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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 851014, 9 pages
http://dx.doi.org/10.1155/2015/851014
Research Article

Image-Processing Scheme to Detect Superficial Fungal Infections of the Skin

1Institute of Medical Physics and Radiation Protection, Technische Hochschule Mittelhessen - University of Applied Sciences, 35390 Giessen, Germany
2Helmut Hund GmbH, Artur Herzog Straße 2, 35580 Wetzlar, Germany
3Institute of Bioprocess Engineering and Pharmaceutical Technology, Technische Hochschule Mittelhessen - University of Applied Sciences, 35390 Giessen, Germany
4Department of Dermatology, Venereology and Allergology, Justus Liebig University Giessen, 35390 Giessen, Germany

Received 12 August 2015; Revised 16 October 2015; Accepted 21 October 2015

Academic Editor: Edite Figueiras

Copyright © 2015 Ulf Mäder 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. B. Havlickova, V. A. Czaika, and M. Friedrich, “Epidemiological trends in skin mycoses worldwide,” Mycoses, vol. 51, no. 4, pp. 2–15, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. M. F. R. G. Dias, M. V. P. Quaresma-Santos, F. Bernardes-Filho, A. G. D. F. Amorim, R. C. Schechtman, and D. R. Azulay, “Update on therapy for superficial mycoses: review article part I,” Anais Brasileiros de Dermatologia, vol. 88, no. 5, pp. 764–774, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Ameen, “Epidemiology of superficial fungal infections,” Clinics in Dermatology, vol. 28, no. 2, pp. 197–201, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. B. P. Kelly, “Superficial fungal infections,” Pediatrics in Review, vol. 33, no. 4, pp. e22–e37, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Hay, “Superficial fungal infections,” Medicine, vol. 37, no. 11, pp. 610–612, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. S. C. Deorukhkar and S. Saini, “Laboratory approach for diagnosis of candidiasis through ages,” International Journal of Current Microbiology and Applied Sciences, vol. 3, no. 1, pp. 206–218, 2014. View at Google Scholar
  7. J. Aslanzadeh and G. D. Roberts, “Direct microscopic examination of clinical specimens for the laboratory diagnosis of fungal infections,” Clinical Microbiology Newsletter, vol. 13, no. 24, pp. 185–188, 1991. View at Publisher · View at Google Scholar · View at Scopus
  8. D. J. M. Haldane and E. Robart, “A comparison of calcofluor white, potassium hydroxide, and culture for the laboratory diagnosis of superficial fungal infection,” Diagnostic Microbiology and Infectious Disease, vol. 13, no. 4, pp. 337–339, 1990. View at Publisher · View at Google Scholar · View at Scopus
  9. M. F. de Chauvin, “New diagnostic techniques,” Journal of the European Academy of Dermatology and Venereology, vol. 19, supplement 1, pp. 20–24, 2005. View at Publisher · View at Google Scholar
  10. U. Reichl, T. K. Buschulte, and E. D. Gilles, “Study of the early growth and branching of Streptomyces tendae by means of an image processing system,” Journal of Microscopy, vol. 158, no. 1, pp. 55–62, 1990. View at Publisher · View at Google Scholar · View at Scopus
  11. U. Reichl, H. Yang, E.-D. Gilles, and H. Wolf, “An improved method for measuring the interseptal spacing in hyphae of Streptomyces tendae by fluorescence microscopy coupled with image processing,” FEMS Microbiology Letters, vol. 67, no. 1-2, pp. 207–209, 1990. View at Publisher · View at Google Scholar · View at Scopus
  12. P. W. Cox and C. R. Thomas, “Classification and measurement of fungal pellets by automated image analysis,” Biotechnology and Bioengineering, vol. 39, no. 9, pp. 945–952, 1992. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Papagianni, “Characterization of fungal morphology using digital image analysis techniques,” Journal of Microbial & Biochemical Technology, vol. 6, no. 4, pp. 189–194, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Pulli, A. Baksheev, K. Kornyakov, and V. Eruhimov, “Real-time computer vision with OpenCV,” Communications of the ACM, vol. 55, no. 6, pp. 61–69, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Illingworth and J. Kittler, “A survey of the hough transform,” Computer Vision, Graphics, and Image Processing, vol. 44, no. 1, pp. 87–116, 1988. View at Publisher · View at Google Scholar · View at Scopus
  17. C. E. Metz, “Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems,” Journal of the American College of Radiology, vol. 3, no. 6, pp. 413–422, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. T. Liu, H. Xu, W. Jin, Z. Liu, Y. Zhao, and W. Tian, “Medical image segmentation based on a hybrid region-based active contour model,” Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 890725, 10 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. I. M. Inglis and A. J. Gray, “An evaluation of semiautomatic approaches to contour segmentation applied to fungal hyphae,” Biometrics, vol. 57, no. 1, pp. 232–239, 2001. View at Publisher · View at Google Scholar · View at MathSciNet
  20. T. Baum, A. Navarro-Quezada, W. Knogge, D. Douchkov, P. Schweizer, and U. Seiffert, “HyphArea—automated analysis of spatiotemporal fungal patterns,” Journal of Plant Physiology, vol. 168, no. 1, pp. 72–78, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. R. P. Kumar, F. Albregtsen, M. Reimers, B. Edwin, T. Langø, and O. J. Elle, “Three-dimensional blood vessel segmentation and centerline extraction based on two-dimensional cross-section analysis,” Annals of Biomedical Engineering, vol. 43, no. 5, pp. 1223–1234, 2015. View at Publisher · View at Google Scholar
  22. O. Daniel, F. Schonholzer, and J. Zeyer, “Quantification of fungal hyphae in leaves of deciduous trees by automated image analysis,” Applied and Environmental Microbiology, vol. 61, no. 11, pp. 3910–3918, 1995. View at Google Scholar · View at Scopus
  23. K. G. Tucker, T. Kelly, P. Delgrazia, and C. R. Thomas, “Fully-automatic measurement of mycelial morphology by image analysis,” Biotechnology Progress, vol. 8, no. 4, pp. 353–359, 1992. View at Publisher · View at Google Scholar · View at Scopus
  24. C. J. Vyborny, “Can computers help radiologists read mammograms?” Radiology, vol. 191, no. 2, pp. 315–317, 1994. View at Publisher · View at Google Scholar · View at Scopus
  25. K. Doi, “Current status and future potential of computer-aided diagnosis in medical imaging,” The British Journal of Radiology, vol. 78, supplement 1, pp. s3–s19, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. J. Shiraishi, Q. Li, D. Appelbaum, and K. Doi, “Computer-aided diagnosis and artificial intelligence in clinical imaging,” Seminars in Nuclear Medicine, vol. 41, no. 6, pp. 449–462, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “Histopathological image analysis: a review,” IEEE Reviews in Biomedical Engineering, vol. 2, pp. 147–171, 2009. View at Publisher · View at Google Scholar · View at Scopus