title: Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames.
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.
authors.affiliation: MedGIFT Group, Business Information Systems, University of Applied Sciences Western Switzerland, Sierre 3960, Switzerland. adrien.depeursinge@hevs.ch
authors.forename: Adrien
authors.initials: A
authors.lastname: Depeursinge
authors.forename: Dimitri
authors.initials: D
authors.lastname: Van de Ville
authors.forename: Alexandra
authors.initials: A
authors.lastname: Platon
authors.forename: Antoine
authors.initials: A
authors.lastname: Geissbuhler
authors.forename: Pierre-Alexandre
authors.initials: PA
authors.lastname: Poletti
authors.forename: Henning
authors.initials: H
authors.lastname: Müller
mesh_terms: D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000033:anatomy & histology* / Q000473:pathology*; D017563:Lung Diseases, Interstitial / Q000473:pathology; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*; D058067:Wavelet Analysis