ICTACT Journal on Image and Video Processing

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Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations


Affiliations
1 Department of Electronics and Communication Engineering, University of Alberta, Canada
     

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Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

Keywords

Appearance Model, Contourlet, Sparsity, Independent Component Analysis, Pathology Deformations.
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  • Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Abstract Views: 190  |  PDF Views: 6

Authors

Rami Zewail
Department of Electronics and Communication Engineering, University of Alberta, Canada
Ahmed Hag-Elsafi
Department of Electronics and Communication Engineering, University of Alberta, Canada

Abstract


Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

Keywords


Appearance Model, Contourlet, Sparsity, Independent Component Analysis, Pathology Deformations.

References