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Quantitative Analysis of HRCT Images for Characterization of Pulmonary Emphysema
Abstract
This paper aims at improving quantitative measures of
emphysema in computed tomography images of lungs. Pulmonary Emphysema is a chronic obstructive lung disease which is characterized by limitation of air flow. Detection and quantification of emphysema is important, since it is the main cause of shortness of breath and disability in Chronic Obstructive Pulmonary Disease. Current standard measures like Relative Area method, Pulmonary Function Test and Mean Lung Density methods rely on a single
intensity threshold on individual pixels, ignoring any interrelations between pixels. HRCT is a sensitive method for diagnosing emphysema, assessing its severity, and determining its subtype. In
this project texture based classification system is used. A study is
performed by choosing Local binary Pattern (LBP) as texture feature
and classification is performed using KNN classifier. Comparison is
done with another texture feature i.e. Gaussian. The ROI
classification using LBP showed good classification performance,
compared to Gaussian. Thus LBP seems to perform better than
Gaussian in finding the quantitative value. Also KNN have a greater
sensitivity to emphysema. LBP analysis is a sensitive method for
diagnosing emphysema, assessing its severity, and determining its
subtype since both visual and quantitative HRCT assessment are
closely correlated with the pathological extent of emphysema.
Classification accuracy and quantification shows that KNN classifier
performs better when LBP is used as texture feature in determining
Pulmonary Emphysema.
Keywords
References
A. H. Mir, M. Hanmandlu, and S. N. Tandon, “Texture analysis of CT
images,”IEEE Eng. Med. Biol. Mag. ,vol. 14, no. 6, pp. 781–786,Nov./
Dec. 1995
N. L. Müller, C. A. Staples, R. R. Miller, and R. T. Abboud, “Density
mask- An objective method to quantitate emphysema using computed
tomography,” Chest, vol. 94, no. 4, pp. 782–787, Oct. 1996
R. Uppaluri, T. Mitsa, M. Sonka, E. A. Hoffman, and G.
McLennan,“Quantification of pulmonary emphysema from lung
computed tomography images,” Amer. J. Respir. Crit. Care Med., vol.
, no. 1, pp.248–254, Jul. 1997
M. Tuceryan and A. K. Jain, “Texture analysis,” in The Handbook of
Pattern Recognition and Computer Vision, 2nd ed. Singapore:World
Scientific, 1998, pp. 207–248.
“Reproducibility of Spirometrically Controlled CT Lung Densitometry
in a Clinical Setting” -R.J.S. Lamers, G.J. Kemerink, M. Drent, J.M.A.
van Engelshoven. ERS Journals Ltd 1998
“Discrimination Between Healthy Subjects And Patients With
Pulmonary Emphysema By Detection Of Abnormal Respiration”
Masaru Yamashita, Shoichi Matsunaga and Sueharu
Miyahara.Department of Computer and Information Sciences, Nagasaki
University, JAPAN,August-1999
W. R. Webb, N. Müller, and D. Naidich, “High-Resolution CT of the
Lung”, J.-R. John, Ed., 3rd ed. Baltimore, MD: Lippincott Williams &
Wilkins, 2000.
Shiying Hu, Eric A. Hoffman, Member, IEEE, and Joseph M. Reinhardt
“Automatic Lung Segmentation for Accurate Quantification of
Volumetric X-Ray CT Images” IEEE transaction on medical imaging,
VOL. 20, NO. 6, June 2001
T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of
texture measures with classification based on featured distributions,”
Pattern Recognit., Jan. 2001
“Automated Detection System for Pulmonary Emphysema on 3D Chest
Image” Takeshi Hara, Akira Yamamoto, Xiangrong Zhou, Shingo
Iwano*, Shigeki Itoh*,Hiroshi Fujita, and Takeo Ishigaki*Department of
Intelligent Image Information ,Nagoya University,August 2002
N. L Müller, C A Staples, R R Miller and R T Abboud , “Quantitative
emphysema using computed Density mask. An objective method”
American College of Chest Physicians by guest on September 25 ,2003
“Hot Spot Detection Based on Feature Space Representation of Visual
Search” Xiao-Peng Hu, Laura Dempere-Marco, and Guang-Zhong
Yang*IEEE Transactions On Medical Imaging, vol. 22, no. 9,
september2003
Ching Ming Jimmy Wang Mamatha Rudrapatna Arcot Sowmya “Lung
Disease Detection Using Frequency Spectrum Analysis” Amer. J.
Respir. Crit. Care Med.Apr. 2005
Daniel I. Morariu, Lucian N. Vintan, and Volker Tresp, “Meta-
Classification using SVM Classifiers for Text Documents” International
Journal of Mathematical and Computer Sciences-Jan 2005
T. Stavngaard, S. B. Shaker, K. S. Bach, B. C. Stoel, and A.
Dirksen,“Quantitative assessment of regional emphysema distribution in
patients with chronic obstructive pulmonary disease (COPD),” Acta
Radiol. , Nov. 2006.
“Local Binary Pattern Descriptors for Dynamic Texture Recognition”
Guoying Zhao and Matti Pietikäinen Machine Vision Group, Infotech
Oulu and Department of Electrical and Information Engineering, The
th International Conference on Pattern Recognition2006IEEE
“Adaptive multiple feature method (AMFM) for early detecton of
parenchymal pathology in a smoking population” Renuka Uppaluri;
Geoffrey McLennan M.D.; Paul Enright; James Standen; Pamela Boyer-
Pfersdorf; Eric A. Hoffman [2007]
G. Zhao and M. Pietikäinen, “Dynamic texture recognition using local
binary patterns with an application to facial expressions,” IEEE Trans.
Pattern Anal. Mach . Intell., Jun. 2007.
A. Depeursinge, D. Sage, A. Hidki, A. Platon, P.-A. Poletti, M. Unser,
and H. Muller, “Lung tissue classification using wavelet frames,” in
Proc. 29th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS 2007,
Aug. 2007, pp. 6259–6262
L. Sørensen, S. B. Shaker, and M. de Bruijne, “Texture classification in
lung CT using local binary patterns,” in MICCAI (1), ser. (Lecture
Notes in Computer Science 5241), D. N. Metaxas, L. Axel, G.
Fichtinger, and G. Székely, Eds. New York: Springer-Verlag, Sep.2008,
pp. 934–941..
“A Structural and Functional Assessment of the Lung via Multidetector-
Row Computed Tomography”-Phenotyping Chronic Obstructive
Pulmonary Disease, Eric A. Hoffman, Brett A. Simon, and Geoffrey
McLennan,2008
Y. S. Park, J. B. Seo, N. Kim, E. J. Chae, Y. M. Oh, S. D. Lee, Y. Lee,
and S.-H. Kang, “Texture-based quantification of pulmonary
emphysema on high-resolution computed tomography: Comparison with
density-based quantification and correlation with pulmonary function
test,” Invest Radiol. Jun. 2008 .
Michael Fitzpatrick, Milan Sonka, ”Automatic detection system for
pulmonary emphysema on 3-D Chest images". Proceedings of SPIEmar,
A. Bharathi Dr.A.M.Natarajan, “Minimal feature selection using SVM
based on anova” Journal of Theoretical and Applied Information
Technology-Mar2009
G. Zhao and M. Pietikäinen, “Dynamic texture recognition using local
binary patterns with an application to facial expressions,” IEEE
Trans.Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 915–928,Jun.2009.
Lauge Sørensen, Saher B. Shaker, and Marleen de Bruijne “Quantitative
Analysis of Pulmonary Emphysema Using Local Binary Patterns ” IEEE
transaction on medical imaging, vol. 29, no. 2, February 2010
“Monogenic-LBP: A New Approach For Rotation Invariant Texture
Classification” Lin Zhang, Lei Zhang1, Zhenhua Guo, and David
Zhang(Proceeding Paper) Biometrics Research Centre, Dept. of
Computing, The Hong Kong Polytechnic University, Hong Kong
,September 2010,
“Severity Analysis of Pulmonary Emphysema Based on the Comparison
of Expiratory and Inspiratory States”, Kazuaki Neda, Toshiyuki Tanaka,
Toru Shirahata, Hiroaki Sugiura, SICE Annual Conference 2010, August
“Comparative Analysis of Texture Models for Image Segmentation” -
Murugswari G, Suruliandi A International Conference on Computer,
Communication and Electrical Technology – ICCCET 2011,March-2011
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