Abstract
Supervised classification methods require reliable and consistent training sets. In image analysis, where class labels are often assigned to the entire image, the manual generation of pixel-accurate class labels is tedious and time consuming. We present an independent component analysis (ICA)-based method to generate these pixel-accurate class labels with minimal user interaction. The algorithm is applied to the detection of skin cancer in hyperspectral images. Using this approach it is possible to remove artifacts caused by sub-optimal image acquisition. We report on the classification results obtained for the hyper-spectral skin cancer data set with 300 images using support vector machines (SVM) and model-based discriminant analysis (MclustDA, MDA).
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© 2008 Springer-Verlag Berlin Heidelberg
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Kazianka, H., Leitner, R., Pilz, J. (2008). Segmentation and Classification of Hyper-Spectral Skin Data. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_29
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DOI: https://doi.org/10.1007/978-3-540-78246-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
Online ISBN: 978-3-540-78246-9
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