Abstract
When attempting to recover the surface color from an image, modelling the illumination contribution per-pixel is essential. In this work we present a novel approach for illumination compensation using multispectral image data. This is done by means of a low-rank decomposition of representative spectral bands with prior knowledge of the reflectance spectra of the imaged surface. Experimental results on synthetic data, as well as on images of real lesions acquired at the university clinic, show that the proposed method significantly improves the contrast between the lesion and the background.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Filter-wheel multispectral cameras sequentially acquires multiple images of different spectral bands, each time exposing the sensor through a different filter.
References
Basri, R., Jacobs, D., Kemelmacher, I.: Photometric stereo with general, unknown lighting. Int. J. Comput. Vis. 72(3), 239–257 (2007)
Chen, W., Er, M.J., Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(2), 458–466 (2006)
Cheng, Y.I., Swamisai, R., Umbaugh, S.E., Moss, R.H., Stoecker, W.V., Teegala, S., Srinivasan, S.K.: Skin lesion classification using relative color features. Skin Res. Technol. 14(1), 53–64 (2008)
Cui, X., Huang, J., Zhang, S., Metaxas, D.N.: Background subtraction using low rank and group sparsity constraints. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 612–625. Springer, Heidelberg (2012)
Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans Graph. (TOG). 21, 257–266 (2002)
Fattal, R., Agrawala, M., Rusinkiewicz, S.: Multiscale shape and detail enhancement from multi-light image collections. ACM Trans. Graph. 26(3), 51 (2007)
Li, Y., Sharan, L., Adelson, E.H.: Compressing and companding high dynamic range images with subband architectures. ACM Trans. Graph. (TOG). 24, 836–844 (2005)
Nguyen, N.H., Lee, T.K., Atkins, M.S.: Segmentation of light and dark hair in dermoscopic images: a hybrid approach using a universal kernel. In: SPIE Medical Imaging. p. 76234N, International Society for Optics and Photonics (2010)
Park, J.I., Lee, M.H., Grossberg, M.D., Nayar, S.K.: Multispectral imaging using multiplexed illumination. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8 (2007)
Samaras, D., Metaxas, D., Fua, P., Leclerc, Y.G.: Variable albedo surface reconstruction from stereo and shape from shading. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. vol. 1, pp. 480–487. IEEE (2000)
Shi, Z., Govindaraju, V.: Historical document image enhancement using background light intensity normalization. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 1, pp. 473–476. IEEE (2004)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, 1998, pp. 839–846. IEEE (1998)
Vogel, A., Chernomordik, V.V., Demos, S.G., Pursley, R., Little, R.F., Tao, Y., Gandjbakhche, A.H., Yarchoan, R., Riley, J.D., Hassan, M., et al.: Using noninvasive multispectral imaging to quantitatively assess tissue vasculature. J. Biomed. Opt. 12(5), 051604–051604 (2007)
Zheng, J., Li, Z., Rahardja, S., Yao, S., Yao, W.: Collaborative image processing algorithm for detail refinement and enhancement via multi-light images. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1382–1385. IEEE (2010)
Acknowledgements
This work was partially funded by the TUM Graduate School of Information Science in Health.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Duliu, A., Brosig, R., Ognawala, S., Lasser, T., Ziai, M., Navab, N. (2015). Illumination Compensation and Normalization Using Low-Rank Decomposition of Multispectral Images in Dermatology. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-19992-4_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19991-7
Online ISBN: 978-3-319-19992-4
eBook Packages: Computer ScienceComputer Science (R0)