Multiscale detection of curvilinear structures in 2-D and 3-D image data

This paper presents a novel, parameter-free technique for the segmentation and local description of line structures on multiple scales, both in 2-D and 3-D. The algorithm is based on a nonlinear combination of linear filters and searches for elongated, symmetric line structures, while suppressing the response to edges. The filtering process creates one sharp maximum across the line-feature profile and across scale-space. The multiscale response reflects local contrast and is independent of the local width. The filter is steerable in orientation and scale domain, leading to an efficient, parameter-free implementation. A local description is obtained that describes the contrast, the position of the center-line, the width, the polarity, and the orientation of the line. Examples of images from different application domains demonstrate the generic nature of the line segmentation scheme. The 3-D filtering is applied to magnetic resonance volume data in order to segment cerebral blood vessels.