Abstract
In this paper, we present variants of the Dual-Tree Complex Wavelet Transform (DT-CWT) in order to automatically classify endoscopic images with respect to the Marsh classification. The feature vectors either consist of the means and standard deviations of the subbands from a DT-CWT variant or of the Weibull parameter of these subbands. To reduce the effects of different distances and perspectives toward the mucosa, we enhanced the scale invariance by applying the discrete Fourier transform or the discrete cosine transform across the scale dimension of the feature vector.
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Uhl, A., Vécsei, A., Wimmer, G. (2011). Complex Wavelet Transform Variants in a Scale Invariant Classification of Celiac Disease. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_92
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DOI: https://doi.org/10.1007/978-3-642-21257-4_92
Publisher Name: Springer, Berlin, Heidelberg
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