Texture classification in pulmonary CT

Lung Imaging and Computer Aided Diagnosis

Volumn , Issue , 2016, Pages 343-367

  Sørensen, Lauge ^a   Gangeh, Mehrdad J ^b   Shaker, Saher B ^c   de Bruijne, Marleen ^d  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b UNIVERSITY OF WATERLOO   (Canada)

^c GENTOFTE UNIVERSITY HOSPITAL   (Denmark)

^d ERASMUS MEDICAL CENTER   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTERS;

TEXTURE CLASSIFICATION;

COMPUTER SCIENCE;

EID: 84881627755     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (47)

1. Barnes, P. J. 2000. Chronic obstructive pulmonary disease. The New England Journal of Medicine, 343(4):269-280.
2. Chabat, F., G.-Z. Yang, and D. M. Hansell. 2003. Obstructive lung diseases: texture classification for differentiation at CT. Radiology, 228(3):871-877.
3. Cula, O. G., and K. J. Dana. 2004. 3D texture recognition using bidirectional feature histograms. International Journal of Computer Vision, 59(1):33-60.
4. Dietterich, T. G. 1998. Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation, 10(7):1895-1923.
5. Duda, R. O., P. E. Hart, and D. G. Stork. 2000. Pattern Classification (2nd ed.). Wiley-Interscience.
6. Duin, R. P. W., D. de Ridder, and D. M. J. Tax. 1998. Featureless pattern classification. Kybernetika, 34(4):399-404.
7. Gangeh, M. J., L. Sørensen, S. B. Shaker, M. S. Kamel, M. de Bruijne, and M. Loog. 2010. A texton-based approach for the classification of lung parenchyma in CT images. In T. Jiang, N. Navab, J. P. W. Pluim, and M. A. Viergever (eds.), Medical Image Computing and Computer Assisted Intervention, volume 6363 of Lecture Notes in Computer Science, pp. 596-603. Springer.
8. Haralick, R. M. 1979. Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):786-804.
9. Jain, A. K., R. P. W. Duin, and J. Mao. 2000. Statistical pattern recognition: a review. IEEE Transactions Pattern Analysis and Machine Intelligence, 22(1):4-37.
10. Juelsz, B. 1981. Textons, the elements of texture-perception, and their interactions. Nature, 290(5802):91-97.
11. Kittler, J., and F. M. Alkoot. 2002. Moderating k-NN classifiers. Pattern Analysis and Applications, 5(3):326-332.
12. Kuncheva, L. I. 2004. Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience,.
13. Leung, T., and J. Malik. 2001. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43(1):29-44.
14. Liao, S., and A. C. S. Chung. 2009. Non-rigid image registration with uniform spherical structure patterns. In J. L. Prince, D. L. Pham, and K. J. Myers (eds.), Information Processing in Medical Imaging, volume 5636 of Lecture Notes in Computer Science, pp. 163-175. Springer.
15. Mirmehdiand, M., X. Xie, and J. Suri (eds.). 2008. Handbook of Texture Analysis. Imperial College Press.
16. Ojala, T., M. Pietikäinen, and D. Harwood. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51-59.
17. Ojala, T., M. Pietikäinen, and T. Mäenpää. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions Pattern Analysis and Machine Intelligence, 24(7):971-987.
18. Park, Y. S., J. B. Seo, N. Kim, E. J. Chae, Y. M. Oh, S. D. Lee, Y. Lee, and S.-H. Kang. 2008. Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: comparison with density-based quantification and correlation with pulmonary function test. Investigative Radiology, 43(6):395-402.
19. Paulhac, L., P. Makris, and Y.-J. Ramel. 2008. Comparison between 2D and 3D local binary pattern methods for characterisation of three-dimensional textures. In A. C. Campilho and M. S. Kamel (eds.), Image Analysis and Recognition, volume 5112 of Lecture Notes in Computer Science, pp. 670-679. Springer.
20. Prasad, M., A. Sowmya, and I. Koch. 2008. Designing relevant features for continuous data sets using ICA. International Journal of Computational Intelligence and Applications, 7(4):447-468.
21. Prasad, M., A. Sowmya, and P. Wilson. 2009. Multi-level classification of emphysema in HRCT lung images. Pattern Analysis and Applications, 12(1):9-20.
22. Rabe, K. F., S. Hurd, A. Anzueto, P. J. Barnes, S. A. Buist, P. Calverley, Y. Fukuchi, C. Jenkins, R. Rodriguez-Roisin, C. van Weel, J. Zielinski, and Global Initiative for Chronic Obstructive Lung Disease. 2007. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. American Journal of Respiratory and Critical Care Medicine, 176(6):532-555.
23. Schmid, C. 2001. Constructing models for content-based image retrieval. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 39-45. IEEE Computer Society Press.
24. Schmid, C. 2004. Weakly supervised learning of visual models and its application to content-based retrieval. International Journal of Computer Vision, 56(1-2, Sp. Iss. SI):7-16.
25. Shaker, S. B., A. Dirksen, K. S. Bach, and J. Mortensen. 2007. Imaging in chronic obstructive pulmonary disease. COPD, 4(2):143-161.
26. Shaker, S. B., K. A. von Wachenfeldt, S. Larsson, I. Mile, S. Persdotter, M. Dahlbäck, P. Broberg, B. Stoel, K. S. Bach, M. Hestad, T. E. Fehniger, and A. Dirksen. 2008. Identification of patients with chronic obstructive pulmonary disease (COPD) by measurement of plasma biomarkers. The Clinical Respiratory Journal, 2(1):17-25.
27. Sluimer, I. C., M. Prokop, I. Hartmann, and B. van Ginneken. 2006. Automated classification of hyperlucency, fibrosis, ground glass, solid and focal lesions in high resolution CT of the lung. Medical Physics, 33(7):2610-2620.
28. Sluimer, I. C., A. Schilham, M. Prokop, and B. van Ginneken. 2006. Computer analysis of computed tomography scans of the lung: a survey. IEEE Transactions on Medical Imaging, 25(4):385-405.
29. Sluimer, I. C., P. F. van Waes, M. A. Viergever, and B. van Ginneken. 2003. Computer-aided diagnosis in high resolution CT of the lungs. Medical Physics, 30(12):3081-3090.
30. Sørensen, L., P. Lo, H. Ashraf, J. Sporring, M. Nielsen, and M. de Bruijne. 2009. Learning COPD sensitive filters in pulmonary CT. In G.-Z. Yang, D. J. Hawkes, D. Rueckert, J. A. Noble, and C. J. Taylor (eds.), Medical Image Computing and Computer Assisted Intervention, volume 5761 of Lecture Notes in Computer Science, pp. 699-706. Springer.
31. Sørensen, L., M. Loog, P. Lo, H. Ashraf, A. Dirksen, R. P. W. Duin, and M. de Bruijne. 2010. Image dissimilarity-based quantification of lung disease from CT. In T. Jiang, N. Navab, J. P. W. Pluim, and M. A. Viergever (eds.), Medical Image Computing and Computer Assisted Intervention, volume 6361 of Lecture Notes in Computer Science. Springer.
32. Sørensen, L., S. B. Shaker, and M. de Bruijne. 2008. Texture based emphysema quantification in lung CT. In M. Brown, M. de Bruijne, B. van Ginneken, A. Kiraly, J.-M. Kuhnigk, C. Lorenz, K. Mori, and J. Reinhardt (eds.), Proc. of The First International Workshop on Pulmonary Image Analysis, pp. 5-14.
33. Sørensen, L., S. B. Shaker, and M. de Bruijne. 2008. Texture classification in lung CT using local binary patterns. In D. N. Metaxas, L. Axel, G. Fichtinger, and G. Székely (eds.), Medical Image Computing and Computer Assisted Intervention, volume 5241 of Lecture Notes in Computer Science, pp. 934-941. Springer.
34. Sørensen, L., S. B. Shaker, and M. de Bruijne. 2010. Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Transactions on Medical Imaging, 29(2):559-569.
35. Swain, M. J., and D. H. Ballard. 1991. Color indexing. International Journal of Computer Vision, 7(1):11-32.
36. ter Haar Romeny, B. M. 1997. Applications of scale-space theory. Gaussian Scale-Space Theory, pp. 3-19. Kluwer Academic Publishers.
37. Tuceryan, M., and A. K. Jain. 1998. Texture analysis. The Handbook of Pattern Recognition and Computer Vision (2nd ed.), pp. 207-248. World Scientific Publishing.
38. Uppaluri, R., E. A. Hoffman, M. Sonka, P. G. Hartley, G. W. Hunninghake, and G. McLennan. 1999. Computer recognition of regional lung disease patterns. American Journal of Respiratory and Critical Care Medicine, 160(2):648-654.
39. Uppaluri, R., T. Mitsa, M. Sonka, E. A. Hoffman, and G. McLennan. 1997. Quantification of pulmonary emphysema from lung computed tomography images. American Journal of Respiratory and Critical Care Medicine, 156(1):248-254.
40. van Ginneken, B., and B. M. ter Haar Romeny. 2003. Multi-scale texture classification from generalized locally orderless images. Pattern Recognition, 36(4):899-911.
41. Varma, M., and A. Zisserman. 2003. Texture classification: are filter banks necessary? In IEEE Conference on Computer Vision and Pattern Recognition, pp. 691-698. IEEE Computer Society.
42. Varma, M., and A. Zisserman. 2005. A statistical approach to texture classification from single images. International Journal of Computer Vision, 62(1-2):61-81.
43. Varma, M., and A. Zisserman. 2009. A statistical approach to material classification using image patch exemplars. IEEE Transactions Pattern Analysis and Machine Intelligence, 31(11):2032-2047.
44. Webb, W. R., N. L. Müller, and D. P. Naidich. 2001. High-Resolution CT of the Lung (3rd ed.). Lippincott Williams &Wilkins.
45. Xu, Y., M. Sonka, G. McLennan, J. Guo, and E. A. Hoffman. 2006. MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies. IEEE Transactions on Medical Imaging, 25(4):464-475.
46. Zhao, G., and M. Pietikäinen. 2007. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions Pattern Analysis and Machine Intelligence, 29(6):915-928.
47. Zhu, S. C., Y. Wu, and D. Mumford. 1998. Filters, random fields and maximum entropy (frame): towards a unified theory for texture modeling. International Journal of Computer Vision, 27(2):107-126.