Group sparsity based classification for cervigram segmentation

Proceedings - International Symposium on Biomedical Imaging

Volumn , Issue , 2011, Pages 1425-1429

  Yu, Yang ^a   Huang, Junzhou ^a   Zhang, Shaoting ^a   Restif, Christophe ^a   Huang, Xiaolei ^b   Metaxas, Dimitris ^a  

^a RUTGERS UNIVERSITY   (United States)

^b Lehigh University   (United States)

Author keywords

biomarker AcetoWhite; cervix image; classification; feature selection; group sparsity; segmentation

Indexed keywords

ABNORMAL TISSUES; ACETOWHITE; CERVIX IMAGES; CLASSIFICATION METHODS; COMPUTATIONAL COSTS; DATA SETS; FEATURE SPACE; GROUP SPARSITY; IMAGE APPEARANCE; MANUAL SEGMENTATION; SEGMENTATION PROCESS; SEGMENTATION QUALITY; SPECULAR REFLECTIONS; TEXTURE INFORMATION; UTERINE CERVIX; VISUAL NOISE;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; FEATURE EXTRACTION; MEDICAL IMAGING; TISSUE;

IMAGE SEGMENTATION;

EID: 80055044026     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2011.5872667     Document Type: Conference Paper

References (21)

1. Y. Artan and X. Huang. Combining multiple 2v-svm classifiers for tissue segmentation. In ISBI, pages 488-491, 2008.
2. S. Gordon and H. Greenspan. Segmentation of non-convex regions within uterine cervix images. In ISBI, 2007.
3. S. Gordon, S. Lotenberg, and H. Greenspan. Shape priors for segmentation of the cervix region within uterine cervix images. In SPIE, Medical Imaging, 2008.
4. H. Greenspan, S. Gordon, G. Zimmerman, S. Lotenberg, J. Jeronimo, S. Antani, and R. Long. Automatic detection of anatomical landmarks in uterine cervix images. IEEE Trans. Med. Imag., 28(3):454-468, 2009.
5. J. Huang and T. Zhang. The benefit of group sparsity. In Technical Report arXiv:0901.2962, Rutgers University, 2009.
6. J. Huang, T. Zhang, and D. Metaxas. Learning with structured sparsity. In ICML, pages 417-424, 2009.
7. X. Huang, W. Wang, Z. Xue, S. Antani, L. R. Long, and J. Jeronimo. Tissue classification using cluster features for lesion detection in digital cervigrams. In SPIE, Medical Imaging, 2008.
8. J. Jeronimo, L. Long, L. Neve, M. Bopf, S. Antani, and M. Schiffman. Digital tools for collecting data from cervigrams for research and training in colposcopy. In Colposcopy, J Low Gen Tract Disease, pages 16-25, 2006.
9. Q. Ji, J. Engel, and E. Craine. Texture analysis for classification of cervix lesions. IEEE Trans. Med. Imag., 19(11):1144-1149, 2000.
10. K. Sande, T., and C. Snoek. Evaluating color descriptors for object and scene recognition. PAMI, 99(1), 2009.
11. M. Schmidt, K. Murphy, G. Fung, and R. Rosales. Structure learning in random fields for heart motion abnormality detection. In CVPR, 2008.
12. F. Schroff, A. Criminisi, and A. Zisserman. Single-histogram class models for image segmentation. In ICVGIP, 2006.
13. J. Shotton, J. Winn, C. Rother, and A. Criminisi. Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In ECCV, 2006.
14. B. Tulpule, D. Hernes, Y. Srinivasan, S. Yang, S. Mitra, Y. Sriraja, B. Nutter, B. Phillips, L. Long, and D. Ferris. A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features. In SPIE, Medical Imaging, pages 995-1003, 2005.
15. W. Wang and X. Huang. Distance guided selection of the best base classifier in an ensemble with application to cervigram image segmentation. In MMBIA, 2009.
16. S. K. Warfield, K. H. Zou, and W. M. Wells. Simultaneous truth and performance level estimation (staple): An algorithm for the validation of image segmentation. In IEEE Trans. on Med. Imag., pages 903-921, 2004.
17. M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 68:49-67, 2006.
18. J. Zhang and Y. Liu. Cervical cancer detection using svm based feature screening. In MICCAI, pages 873 - 880, 2004.
19. S. Zhang, J. Huang, Y. Huang, Y. Yu, H. Li, and D. Metaxas. Automatic image annotation using group sparsity. In CVPR, pages 3312-3319, 2010.
20. S. Zhang, J. Huang, D. Metaxas, W. Wang, and X. Huang. Discriminative sparse representations for cervigram image segmentation. In ISBI, pages 133 - 136, 2010.
21. S. Zhang, J. Huang, W. Wang, X. Huang, and D. Metaxas. Cervigram image segmentation based on reconstructive sparse representations. In SPIE, Medical Imaging, 2010.