Feature-based image patch approximation for lung tissue classification

Yang Song, Weidong Cai, Yun Zhou, David Dagan Feng

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number6415277
Pages (from-to)797-808
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number4
DOIs
StatePublished - 2013

Keywords

  • Adaptive
  • gradient
  • reference
  • texture

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software

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