Novel approach for the automated detection of allergy test using spectral imaging
Author(s)
Issa Ibraheem*
ABSTRACT
This paper proposes a novel approach for the automatic detection of allergy test (allergy lesion). A hyperspectral microscope system was used to image the test samples which were diagnosed by dermatologist. It was found that allergy of different levels, and healthy skin cells show absorption spectra, which are sufficiently characteristic and yet reproducible enough to allow for differentiation when using a spectroscopic system. Principal components analysis was used to extract relevant features that could be used for classification from these spectra. Preliminary results indicate that the different types of allergy cells can be reliably distinguished by these features. We conclude that hyperspectral microscopic analysis is a promising approach for improving and automating the diagnosis of allergy test as well as another skin lesions.
This paper proposes a novel approach for the automatic detection of allergy test (allergy lesion). A hyperspectral microscope system was used to image the test samples which were diagnosed by dermatologist. It was found that allergy of different levels, and healthy skin cells show absorption spectra, which are sufficiently characteristic and yet reproducible enough to allow for differentiation when using a spectroscopic system. Principal components analysis was used to extract relevant features that could be used for classification from these spectra. Preliminary results indicate that the different types of allergy cells can be reliably distinguished by these features. We conclude that hyperspectral microscopic analysis is a promising approach for improving and automating the diagnosis of allergy test as well as another skin lesions.
Cite this paper
Ibraheem, I. (2012) Novel approach for the automated detection of allergy test using spectral imaging. Journal of Biomedical Science and Engineering, 5, 416-421. doi: 10.4236/jbise.2012.58053.
Ibraheem, I. (2012) Novel approach for the automated detection of allergy test using spectral imaging. Journal of Biomedical Science and Engineering, 5, 416-421. doi: 10.4236/jbise.2012.58053.
References
[1] Farkas, D.L., et al. (2001) Applications of spectral imaging: Detection and analysis of human melanoma and its precursors. Pigment Cell Research, 14, 2-8. doi:10.1034/j.1600-0749.2001.140102.x [2] Leitner, R., Ibraheem, I. and Kercek, A. (2005) Spectral imaging as a modern tool for medical diagnostics. Austrian Computer Society, Vienna, 31-34. [3] Ibraheem, I., Leitner, R., Mairer, H., Cerroni, L. and Smolle, J. (2006) Hyperspectral analysis of stained histological preparations for the detection of melanoma. Proceeding of 3rd International Workshop on Spectral Imaging, Graz, 13 May 2006. [4] Duda, I.R.O., Hart, P.E. andStork, D.G. (2001) Pattern classification. 2nd Edition, John Wiley & Sons, New York. [5] Fix, E. and Hodges, J.L. (1989) Discriminatory analysis—nonparam etric discrimination: Consistency properties. International Statistical Review, 57, 238247. [6] Bajcsy, P. and Groves, P. (2004) Methodology for hyperspectral band selection. Photogrammetric Engineering and Remote Sensing Journal, 70, 793-802. [7] Sotoca, J.M., Plam F. and Klaren, A.C. (2004) Unsupervised band selection for multispectral images using information theory. International Conference on Pattern Recognition, 23-26 August 2006, 510-513. [8] Hillenkamp, F. and Grabbe, S. (1993) Die physikalischen grundlagen optischer strahlung auf die haut. In: E. Macher, G. Kolde and E.B. Brcker, Jahrbuch der dermatologie, Zulpich, Biermann-Verlag, 13-30. [9] Ibraheem I. (2011) Linear and quadratic classifier to detection of skin lesions “epicutaneus”. 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, 31 May 2011, 1-5.
[1] Farkas, D.L., et al. (2001) Applications of spectral imaging: Detection and analysis of human melanoma and its precursors. Pigment Cell Research, 14, 2-8. doi:10.1034/j.1600-0749.2001.140102.x [2] Leitner, R., Ibraheem, I. and Kercek, A. (2005) Spectral imaging as a modern tool for medical diagnostics. Austrian Computer Society, Vienna, 31-34. [3] Ibraheem, I., Leitner, R., Mairer, H., Cerroni, L. and Smolle, J. (2006) Hyperspectral analysis of stained histological preparations for the detection of melanoma. Proceeding of 3rd International Workshop on Spectral Imaging, Graz, 13 May 2006. [4] Duda, I.R.O., Hart, P.E. andStork, D.G. (2001) Pattern classification. 2nd Edition, John Wiley & Sons, New York. [5] Fix, E. and Hodges, J.L. (1989) Discriminatory analysis—nonparam etric discrimination: Consistency properties. International Statistical Review, 57, 238247. [6] Bajcsy, P. and Groves, P. (2004) Methodology for hyperspectral band selection. Photogrammetric Engineering and Remote Sensing Journal, 70, 793-802. [7] Sotoca, J.M., Plam F. and Klaren, A.C. (2004) Unsupervised band selection for multispectral images using information theory. International Conference on Pattern Recognition, 23-26 August 2006, 510-513. [8] Hillenkamp, F. and Grabbe, S. (1993) Die physikalischen grundlagen optischer strahlung auf die haut. In: E. Macher, G. Kolde and E.B. Brcker, Jahrbuch der dermatologie, Zulpich, Biermann-Verlag, 13-30. [9] Ibraheem I. (2011) Linear and quadratic classifier to detection of skin lesions “epicutaneus”. 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, 31 May 2011, 1-5.