Multimodal sparse representation-based classification for lung needle biopsy images

IEEE Transactions on Biomedical Engineering

Volumn 60, Issue 10, 2013, Pages 2675-2685

  Shi, Yinghuan ^a   Gao, Yang ^a   Yang, Yubin ^a   Zhang, Ying ^b   Wang, Dong ^b  

^a NANJING UNIVERSITY   (China)

^b People s Liberation Army Cancer Center Bayi Hospital   (China)

Author keywords

Dictionary learning; Genetic algorithm; Lung cancer image classification; Sparse representation based classification (src)

Indexed keywords

DICTIONARY LEARNING; HIERARCHICAL FUSIONS; LUNG CANCER; LUNG CANCER DIAGNOSIS; MULTI-MODAL INFORMATION; SPARSE REPRESENTATION-BASED CLASSIFICATION (SRC); STATE-OF-THE-ART METHODS; TEXTURE INFORMATION;

BIOLOGICAL ORGANS; BIOPSY; COMPUTER AIDED DIAGNOSIS; DISEASES; GENETIC ALGORITHMS; TEXTURES;

IMAGE CLASSIFICATION;

ARTICLE; CANCER CLASSIFICATION; CELL NUCLEUS; CLINICAL FEATURE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISCRIMINATION LEARNING; GENETIC ALGORITHM; HUMAN; HUMAN CELL; IMAGE ANALYSIS; INTERMETHOD COMPARISON; LUNG BIOPSY; MULTIMODAL SPARSE REPRESENTATION BASED CLASSIFICATION; NEEDLE BIOPSY; PHYSICAL PARAMETERS; PREDICTION;

ALGORITHMS; BIOPSY, NEEDLE; CELL NUCLEUS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LUNG; LUNG NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84884554301     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2013.2262099     Document Type: Article

References (38)

1. World Health Organization (WHO), Cancer. (2012). [Online]. Available: http://www.who.int/mediacentre/factsheets/fs297/en/index.html
2. H. Huang, L. Shen, J. Ford, L. Gao, and J. Pearlman, "Early lung cancer detection based on registered perfusion MRI," J. Oncol. Rep., vol. 15, pp. 1080-1084, 2005.
3. A. Depeursinge, D. Racoceanu, J. Iavindrasana, G. Cohen, A. Platon, P. A. Poletti, and H. M?uller, "Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography," Artif. Intell. Med., vol. 50, pp. 13-21, 2011.
4. S. Diciotti, S. Lombardo, M. Falchini, G. Picozzi, and M. Mascalchi, "Automated segmentation refinement of small lung nodules in CT scans by local shape analysis," IEEE Trans. Biomed. Eng., vol. 58, no. 12, pp. 3418-3428, Dec. 2011.
5. Q. Wei, Y. Hu, G. Gelfand, and J. H. MacGregor, "Segmentation of lung lobes in high-resolution isotropic CT images," IEEE Trans. Biomed. Eng., vol. 56, no. 5, pp. 1383-1393, May 2009.
6. K.Mori, J. Hasegawa, J. Toriwaki, H. Anno, and K. Katada, "Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system," in Proc. Int. Conf. Pattern Recognit., 1996, pp. 528-532.
7. C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 1-27, 2011.
8. Y. Shi, Y. Gao, R. Wang, Y. Zhang, and D. Wang, "Transductive costsensitive lung cancer image classification," Appl. Intell., vol. 38, no. 1, pp. 16-28, 2013.
9. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2009.
10. W. Wang and Z.-H. Zhou, "Analyzing co-training style algorithms," in Proc. Eur. Conf. Mach. Learn., 2007, pp. 454-465.
11. Y. Yu, Y.-F. Li, and Z.-H. Zhou, "Diversity regularized machine," in Proc. Int. Joint Conf. Artif. Intell., 2011, pp. 1603-1608.
12. J. Yang, K. Yu, Y. Gong, and T. Huang, "Linear spatial pyramid matching using sparse coding for image classification," in Proc. IEEE Conf.Comput. Vis. Pattern Recognit., Jun. 2009, pp. 1794-1801.
13. M. Liu, D. Zhang, and D. Shen, "Ensemble sparse classification of Alzheimer's disease," Neuroimage, vol. 60, no. 2, pp. 1106-1116, 2012.
14. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with cotraining," in Proc. Annu. Conf. Comput. Learn. Theory, 1998, pp. 92-100.
15. Z.-H. Zhou, Y. Jiang, Y.-B. Yang, and S.-F. Chen, "Lung cancer cell identification based on aritificial neural network ensembles," Artif. Intell. Med., vol. 24, no. 1, pp. 25-36, 2002. (Pubitemid 34084734)
16. L. Zhu, B. Zhao, and Y. Gao, "Multi-class multi-instance learning approach for lung cancer cell classification based on bag feature selection," in Proc. Int. Conf. Fuzzy Syst. Knowl. Discovery, Oct. 2008, pp. 487-492.
17. Y. Freund and R. Schapire, "Experiments with a new boosting algorithm," in Proc. Int. Conf. Mach. Learn., 1996, pp. 325-332.
18. C. M. Christoudias, R.Urtasun, and T.Darrell, "Multi-viewlearning in the presence of view disagreement," in Proc. Conf. Uncertainty Artif. Intell., 2008, pp. 88-96.
19. Z.-H. Zhou and M. Li, "Semi-supervised learning by disagreement," Knowl. Inf. Syst., vol. 24, pp. 415-439, 2010.
20. P. Campadelli, E. Casiraghi, and G. Valentini, "Support vector machines for candidate nodules classification," Neurocomputing, vol. 68, pp. 281- 288, 2005. (Pubitemid 41244506)
21. V. Kovalev, N. Harder, B. Neumann, M. Held, U. Liebel, H. Erfle, J. Ellenberg, R. Ellis, and K. Rohr, "Feature selection for evaluating florescence microscopy images in geneme-wide cell screens," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Jun. 2006, pp. 276- 283. (Pubitemid 44931341)
22. M. C. Lee, L. Boroczky, K. Sungur-Stasik, A. D. Cann, A. C. Borczuk, S. M. Kawut, and C. A. Powell, "Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction," Artif. Intell. Med., vol. 50, pp. 43-53, 2010.
23. S. Yu, B. Krishnapnram, R. Rosales, and R. B. Rao, "Bayesian cotraining," J. Mach. Learn. Res., vol. 12, pp. 2649-2680, 2011.
24. M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, "An ensemble classification-based approach applied to retinal blood vessel segmentation," IEEE Trans. Biomed. Eng., vol. 59, no. 9, pp. 2538-2548, Sep. 2012.
25. D. Zhang and D. Shen, "Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease," Neuroimage, vol. 59, no. 2, pp. 895-907, 2012.
26. P. Tiwari, S. E.Viswanath,D. Lee, andA.Madabhushi, "Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data," in Proc. IEEE Int. Symp. Biomed. Imag., Mar.-Apr. 2011, pp. 165-168.
27. A. Madabhushi, M. D. Feldman, D. N. Metaxas, J. Tomaszeweski, and D. Chute, "Automated detection of prostatic adenocarcinoma from highresolution ex vivo MRI," IEEE Trans. Med. Imag., vol. 24, no. 12, pp. 1611-1625, Dec. 2005. (Pubitemid 41812028)
28. G. Dasovich, R. Kim, D. Raicu, and J. Furst, "A model for the relationship between semantic and content based similarity using LIDC," in Proc. SPIE Med. Imag., vol. 7624, pp. 31-43, 2010.
29. C. Suzuki, J. F. Gomes, A. X. Falcao, J. P. Papa, and S. Hoshino-Shimizu, "Automatic segmentation and classification of human intestinal parasites from microscopy images," IEEE Trans. Biomed. Eng., vol. 60, no. 3, pp. 803-812, Feb. 2012.
30. J. A. Tropp, "Greed is good: Algorithmic results for sparse approximation," IEEE Trans. Inf. Theory, vol. 50, no. 10, pp. 2231-2242, Oct. 2004.
31. K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, "Dictionary learning algorithms for sparse representation," Neural Comput., vol. 12, no. 2, pp. 349-396, 2003. (Pubitemid 37049825)
32. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online dictionary learning for sparse coding," in Proc. 26th Int. Conf. Mach. Learn., 2009, pp. 689-696.
33. M. Yang, L. Zang, X. Feng, and D. Zhang, "Fisher discrimination dictionary learning for sparse representation," in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 543-550.
34. M. Mitchell, An Introduction to Genetic Algorithm. Cambridge, MA, USA: MIT Press, 1998.
35. J. J. Grefenstette, "Optimization of control parametes for genetic algorithm," IEEE Trans. Syst. Man Cybern., vol. SMC-16, no. 1, pp. 122-128, Jan. 1986. (Pubitemid 16187857)
36. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples," J. Mach. Learn. Res., vol. 7, pp. 2399-2434, 2006. (Pubitemid 44708005)
37. L. Zhang, W. D. Zhou, P. C. Chang, J. Liu, Z. Yan, T. Wang, and F. Z. Li, "Kernel sparse representation-based classifier," IEEE Trans. Signal Process., vol. 60, no. 4, pp. 1684-1695, Apr. 2012.
38. W. D. Travis, E. Brambilla, H. K. Muller-Hermelink, and C. C. Harris, Pathology and Genetics of Tumours of the Lung, Pleura, Thymus and Hear, (World Health Organization Classification of Tumours Series). Lyon, France: IARC Press, 2004.