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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
Abstract
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.
Download in postscript format
@InProceedings{eth_biwi_00069,
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}
}