Early detection of melanoma using multispectral imaging and artificial intelligence techniques
American Journal of Biomedical and Life Sciences
Volume 3, Issue 2-3, April 2015, Pages: 29-33
Received: Dec. 18, 2014;
Accepted: Dec. 19, 2014;
Published: Aug. 7, 2015
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Moataz Aboras, Department of Biomedical Engineering, FMEE, Damascus University and Faculty of Informatics and Communication Engineering, Arab International University, Damascus, Syria
Hani Amasha, Department of Biomedical Engineering, FMEE, Damascus University and Faculty of Informatics and Communication Engineering, Arab International University, Damascus, Syria
Issa Ibraheem, Department of Biomedical Engineering, FMEE, Damascus University and Faculty of Informatics and Communication Engineering, Arab International University, Damascus, Syria
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Biomedical spectral imaging is a non-invasive, non-destructive method, and has an important role in melanoma detection and all skin lesions monitoring during their various stages. In addition to spatial information, it contains spectral information that describes structure such as melanin content, and melanoma thickness, which, very well improve the sensitivity and specificity of melanoma detection. This article aims to describe the design of a multispectral imaging system that utilizes Artificial Neural Networks and Genetic Algorithm (Artificial Intelligence) for spectral images classification, in order to reduce the processing time of spectral images, memory and cost of the system. All system (Hardware and Software) works as an automatic detection system for malignant melanoma, which identifies malignant melanoma and common (benign) nevi by using wavelength scanning method with; CCD camera, filters wheel, and only eight optical filters range from 430nm to 620nm. 47 study cases were imaged. Good results were obtained: the sensitivity 91.67% and the specificity 91.43%.
Melanoma Detection, Spectral Imaging, Artificial Intelligence, Artificial Neural Networks, Genetic Algorithm, Images Classification
To cite this article
Early detection of melanoma using multispectral imaging and artificial intelligence techniques, American Journal of Biomedical and Life Sciences. Special Issue: Spectral Imaging for Medical Diagnosis “Modern Tool for Molecular Imaging”.
Vol. 3, No. 2-3,
2015, pp. 29-33.
Arnold T., Leitner R., Wuertz G. F., and Elbischger J. P., “Spot Counting for Automated Analysis of Unmixed Hyper-Spectral M-FISH Images”, World Academy of Science, Engineering and Technology 44, pp. 247-248, 2008.
De Raeve L., “Risk factors for paediatric melanoma”, 3rd World Meeting of Interdisciplinary Melanoma/Skin Cancer Centers, November 19–21, Ellington Hotel, Berlin, Germany, pp. 5, 2009.
Diebele I., Kuzmina I., Lihachev A., Kapostinsh J., Derjabo A., Valeine L., and Spigulis J., “Clinical Evaluation of Melanomas and Common Nevi by Spectral Imaging”, Biomedical Optics Express, Vol. 3, No. 3, pp. 467-471, 2012.
Gomez B. R., “Hyperspectral Imaging: a Useful Technology for Transportation Analysis”, Optical Engineering, Vol. 41, No. 9, pp. 2137-2139, 2002.
Ibraheem I., Leitner R., Mairer H., Cerroni L., and Smolle J., “Hyper-spectral Analysis of Stained Histological Preparations for the Detection of Melanoma”, Proceedings of the 3rd International Spectral Imaging Workshop, May 13, Graz, Austria. pp. 24-30, 2006.
Kise M., Park B., Heitschmidt W. G., Lawrence C. K., and Windham R. W., “Multispectral Imaging System with Interchangeable Filter Design”, Computers and Electronics in Agriculture 72, pp. 61-68, 2010.
Patwardhan V. S., Dai S., and Dhawan P. A., “Multi-spectral Image Analysis and Classification of Melanoma Using Fuzzy Membership Based Partitions”, Computerized Medical Imaging and Graphics 29, pp. 287-295, 2005.
Psaty L. E., and Halpern C. A., “Current and Emerging Technologies in Melanoma Diagnosis: The state of The art”, Clinics in Dermatology 27, pp. 35-39, 2009.