Abstract
In this work we introduce the composed segmentation (C-segmentation), that is a priori composition of sources to obtain a single one segmentation result according to specific logic combinations. The approach and the segmentation model are general but we apply the C-segmentation technique to the challenging problem of segmenting tubular-like structures. The reconstruction is obtained by continuously deforming an initial distance function following the Partial Differential Equation (PDE)-based diffusion model derived from a minimal volume-like variational formulation. The gradient flow for this functional leads to a nonlinear curvature motion model. An anisotropic variant is provided which includes a diffusion tensor aimed to follow the tube geometry. Numerical examples demonstrate the ability of the proposed method to produce high quality 2D/3D segmentations of complex and eventually incomplete synthetic and real data.
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References
Drblikova, O., Mikula, K.: Semi-implicit Diamond-cell Finite volume Scheme for 3D Nonlinear Tensor Diffusion in Coherence Enhancing Image Filtering. In: Eymard, R., Herard, J.M. (eds.) Finite Volumes for Complex Applications V: Problems and Perspectives, ISTE and WILEY, London, pp. 343–350 (2008)
Corsaro, S., Mikula, K., Sarti, A., Sgallari, F.: Semi-implicit covolume method in 3D image segmentation. SIAM J. Sci. Comput. 28(6), 2248–2265 (2006)
Weickert, J., Scharr, H.: A scheme for coherence enhancing diffusion filtering with optimized rotation invariance. Journal of Visual Communication and Image Representation 13(1/2), 103–118 (2002)
Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Computing Surveys 36(2), 81–121 (2004)
Hassan, H., Farag, A.A.: Cerebrovascular segmentation for MRA data using levels set. International Congress Series, vol. 1256, pp. 246–252 (2003)
Scherl, H., et al.: Semi automatic level set segmentation and stenosis quatification of internal carotid artery in 3D CTA data sets. Medical Image Analysis 11, 21–34 (2007)
Cohen, L.D., Deschamps, T.: Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging. Computer Methods in Biomechanics and Biomedical Engineering 10(4), 289–305 (2007)
Gooya, A., Liao, H., et al.: A variational method for geometric regularization of vascular segmentation in medical images. IEEE Transaction on image processing 17, 1295–1312 (2008)
Sandberg, B., Chan, T.F.: A logic framework for active contours on multi-channel images. J. Vis. Commun. Image R. 16, 333–358 (2005)
Westin, C.-F., Lorigo, L.M., Faugeras, O.D., Grimson, W.E.L., Dawson, S., Norbash, A., Kikinis, R.: Segmentation by Adaptive Geodesic Active Contours. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 266–275. Springer, Heidelberg (2000)
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Franchini, E., Morigi, S., Sgallari, F. (2009). Composed Segmentation of Tubular Structures by an Anisotropic PDE Model. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_7
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DOI: https://doi.org/10.1007/978-3-642-02256-2_7
Publisher Name: Springer, Berlin, Heidelberg
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