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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  • T.F. Cootes, G.J. Edwards and C.J. Taylor, “Active Appearance Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 6, pp. 681685, 2001.

  • M. Roberts, T. Cootes and J. Adams, “Automatic Segmentation of Lumbar Vertebrae on Digitised Radiographs using Linked Active Appearance Models”, Proceedings of Medical Image Understanding and Analysis, Vol. 2, pp. 120-124, 2006.

  • K. Delac, M. Grgic and P. Liatsis, “Appearance-based Statistical methods for Face Recognition”, Proceedings of 47th International Symposium Focus on Multimedia Systems and Applications, pp. 151-158, 2005.

  • X. Xu, C. Zhang and T. Huang, “Active Morphable Model: An Efficient Method for Face Analysis”, Proceedings of 6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 837-842, 2004.

  • Mikkel B. Stegmann, Karl Sjostrand and Rasmus Larsen, “Sparse Modeling of Landmark and Texture Variability using the Orthomax Criterion”, Proceedings of International Symposium on Medical Imaging, Vol. 6144, pp. 1-12, 2006.

  • C. Wolstenholme and C. Taylor, “Wavelet Compression of Active Appearance Models”, Proceedings of 2nd International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 544-554, 1999.

  • M.B. Stegmann, S. Forchhammer and T.F. Cootes, “Wavelet Enhanced Appearance Modelling”, Proceedings of Symposium on Medical Imaging, Vol. 5370, pp. 1823-1832, 2004.

  • R. Larsen, M.B. Stegmann, S. Darkner, S. Forchhammer, T.F. Cootes and B.K. Ersboll, “Texture Enhanced Appearance Models”, Computer Vision and Image Understanding, Vol. 106, No. 1, pp. 20-30, 2007.

  • Ki Gia Quach, Chi Nhann Duong, Khou Luu and Bac Le, “Gabor Wavelet-based Appearance Models”, Proceedings of International Conference of Computing and Communication Technologies, Research, Innovation, and Vision for the Future, pp. 1-6, 2012.

  • Qiang Zhang, Abhir Bhalerao, Caron Parsons, Emma Helm and Charles Hutchinson, “Wavelet Appearance Pyramids for Landmark Detection and Pathology Classification: Application to Lumbar Spinal Stenosis”, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 274-282, 2016.

  • Abbas Hanon Hassin Al Asadi, “Contourlet Transform based Medical Image Denoising”, International Journal of Image Processing, Vol. 9, No. 1, pp. 22-31, 2015.

  • B. Abboud, F. Davoine, and M. Dang, “Facial Expression Recognition and Synthesis based on An Appearance Model”, Signal Processing: Image Communication, Vol. 19, No. 8, pp. 723-740, 2004.

  • W. Seales and C. Yaun, “Improved Image Classification using Morphing”, Proceedings of 3rd Asian Conference on Computer Vision, pp. 233-240, 1998.

  • S. Wolberg and G. Shin, “Polymorph: Morphing among Multiple Images”, IEEE Computer Graphics and Applications, Vol. 18, No. 1, pp. 58-71, 1998.

  • Ghassan Hamarneh, Preet Jassi and Lisa Tang, “Simulation of Ground Truth Validation Data via physically-and Statistically-based Warps”, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 459-467, 2008.

  • C.J. Rose and C.J. Taylor, “A Statistical Model of Texture for Medical Image Synthesis and Analysis”, Proceedings of Medical Image Understanding and Analysis, pp. 1-4, 2003.

  • Ahmed Elsafi, Rami Zewail, and Nelson Durdle, “Statistical Simulation of Deformations using Wavelet Independent Component Analysis”, Proceedings of Symposium on Security and Defence Visual Information Processing, Vol. 6978, pp. 1-8, 2008.

  • Z. Xue, D. Shen, B. Karacali, J. Stern, D. Rottenberg, and C. Davatzikos, “Simulating Deformations of MR Brain Images for Validation of Atlas-based Segmentation and Registration Algorithms”, Neuroimage, Vol. 33, No. 3, pp. 855-866, 2006.

  • Ruida Cheng et al., “Active Appearance Model and Deep Learning for more Accurate Prostate Segmentation on MRI”, Proceedings of Medical Imaging, pp. 1-9, 2016.

  • R. Nithya and S.Elayaraja, “Medical Image Fusion Schemes using Contourlet Transform and PCA Bases”, Asian Journal of Electronics Sciences, Vol. 4, No. 1, pp. 27-33, 2015.

  • M.N. Do and M. Vetterli, “Contourlets in Beyond Wavelets”, Academic Press, 2003.

  • M. Nordstom et al., “The IMM Face Database: An Annotated Dataset of 240 Face Images, Informatics and Mathematical Modelling”, Technical University of Denmark, 2004.

  • L. Long, S. Antani and G. Thoma, “Image informatics at a National Research Center”, Computerized Medical Imaging and Graphics, Vol. 29, No. 2-3, pp. 171-193, 2005.

  • Rami Zewail, Ahmed ElSafi and Nelson Durdle, “Vertebral segmentation using Contourlet-based Salient Matching and Localized Multi-Scale Shape Prior”, Proceedings of Medical Imaging, pp. 1-9, 2009.


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  • Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Abstract Views: 246  |  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