Classification of Emphysema Subtypes: Comparative Assessment of Local Binary Patterns and Related Texture Features

DOI: 10.4236/act.2015.43007   PDF   HTML   XML   3,647 Downloads   4,536 Views   Citations


The purpose of this study was to assess usefulness of local binary patterns (LBP) and related texture features, namely completed local binary patterns (CLBP) and local ternary patterns (LTP), for the classification of emphysema subtypes on low-dose CT images. Fifty patients (34 men and 16 women; age, 67.5 ± 10.1 years) who underwent low-dose CT (60 mAs) were included. They were comprised of 17 never smokers, 13 smokers without COPD, and 20 smokers with COPD. By consensus reading of low-dose CT images from these patients, two radiologists selected 3681 nonoverlapping regions of interest (ROIs) and annotated them as one of the following three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. From these ROIs, histogram of CT densities, LBP, CLBP, and LTP were calculated, and the 3 types of texture histograms were concatenated with the CT density histogram. These 3 types of histograms (referred to as combined LBP, combined CLBP, and combined LTP) were used to classify ROI using linear support vector machine. For each type of the combined histogram, the accuracy of classification was determined by patient-based 10-fold cross validation. The best accuracy of combined LBP, combined CLBP, and combined LTP were 81.36%, 82.99%, and 83.29%, respectively. Compared to the classification accuracies obtained with combined LBP, those with combined LTP or combined CLBP were consistently improved. In conclusion, the results of this study suggest that, on low-dose CT, LTP and CLBP were more useful for the classification of emphysema subtypes than LBP.

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Nishio, M. , Koyama, H. , Ohno, Y. and Sugimura, K. (2015) Classification of Emphysema Subtypes: Comparative Assessment of Local Binary Patterns and Related Texture Features. Advances in Computed Tomography, 4, 47-55. doi: 10.4236/act.2015.43007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Vestbo, J., Hurd, S.S., Agustí, A.G., Jones, P.W., Vogelmeier, C., Anzueto A., et al. (2013) Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: GOLD Executive Summary. American Journal of Respiratory and Critical Care Medicine, 187, 347-365.
[2] Mathers, C.D. and Loncar, D. (2006) Projections of Global Mortality and Burden of Disease from 2002 to 2030. PLoS Medicine, 3, e442.
[3] Webb, W.R. (2006) Thin-Section CT of the Secondary Pulmonary Lobule: Anatomy and the Image—The 2004 Fleischer Lecture. Radiology, 239, 322-338.
[4] Stern, E.J. and Frank, M.S. (1994) CT of the Lung in Patients with Pulmonary Emphysema: Diagnosis, Quantification, and Correlation with Pathologic and Physiologic Findings. American Journal of Roentgenology, 162, 791-798.
[5] Goddard, P.R., Nicholson, E.M., Laszlo, G. and Watt, I. (1982) Computed Tomography in Pulmonary Emphysema. Clinical Radiology, 33, 379-387.
[6] Koyama, H., Ohno, Y., Yamazaki, Y., Nogami, M., Murase, K., Onishi, Y., et al. (2010) Quantitative and Qualitative Assessments of Lung Destruction and Pulmonary Functional Loss from Reduced-Dose Thin-Section CT in Pulmonary Emphysema Patients. Academic Radiology, 17, 163-168.
[7] COPDGene CT Workshop Group, Barr, R.G., Berkowitz, E.A., Bigazzi, F., Bode, F., Bon, J., et al. (2012) A Combined Pulmonary-Radiology Workshop for Visual Evaluation of COPD: Study Design, Chest CT Findings and Concordance with Quantitative Evaluation. COPD, 9, 151-159.
[8] Sørensen, L., Shaker, S. and de Bruijne, M. (2010) Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns. IEEE Transactions on Medical Imaging, 29, 559-569.
[9] Ginsburg, S.B., Lynch, D.A., Bowler, R.P. and Schroeder, J.D. (2012) Automated Texture-Based Quantification of Centrilobular Nodularity and Centrilobular Emphysema in Chest CT Images. Academic Radiology, 19, 1241-1251.
[10] Xu, Y., van Beek, E.J., Hwanjo, Y., Guo, J., McLennan, G. and Hoffman, E.A. (2006) Computer-Aided Classification of Interstitial Lung Diseases via MDCT: 3D Adaptive Multiple Feature Method (3D AMFM). Academic Radiology, 13, 969-978.
[11] Sluimer, I.C., van Waes, P.F., Viergever, M.A. and van Ginneken, B. (2003) Computer-Aided Diagnosis in High Resolution CT of the Lungs. Medical Physics, 30, 3081-3090.
[12] Sørensen, L., Nielsen, M., Lo, P., Ashraf, H., Pedersen, J.H. and de Bruijne, M. (2012) Texture-Based Analysis of COPD: A Data-Driven Approach. IEEE Transactions on Medical Imaging, 31, 70-78.
[13] Sørensen, L., Loog, M., Lo, P., Ashraf, H., Dirksen, A., Duin, R.P., et al. (2010) Image Dissimilarity-Based Quantification of Lung Disease from CT. Medical Image Computing and Computer-Assisted Intervention, 13, 37-44.
[14] Prasad, M., Sowmya, A. and Wilson, P. (2009) Multi-Level Classification of Emphysema in HRCT Lung Images. Pattern Analysis and Applications, 12, 9-20.
[15] Chabat, F., Yang, G.Z. and Hansell, D.M. (2003) Obstructive Lung Diseases: Texture Classification for Differentiation at CT. Radiology, 228, 871-877.
[16] Uppaluri, R., Mitsa, T., Sonka, M., Hoffman, E.A. and McLennan, G. (1997) Quantification of Pulmonary Emphysema from Lung Computed Tomography Images. American Journal of Respiratory and Critical Care Medicine, 156, 248-254.
[17] Ojala, T., Pietikäinen, M. and Mäenpää, T. (2002) Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971-987.
[18] National Lung Screening Trial Research Team, Aberle, D.R., Adams, A.M., Berg, C.D., Black, W.C., Clapp, J.D., et al. (2011) Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. New England Journal of Medicine, 65, 395-409.
[19] Zurawska, J.H., Jen, R., Lam, S., Coxson, H.O., Leipsic, J. and Sin, D.D. (2012) What to Do When a Smoker’s CT Scan is “Normal”?: Implications for Lung Cancer Screening. Chest, 141, 1147-1152.
[20] Zulueta, J.J., Wisnivesky, J.P., Henschke, C.I., Yip, R., Farooqi, A.O., McCauley, D.I., et al. (2012) Emphysema Scores Predict Death from COPD and Lung Cancer. Chest, 141, 1216-1223.
[21] Guo, Z.H., Zhang, L. and Zhang, D. (2010) A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing, 19, 1657-1663.
[22] Tan, X. and Triggs, B. (2010) Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions. IEEE Transactions on Image Processing, 19, 1635-1650.
[23] Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R. and Lin, C.-J. (2008) LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research, 9, 1871-1874.
[24] Müller, N.L., Staples, C.A., Miller, R.R. and Abboud, R.T. (1988) “Density Mask”: An Objective Method to Quantitate Emphysema Using Computed Tomography. Chest, 94, 782-787.
[25] Willemink, M.J., Leiner, T., de Jong, P.A., de Heer, L.M., Nievelstein, R.A., Schilham, A.M., et al. (2013) Iterative Reconstruction Techniques for Computed Tomography Part 2: Initial Results in Dose Reduction and Image Quality. European Radiology, 23, 1632-1642.
[26] Nishio, M., Matsumoto, S., Seki, S., Koyama, H., Ohno, Y., Fujisawa, Y., et al. (2014) Emphysema Quantification on Low-Dose CT Using Percentage of Low-Attenuation Volume and Size Distribution of Low-Attenuation Lung Regions: Effects of Adaptive Iterative Dose Reduction Using 3D Processing. European Journal of Radiology, 83, 2268-2276.

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