title: Feature-based image patch approximation for lung tissue classification.
abstract: In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.
authors: Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney 2006, Australia. yson1723@uni.sydney.edu.au
authors: Yang
authors: Y
authors: Song
authors: Weidong
authors: W
authors: Cai
authors: Yun
authors: Y
authors: Zhou
authors: David Dagan
authors: DD
authors: Feng
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000166:cytology* / Q000000981:diagnostic imaging*; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging* / Q000473:pathology*; D008954:Models, Biological; D014057:Tomography, X-Ray Computed