Published October 2, 2017 | Version 10008223
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Scattering Operator and Spectral Clustering for Ultrasound Images: Application on Deep Venous Thrombi

Description

Deep Venous Thrombosis (DVT) occurs when a
thrombus is formed within a deep vein (most often in the legs). This
disease can be deadly if a part or the whole thrombus reaches the
lung and causes a Pulmonary Embolism (PE). This disorder, often
asymptomatic, has multifactorial causes: immobilization, surgery,
pregnancy, age, cancers, and genetic variations. Our project aims to
relate the thrombus epidemiology (origins, patient predispositions,
PE) to its structure using ultrasound images. Ultrasonography and
elastography were collected using Toshiba Aplio 500 at Brest
Hospital. This manuscript compares two classification approaches:
spectral clustering and scattering operator. The former is based on
the graph and matrix theories while the latter cascades wavelet
convolutions with nonlinear modulus and averaging operators.

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References

  • R. W. Colman, Hemostasis and thrombosis: basic principles and clinical practice. Philadelphia, PA, USA: Lippincott Williams & Wilkins, 2006.
  • A. T. Cohen, G. Agnelli, F. A. Anderson, J. I. Arcelus, D. Bergqvist, J. G. Brecht, I. A. Greer, J. A. Heit, J. L. Hutchinson, A. Kakkar et al., "Venous thromboembolism (vte) in europe," Thrombosis and Haemostasis, vol. 98, no. 4, pp. 756–764, 2007.
  • J. P. Carpenter, G. A. Holland, R. A. Baum, R. S. Owen, J. T. Carpenter, and C. Cope, "Magnetic resonance venography for the detection of deep venous thrombosis: comparison with contrast venography and duplex doppler ultrasonography," Journal of vascular surgery, vol. 18, no. 5, pp. 734–741, 1993.
  • A. Dahabiah, J. Puentes, B. Guias, L. Bressollette, and B. Solaiman, "Comparative neural network based venous thrombosis echogenicity and echostructure characterization using ultrasound images," in Information and Communication Technologies, 2006. ICTTA'06. 2nd, vol. 1. Damascus, Syria: IEEE, 2006, pp. 992–997.
  • B. Geier, L. Barbera, D. Muth-Werthmann, S. Siebers, H. Ermert, S. Philippou, A. Mumme et al., "Ultrasound elastography for the age determination of venous thrombi evaluation in an animal model of venous thrombosis," Thrombosis and haemostasis, vol. 93, no. 2, pp. 368–374, 2005.
  • E. Mfoumou, J. Tripette, M. Blostein, and G. Cloutier, "Time-dependent hardening of blood clots quantitatively measured in vivo with shear-wave ultrasound imaging in a rabbit model of venous thrombosis," Thrombosis research, vol. 133, no. 2, pp. 265–271, 2014.
  • B. S. Garra, "Elastography: history, principles, and technique comparison," Abdominal imaging, vol. 40, no. 4, pp. 680–697, 2015.
  • T. Berthomier, A. Mansour, L. Bressollette, F. Le Roy, and D. Mottier, "Deep venous thrombosis: Database creation and image preprocessing," in Frontiers of Signal Processing (ICFSP), International Conference on. Warsaw, Poland: IEEE, 2016, pp. 87–92.
  • K. Zuiderveld, "Contrast limited adaptive histogram equalization," in Graphics Gems IV, P. S. Heckbert, Ed. San Diego, CA, USA: Academic Press Professional, Inc., 1994, pp. 474–485. [10] T. Berthomier, A. Mansour, L. Bressollette, F. L. Roy, and D. Mottier, "Deep venous thrombus characterization : ultrasonography , elastography and scattering operator," Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 48–59, 2017. [11] J. Bruna and S. Mallat, "Invariant scattering convolution networks," IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 8, pp. 1872–1886, 2013. [12] L. Sifre and S. Mallat, "Rotation, scaling and deformation invariant scattering for texture discrimination," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 1233–1240. [13] N. Valeyrie, Y. Pailhas, C. Capus, and Y. Petillot, "Texture recognition in synthetic aperture sonar images with scattering operators," in 4th International Conference and Exhibition on Underwater Acoustic Measurements: Technologies & Results, 2011. [14] U. Von Luxburg, "A tutorial on spectral clustering," Statistics and computing, vol. 17, no. 4, pp. 395–416, 2007. [15] D. Hamad and P. Biela, "Introduction to spectral clustering," in Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on. Damascus, Syria: IEEE, 2008, pp. 1–6. [16] N. Archip, R. Rohling, P. Cooperberg, H. Tahmasebpour, and S. Warfield, "Spectral clustering algorithms for ultrasound image segmentation," Medical Image Computing and Computer-Assisted Intervention–MICCAI 2005, pp. 862–869, 2005. [17] P. Chuzel, A. Mansour, J. Ognard, J. Gentric, L. Bressollette, D. Hamad, and N. Betrouni, "Automatic clustering for mri images, application on perfusion mri of brain," in Frontiers of Signal Processing (ICFSP), International Conference on. Warsaw, Poland: IEEE, 2016, pp. 63–66. [18] A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," in Advances in neural information processing systems, Cambridge, MA, USA, 2002, pp. 849–856. [19] E. Oyallon and S. Mallat, "Deep roto-translation scattering for object classification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2865–2873. [20] M. Fritz, E. Hayman, B. Caputo, and J.-O. Eklundh, "The kth-tips database," 2004. [21] H.-G. Nguyen, R. Fablet, and J.-M. Boucher, "Visual textures as realizations of multivariate log-gaussian cox processes," in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011, pp. 2945–2952. [22] M. Crosier and L. D. Griffin, "Texture classification with a dictionary of basic image features," in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008, pp. 1–7. [23] L. Liu, P. Fieguth, G. Kuang, and H. Zha, "Sorted random projections for robust texture classification," in Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011, pp. 391–398. [24] X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, "An improved method to construct basic probability assignment based on the confusion matrix for classification problem," Information Sciences, vol. 340, pp. 250–261, 2016. [25] T. Berthomier, A. Mansour, L. Bressollette, F. Le Roy, and D. Mottier, "Venous blood clot structure characterization using scattering operator," in Frontiers of Signal Processing (ICFSP), International Conference on. Warsaw, Poland: IEEE, 2016, pp. 73–80.