Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning

IEEE Transactions on Medical Imaging

Volumn 35, Issue 3, 2016, Pages 738-751

  Vu, Tiep Huu ^a   Mousavi, Hojjat Seyed ^a   Monga, Vishal ^a   Rao, Ganesh ^b   Rao, U K Arvind ^b  

^a The Pennsylvania State University   (United States)

^b UNIVERSITY OF TEXAS   (United States)

Author keywords

Cancer grading; dictionary learning; feature extraction; Histopathological image classification; sparse coding

Indexed keywords

CLASSIFICATION (OF INFORMATION); DIAGNOSIS; DISEASES; EXTRACTION; FEATURE EXTRACTION; GRADING; IMAGE CODING; IMAGE PROCESSING; IMAGE SEGMENTATION; MAMMALS; MEDICAL IMAGING;

CLASS-SPECIFIC DICTIONARIES; CLASSIFICATION ACCURACY; DICTIONARY LEARNING; DISCRIMINATIVE FEATURES; HISTOPATHOLOGICAL IMAGE ANALYSIS; HISTOPATHOLOGICAL IMAGES; PENNSYLVANIA STATE UNIVERSITY; SPARSE CODING;

IMAGE CLASSIFICATION;

ANIMAL TISSUE; ARTICLE; AUTOMATION; BIBLIOGRAPHIC DATABASE; BOOK; BRAIN TUMOR; BREAST LESION; CLASSIFICATION; CONTROLLED STUDY; DISCRIMINATION LEARNING; DISCRIMINATIVE FEATURE ORIENTED DICTIONARY LEARNING; HISTOPATHOLOGY; IDENTITY; IMAGE ANALYSIS; KIDNEY; LUNG; MAMMAL; NONHUMAN; SPLEEN; TRAINING; UNITED STATES; CYTOCHEMISTRY; DIAGNOSTIC IMAGING; HUMAN; IMAGE PROCESSING; MACHINE LEARNING; NEOPLASM; PATHOLOGY; PROCEDURES;

HISTOCYTOCHEMISTRY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; KIDNEY; LUNG; MACHINE LEARNING; NEOPLASMS;

EID: 84963758189     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2493530     Document Type: Article

References (41)

1. M. Gurcan et al., "Histopathological image analysis: A review, " IEEE Rev. Biomed. Eng., vol. 2, pp. 147-171, Dec. 2009.
2. U. Srinivas et al., "SHIRC: A simultaneous sparsity model for histopathological image representation and classification, " in Proc. IEEE Int. Symp. Biomed. Imag., Apr. 2013, pp. 1118-1121.
3. U. Srinivas, H. S. Mousavi, V. Monga, A. Hattel, B. Jayarao, "Simultaneous sparsity model for histopathological image representation and classification, " IEEE Trans. Med. Imag., vol. 33, no. 5, pp. 1163-1179, May 2014.
4. N. Nayak, H. Chang, A. Borowsky, P. Spellman, B. Parvin, "Classification of tumor histopathology via sparse feature learning, " in Proc. IEEE Int. Symp. Biomed. Imag., 2013, pp. 1348-1351.
5. H. S. Mousavi, V. Monga, A. U. Rao, G. Rao, "Automated discrimination of lower and higher grade gliomas based on histopathological image analysis, " J. Pathol. Inf., 2015.
6. M. M. Dundar et al., "Computerized classification of intraductal breast lesions using histopathological images, " IEEE Trans. Signal Process., vol. 58, no. 7, pp. 1977-1984, Jul. 2011.
7. A. B. Tosun and C. Gunduz-Demir, "Graph run-length matrices for histopathological image segmentation, " Transmi, vol. 30, no. 3, pp. 721-732, 2011.
8. S. Doyle, S. Agner, A. Madabhushi, M. Feldman, J. Tomaszewski, "Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features, " in Proc. IEEE Int. Symp. Biomed. Imag., 2008, pp. 496-499.
9. L. Hou et al., "Efficient multiple instance convolutional neural networks for gigapixel resolution image classification, " arXiv:1504. 07947 2015.
10. N. Orlov et al., "WND-CHARM: Multi-purpose image classification using compound image transforms, " Pattern Recogn. Lett., vol. 29, no. 11, pp. 1684-1693, 2008.
11. L. Shamir et al., "WNDCHRM-An open source utility for biological image analysis, " Source Code Biol. Med., vol. 3, no. 13, 2008.
12. T. Gultekin, C. Koyuncu, C. Sokmensuer, C. Gunduz-Demir, "Two-tier tissue decomposition for histopathological image representation and classification, " IEEE Trans. Med. Imag., vol. 34, no. 1, pp. 275-283, Jan. 2015.
13. J. Shi, Y. Li, J. Zhu, H. Sun, Y. Cai, "Joint sparse coding based spatial pyramid matching for classification of color medical image, " Comput. Med. Imag. Graph., 2014.
14. S. Minaee, Y. Wang, Y. W. Lui, "Prediction of longterm outcome of neuropsychological tests of MTBI patients using imaging features, " in Signal Proc. Med. Bio. Symp. IEEE, 2013, pp. 1-6.
15. F. Zana and J.-C. Klein, "Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, " IEEE Trans. Image Process., vol. 10, no. 7, pp. 1010-1019, Jul. 2001.
16. O. Chapelle, P. Haffner, V. N. Vapnik, "Support vector machines for histogram-based image classification, " IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 1055-1064, Sep. 1999.
17. M. Unser, A. Aldroubi, A. Laine, "Guest editorial: Wavelets in medical imaging, " IEEE Trans. Med. Imag., vol. 22, no. 3, pp. 285-288, Mar. 2003.
18. E. Ozdemir and C. Gunduz-Demir, "A hybrid classification model for digital pathology using structural and statistical pattern recognition, " IEEE Trans. Med. Imag., vol. 32, no. 2, pp. 474-483, Feb. 2013.
19. I. Kopriva, M. P. Hadžija, M. Hadžija, G. Aralica, "Offset-sparsity decomposition for automated enhancement of color microscopic image of stained specimen in histopathology, " J. Biomed. Opt., vol. 20, no. 7, 2015.
20. Y. Yu et al., "Group sparsity based classification for cervigram segmentation, " in Proc. IEEE Int. Symp. Biomed. Imag., 2011, pp. 1425-1429.
21. Y. Song et al., "Locality-constrained subcluster representation ensemble for lung image classification, " Med. Image Anal., vol. 22, no. 1, pp. 102-113, 2015.
22. Y. Song, W. Cai, H. Huang, Y. Zhou, D. Feng, Y. Wang, M. Fulham, M. Chen, "Large margin local estimate with applications to medical image classification, " vol. 34, no. 6, pp. 1362-1377, 2015.
23. H. Chang, N. Nayak, P. T. Spellman, B. Parvin, "Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching, " in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013, 2013, pp. 91-98, Springer.
24. J. Wright, A. Yang, A. Ganesh, S. Sastry, Y. Ma, "Robust face recognition via sparse representation, " IEEE Trans. Pattern Anal. Mach. Int., vol. 31, no. 2, pp. 210-227, Feb. 2009.
25. K. Huang and S. Aviyente, "Sparse representation for signal classification, " in Adv. Neural Inf. Process. Syst., 2006, pp. 609-616.
26. S. Bahrampour, A. Ray, N. Nasrabadi, K. Jenkins, "Quality-based multimodal classification using tree-structured sparsity, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2014, pp. 4114-4121.
27. H. S. Mousavi et al., "Multi-task image classification via collaborative, hierarchical spike-and-slab priors, " in Proc. IEEE Conf. Image Process., 2014, pp. 4236-4240.
28. Q. Zhang and B. Li, "Discriminative K-SVD for dictionary learning in face recognition, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 2691-2698.
29. Z. Jiang, Z. Lin, L. Davis, "Label consistent K-SVD: Learning a discriminative dictionary for recognition, " IEEE Trans. Pattern Anal. Mach. Int., vol. 35, no. 11, pp. 2651-2664, Nov. 2013.
30. Y. Suo et al., "Group structured dirty dictionary learning for classification, " in Proc. IEEE Conf. Image Process., 2014, pp. 150-154.
31. M. Yang, L. Zhang, X. Feng, D. Zhang, "Fisher discrimination dictionary learning for sparse representation, " in Proc. IEEE Conf. Comput. Vis., Nov. 2011, pp. 543-550.
32. T. H. Vu, H. S. Mousavi, V. Monga, U. Rao, G. Rao, "DFDL: Discriminative feature-oriented dictionary learning for histopathological image classification, " in Proc. IEEE Int. Symp. Biomed. Imag., 2015, pp. 990-994.
33. M. Aharon, M. Elad, A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation, " IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, 2006.
34. J. Mairal, F. Bach, J. Ponce, G. Sapiro, "Online learning for matrix factorization and sparse coding, " J. Mach. Learn. Res., vol. 11, pp. 19-60, 2010.
35. "The cancer genome atlas (TCGA) database, " [online]. Available: http://cancergenome. nih. gov
36. J. Tropp and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit, " IEEE Trans. Info. Theory, vol. 53, no. 12, pp. 4655-4666, 2007.
37. "SPArse Modeling Software, " [online]. Available: http://spams-devel. gforge. inria. fr/
38. R. Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Burlington, MA: Morgan Kaufmann, 1995, pp. 1137-1143.
39. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines, " ACM Trans. Intell. Syst. Technol. vol. 2, pp. 27:1-27:27, 2011 [online]. Available: http://www. csie. ntu. edu. tw/~cjlin/libsvm
40. R. Rubinstein, M. Zibulevsky, M. Elad, "Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit, " CS Technion, vol. 40, no. 8, pp. 1-15, 2008.
41. L. Rosasco, A. Verri, M. Santoro, S. Mosci, S. Villa, Iterative projection methods for structured sparsity regularization MIT Tech. Rep., MIT-CSAIL-TR-2009-050, CBCL-282, 2009.