Abstract
This chapter outlines a new non-invasive method for delineation of skin lesions such as lentigo maligna and lentigo maligna melanoma. The method is based on the analysis of hyperspectral (HS) images taken in vivo before surgical excision of the lesions. For this, characteristic features of the spectral signatures of diseased pixels and healthy pixels are extracted, which combine the intensities in a few selected wavebands with the coefficients of the wavelet frame transforms of the spectral curves. To reduce dimensionality and to reveal the internal structure of the datasets, the diffusion maps (DM) technique is applied. The averaged Nearest Neighbor and the Classification and Regression Tree (CART) classifiers are utilized as the decision units. To reduce false alarms by the CART classifier, the Aisles procedure is used. The chapter is based on the paper (Zheludev et al, Biomed Signal Process Control, 16:48–60, (2015), [15]).
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Averbuch, A.Z., Neittaanmäki, P., Zheludev, V.A. (2019). Delineation of Malignant Skin Tumors by Hyperspectral Imaging. In: Spline and Spline Wavelet Methods with Applications to Signal and Image Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-92123-5_11
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DOI: https://doi.org/10.1007/978-3-319-92123-5_11
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