Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT

Medical Physics

Volumn 35, Issue 12, 2008, Pages 5290-5302

  Korfiatis, Panayiotis ^a   Kalogeropoulou, Christina ^a   Karahaliou, Anna ^a   Kazantzi, Alexandra ^a   Skiadopoulos, Spyros ^a   Costaridou, Lena ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

High resolution computed tomography; Interstitial pneumonia; Lung segmentation; Support vector machine; Texture

Indexed keywords

BIOLOGICAL ORGANS; COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; IMAGE SEGMENTATION; K-MEANS CLUSTERING; STATISTICAL TESTS; SUPPORT VECTOR MACHINES; TEXTURES; WAVELET TRANSFORMS;

COMPUTER AIDED DIAGNOSIS(CAD); HIGH-RESOLUTION COMPUTED TOMOGRAPHY; INTEROBSERVER VARIABILITY; INTERSTITIAL PNEUMONIA; LUNG SEGMENTATION; SEGMENTATION ALGORITHMS; SEGMENTATION PERFORMANCE; STATISTICALLY SIGNIFICANT DIFFERENCE;

ITERATIVE METHODS;

ARTICLE; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISEASE SEVERITY; GEOMETRY; HIGH RESOLUTION COMPUTER TOMOGRAPHY; HUMAN; INTERSTITIAL PNEUMONIA; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; STUDENT T TEST; SUPPORT VECTOR MACHINE;

EID: 56749159136     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.3003066     Document Type: Article

References (38)

1. P. Grenier, D. Valeyre, P. Cluzel, M. W. Brauner, S. Lenoir, and C. Chastang, " Chronic diffuse interstitial lung disease: Diagnostic value of chest radiography and high resolution CT.," Radiology 179, 123-132 (1991).
2. T. Franks, J. Galvin, and A. Frazier, " The use and impact of HRCT in diffuse lung disease.," Curr. Diagn. Pathol. 10, 279-290 (2004).
3. R. D. Suh and J. G. Goldin, " High-resolution computed tomography of interstitial pulmonary fibrosis.," Semin. Respir. Crit. Care Med. 27, 623-633 (2006).
4. C. D. Collins, " Observer variation in pattern type and extent of disease in fibrosing alveolitis on thin section computed tomography and chest radiography.," Clin. Radiol. 49, 236-240 (1994).
5. Z. A. Aziz, " HRCT diagnosis of diffuse parenchymal lung disease: Interobserver variation.," Thorax 59, 506-511 (2004).
6. I. C. Sluimer, M. Prokop, I. Hartmann, and B. van Ginneken, " Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung.," Med. Phys. 33, 2610-2620 (2006).
7. I. C. Sluimer, P. F. van Waes, M. A. Viergever, and B. van Ginneken, " Computer-aided diagnosis in high resolution CT of the lungs.," Med. Phys. 30, 3081-3090 (2003).
8. H.-U. Kauczor, K. Heitmann, C. P. Heussel, D. Marwede, T. Uthmann, and M. Thelen, " Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: Comparison with a density mask.," AJR, Am. J. Roentgenol. 175, 1329-1334 (2000).
9. Y. Uchiyama, " Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography.," Med. Phys. 30, 2440-2454 (2003).
10. A. Depeursinge, " Lung tissue classification using wavelet frames.," IEEE Engineering in Medicine and Biology Society, 2007, Proceedings of the 29th Annual International Conference of the IEEE, 2007, pp. 6260-6263.
11. V. A. Zavaletta, B. J. Bartholmai, and R. A. Robb, " High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis.," Acad. Radiol. 14, 772-787 (2007).
12. S. G. Armato and W. F. Sensakovic, " Automated lung segmentation for thoracic CT Impact on computer-aided diagnosis.," Acad. Radiol. 11, 1011-1021 (2004).
13. S. G. Armato, M. L. Giger, and H. MacMahon, " Automated detection of lung nodules in CT scans: Preliminary results.," Med. Phys. 28, 1552-1561 (2001).
14. S. Hu, E. A. Hoffman, and J. M. Reinhardt, " Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images.," IEEE Trans. Med. Imaging 20, 490-498 (2001).
15. M. S. Brown, " Method for segmenting chest CT image data using an anatomical model: Preliminary results.," IEEE Trans. Med. Imaging 16, 828-839 (1997).
16. S. Ukil and J. M. Reinhardt, " Smoothing lung segmentation surfaces in three-dimensional x-ray CT images using anatomic guidance.," Acad. Radiol. 10, 1502-1511 (2005).
17. X. Sun, H. Zhang, and H. Duan, " 3D computerized segmentation of lung volume with computed tomography.," Acad. Radiol. 13, 670-677 (2006).
18. P. Korfiatis, S. Skiadopoulos, P. Sakellaropoulos, C. Kalogeropoulou, and L. Costaridou, " Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin slice CT.," Br. J. Radiol. 80, 996-1005 (2007).
19. I. C. Sluimer, M. Prokop, and B. van Ginneken, " Toward automated segmentation of the pathological lung in CT.," IEEE Trans. Med. Imaging 24, 1025-1038 (2005).
20. A. Dieudonń, P. Zhang, P. Vannoorenberghe, and I. Gardin, " Semiautomated thoracic and abdominal computed tomography segmentation using the belief functions theory: Application to 3D internal dosimetry.," Cancer Biother. Radiopharm. 22, 275-80 (2007).
21. T. W. Way, " Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours.," Med. Phys. 33, 2323-2337 (2006).
22. M. N. Gurcan, " Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system.," Med. Phys. 29, 2552-2558 (2002).
23. R. M. Haralick, K. Shanmugam, and I. Dinstein, " Textural features for image classification.," IEEE Trans. Syst. Man Cybern. SMC-3, 610-621 (1973).
24. S. Mallat and S. Zhong, " Characterisation of signals from multiscale edges.," IEEE Trans. Pattern Anal. Mach. Intell. 14, 710-732 (1992).
25. L. Costaridou, S. Skiadopoulos, P. Sakellaropoulos, and G. Panayiotakis, " Intelligent Processing of Medical Images in the Wavelet Domain.," in Emerging Artificial Intelligence Applications in Computer Engineering, edited by, I. Maglogiannis, K. Karpouzis, M. Wallace, and, J. Soldatos, (IOS, Netherlands, 2007), Vol. 160, pp. 195-213.
26. L. Dettori and L. Semler, " A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography.," Comput. Biol. Med. 37, 486-498 (2007).
27. V. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, 1995).
28. S. Chen, S. Zhou, F. Yin, L. B. Marks, and S. K. Das, " Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis.," Med. Phys. 34, 3808-3814 (2007).
29. A. Shamsheyeva and A. Sowmya, " The anisotropic Gaussian kernel for SVM classification of HRCT images of the lung.," Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004, pp. 439-444.
30. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, " Choosing multiple parameters for support vector machines.," Mach. Learn. 46, 131-159 (2002).
31. S. S. Keerthi, " Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms.," IEEE Trans. Neural Netw. 13, 1225-1229 (2002).
32. F. van der Heijden, R. P. W. Duin, D. de Ridder, and D. M. J. Tax, Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB (Wiley, Chichester, England, 2004).
33. P. Pudil, F. J. Ferri, J. Novovicova, and J. Kittler, " Floating search methods for feature selection with nonmonotonic criterion functions.," IEEE 12th International Conference on Pattern Recognition, 1994, Vol. II, pp. 279-283.
34. J. A. Hanley and B. J. McNeil, " The meaning and use of the area under a receiver operating characteristic (ROC) curve.," Radiology 143, 29-36 (1982).
35. J. Kittler and J. Illingworth, " Minimum error thresholding.," Pattern Recogn. 19, 41-47 (1986).
36. LIDC: Datasets as a public resource. Available from: http://imaging. cancer.gov/reportsandpublications/reportsandpresentations/firstdataset (accessed 1 August 2008).
37. F. Chabat, G.-Z. Yang, and D. M. Hansell, " Obstructive lung diseases: Texture classification for differentiation at CT.," Radiology 228, 871-877 (2003).
38. Y. Xu, E. J. R. van Beek, Y. Hwanjo, J. Guo, G. McLennan, and E. A. Hoffman, " Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM).," Acad. Radiol. 13, 969-978 (2006).