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Lung Tissue Classification Using Wavelet Frames

A. Depeursinge, D. Sage, A. Hidki, A. Platon, P.-A. Poletti, M. Unser, H. Müller

Proceedings of the Twenty-Ninth Annual International Conference of the IEEE Engineering in Medicine and Biology Society, in conjunction with the biennial Conference of the French Society of Biological and Medical Engineering (EMBC'07), Lyon, French Republic, August 23-26, 2007, pp. 6259-6262.



We describe a texture classification system that identifies lung tissue patterns from high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD). This pattern recognition task is part of an image-based diagnostic aid system for ILDs. Five lung tissue patterns (healthy, emphysema, ground glass, fibrosis and microdules) selected from a multimedia database are classified using the overcomplete discrete wavelet frame decompostion combined with grey-level histogram features. The overall multiclass accuracy reaches 92.5% of correct matches while combining the two types of features, which are found to be complementary.


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