MULTISCALE SPARSE APPEARANCE MODELING AND SIMULATION OF PATHOLOGICAL DEFORMATIONS

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.

Authors
Rami Zewail1, Ahmed Hag-ElSafi2
University of Alberta, Canada1, Empower Innovation Labs Inc., Canada2

Keywords
Appearance Model, Contourlet, Sparsity, Independent Component Analysis, Pathology Deformations
Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 8 , Issue: 1 )
Date of Publication :
August 2017

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