Title
Multiscale detection of curvilinear structures in 2-D and 3-D image data
Abstract
Presents a novel, parameter-free technique for the segmentation and local description of line structures on multiple scales, both in 2D and in 3D. 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 the scale-space. The multi-scale response reflects local contrast and is independent of the local width. The filter is steerable in both the orientation and scale domains, 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 3D filtering is applied to magnetic resonance volume data in order to segment cerebral blood vessels.
Year
DOI
Venue
1995
10.1109/ICCV.1995.466846
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Keywords
Field
DocType
local contrast,local width,parameter-free implementation,3-d image data,local description,parameter-free technique,multiscale detection,line structure,symmetric line structure,line segmentation scheme,different application domain,curvilinear structure,multi-scale response,computer vision,scale space,pixel,3d imaging,biomedical imaging,image segmentation,polarity,detectors,linear filtering,linear filters,filtering,shape
Computer vision,Nonlinear system,Linear filter,Pattern recognition,Computer science,Segmentation,Filter (signal processing),Scale space,Image segmentation,Curvilinear coordinates,Artificial intelligence,Steerable filter
Conference
Volume
Issue
ISBN
1995
1
0-8186-7042-8
Citations 
PageRank 
References 
168
27.45
10
Authors
4
Search Limit
100168
Name
Order
Citations
PageRank
Thomas Koller116827.45
Guido Gerig24795540.21
Gábor Székely325435.35
Daniel Dettwiler416827.45