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The Hough Transform and the Impact of Chronic Leukemia on the Compact Bone Tissue from CT-Images Analysis

  • Anna Maria MassoneEmail author
  • Cristina Campi
  • Francesco Fiz
  • Mauro Carlo Beltrametti
Chapter
First Online:
Part of the Springer INdAM Series book series (SINDAMS, volume 36)

Abstract

Computational analysis of X-ray Computed Tomography (CT) images allows the assessment of alteration of bone structure in adult patients with Advanced Chronic Lymphocytic Leukemia (ACLL), and may even offer a powerful tool to assess the development of the disease (prognostic potential). The crucial requirement for this kind of analysis is the application of a pattern recognition method able to accurately segment the intra-bone space in clinical CT images of the human skeleton. Our purpose is to show how this task can be accomplished by a procedure based on the use of the Hough transform technique for special families of algebraic curves. The dataset used for this study is composed of sixteen subjects including eight control subjects, one ACLL survivor, and seven ACLL victims. We apply the Hough transform approach to the set of CT images of appendicular bones for detecting the compact and trabecular bone contours by using ellipses, and we use the computed semi-axes values to infer information on bone alterations in the population affected by ACLL. The effectiveness of this method is proved against ground truth comparison. We show that features depending on the semi-axes values detect a statistically significant difference between the class of control subjects plus the ACLL survivor and the class of ACLL victims.

Keywords

X-ray tomography Image processing Pattern recognition Hough transform Algebraic plane curves 
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Notes

Acknowledgements

We acknowledge support from INDAM grant (intensive period on Computational Methods for Inverse Problems in Imaging). We wish to thank Gianmario Sambuceti, Head of the Nuclear Medicine Unit of the IRCCS San Martino - IST, Università degli Studi di Genova, and our colleague and friend Michele Piana for many helpful discussions. Special thanks should be given to Annalisa Perasso for her contribution to the data analysis of this project.

References

  1. 1.
    Aslan, M.S., Ali, A., Rara, H., et al.: A Novel 3D Segmentation of Vertebral Bones From Volumetric CT Images Using Graph Cuts. Advances in Visual Computing, Lecture Notes in Computer Science, vol. 5876, pp. 519–528 (2009)Google Scholar
  2. 2.
    Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 13, 111–122 (1981)CrossRefGoogle Scholar
  3. 3.
    Beltrametti, M.C., Campi, C., Massone, A.M., Torrente, M.-L.: Geometry of the Hough transforms with applications to synthetic data. arXiv:1904.02587 [cs.CV]
  4. 4.
    Beltrametti, M.C., Massone, A.M., Piana, M.: Hough transform of special classes of curves. SIAM J. Imaging Sci. 6, 391–412 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Bonci, A., Leo, T., Longhi, S.: A bayesian approach to the Hough transform for line detection. IEEE Trans. Syst. Man. Cybern. Syst.—Part A: Syst. Humans 35, 945–955 (2005)Google Scholar
  6. 6.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70, 109–131 (2006)CrossRefGoogle Scholar
  7. 7.
    Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. Int. Conf. Comput. Vis. I, 105–112 (2001)Google Scholar
  8. 8.
    Campi, C., Perasso, A., Beltrametti, M.C., Massone, A.M., Sambuceti, G., Piana, M.: Pattern recognition in medical imaging by means of the Hough transform of curves. In: Proceedings of 8th International Symposium on Image and Signal Processing and Analysis (ISPA 2013), pp. 280–283Google Scholar
  9. 9.
    Campi, C., Perasso, A., Beltrametti, M.C., Sambuceti, G., Massone, A.M., Piana, M.: HT-BONE: a graphical user interface for the identification of bone profiles in CT images via extended Hough transform. In: Proceedings of SPIE 9784, Medical Imaging 2016: Image Processing, pp. 978423Google Scholar
  10. 10.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)Google Scholar
  11. 11.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22, 61–79 (1997)CrossRefGoogle Scholar
  12. 12.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)CrossRefGoogle Scholar
  13. 13.
    Ciliberto, C.: Algebra Lineare. Elettronica. Bollati Boringhieri, Programma di Matematica, Fisica (1994)Google Scholar
  14. 14.
    Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM. 15, 11–15 (1972)CrossRefGoogle Scholar
  15. 15.
    Fiz, F., Marini, C., Piva, R., et al.: Adult advanced chronic lymphocytic leukemia: computational analysis of whole-body CT documents a bone structure alteration. Radiology 271, 805–813 (2014)CrossRefGoogle Scholar
  16. 16.
    Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, 1st edn. Addison-Wesley Longman Publishing Co Inc, Boston (1992)Google Scholar
  17. 17.
    Hough, P.V.C.: Method and means for recognizing complex patterns. U.S. Patent 3069654 (1962)Google Scholar
  18. 18.
    Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans. Med. Imaging 13, 601–609 (1994)CrossRefGoogle Scholar
  19. 19.
    Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53, 457–481 (1958)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17, 158–175 (1995)CrossRefGoogle Scholar
  21. 21.
    Massone, A.M., Perasso, A., Campi, C., et al.: Profile detection in medical and astronomical images by means of the Hough transform of special classes of curves. J. Math. Imaging Vis. 51, 296–310 (2015)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Paragios, N., Mellina-Gottardo, O., Ramesh, V.: Gradient vector flow fast geometric active contours. IEEE Trans. Pattern Anal. Mach. Intell. 26, 402–407 (2004)CrossRefGoogle Scholar
  23. 23.
    Philip, K.P., Dove, E.L., McPherson, D.D., et al.: The fuzzy Hough transform-feature extraction in medical images. IEEE Trans. Med. Imaging 13, 235–240 (1994)Google Scholar
  24. 24.
    Ricca, G., Beltrametti, M.C., Massone, A.M.: Piecewise recognition of bone skeleton profiles via an iterative Hough transform approach without re-voting. In: Medical Imaging 2015: Image Processing, Proceedings of SPIE, vol. 9413, 94132M:1–8 (2015)Google Scholar
  25. 25.
    Ricca, G., Beltrametti, M.C., Massone, A.M.: Detecting curves of symmetry in images via Hough transform. Math. Comput. Sci., Spec. Issue Geom. Comput. 10, 179–205 (2016)Google Scholar
  26. 26.
    Robbiano, L.: Hyperplane Sections, Gröbner bases, and Hough transforms. J. Pure Appl. Algebra 219, 2434–2448 (2015)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Rosset, A., Spadola, L., Ratib, O.: OsiriX: An open-source software for navigating in multidimensional DICOM images. J. Digit. Imaging 17, 205–216 (2004)CrossRefGoogle Scholar
  28. 28.
    Sambuceti, G., Brignone, M., Marini, C., et al.: Estimating the whole bone-marrow asset in humans by a computational approach to integrated PET/CT imaging. Eur. J. Nucl. Med. Mol. Imaging 39, 1326–1338 (2012)CrossRefGoogle Scholar
  29. 29.
    Torrente, M.-L., Beltrametti, M.C.: Almost-vanishing polynomials and an application to the Hough transform. J. Algebra Appl. 13, 39 (2014)Google Scholar
  30. 30.
    Truc, P.T.H., Kim, Y.H., Lee, Y.K., et al.: Evaluation of active contour-based techniques toward bone segmentation from CT images. In: World Congress on Medical Physics and Biomedical Engineering 2006, IFMBE Proceedings, vol. 14, pp. 3121–3125. Springer, Berlin (2007)Google Scholar
  31. 31.
    Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. 50, 271–293 (2002)CrossRefGoogle Scholar
  32. 32.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7, 359–369 (1998)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Yezzi, A., Kichenassamy, S., Kumar, A., et al.: A geometric snake model for segmentation of medical imagery. IEEE Trans. Med. Imaging 16, 199–209 (1997)CrossRefGoogle Scholar
  34. 34.
    Zhang, J., Yan, C.H., Chui, C.K., et al.: Fast segmentation of bone in CT images using 3D adaptive thresholding. Comput. Biol. Med. 40, 231–236 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anna Maria Massone
    • 1
    Email author
  • Cristina Campi
    • 2
  • Francesco Fiz
    • 3
  • Mauro Carlo Beltrametti
    • 1
  1. 1.Dipartimento di MatematicaUniversità di GenovaGenovaItaly
  2. 2.Dipartimento di MatematicaTullio Levi-Civita, Università di PadovaPadovaItaly
  3. 3.Nuclear Medicine Unit, Department of RadiologyUniversity of TuebingenTübingenGermany

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