A Robust Algorithm for Characterizing Anisotropic Local Structures

Kazunori Okada 1 Dorin Comaniciu 1 Navneet Dalal 2 Arun Krishnan 3
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : This paper proposes a robust estimation and validation framework for characterizing local structures in a positive multi-variate continuous function approximated by a Gaussian-based model. The new solution is robust against data with large deviations from the model and margin-truncations induced by neighboring structures. To this goal, it unifies robust statistical estimation for parametric model fitting and multi-scale analysis based on continuous scale-space theory. The unification is realized by formally extending the mean shift-based density analysis towards continuous signals whose local structure is characterized by an anisotropic fully-parameterized covariance matrix. A statistical validation method based on analyzing residual error of the chi-square fitting is also proposed to complement this estimation framework. The strength of our solution is the aforementioned robustness. Experiments with synthetic 1D and 2D data clearly demonstrate this advantage in comparison with the gamma-normalized Laplacian approach [12] and the standard sample estimation approach [13, p.179]. The new framework is applied to 3D volumetric analysis of lung tumors. A 3D implementation is evaluated with high-resolution CT images of 14 patients with 77 tumors, including 6 part-solid or ground-glass opacity nodules that are highly non-Gaussian and clinically significant. Our system accurately estimated 3D anisotropic spread and orientation for 82% of the total tumors and also correctly rejected all the failures without any false rejection and false acceptance. This system processes each 32-voxel volume-of-interest by an average of two seconds with a 2.4GHz Intel CPU. Our framework is generic and can be applied for the analysis of blob-like structures in various other applications.
keyword : SCR
Type de document :
Communication dans un congrès
Jirí Matas and Tomás Pajdla. European Conference on Computer Vision (ECCV '04), May 2004, Prague, Czech Republic. Springer-Verlag Heidelberg, 3021, pp.549--561, 2004, Lecture Notes in Computer Science (LNCS). 〈http://www.springerlink.com/content/7exfd872ank5a4kc/〉. 〈10.1007/978-3-540-24670-1_42〉
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Kazunori Okada, Dorin Comaniciu, Navneet Dalal, Arun Krishnan. A Robust Algorithm for Characterizing Anisotropic Local Structures. Jirí Matas and Tomás Pajdla. European Conference on Computer Vision (ECCV '04), May 2004, Prague, Czech Republic. Springer-Verlag Heidelberg, 3021, pp.549--561, 2004, Lecture Notes in Computer Science (LNCS). 〈http://www.springerlink.com/content/7exfd872ank5a4kc/〉. 〈10.1007/978-3-540-24670-1_42〉. 〈inria-00548536〉

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