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Feature-Based Image Patch Approximation for Lung Tissue Classification

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4 Author(s)
Yang Song ; Biomed. & Multimedia Inf. Technol. (BMIT) Res. Group, Univ. of Sydney, Sydney, NSW, Australia ; Weidong Cai ; Yun Zhou ; Feng, D.D.

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.

Published in:

Medical Imaging, IEEE Transactions on  (Volume:32 ,  Issue: 4 )