Lung tissue classification using wavelet frames.
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.
Depeursinge
Adrien
A
Service of Medical Informatics, University & University Hospitals of Geneva (HUG), Geneva, Switzerland. adrien.depeursinge@sim.hcuge.ch
Sage
Daniel
D
Hidki
Asmâa
A
Platon
Alexandra
A
Poletti
Pierre-Alexandre
PA
Unser
Michael
M
Müller
Henning
H
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