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No-reference Blur Assessment of Dermatological Images Acquired via Mobile Devices

  • Maria João M. Vasconcelos
  • Luís Rosado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8509)

Abstract

One of the most important challenges of dealing with digital images acquired under uncontrolled conditions is the capability to assess if the image has enough quality to be further analyzed. In this scenario, blur can be considered as one of the most common causes for quality degradation in digital pictures, particularly in images acquired using mobile devices. In this study, we collected a set of 78 features related with blur detection and further analyzed its individual discriminatory ability for two dermatologic image datasets. For the dataset of dermoscopic images with artificially induced blur, high separation levels were obtained for the features calculated using DCT/DFT and Lapacian groups, while for the dataset of mobile acquired images, the best results were obtained for features that used Laplacian and Gradient groups.

Keywords

Mobile image assessment dermatology blur distortion feature extraction 

References

  1. 1.
    Kittler, H., Pehamberger, H., Wolff, K., Binder, M.: Diagnostic accuracy of dermoscopy. The Lancet Oncology 3(3), 159–165 (2002)CrossRefGoogle Scholar
  2. 2.
    Thung, K.H., Raveendran, P.: A survey of image quality measures. In: International Conference for Technical Postgraduates (TECHPOS), pp. 1–4. IEEE (2009)Google Scholar
  3. 3.
    Chandler, D.M.: Seven Challenges in Image Quality Assessment: Past, Present, and Future Research. ISRN Signal Processing 2013, 1–53 (2013)Google Scholar
  4. 4.
    Pertuz, S., Puig, D., Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recognition 46(5), 1415–1432 (2013)CrossRefzbMATHGoogle Scholar
  5. 5.
    Ko, J., Kim, C.: Low cost blur image detection and estimation for mobile devices. In: 11th International Conference on Advanced Communication Technology (ICACT 2009), pp. 1605–1610. IEEE (2009)Google Scholar
  6. 6.
    Nunnagoppula, G., Deepak, K.S., Harikrishna, G., Rai, N., Krishna, P.R., Vesdapunt, N.: Automatic blur detection in mobile captured document images: Towards quality check in mobile based document imaging applications. In: 2013 IEEE Second International Conference on Image Information Processing (ICIIP), pp. 299–304. IEEE (2013)Google Scholar
  7. 7.
    Mendonca, T., Ferreira, P.M., Marques, J.S., Marcal, A.R.S., Rozeira, J.: PH2 - A dermoscopic image database for research and benchmarking. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5437–5440. IEEE EMBC (2013)Google Scholar
  8. 8.
    Fraunhofer Portugal AICOS: Melanoma Detection, Internal Project, http://www.fraunhofer.pt/en/fraunhofer_aicos/projects/internal_research/melanoma_detection.html
  9. 9.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media Inc., Sebastopol (2008)Google Scholar
  10. 10.
    Lopez, X.M., D’Andrea, E., Barbot, P., Bridoux, A.S., Rorive, S., Salmon, I., Decaestecker, C.: An Automated Blur Detection Method for Histological Whole Slide Imaging. PLoS ONE 8,12, e82710 (2013)Google Scholar
  11. 11.
    Subbarao, M., Choi, T.S., Nikzad, A.: Focusing Techniques. Journal of Optical Engineering 32(11), 2824–2836 (1993)CrossRefGoogle Scholar
  12. 12.
    Huang, W., Jing, Z.: Evaluation of focus measures in multi-focus image fusion. Pattern Recognition Letters 28(4), 493–500 (2007)CrossRefGoogle Scholar
  13. 13.
    Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Transactions on Communications 43(12), 2959–2965 (1995)CrossRefGoogle Scholar
  14. 14.
    Santos, A., Ortiz de Solorzano, C., Vaquero, J.J., Pena, J.M., Malpica, N., Del Pozo, F.: Evaluation of autofocus functions in molecular cytogenetic analysis. Journal of Microscopy 188(3), 264–272 (1997)CrossRefGoogle Scholar
  15. 15.
    Krotkov, E., Martin, J.P.: Range from focus. In: IEEE International Conference on Robotics and Automation, vol. 3, pp. 1093–1108. IEEE (1986)Google Scholar
  16. 16.
    Pech-Pacheco, J.L., Cristobal, G., Chamorro-Martinez, J., Fernandez-Valdivia, J.: Diatom autofocusing in brightfield microscopy: A comparative study. In: 15th International Conference on Pattern Recognition, vol. 3, pp. 314–317. IEEE (2000)Google Scholar
  17. 17.
    Nayar, S.K., Nakagawa, Y.: Shape from focus. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(8), 824–831 (1994)CrossRefGoogle Scholar
  18. 18.
    Thelen, A., Frey, S., Hirsch, S., Hering, P.: Improvements in Shape-From-Focus for Holographic Reconstructions With Regard Focus Operators, Neighborhood-Size, Height Value Interpolation. IEEE Trans. on Image Processing 18(1), 151–157 (2009)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Chen, J.S., Huertas, A., Medioni, G.: Fast Convolution with Laplacian-of-Gaussian Masks. IEEE Trans. Pattern Analysis and Machine Intelligence 4, 584–590 (1987)CrossRefGoogle Scholar
  20. 20.
    Firestone, L., Cook, K., Culp, K., Talsania, N., Preston, K.: Comparison of autofocus methods for automated microscopy. Cytometry 12(3), 195–206 (1991)CrossRefGoogle Scholar
  21. 21.
    Baina, J., Dublet, J.: Automatic focus and iris control for video cameras. In: Fifth Int. Conference on Image Processing and its Applications, pp. 232–235. IET (1995)Google Scholar
  22. 22.
    Krotkov, E.: Focusing. Intern. Journal of Computer Vision 1(3), 223–237 (1998)CrossRefGoogle Scholar
  23. 23.
    Helmli, F.S., Scherer, S.: Adaptive shape from focus with an error estimation in light microscopy. In: 2nd International Symposium on Image and Signal Processing and Analysis, pp. 188–193. IEEE (2001)Google Scholar
  24. 24.
    Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: A no-reference perceptual blur metric. In: Intern. Conf. on Image Processing, vol. 3, pp. III-57–III-60. IEEE (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maria João M. Vasconcelos
    • 1
  • Luís Rosado
    • 1
  1. 1.Fraunhofer Portugal AICOSPortoPortugal

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