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A 3D Fractal-Based Approach towards Understanding Changes in the Infarcted Heart Microvasculature

  • Polyxeni Gkontra
  • Magdalena M. Żak
  • Kerri-Ann Norton
  • Andrés Santos
  • Aleksander S. Popel
  • Alicia G. Arroyo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

Abstract

The structure and function of the myocardial microvasculature affect cardiac performance. Quantitative assessment of microvascular changes is therefore crucial to understanding heart disease. This paper proposes the use of 3D fractal-based measures to obtain quantitative insight into the changes of the microvasculature in infarcted and non-infarcted (remote) areas, at different time-points, following myocardial infarction. We used thick slices (~100μm) of pig heart tissue, stained for blood vessels and imaged with high resolution microscope. Firstly, the cardiac microvasculature was segmented using a novel 3D multi-scale multi-thresholding approach. We subsequently calculated: i) fractal dimension to assess the complexity of the microvasculature; ii) lacunarity to assess its spatial organization; and iii) succolarity to provide an estimation of the microcirculation flow. The measures were used for statistical change analysis and classification of the distinct vascular patterns in infarcted and remote areas, demonstrating the potential of the approach to extract quantitative knowledge about infarction-related alterations.

Keywords

Fractal Dimension Remote Area Post Myocardial Infarction Microvascular Coronary Dysfunction High Resolution Microscope 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Allain, C., Cloitre, M.: Characterising the lacunarity of random and deterministic fractal sets. Phys. Rev. Lett. 44, 3552–3558 (1991)MathSciNetGoogle Scholar
  2. 2.
    Berntson, G.M., Stoll, P.: Correcting for Finite Spatial Scales of Self-Similarity When Calculating Fractal Dimensions of Real-World Structures. Proceedings of the Royal Society B: Biological Sciences 264(1387), 1531–1537 (1997)CrossRefGoogle Scholar
  3. 3.
    Block, A., von Bloh, W., Schellnhuber, H.J.: Efficient box-counting determination of generalized Fractal Dimensions. Physical Review A 42, 1869–1874 (1990)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: CVPR (2), pp. 60–65 (2005)Google Scholar
  5. 5.
    Dougherty, G., Henebry, G.M.: Fractal signature and lacunarity in the measurement of the texture of trabecular bone in clinical CT images. Med. Eng. Phys. 23(6), 369–380 (2001)CrossRefGoogle Scholar
  6. 6.
    Fernández-Jiménez, R., Sánchez-González, J., Agüero, J., et al.: Myocardial Edema After Ischemia/Reperfusion Is Not Stable and Follows a Bimodal Pattern: Imaging and Histological Tissue Characterization. J. Am. Coll. Cardiol. 65(4), 315–323 (2015)CrossRefGoogle Scholar
  7. 7.
    Gould, D.J., Vadakkan, T.J., Poché, R.A., Dickinson, M.E.: Multifractal and Lacunarity Analysis of Microvascular Morphology and Remodeling. Microcirculation 18(2), 136–151 (2011)CrossRefGoogle Scholar
  8. 8.
    Lopes, R., Betrouni, N.: Fractal and multifractal analysis: a review. Med. Image Anal. 13(4), 634–649 (2009)CrossRefGoogle Scholar
  9. 9.
    Lorthois, S., Cassot, F.: Fractal analysis of vascular networks: insights from morphogenesis. J. Theor. Biol. 262(4), 614–633 (2010)CrossRefGoogle Scholar
  10. 10.
    Mandelbrot, B.B.: Fractal geometry of nature. Freeman, New York (1977)zbMATHGoogle Scholar
  11. 11.
    Melo, R.H.C., Conci, A.: How Succolarity could be used as another fractal measure in image analysis. Telecommunication Systems 52(3), 1643–1655 (2013)CrossRefGoogle Scholar
  12. 12.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Pawley, J.B.: Handbook of Biological Confocal Microscopy. Springer (2006)Google Scholar
  14. 14.
    Petersen, J.W., Pepine, C.J.: Microvascular coronary dysfunction and ischemic heart disease: Where are we in 2014? Trends Cardiovasc. Med. 25(2), 98–103 (2015)CrossRefGoogle Scholar
  15. 15.
    Ren, G., Michael, L.H., Entman, M.L., Frangogiannis, N.G.: Morphological characteristics of the microvasculature in healing myocardial infarcts. J. Histochem. Cytochem. 50(1), 71–79 (2002)CrossRefGoogle Scholar
  16. 16.
    Weaver, M.E., Pantely, G.A., Bristow, J.D., Ladley, H.D.: A quantitative study of the anatomy and distribution of coronary arteries in swine in comparison with other animals and man. Cardiovasc. Res. 20(12), 907–917 (1986)CrossRefGoogle Scholar
  17. 17.
    Wu, X., Kumar, V., Quinlan, J.R., et al.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1), 1–37 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Polyxeni Gkontra
    • 1
  • Magdalena M. Żak
    • 1
  • Kerri-Ann Norton
    • 2
  • Andrés Santos
    • 3
  • Aleksander S. Popel
    • 2
  • Alicia G. Arroyo
    • 1
  1. 1.Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC)MadridSpain
  2. 2.Department of Biomedical Engineering, School of MedicineJohns Hopkins UniversityBaltimoreUS
  3. 3.Universidad Politécnica de Madrid and CIBERBBNMadridSpain

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