Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT

Abstract

Identification and characterization of diffuse parenchyma lung disease (DPLD) patterns challenges computer-aided schemes in computed tomography (CT) lung analysis. In this study, an automated scheme for volumetric quantification of interstitial pneumonia (IP) patterns, a subset of DPLD, is presented, utilizing a multidetector CT (MDCT) dataset. Initially, lung-field segmentation is achieved by 3-D automated gray-level thresholding combined with an edge-highlighting wavelet preprocessing step, followed by a texture-based border refinement step. The vessel tree volume is identified and removed from lung field, resulting in lung parenchyma (LP) volume. Following, identification and characterization of IP patterns is formulated as a three-class pattern classification of LP into normal, ground glass, and reticular patterns, by means of k-nearest neighbor voxel classification, exploiting 3-D cooccurrence features. Performance of the proposed scheme in indentifying and characterizing ground glass and reticular patterns was evaluated by means of volume overlap (ground glass: 0.734 +/- 0.057, reticular: 0.815 +/- 0.037), true-positive fraction (ground glass: 0.638 +/- 0.055, reticular: 0.942 +/- 0.023) and false-positive fraction (ground glass: 0.361 +/- 0.027, reticular: 0.147 +/- 0.032) on five MDCT scans.