AUTHORS: Depeursinge A, Van de Ville D, Platon A, Geissbuhler A, Poletti PA, Muller H

IEEE Transactions on Information Technology in Biomedicine, 16(4): 665-75, July 2012

ABSTRACT

We propose near-affine-invariant texture descriptors derived from isotropic wavelet frames for the characterization of lung tissue patterns in high-resolution computed tomography (HRCT) imaging. Affine invariance is desirable to enable learning of nondeterministic textures without a priori localizations, orientations, or sizes. When combined with complementary gray-level histograms, the proposed method allows a global classification accuracy of 76.9% with balanced precision among five classes of lung tissue using a leave-one-patient-out cross validation, in accordance with clinical practice.

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