Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 545809, 13 pages
http://dx.doi.org/10.1155/2015/545809
Research Article

Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets

1Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand Johannesburg, Private Bag 3, Johannesburg 2050, South Africa
2Department of Biomedicine, University of Aarhus, 8000 Aarhus C, Denmark
3Department of Histology and Embryology, China Medical University, Shenyang, Liaoning 110122, China

Received 26 February 2015; Revised 16 May 2015; Accepted 28 May 2015

Academic Editor: Giancarlo Ferrigno

Copyright © 2015 Charita Bhikha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. T. Layton, T. L. Pannabecker, W. H. Dantzler, and H. E. Layton, “Functional implications of the three-dimensional architecture of the rat renal inner medulla,” The American Journal of Physiology—Renal Physiology, vol. 298, no. 4, pp. F973–F987, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. T. L. Pannabecker and W. H. Dantzler, “Three-dimensional architecture of collecting ducts, loops of Henle, and blood vessels in the renal papilla,” American Journal of Physiology—Renal Physiology, vol. 293, no. 3, pp. F696–F704, 2007. View at Publisher · View at Google Scholar
  3. T. L. Pannabecker, D. E. Abbott, and W. H. Dantzler, “Three-dimensional functional reconstruction of inner medullary thin limbs of Henle's loop,” The American Journal of Physiology: Renal Physiology, vol. 286, no. 1, pp. F38–F45, 2004. View at Google Scholar · View at Scopus
  4. W. Kriz, “The architectonic and functional structure of the rat kidney,” Zeitschrift für Zellforschung und Mikroskopische Anatomie, vol. 82, no. 4, pp. 495–535, 1967. View at Publisher · View at Google Scholar
  5. T. L. Pannabecker, “Comparative physiology and architecture associated with the mammalian urine concentrating mechanism: role of inner medullary water and urea transport pathways in the rodent medulla,” The American Journal of Physiology—Regulatory Integrative and Comparative Physiology, vol. 304, no. 7, pp. R488–R503, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Ren, N.-Y. Liu, A. Andreasen et al., “Direct physical contact between intercalated cells in the distal convoluted tubule and the afferent arteriole in mouse kidneys,” PLoS ONE, vol. 8, no. 9, Article ID e70898, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. X.-Y. Zhai, J. S. Thomsen, H. Birn, I. B. Kristoffersen, A. Andreasen, and E. I. Christensen, “Three-dimensional reconstruction of the mouse nephron,” Journal of the American Society of Nephrology, vol. 17, no. 1, pp. 77–88, 2006. View at Google Scholar
  8. E. I. Christensen, B. Grann, I. B. Kristoffersen, E. Skriver, J. S. Thomsen, and A. Andreasen, “Three-dimensional reconstruction of the rat nephron,” American Journal of Physiology—Renal Physiology, vol. 306, no. 6, pp. F664–F671, 2014. View at Publisher · View at Google Scholar
  9. P. Campadelli, E. Casiraghi, and S. Pratissoli, “Automatic segmentation of abdominal organs from CT scans,” in Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI '07), vol. 1, pp. 513–516, IEEE, Patras, Greece, October 2007. View at Publisher · View at Google Scholar
  10. H.-Y. Lee, N. C. F. Codella, M. D. Cham, J. W. Weinsaft, and Y. Wang, “Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 4, pp. 905–913, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Can, H. Shen, J. N. Turner, H. L. Tanenbaum, and B. Roysam, “Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms,” IEEE Transactions on Information Technology in Biomedicine, vol. 3, no. 2, pp. 125–138, 1999. View at Publisher · View at Google Scholar
  12. T. Yedidya and R. Hartley, “Tracking of blood vessels in retinal images using Kalman filter,” in Proceedings of the Digital Image Computing: Techniques and Applications (DICTA '08), pp. 52–58, IEEE, Canberra, Australia, 2008. View at Publisher · View at Google Scholar
  13. B. Karasulu, “Automatic extraction of retinal blood vessels: a software implementation,” European Scientific Journal, vol. 8, no. 30, 2012. View at Google Scholar
  14. X. Kang, Q. Zhao, K. Sharma, R. Shekhar, B. J. Wood, and M. G. Linguraru, “Automatic labeling of liver veins in CT by probabilistic backward tracing,” in Proceedings of the IEEE 11th International Symposium on Biomedical Imaging (ISBI ’14), pp. 1115–1118, IEEE, Beijing, China, April-May 2014. View at Publisher · View at Google Scholar
  15. R. N. Douglas-Denton, J. F. Bertram, B. Diouf, M. D. Hughson, and W. E. Hoy, “Human nephron number: implications for health and disease,” Pediatric Nephrology, vol. 26, no. 9, pp. 1529–1533, 2011. View at Publisher · View at Google Scholar
  16. Y. L. Zhang, S. J. Chang, X. Y. Zhai, J. S. Thomsen, E. I. Christensen, and A. Andreasen, “Non-rigid landmark-based large-scale image registration in 3-D reconstruction of mouse and rat kidney nephrons,” Micron, vol. 68, pp. 122–129, 2015. View at Publisher · View at Google Scholar
  17. J. S. Thomsen, L. Mosekilde, J. Barlach, C. H. Søgaard, and E. Mosekilde, “Computerized determination of 3-D connectivity density in human iliac crest bone biopsies,” Mathematics and Computers in Simulation, vol. 40, no. 3-4, pp. 411–423, 1996. View at Publisher · View at Google Scholar · View at Scopus
  18. K. B. Wagholikar, V. Sundararajan, and A. W. Deshpande, “Modeling paradigms for medical diagnostic decision support: a survey and future directions,” Journal of Medical Systems, vol. 36, no. 5, pp. 3029–3049, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Stoitsis, I. Valavanis, S. G. Mougiakakou, S. Golemati, A. Nikita, and K. S. Nikita, “Computer aided diagnosis based on medical image processing and artificial intelligence methods,” Nuclear Instruments and Methods in Physics Research A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 569, no. 2, pp. 591–595, 2006. View at Publisher · View at Google Scholar
  20. MATLAB Version R2012a, MathWorks, Image Processing Toolbox; Neural Network Toolbox; Statistics Toolbox.
  21. E. R. Davies, Computer & Machine Vision: Theory, Algorithms, Practicalities, Elsevier, Egham, UK, 2012.
  22. L. C. Junqueira and J. Carneiro, Basic Histology—Text & Atlas, The Urinary System, McGraw-Hill, New York, NY, USA, 2005.
  23. W. A. Beresford, “Urinary system,” in Histology Full-Text, chapter 23, Anatomy Department, West Virginia University, 2014, http://wberesford.hsc.wvu.edu/histolch23.htm. View at Google Scholar
  24. P. Henderson, R. Seaby, and R. Somes, Growth II: Types of Growth Curve—Logistic Curve, Pisces Conservation Limited, Hampshire, UK, 2006.
  25. J. Zhang and J. Fan, “Medical image segmentation based on wavelet transformation and watershed algorithm,” in Proceedings of the IEEE International Conference on Information Acquisition, pp. 484–488, IEEE, Shandong, China, August 2006. View at Publisher · View at Google Scholar
  26. G. Gan, C. Ma, and W. Jianhong, “Center-based clustering algorithms,” in Data Clustering Theory, Algorithms and Applications, ASA-SIAM Series on Statistics and Applied Probability, chapter 9, SIAM, Philadelphia, Pa, USA; ASA, Alexandria, Va, USA, 2007. View at Google Scholar
  27. L. Wojnar and K. J. Kurzydłowski, Practical Guide to Image Analysis, ASM International, 2000.
  28. D. H. Ballard and C. M. Brown, Computer Vision, Prentice Hall, Rochester, NY, USA, 1982.
  29. A. Patel, “Stanford Theory Group: Introduction to A*,” 2014, http://theory.stanford.edu/~amitp/GameProgramming/AStarComparison.html.
  30. B. Zitová and J. Flusser, “Image registration methods: a survey,” Image and Vision Computing, vol. 21, no. 11, pp. 977–1000, 2003. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Stoitsisa, I. Valavanis, S. G. Mougiakakou, S. Golemati, A. Nikita, and K. S. Nikita, “Computer aided diagnosis based on medical image processing and artificial intelligence methods,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 569, no. 2, pp. 591–595, 2006. View at Publisher · View at Google Scholar
  32. N. G. Andrew, Machine Learning Course. Coursera Online Courses, 2014, https://class.coursera.org/ml-005.