Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

Abstract--We aim at improving quantitative measures of emphysema in computed tomography (CT) images of the lungs. Current standard measures, such as the relative area of emphysema (RA), rely on a single intensity threshold on individual pixels, thus ignoring any interrelations between pixels. Texture analysis allows for a much richer representation that also takes the local structure around pixels into account. This paper presents a texture classification-based system for emphysema quantification in CT images. Measures of emphysema severity are obtained by fusing pixel posterior probabilities output by a classifier. Local binary patterns (LBP) are used as texture features, and joint LBP and intensity histograms are used for characterizing regions of interest (ROIs). Classification is then performed using a nearest neighbor classifier with a histogram dissimilarity measure as distance. A 95.2% classification accuracy was achieved on a set of 168 manually annotated ROIs, comprising the t...

• Type: Journal
• Year: 2010
• Where: TMI
• Authors: Lauge Sørensen, Saher B. Shaker, Marleen de Bruijne
• Keywords: Emphysema, Emphysema Severity, Management, Pixel, TMI 2010