Automatic segmentation of ground-glass opacities in lung CT images by using markov rand om field-based algorithms

Journal of Digital Imaging

Volumn 25, Issue 3, 2012, Pages 409-422

  Zhu, Yanjie ^a   Tan, Yongqing ^a   Hua, Yanqing ^b   Zhang, Guozhen ^b   Zhang, Jianguo ^a  

^a SHANGHAI INSTITUTE OF TECHNICAL PHYSICS   (China)

^b HUADONG HOSPITAL AFFILIATED TO FUDAN UNIVERSITY   (China)

Author keywords

Image segmentation; Lung diseases; Markov chains; Tomography; X ray computed

Indexed keywords

AUTOMATIC SEGMENTATIONS; AVERAGE SENSITIVITIES; COMBINATORIAL OPTIMIZATION PROBLEMS; COMPUTED TOMOGRAPHY; CT IMAGE; DIAGNOSTIC PROCEDURE; DIFFUSE LUNG DISEASE; DISEASE SEVERITY; FALSE POSITIVE; GIBBS SAMPLERS; GROUND-GLASS OPACITY; HIGH RESOLUTION CT; LOCAL DISTRIBUTIONS; LUNG CT; MAP ESTIMATOR; MARKOV RANDOM FIELDS; MAXIMUM A POSTERIORI; RECOVERY STAGES; SEGMENTATION RESULTS; SIMULATED ANNEALING ALGORITHMS; SUBJECTIVE EVALUATIONS; SUPPORT VECTOR; X-RAY COMPUTED;

ALGORITHMS; COMBINATORIAL OPTIMIZATION; DIAGNOSIS; GLASS; IMAGE SEGMENTATION; MARKOV PROCESSES; OPACITY; PULMONARY DISEASES; RATING; TOMOGRAPHY;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTICLE; AUTOMATION; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; LUNG DISEASE; METHODOLOGY; PROBABILITY; RADIOGRAPHY;

ALGORITHMS; AUTOMATION; BAYES THEOREM; HUMANS; LUNG DISEASES; MARKOV CHAINS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84861592200     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-011-9435-5     Document Type: Article

References (28)

1. Ground-glass attenuation at medcyclopaedia. Available at: http:// www.medcyclopaedia.com/library/topics/volume-v-1/g/ground- glass-attenuation. aspx
2. Shimizu K, Johkoh T, Ikezoe J, Ichikado K, et al: Fractal analysis for classification of ground-glass opacity on highresolution CT: an in vitro study. J Comput Assist Tomogr 21 (6):955-962, 1997
3. Ground-glass nodule features on CT reveal malignancy risk. Available at: http://www.auntminnie.com/index.asp?sec=sersub= defpag=disItemID=80914. Accessed April 29, 2008
4. Matsuki Y, Nakamura K, Watanabe H, Aoki T, Nakata H, Katsuragawa S, Doi K: Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. Am J Roentgenol 178(3):657-663, 2002 (Pubitemid 34178147)
5. Nakamura K, Yoshida H, Engelmann R, MacMahon H, et al: Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. Radiology 214:823-830, 2000 (Pubitemid 30115386)
6. Shah SK, McNitt-Gray MF, Rogers SR, et al: Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features. Acad Radiol 12(10):1310-1319, 2005 (Pubitemid 41354257)
7. Zhu Y, Tan Y, Hua Y, Wang M, Zhang G, Zhang J: Feature selection and performance evaluation of support vector machine (SVM) based classifier for differentiating benign and malignant pulmonary nodules by computed tomography. J Digit Imaging 23:51-65, 2010
8. Delorme S, Keller-Reichenbecher M, Zuna I, Schlegel W, Van Kaick G: Usual interstitial pneumonia: quantitative assessment of high-resolution computed tomography findings by computerassisted texture-based image analysis. Invest Radiol 32(9):566-574, 1997 (Pubitemid 28049503)
9. Uppaluri R, Hoffman E, Sonka M, Hartley PG, Hunninghake GW, McLennan G: Computer recognition of regional lung disease patterns. Am J Respir Crit Care Med 160(2):648-654, 1999 (Pubitemid 29380686)
10. Heitmann KR, Kauczor H, Mildenberger P, Uthmann T, Perl J, Thelen M: Automatic detection of ground glass opacities on lung HRCT using multiple neural networks. Eur Radiol 7:1463-1472, 1997 (Pubitemid 127538386)
11. Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, Thelen M: Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask. Am J Roentgenol 175(5):1329-1334, 2000
12. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang CT, Suzuki K, Doi K: Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys 30(9):2440-2454, 2003 (Pubitemid 37129750)
13. Zhang L, Zhang T, Novak CL, et al: A computer-based method of segmenting ground glass nodules in pulmonary CT images: comparison to expert radiologists' interpretations. SPIE Med Imaging 5747:113-123, 2005 (Pubitemid 41146652)
14. Mignotte M, Collet C, Perez P, Bouthemy P: Sonar image segmentation using an unsupervised hierarchical MRF model. IEEE Trans Image Process 9(7):1216-1231, 2000
15. Smits PC, Dellepiane SG: Synthetic aperture radar image segmentation by a detail preserving Markov random field approach. IEEE Trans Geosci Remote Sens 35(4):844-857, 1997 (Pubitemid 127771124)
16. Held K, Kops ER, Krause BJ, et al: Markov random field segmentation of brain MR images. IEEE Trans Med Imaging 16 (6):878-886, 1997 (Pubitemid 127770446)
17. Hammersley JM, Clifford P: Markov field on finite graphs and lattices. Unpublished, 1971
18. Geman S, Geman D: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEEE Trans Pattern Anal Machine Intell 6:721-741, 1984 (Pubitemid 15453722)
19. Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E: Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087-1092, 1953
20. Zrimec T, Busayarat S, Wilson P: A 3D model of the human lung with lung regions characterization. ICIP 2004 Proc IEEE Int Conf Image Process 2, 2004, pp 1149-1152 (Pubitemid 40820242)
21. Shamsheyeva A, Sowmya A: The anisotropic Gaussian kernel for SVM classification of HRCT images of the lung. Proc IEEE ISSNIP (14-17):439-444, 2004 (Pubitemid 41775852)
22. Manjunath BS, Ma WY: Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18 (8):837-842, 1996 (Pubitemid 126772825)
23. Kaplan LM, Murenzi R: Texture segmentation using multiscale Hurst features. ICIP'97. 3:205, 1997
24. Unser M: Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4(11):1549-1560, 1995
25. Cesmeli E, Wang D: Texture segmentation using Gaussian-Markov random fields and neural oscillator networks. IEEE Trans Neural Networks 12(2):394-404, 2001 (Pubitemid 32371493)
26. LIBSVM: a library for support vector machines, 2001. Software. Available at http://www.csie.ntu.edu.tw/?cjlin/libsvm. Accessed July 28, 2009
27. Guyon I, Weston J, Barnhill S, Vapnik V: Gene selection for cancer classification using support vector machines. Mach Learn 46(1-3):389-422, 2002 (Pubitemid 34129977)
28. THenschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS, ELCAP Group: CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR AM J Roentgenol 178:1053-1057, 2002 (Pubitemid 34411393)