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Multiscale Detection of Curvilinear Structures in 2-D and 3-D Image Data

T. Koller, G. Gerig, G. Székely and D. Dettwiler
Proceedings Fifth Int. Conf. on Computer Vision (ICCV95)
June 1995


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.

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  author = {T. Koller and G. Gerig and G. Székely and D. Dettwiler},
  title = {Multiscale Detection of Curvilinear Structures in 2-D and 3-D Image Data},
  booktitle = {Proceedings Fifth Int. Conf. on Computer Vision (ICCV95)},
  year = {1995},
  month = {June},
  pages = {864-869},
  publisher = {IEEE Computer Society Press},
  keywords = {filtering, skeletonization, segmentation, scale space, non-linear}