Multidimensional Texture Analysis for Improved Prediction of Ultrasound Liver Tumor Response to Chemotherapy Treatment
The number density of scatterers in tumor tissue contribute to a heterogeneous ultrasound speckle pattern that can be difficult to discern by visual observation. Such tumor stochastic behavior becomes even more challenging if the tumor texture heterogeneity itself is investigated for changes related to response to chemotherapy treatment. Here we define a new tumor texture heterogeneity model for evaluating response to treatment. The characterization of the speckle patterns is performed via state-of-the-art multi-orientation and multi-scale circular harmonic wavelet (CHW) frames analysis of the envelope of the radio-frequency signal. The lacunarity measure – corresponding to scatterer number density – is then derived from fractal dimension texture maps within the CHW decomposition, leading to a localized quantitative assessment of tumor texture heterogeneity. Results indicate that evaluating tumor heterogeneity in a multidimensional texture analysis approach could potentially impact on designing an early and effective chemotherapy treatment.
KeywordsFractal Dimension Speckle Pattern Fractal Texture Nakagami Distribution Localize Fractal Dimension
We would like to thank Dr. Daniel Y.F. Chung for providing the ultrasound dataset. This work was partially supported by the Swiss National Science Foundation (grant PZ00P2_154891) and the Arab Fund (grant 2015-02-00627).
- 1.Bae, Y.H., Mrsny, R., Park, K.: Cancer Targeted Drug Delivery: An Elusive Dream, pp. 689–707. Springer, New York (2013)Google Scholar
- 2.Sadeghi-Naini, A., Papanicolau, N., Falou, O., Zubovits, J., Dent, R., Verma, S., Trudeau, M., Boileau, J.F., Spayne, J., Iradji, S., Sofroni, E., Lee, J., Lemon-Wong, S., Yaffe, M., Kolios, M.C., Czarnota, G.J.: Quantitative ultrasound evaluation of tumor cell death response in locally advanced breast cancer patients receiving chemotherapy. Clin. Cancer Res. 19(8), 2163–2174 (2013)CrossRefGoogle Scholar
- 13.Depeursinge, A., Püspöki, Z., et al.: Steerable wavelet machines (SWM): learning moving frames for texture classification. IEEE Trans. Image Process. (submitted)Google Scholar