Scattering Operator and Spectral Clustering for Ultrasound Images: Application on Deep Venous Thrombi
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.
[1] R. W. Colman, Hemostasis and thrombosis: basic principles and clinical
practice. Philadelphia, PA, USA: Lippincott Williams & Wilkins, 2006.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] B. S. Garra, “Elastography: history, principles, and technique
comparison,” Abdominal imaging, vol. 40, no. 4, pp. 680–697, 2015.
[8] 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.
[9] 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.
[1] R. W. Colman, Hemostasis and thrombosis: basic principles and clinical
practice. Philadelphia, PA, USA: Lippincott Williams & Wilkins, 2006.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] B. S. Garra, “Elastography: history, principles, and technique
comparison,” Abdominal imaging, vol. 40, no. 4, pp. 680–697, 2015.
[8] 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.
[9] 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.
@article{"International Journal of Medical, Medicine and Health Sciences:76644", author = "Thibaud Berthomier and Ali Mansour and Luc Bressollette and Frédéric Le Roy and Dominique Mottier and Léo Fréchier and Barthélémy Hermenault", title = "Scattering Operator and Spectral Clustering for Ultrasound Images: Application on Deep Venous Thrombi", abstract = "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.", keywords = "Deep venous thrombosis, ultrasonography,
elastography, scattering operator, wavelet, spectral clustering.", volume = "11", number = "11", pages = "647-8", }
