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Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

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3 Author(s)
Sorensen, L. ; Dept. of Comput. Sci., Univ. of Copenhagen, Copenhagen, Denmark ; Shaker, S.B. ; de Bruijne, M.

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 k 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 three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. The measured emphysema severity was in good agreement with a pulmonary function test (PFT) achieving correlation coefficients of up to |r| = 0.79 in 39 subjects. The results were compared to RA and to a Gaussian filter bank, and the texture-based measures correlated significantly better with PFT than did RA.

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Medical Imaging, IEEE Transactions on  (Volume:29 ,  Issue: 2 )