Recursive tracking of vascular tree axes in 3D medical images
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Object This article describes a method for automated extraction of branching structures in three dimensional (3D) medical images.
Materials and methods The algorithm recursively tracks branches and detects bifurcations by analyzing the binary connected components on the surface of a sphere that moves along the vessels. Local segmentation within the sphere is performed using a clustering algorithm based on both geometric and intensity information. It minimizes a combination of the intra-class intensity variances and of the inertia moment of the “vessel” class, which emphasizes the cylindrical structures. The algorithm was applied to 16 MRA and 12 CTA 3D images of different anatomic regions. Its capability of extracting all the branches and avoiding spurious detections was evaluated by comparing the number of extracted branches with the number of branches found by visual inspection of the datasets. Its reproducibility and sensitivity to parameter variation were also assessed.
Results With a fixed parameter setting, 68 out of 286 perceptible branches were missed or partly extracted and 11 spurious branches were obtained. Increasing the weight of the geometric criterion helped in tracking the principal branches in noisy data but increased the number of missed branches. Processing time was within 5 min per dataset.
Conclusion From one initial point, the algorithm extracts a vascular tree where the differences of size and of intensity between the branches are not large. Missed sub-trees can be recovered using additional starting points.
KeywordsVascular diseases Magnetic resonance angiography Computed tomography angiography Three-dimensional image Computer assisted image processing
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- 2.Tizon X (2004) Algorithms for the analysis of 3D magnetic resonance angiography images (Acta Universitatis Agriculturæ Sueciæ, Uppsala, ISBN 91-576-670-4Google Scholar
- 3.Wink O (2004) Vessel axis determination (Print Partner Ipskamp, Amsterdam, ISBN 90-393-3698-9Google Scholar
- 5.Hernández Hoyos M, Orkisz M, Douek PC, Magnin IE (2005). Assessment of carotid artery stenoses in 3D contrast-enhanced magnetic resonance angiography, based on improved generation of the centerline. Mach Graph Vis 22:349–378Google Scholar
- 9.Shim H, Yun ID, Lee KM, Lee SU (2005) Partition-based extraction of cerebral arteries from CT angiography with emphasis on adaptive tracking. In: Information processing med imaging, Glenwood Springs. LNCS, vol 3565. Springer, Heidelberg, pp 357–368Google Scholar
- 11.Descoteaux M, Collins L, Siddiqi K (2004) Geometric flows for segmenting vasculature in MRI: theory and validation. In: MICCAI—med image computing and computer-assisted intervention, Saint-Malo, France, Springer Verlag LNCS 3216:500–507Google Scholar
- 12.Manniesing R, Niessen W (2004) Local speed functions in level set based vessel segmentation. In: MICCAI—med image computing and computer-assisted intervention, Saint-Malo. LNCS, vol 3216. Springer, Heidelberg, pp 475–482Google Scholar
- 13.Lorenz C, Renisch S, Schlathölter T, Bülow T (2003). Simultaneous segmentation and tree reconstruction of the coronary arteries in MSCT images. In: Med imaging, San Diego. Proc SPIE 5031:167–177Google Scholar
- 14.Shikata H, Hoffman EA, Sonka M (2004) Automated segmentation of pulmonary vascular tree from 3D CT images. In: med imaging, San Diego. Proc SPIE 5369:107–116, doi: 10.1117/12.537032Google Scholar
- 18.Carrillo JF, Orkisz M, Hernández Hoyos M (2005) Extraction of 3D vascular tree skeletons based on the analysis of connected components evolution. In: CAIP’2005—11th Int IAPR Conference computer analysis of images and patterns, Versailles. LNCS, vol 3691. Springer, Heidelberg, pp 604–611Google Scholar
- 19.MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium Math Stat and Prob, Berkeley, University of California Press 1:281–297Google Scholar
- 20.Otsu N (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cyber. (SMC) 9(1):63–66Google Scholar
- 21.Mukundan R, Ramakrishnan KR (1998). Moment functions in image analysis, theory and applications. World Scientific Publishing Co. Pte. Ltd., Singapore, 150 pGoogle Scholar