SHIRC: A simultaneous sparsity model for histopathological image representation and classification

Proceedings - International Symposium on Biomedical Imaging

Volumn , Issue , 2013, Pages 1118-1121

  Srinivas, Umamahesh ^a   Mousavi, Hojjat ^a   Jeon, Charles ^b   Monga, Vishal ^a   Hattel, Arthur ^a   Jayarao, Bhushan ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED CLASSIFICATION; GEOMETRIC INFORMATION; GEOMETRIC STRUCTURE; HISTOPATHOLOGICAL IMAGES; LINEAR COMBINATIONS; OPTIMIZATION PROBLEMS; PENNSYLVANIA STATE UNIVERSITY; SIMULTANEOUS SPARSITY;

FEATURE EXTRACTION; MEDICAL IMAGING;

IMAGE CLASSIFICATION;

EID: 84881650786     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2013.6556675     Document Type: Conference Paper

References (14)

1. M. M. Dundar, et al., "Computerized classification of intraductal breast lesions using histopathological images," IEEE Trans. Biomed. Eng., vol. 58, no. 7, pp. 1977-1984, 2011.
2. L. E. Boucheron, "Object-and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer," Ph.D. dissertation, Dept. of ECE, University of California, Santa Barbara, 2008.
3. A. Madabhushi, "Digital pathology image analysis: opportunities and challenges," Imaging in Medicine, vol. 1, no. 1, pp. 7-10, 2009.
4. M. N. Gurcan, et al., "Histopathological image analysis: a review," IEEE Rev. Biomed. Eng., vol. 2, pp. 147-171, 2009.
5. H. Fox, "Is H&E morphology coming to an end?" J. Clin. Pathol., vol. 53, pp. 38-40, 2000.
6. V. N. Vapnik, The nature of statistical learning theory. New York, USA: Springer, 1995.
7. S. Naik, et al., "Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology," in ISBI Workshop Comput. Histopathology, 2008, pp. 284-287.
8. O. Sertel, et al., "Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading," J. Signal Process. Syst., vol. 55, no. 1-3, pp. 169-183, 2009.
9. J. Wright, et al., "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Machine Intell., vol. 31, no. 2, pp. 210-227, Feb. 2009.
10. S. Zhang, et al., "Discriminative sparse representations for cervigram image segmentation," in Proc. IEEE Int. Symp. Biomed. Imag., 2010, pp. 133-136.
11. Y. Yu, et al., "Group sparsity based classification for cervigram segmentation," in Proc. IEEE Int. Symp. Biomed. Imag., 2011, pp. 1425-1429.
12. M. Liu, et al., "Sparse classification for computer aided diagnosis using learned dictionaries," in Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), vol. 6893, 2011, pp. 41-48.
13. J. A. Tropp, A. C. Gilbert, and M. J. Strauss, "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit," Signal Processing, vol. 86, pp. 572-588, Apr. 2006. (Pubitemid 43106572)
14. C. Jeon, U. Srinivas, and V. Monga, "A greedy pursuit approach to classification using multi-task multivariate sparse representations," Pennsylvania State University, Tech. Rep. PSUEE-TR-0112, July 2012. [Online]. Available: http://signal.ee.psu.edu/techrep/mtmv-somp.pdf