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Washington, DC, USA
June 27, 2004 to July 2, 2004
ISBN: 0-7695-2158-4
pp: 594-601
Kazunori Okada , Siemens Corporate Research, Inc.
Dorin Comaniciu , Siemens Corporate Research, Inc.
Arun Krishnan , Siemens Medical Solutions USA, Inc.
A unified approach for treating the scale selection problem in the anisotropic scale-space is proposed. The anisotropic scale-space is a generalization of the classical isotropic Gaussian scale-space by considering the Gaussian kernel with a fully parameterized analysis scale (bandwidth) matrix. The "maximum-over-scales" and the "most-stable-over-scales" criteria are constructed by employing the "l-normalized scale-space derivatives", i.e., response-normalized derivatives in the anisotropic scale-space. This extension allows us to directly analyze the anisotropic (ellipsoidal) shape of local structures. The main conclusions are (i) the norm of the \gamma - and L-normalized anisotropic scale-space derivatives with a constant \gamma =1/2 are maximized regardless of the signal?s dimension if the analysis scale matrix is equal to the signal?s covariance and (ii) the most-stable-over-scales criterion with the isotropic scale-space outperforms the maximum-over-scales criterion in the presence of noise. Experiments with 1D and 2D synthetic data confirm the above findings. 3D implementations of the most-stable-over-scales methods are applied to the problem of estimating anisotropic spreads of pulmonary tumors shown in high-resolution computed-tomography (HRCT) images. Comparison of the first- and second-order methods shows the advantage of exploiting the second-order information.
Kazunori Okada, Dorin Comaniciu, Arun Krishnan, "Scale Selection for Anisotropic Scale-Space: Application to Volumetric Tumor Characterization", CVPR, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2004, pp. 594-601, doi:10.1109/CVPR.2004.217
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