Model-Based Detection of Tubular Structures in 3D Images
✍ Scribed by Karl Krissian; Grégoire Malandain; Nicholas Ayache; Régis Vaillant; Yves Trousset
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 983 KB
- Volume
- 80
- Category
- Article
- ISSN
- 1077-3142
No coin nor oath required. For personal study only.
✦ Synopsis
Detection of tubular structures in 3D images is an important issue for vascular medical imaging. We present in this paper a new approach for centerline detection and reconstruction of 3D tubular structures. Several models of vessels are introduced for estimating the sensitivity of the image second-order derivatives according to elliptical cross section, to curvature of the axis, or to partial volume effects. Our approach uses a multiscale analysis for extracting vessels of different sizes according to the scale. For a given model of vessel, we derive an analytic expression of the relationship between the radius of the structure and the scale at which it is detected. The algorithm gives both centerline extraction and radius estimation of the vessels allowing their reconstruction. The method has been tested on synthetic images, an image of a phantom, and real images, with encouraging results.
📜 SIMILAR VOLUMES
Estimation of local second-degree variation should be a natural first step in computerized image analysis, just as it seems to be in human vision. A prevailing obstacle is that the second derivatives entangle the three features, signal strength (i.e., magnitude or energy), orientation, and shape. To
Point-based registration of images strongly depends on the extraction of suitable landmarks. Recently, different 3D operators have been proposed in the literature for the detection of anatomical point landmarks in 3D images. In this paper, we investigate nine 3D differential operators for the detect