Mining Patterns of Lung Infections in Chest Radiographs

Abstract

Chest radiography is a reference standard and the initial diagnostic test performed in patients who present with signs and symptoms suggesting a pulmonary infection. The most common radiographic manifestation of bacterial pulmonary infections is foci of consolidation. These are visible as bright shadows interfering with the interior lung intensities. The discovery and the assessment of bacterial infections in chest radiographs is a challenging computational task. It has been limitedly addressed as it is subject to image quality variability, content diversity, and deformability of the depicted anatomic structures. In this paper, we propose a novel approach to the discovery of consolidation patterns in chest radiographs. The proposed approach is based on non-negative matrix factorization (NMF) of statistical intensity signatures characterizing the densities of the depicted anatomic structures. Its experimental evaluation demonstrates its capability to recover semantically meaningful information from chest radiographs of patients with bacterial pulmonary infections. Moreover, the results reveal its comparative advantage over the baseline fuzzy C-means clustering approach.