title: Lung tissue classification using wavelet frames.
abstract: 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.
authors.affiliation: Service of Medical Informatics, University & University Hospitals of Geneva (HUG), Geneva, Switzerland. adrien.depeursinge@sim.hcuge.ch
authors.forename: Adrien
authors.initials: A
authors.lastname: Depeursinge
authors.forename: Daniel
authors.initials: D
authors.lastname: Sage
authors.forename: Asmâa
authors.initials: A
authors.lastname: Hidki
authors.forename: Alexandra
authors.initials: A
authors.lastname: Platon
authors.forename: Pierre-Alexandre
authors.initials: PA
authors.lastname: Poletti
authors.forename: Michael
authors.initials: M
authors.lastname: Unser
authors.forename: Henning
authors.initials: H
authors.lastname: Müller
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted