; Regression; Robustness; Three dimensional; Three-dimensional displays; Training; Trains; Transforms; Two dimensional; Vectors
• ISSN: 1063-6919; 1063-6919; 2575-7075
• DOI: 10.1109/CVPR.2014.351

We propose a robust and accurate method to extract the centerlines and scale of tubular structures in 2D images and 3D volumes. Existing techniques rely either on filters designed to respond to ideal cylindrical structures, which lose accuracy when the linear structures become very irregular, or on classification, which is inaccurate because locations on centerlines and locations immediately next to them are extremely difficult to distinguish. We solve this problem by reformulating centerline detection in terms of a regression problem. We first train regressors to return the distances to the closest centerline in scale-space, and we apply them to the input images or volumes. The centerlines and the corresponding scale then correspond to the regressors local maxima, which can be easily identified. We show that our method outperforms state-of-the-art techniques for various 2D and 3D datasets.