Model-Based Detection of Tubular Structures in 3D Images

By Karl Krissian, Grégoire Malandain, Nicholas Ayache, Régis Vaillant, Y. Trousset. Computer Vision and Image Understanding, 2000

527 citations recorded by the source corpus.

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.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4aa6f029a4d63da6f4d9b5a5cb87f8207aab88b0

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In Biophysics

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Citations in the corpus

Submitted to Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiscale Centerline Detection (unknown): “Most existing techniques rely on filters designed to respond to locally cylindrical structures [15], [24], [27], [35], [44], [58], optimized for specific profiles [22], or learnt [5], [17], [43].”
A review of vessel extraction techniques and algorithms (2004, methodology): “Mao et al. [1992] describe their algorithm to extract structural features in digital subtraction angiograms.”
CURVES: Curve evolution for vessel segmentation (2001, methodology): “Multiscale filtering has been proposed for the segmentation of curvilinear structures in three-dimensional medical images (Sato et al. 1998; Krissian et al. 1999; Krissian et al. 1998; Frangi et al. 1998; Lorenz et al. 1997).”
Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images (2003, methodology): “applications have included time-of-flight magnetic resonance angiogram (MRA) segmentation based on adaptive statistical segmentation [30], active shape models for segmenting acute abdominal aneurysms [31], and a method for detection and measurement of vascular structures based on a Gaussian model [ ”
Sensing and Automation in Pruning of Apple Trees: A Review (2018, background): “Through this comprehensive review and discussion, the following statement could be concluded.”