A reinforcement agent for object segmentation in ultrasound images

Expert Systems with Applications

Volumn 35, Issue 3, 2008, Pages 772-780

  Sahba, Farhang ^a   Tizhoosh, Hamid R ^a   Salama, Magdy M M A ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Image segmentation; Reinforcement learning; Ultrasound imaging

Indexed keywords

ACOUSTIC WAVES; ARCHITECTURAL DESIGN; COMPUTER NETWORKS; IMAGE PROCESSING; IMAGE SEGMENTATION; INTELLIGENT SYSTEMS; MATRIX ALGEBRA; REINFORCEMENT; REINFORCEMENT LEARNING; ULTRASONIC IMAGING; ULTRASONIC TRANSMISSION; ULTRASONICS;

ELSEVIER (CO); EXPERIMENTAL RESULTS; GENERAL FRAMEWORK; HIGH POTENTIAL; INPUT IMAGES; OBJECT OF INTEREST (OOI); OBJECT SEGMENTATION; PROSTATE SEGMENTATION; REWARD FUNCTIONS; SEGMENTED IMAGES; SUB-IMAGES; TRANS-RECTAL ULTRASOUND (TRUS) IMAGES; ULTRASOUND (US) IMAGES;

IMAGE ENHANCEMENT;

EID: 44949248275     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.07.057     Document Type: Article

References (31)

1. Betrounia N., Vermandela M., Pasquierc D., Maoucheb S., and Rousseaua J. Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter. Computerized Medical Imaging and Graphics 29 (2005) 43-51
2. Cancer Facts and Figures (2002). American Cancer Society. .
3. Chiu B., Freeman G.H., Salama M.M.A., and Fenster A. Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour. Physics in Medicine and Biology 49 (2004) 4943-4960
4. Gelb A. Applied optimal estimation (1974), MIT Press
5. Gong L., Pathak S., Haynor D., Cho P., and Kim Y. Parametric shape modeling using deformable superellipses for prostate segmentation. IEEE Transactions on Medical Imaging 23 (2004) 340-349
6. Gonzalez R.C., and Woods R.E. Digital image processing. second ed. (2002), Prentice Hall
7. Holt R.J., and Netravali A.N. Using line correspondences in invariant signatures for curve recognition. Image and Vision Computing 11 (1993) 440-446
8. Insana M.F., and Brown D.G. Acoustic scattering theory applied to soft biological tissues, ultrasonic scattering in biological tissues (1993), CRC Press, Boca Raton (pp. 76-124)
9. Ladak H.M., Mao F., Wang Y., Downey D.B., Steinman D.A., and Fenster A. Prostate boundary segmentation from 2D ultrasound images. Medical Physics 27 (2000) 1777-1788
10. Nanayakkara N.D., Samarabandu J., and Fenster A. Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations. Physics in Medicine and Biology 51 (2006) 1831-1848
11. Noble J.A., and Boukerroui D. Ultrasound image segmentation: A survey. IEEE Transactions on Medical Imaging 25 (2006) 987-1010
12. Pathak S.D.V., Chalana D.R., Haynor D., and Kim Y. Edge-guided boundary delineation in prostate ultrasound images. IEEE Transactions on Medical Imaging 19 (2000) 1211-1219
13. Prater J.S., and Richard W.D. Segmenting ultrasound images of the prostate using neural networks. Ultrasound Imaging 14 (1992) 159-185
14. Rodriguez W., Lastb M., Kandel A., and Bunked H. 3-Dimensional curve similarity using string matching. Robotics and Autonomous Systems 49 (2004) 165-172
15. Russell S.J., and Norvig P. Artificial intelligence: A modern approach (1995), Prentice Hall, Englewood Cliffs, NJ
16. Sahba, F., & Tizhoosh, H. R. (2003). Filter fusion for image enhancement using reinforcement learning, (pp. 847-850). CCECE03, May 2003.
17. Sahba, F., Tizhoosh, H. R., & Salama, M. M. A. (2005a). Using reinforcement learning for filter fusion in image enhancement, In The fourth IASTED international conference on computational intelligence, July 2005 (pp. 262-266). Calgary, Canada.
18. Sahba F., Tizhoosh H.R., and Salama M.M.A. A coarse-to-fine approach to prostate boundary segmentation in ultrasound images. Biomedical Engineering 58 4 (2005)
19. Sahba, F., Tizhoosh, H. R., & Salama, M. M. A. (2006a). A reinforcement learning framework for medical image segmentation, In The IEEE world congress on computational intelligence (WCCI), July 2006 (pp. 1238-1244). Vancouver, Canada.
20. Sahba, F., Tizhoosh, H. R., & Salama, M. M. A. (2006b). Increasing object recognition rate using reinforced segmentation, In The IEEE international conference on image processing (ICIP), October 2006, Atlanta.
21. Shen D., Zhan Y., and Davatzikos C. Segmentation of prostate boundaries from ultrasound images using statistical shape model. IEEE Transactions on Medical Imaging 22 (2003) 539-551
22. Shokri, M., & Tizhoosh, H. R. (2004). Q(λ)-based image thresholding, In: Canadian conference on computer and robot vision.
23. Singh S., Norving P., and Cohn D. Introduction to reinforcement learning (1996), Harlequin Inc
24. Sutton R.S., and Barto A.G. Reinforcement learning (1998), MIT Press, Cambridge, MA
25. Taylor, G. A. (2006). A reinforcement learning framework for parameter control in computer vision applications. In: Proceedings of the first Canadian conference on computer and robot vision.
26. Tizhoosh H.R., and Taylor G.W. Reinforced contrast adaptation. International Journal of Image and Graphic 6 (2006) 377-392
27. Wang Y., Cardinal H., Downey D., and Fenster A. Semiautomatic three-dimensional segmentation of the prostate using twodimensional ultrasound images. Medical Physics 30 (2003) 887-897
28. Watkins C.J.C.H., and Dayan P. Q-Learning. Machine Learning 8 (1992) 279-292
29. Watkins C.J.C.H. Learning from delayed rewards (1989), Cambridge University, Cambridge
30. Yin P. Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization. Signal Processing 82 (2002) 993-1006
31. Zhang Y.J. A survey on evaluation methods for image segmentation. Pattern Recognition Letter 29 (1996) 1335-1346