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A Robust Algorithm for Characterizing Anisotropic Local Structures

  • Kazunori Okada
  • Dorin Comaniciu
  • Navneet Dalal
  • Arun Krishnan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)

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 γ-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.

Keywords

Robust Algorithm Shift Vector Validation Framework Shift Procedure Robust Statistical Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kazunori Okada
    • 1
  • Dorin Comaniciu
    • 1
  • Navneet Dalal
    • 2
  • Arun Krishnan
    • 3
  1. 1.Real-Time Vision & Modeling DepartmentSiemens Corporate Research, Inc.PrincetonUSA
  2. 2.INRIA Rhône-AlpesMontbonnotFrance
  3. 3.CAD ProgramSiemens Medical Solutions USA, Inc.MalvernUSA

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