Premium
Efficient center‐line extraction for quantification of vessels in confocal microscopy images
Author(s) -
Maddah Mahnaz,
AfzaliKusha Ali,
SoltanianZadeh Hamid
Publication year - 2003
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1533747
Subject(s) - artificial intelligence , computer vision , skeletonization , computer science , distance transform , medial axis , hough transform , computation , line (geometry) , pattern recognition (psychology) , mathematics , geometry , image (mathematics) , algorithm
In this paper we present a novel method for the three‐dimensional (3‐D) centerline extraction of elongated objects such as vessels. This method combines the basic ideas in distance transform‐based, thinning, and path planning methods to extract thin and connected centerlines. This efficient approach needs no user interaction or any prior knowledge of the object shape. We used the path planning approach, which has exclusively been used in the virtual endoscopy or robotics, to obtain the medial curve of the objects. To make our approach fully automated, a distance transform mapping is used to identify the end points of the object branches. The initial paths are also constructed on the surface of the object, traversing the same distance map. Then a thinning algorithm centralizes the paths. The proposed approach is especially efficient for centerline extraction of the complex branching structures. The method has been applied on the confocal microscopy images of rat brains and the results confirm its efficiency in extracting the medial curve of vessels, essential for the computation of quantitative parameters.