Mathematik Preprint Nr . 237 Adaptive Continuous-Scale Morphology for Matrix Fields

In this article we consider adaptive, PDE-driven morphological operations for 3D matrix fields arising e.g. in diffusion tensor magnetic resonance imaging (DT-MRI). The anisotropic evolution is steered by a matrix constructed from a structure tensor for matrix valued data. An important novelty is an intrinsically one-dimensional directional variant of the matrix-valued upwind schemes such as the Rouy-Tourin scheme. It enables our method to complete or enhance anisotropic structures effectively. A special advantage of our approach is that upwind schemes are utilised only in their basic one-dimensional version. No higher dimensional variants of the schemes themselves are required. Experiments with synthetic and real-world data substantiate the gap-closing and line-completing properties of the proposed method.

[1]  Michael Breuß,et al.  A Directional Rouy-Tourin Scheme for Adaptive Matrix-Valued Morphology , 2009, ISMM.

[2]  Michael Breuß,et al.  Highly Accurate PDE-Based Morphology for General Structuring Elements , 2009, SSVM.

[3]  Michael Breuß,et al.  PDE-Driven Adaptive Morphology for Matrix Fields , 2009, SSVM.

[4]  Luc Florack,et al.  A generic approach to diffusion filtering of matrix-fields , 2007, Computing.

[5]  Michael Breuß,et al.  Anisotropic Continuous-Scale Morphology , 2007, IbPRIA.

[6]  Joachim Weickert,et al.  Morphology for matrix data: Ordering versus PDE-based approach , 2007, Image Vis. Comput..

[7]  Michael Breuß,et al.  A Shock-Capturing Algorithm for the Differential Equations of Dilation and Erosion , 2006, Journal of Mathematical Imaging and Vision.

[8]  Joachim Weickert,et al.  Curvature-Driven PDE Methods for Matrix-Valued Images , 2006, International Journal of Computer Vision.

[9]  Joachim Weickert,et al.  Coherence-Enhancing Shock Filters , 2003, DAGM-Symposium.

[10]  Jean-Michel Morel,et al.  A Note on Two Classical Enhancement Filters and Their Associated PDE's , 2003, International Journal of Computer Vision.

[11]  Mohamed Cheriet,et al.  Numerical Schemes of Shock Filter Models for Image Enhancement and Restoration , 2003, Journal of Mathematical Imaging and Vision.

[12]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[13]  Yehoshua Y. Zeevi,et al.  Regularized Shock Filters and Complex Diffusion , 2002, ECCV.

[14]  Rachid Deriche,et al.  Constrained Flows of Matrix-Valued Functions: Application to Diffusion Tensor Regularization , 2002, ECCV.

[15]  Kristel Michielsen,et al.  Morphological image analysis , 2000 .

[16]  Marcel J. T. Reinders,et al.  Image sharpening by morphological filtering , 2000, Pattern Recognit..

[17]  Rein van den Boomgaard,et al.  Numerical Solution Schemes for Continuous-Scale Morphology , 1999, Scale-Space.

[18]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[19]  J. Boris,et al.  Flux-Corrected Transport , 1997 .

[20]  L. Álvarez,et al.  Signal and image restoration using shock filters and anisotropic diffusion , 1994 .

[21]  Guillermo Sapiro,et al.  Implementing continuous-scale morphology via curve evolution , 1993, Pattern Recognit..

[22]  P. Lions,et al.  Axioms and fundamental equations of image processing , 1993 .

[23]  Luc Vincent,et al.  Mathematical morphology: The Hamilton-Jacobi connection , 1993, 1993 (4th) International Conference on Computer Vision.

[24]  E. Rouy,et al.  A viscosity solutions approach to shape-from-shading , 1992 .

[25]  Stanley Osher,et al.  Shocks and other nonlinear filtering applied to image processing , 1991, Optics & Photonics.

[26]  Johan Wiklund,et al.  Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  L. Rudin,et al.  Feature-oriented image enhancement using shock filters , 1990 .

[28]  Lucas J. van Vliet,et al.  A nonlinear laplace operator as edge detector in noisy images , 1989, Comput. Vis. Graph. Image Process..

[29]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[30]  J. Boris,et al.  Flux-corrected transport. III. Minimal-error FCT algorithms , 1976 .

[31]  G. Matheron Random Sets and Integral Geometry , 1976 .

[32]  David L. Book,et al.  Flux-corrected transport II: Generalizations of the method , 1975 .

[33]  Henry P. Kramer,et al.  Iterations of a non-linear transformation for enhancement of digital images , 1975, Pattern Recognit..

[34]  Michael Breuß,et al.  PDE-based Morphology for Matrix Fields: Numerical Solution Schemes , 2009, Tensors in Image Processing and Computer Vision.

[35]  Joachim Weickert,et al.  A general structure tensor concept and coherence-enhancing diffusion filtering for matrix fields , 2009 .

[36]  Thomas Brox,et al.  Nonlinear structure tensors , 2006, Image Vis. Comput..

[37]  Preprint Nr,et al.  Mathematical Morphology for Tensor Data Induced by the Loewner Ordering in Higher Dimensions , 2005 .

[38]  T. Brox,et al.  Diffusion and regularization of vector- and matrix-valued images , 2002 .

[39]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[40]  Silvano Di Zenzo,et al.  A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..

[41]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[42]  Jay P. Boris,et al.  Flux-corrected transport. I. SHASTA, a fluid transport algorithm that works , 1973 .

[43]  G. Matheron Éléments pour une théorie des milieux poreux , 1967 .