Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

By Lauge Sørensen, Saher B. Shaker, Marleen de Bruijne. IEEE Transactions on Medical Imaging, 2010

382 citations recorded by the source corpus.

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.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a89d2b0986bb2a35f639a89e73913e4e3b71ec5f

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In Pulmonary and Respiratory Medicine

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Citations in the corpus

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network (2016, methodology): “Such systems employed filter banks [7]–[9], morphological operations followed by geometric measures [10], wavelet and contourlet transformations [11], [12], histograms of oriented gradients [8] and local binary patterns (LBP) [13].”
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (2019, methodology): “Most commonly used features include texture descriptors, such as the gray level co-occurrence matrix (GLCM)-based features [13], [14], [15], local binary pattern (LBP) [16] and histogram of oriented gradients (HOG) [13], and shape descriptors such as the Fourier shape descriptor [17], [18] and spher”
An appendix to "Texture databases - A comprehensive survey" (2014, methodology): “The dataset has been the basis for testing the effectiveness of some texture descriptors in this field (Gangeh et al., 2011; Sørensen et al., 2010).”