Active learning guided interactions for consistent image segmentation with reduced user interactions

Proceedings - International Symposium on Biomedical Imaging

Volumn , Issue , 2011, Pages 1645-1648

  Veeraraghavan, Harini ^a   Miller, James V ^a  

^a GE GLOBAL RESEARCH   (United States)

Author keywords

Active learning; interactive segmentation; learning based user guidance; SVM classification

Indexed keywords

ACTIVE LEARNING; DATA SETS; EXPERIMENTAL EVALUATION; EXPERT KNOWLEDGE; INTERACTIVE SEGMENTATION; INTERACTIVE TECHNIQUES; ROBUST SEGMENTATION; SEGMENTATION ACCURACY; SVM CLASSIFICATION; USER GUIDANCE; USER INTERACTION;

KNOWLEDGE MANAGEMENT; MEDICAL IMAGING; USER INTERFACES;

IMAGE SEGMENTATION;

EID: 80055028391     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2011.5872719     Document Type: Conference Paper

References (16)

1. Y. Boykov. and M.-P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," in IEEE ICCV, 2001, pp. 105-112.
2. L. Grady, "Random walks for image segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1768-1783, 2006.
3. M.M.J. Letteboer, O.F. Olsen, E.B. Dam, P.W.A. Willems, M.A. Viergever, and W.J. Niessen, "Segmentation of brain tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm," Academic Radiology, vol. 11, no. 10, pp. 1125-1138, 2004.
4. V. Vezhnevets and V. Konouchine, "GrowCut - Interactive multi-label N-D image segmentation," in Proc. Graphicon, 2005, pp. 150-156.
5. A. Mishra, A. Wong, W. Zhang, D. Clausi, and P. Feiguth, "Improved interactive medical image segmentation using enhanced intelligent scissors (eis)," in IEEE Intl. Conf. Engineering in Medicine and Biology, 2008, pp. 3083-3086.
6. S. Tong and D. Koller, "Support Vector Machine active learning with applications to text classification," Journal of Machine Learning Research, pp. 45-66, 2001.
7. P. Etyngier, F. Ségonne, and R. Keriven, "Active contour-based image segmentation using machine learning techniques," in MICCAI, 2007, pp. 891-899.
8. S. Gerber, T. Tasdizen, S. Joshi, and R. Whitaker, "On the manifold structure of the space of brain images," in MICCAI, 2009.
9. A. Farhangfar, R. Greiner, and C. Szepesvári, "Learning to segment from a few well-selected training images," in ICML, 2009, pp. 305-312.
10. S. Hoi, R. Jin, J. Zhu, and M. Lyu, "Batch mode active learning and its application to medical image classification," in ICML, 2006.
11. T.R. Raviv, K Van Leemput, B.H. Menze, W.M Wells III, and P. Golland, "Segmentation of image ensembles via latent atlases," Medical Image Analysis, vol. 14, pp. 654-665, 2010.
12. T. Xia, Q. Wu, C. Chen, and Y. Yu, "Lazy texture selection based on active learning," The Visual Computer, vol. 26, no. 3, pp. 157-169, 2009.
13. D. Batra, D. Parikh, L. Jeibo, and T. Chen, "iCoseg: Interactive co-segmentation with intelligent scribble guidance," in IEEE CVPR, 2010, pp. 3169-3176.
14. C. Campbell, N. Cristianni, and A. Smola, "Query learning by large margin classifiers," in ICML, 2000.
15. P. Melville and R.J. Mooney, "Diverse ensembles for active learning," in ICML, 2004, pp. 584-591.
16. M. Kaus, S.K. Warfield, A. Nabavi, P.M. Black, F.A. Jolesz, and R. Kikinis, "Automated segmentation of MRI of brain tumors," Radiology, vol. 218, no. 2, pp. 586-591, Feb 2001.