Journal of Medical Systems

, Volume 35, Issue 5, pp 895–904 | Cite as

A Hand-held Mosaicked Multispectral Imaging Device for Early Stage Pressure Ulcer Detection

Original Paper


The use of a custom filter mosaic overlaying a CMOS/CCD sensor represents a novel idea to multispectral imaging. The innovation provides simple, miniaturized, low cost instrumentation that has many potential biological applications which require a hand-held detector. This makes it extremely adaptable and can serve as an integrated component to distributed diagnosis and home healthcare (D2H2). A mosaicked sensor is a monolithic array of many sensors, arranged in a geometric pattern with each sensor covered by an optical filter sensitive to a specified wavelength. In this way, only one spectral component is sensed at each pixel and the other spectral components must be estimated from neighbors. Although with great potential, one challenge faced by this device, however, is the reconstruction of the high-resolution full-spectral image from the low-resolution input. Due to the physical limitations in fabrication and the usage of the multispectral filter mosaic, two types of degradations exist, including filter misalignment and the missing spectral components, that must be corrected using intelligent algorithms to take full advantage of the hardware capability of the device. In this paper, we first describe a custom geometric correction method to restore the image from the misalignment distortion. We then present a binary tree-based generic demosaicking algorithm to efficiently estimate the missing special components and reconstruct a high-resolution full-spectral image. We choose early detection of pressure ulcer as a targeting area as early stage pressure ulcers and other subcutaneous lesions are nearly invisible in clinical settings, particularly so for dark pigmented skin. We show how the geometric correction and demosaicking algorithms successfully reconstruct a full-spectral image from which apparent contrast enhancement between damaged skin and the normal skin is observed.


Multispectral imaging Filter mosaic Demosaicking Early detection 


  1. 1.
    Karacali, B., and Snyder, W., Automatic target detection using multispectral imaging. In: 31st Applied Imagery Pattern Recognition Workshop. p. 55. Washington, DC, 2002.Google Scholar
  2. 2.
    Chen, Y. R., Chao, K., and Kim, M. S., Machine vision technology for agriculture applications. Comput. Electron. Agric. 36(2–3):173–191, 2002.CrossRefGoogle Scholar
  3. 3.
    Blackman, G., Surface inspection—scanning the surface. In: Imaging and Machine Vision Europe, 2009.Google Scholar
  4. 4.
    Lu, R., and Park, B., Hyperspectral and multispectral imaging for food quality and safety. Sensing and Instrumentation for Food Quality and Safety 2(3):131–132, 2008.CrossRefGoogle Scholar
  5. 5.
    Lu, R., Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Elsevier Journal on Postharvest Biology and Technology 31:147–157, 2004.CrossRefGoogle Scholar
  6. 6.
    Miao, L., Qi, H., and Szu, H., A maximum entropy approach to unsupervised mixed pixel decomposition. IEEE Trans. Image Process 16(4):1008–1021, 2007.CrossRefMathSciNetGoogle Scholar
  7. 7.
    Silva, D. M., and Abileah, R., System and method for multispectral image processing of ocean imagery. United State Patent 6304664, 2010.Google Scholar
  8. 8.
    Wu, Q., Zeng, L., Ke, H., Zheng, H., Gao, X., and Wang, D., A multispectral imaging analysis system for early detection of cervical cancer. In: Medical Imaging: Physics of Medical Imaging. Vol. 5745, pp. 801–809. SPIE, 2005.Google Scholar
  9. 9.
    Levenson, R. M., Lynch, D. T., Kobayashi, H., Backer, J. M., and Backer, M. V., Multiplexing with multispectral imaging: from mice to microscopy. ILAR J. (Institute for Laboratory Animal Research). 49(1):78–88, 2008.Google Scholar
  10. 10.
    Levenson, R. M., and Mansfield, J. R., Multispectral imaging in biology and medicine: Slices of life. Cytometry: Part A. 69A(8):748–758, 2006.CrossRefGoogle Scholar
  11. 11.
    Scribner, D. A., Schuler, J., and Kruer, M. R., Infrared multispectral sensors: re-considering typical design assumptions. Naval Research Lab., Code 5636, 1998.Google Scholar
  12. 12.
    Barrie, J. D., Aitchison, K. A., Rossano, G. S., and Abraham, M. H., Patterning of multilayer dielectric optical coating for multispectral CCDs. Thin Solid Films 270(1–2):6–9, 1995.CrossRefGoogle Scholar
  13. 13.
    Kong, L., Sprigle, S., Duckworth, M., Yi, D., Caspall, J., Wang, J., and Zhao, F., Handheld erythema and bruise detector. In: Proceedings of SPIE—Medical Imaging: Computer-Aided Diagnosis. Vol. 6915, 2008.Google Scholar
  14. 14.
    Kong, L., Yi, D., Sprigle, S., Wang, F., Wang, C., Liu, F., Adibi, A., and Tummala, R., Single sensor that outputs narrowband multispectral images. J. Biomed. Opt. 15:010502, 2010.CrossRefGoogle Scholar
  15. 15.
    Themelis, G., Yoo, J. S., and Ntziachristos, V., Multispectral imaging using multiple-band pass filters. Opt. Lett. 33(9):1023, 2008.CrossRefGoogle Scholar
  16. 16.
    Vila, J., Calpe, J., Pla, F., Gomez, L., Connell, J., Marchant, J., Calleja, J., Mulqueen, M., Munoz, J., and Klaren, A., SmartSpectra: Applying multispectral imaging to industrial environments. Real-Time Imaging 11:85–98, 2005.CrossRefGoogle Scholar
  17. 17.
    Bayer, E. B., Color imaging array. United States Patent 3,971,065, 1976.Google Scholar
  18. 18.
    Packer, O., and Williams, D. R., Light, the retinal image, and photoreceptors. In: Shevell, S. K. (Ed.), The Science of Color. pp. 41–102. Optical Society of America, 2003.Google Scholar
  19. 19.
    Ramanath, R., Snyder, W. E., and Bilbro, G., Demosaicking methods for bayer color arrays. J. Electron. Imaging 11(3):306–315, 2002.CrossRefGoogle Scholar
  20. 20.
    Lukac, R., Martin, K., and Plataniotis, K. N., Demosaikced image postprocessing using local color ratios. EEE Trans. Circuits Syst. Video Technol. 14(6):914–920, 2004.CrossRefGoogle Scholar
  21. 21.
    Chang, L., and Tan, Y. P., Effective use of spatial and spectral correlations for color filter array demosaicking. IEEE Trans. Consum. Electron. 50(1):355–365, 2004.CrossRefGoogle Scholar
  22. 22.
    Gunturk, B. K., Altunbasak, Y., and Mersereau, R. M., Color plane interpolation using alternating projections. IEEE Trans. Image Process. 11(9):997–1013, 2002.CrossRefGoogle Scholar
  23. 23.
    Li, X., and Orchard, M. T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10):1521–1527, 2001.CrossRefGoogle Scholar
  24. 24.
    Miao, L., Qi, H., Ramanath, R., and Snyder, W. E., Binary tree-based generic demosaicking algorithm for multispectral filter arrays. IEEE Trans. Image Process. 15(11):3550–3558, 2006.CrossRefGoogle Scholar
  25. 25.
    Ramanath, R., Snyder, W. E., and Qi, H., Mosaic multispectral focal plane array cameras. In: SPIE Defense and Security Symposium, Orlando (Kissimmee), FL, 12–16 April 2004.Google Scholar
  26. 26.
    Sprigle, S., Zhang, L., and Duckworth, M., Detection of skin erythema in darkly pigmented skin using multispectral images. Skin & Wound Care 22(4):172–179, 2009.CrossRefGoogle Scholar
  27. 27.
    Mersereau, R., The processing of hexagonally samples two-dimensional signals. Proc. IEEE 67(6):930–949, 1979.CrossRefGoogle Scholar
  28. 28.
    Middleton, L., and Sivaswamy, J., Edge detection in a hexagonal-image processing framework. Image Vis. Comput. 19(14):1071–1081, 2001.CrossRefGoogle Scholar
  29. 29.
    Miao, L., and Qi, H., The design and evaluation of a generic method for generating mosaicked multispectral filter arrays. IEEE Trans. Image Process. 15(9):2780–2791, 2006.CrossRefGoogle Scholar
  30. 30.
    Lu, W., and Tan, Y. P., Color filter array demosaicking: new method and performance measures. IEEE Trans. Image Process. 12(10):1194–1210, 2003.CrossRefGoogle Scholar
  31. 31.
    Kimmel, R., Demosaicing: Image reconstruction from color ccd samples. IEEE Trans. Image Process. 8(9):1221–1228, 1999.CrossRefGoogle Scholar
  32. 32.
    Mosby’s Medical Dictionary, 8th edn. Elsevier, 2009.
  33. 33.
    Mcgraw-Hill Concise Dictionary of Modern Medicine. The Mcgraw-Hill Companies, Inc., 2002.

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Hairong Qi
    • 1
  • Linghua Kong
    • 2
  • Chao Wang
    • 2
  • Lidan Miao
    • 3
  1. 1.Electrical Engineering and Computer Science DepartmentUniversity of TennesseeKnoxvilleUSA
  2. 2.Center for Assistive Technology and Environmental AccessGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Microsoft, Inc.SeattleUSA

Personalised recommendations