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Poster communications

Texture Characterization of Tumors

Abstract : A new approach for texture characterization of tumors based on a 2D S-Transform. Previous methods have already been developed using transforms such as the wavelet transform or the Gabor transform. However, these transforms are either not frequency invariant (wavelet transform) or have a fixed resolution (Gabor transform) To solve these problems we employ a 2D S-Transform. The S-Transform, a generalization of the Short-time Fourier transform, provides a time-frequency distribution of a signal. Therefore one can obtain the frequency content of a pixel or of a tumor ROI by averaging pixel spectrums over the tumor . A tool has been developed that computes the S-Transform in real time for a pixel and in 2 or 3 seconds for a tumor, while previous methods take much longer time. To quantify the tumor texture we compute statistics based on pixel spectrums. The first statistic, a texture curve, is the frequency vs its average power at each pixel or over the entire tumor. The second statistic, for a tumor, is a KL-divergence to calculate the deviation of histograms, obtained at each frequency, from a normal distribution. Finally, a map of the area under the texture curve for a band of frequencies shows the average power at each pixel of the tumor. First experiments on images of 20 tumor-bearing patients (10 for the training set, 10 for the test set) using the texture curves allowed us to classify homogeneous and heterogeneous tumors with an accuracy of around 80%.
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Poster communications
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Contributor : Paul Morel <>
Submitted on : Thursday, February 23, 2012 - 3:35:42 PM
Last modification on : Thursday, March 7, 2013 - 11:42:46 AM
Long-term archiving on: : Thursday, May 24, 2012 - 3:02:14 AM


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  • HAL Id : hal-00673453, version 1



Paul Morel, Changizi Neda, Cheng Hing, Mitchell Joseph Ross. Texture Characterization of Tumors. Care About Cancer, Edmonton, AB, Canada, 16/06/2011-18/06/2011, Jun 2011, Edmonton, Alberta, Canada. pp.78, 2011. ⟨hal-00673453⟩



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