Summary
In many vision-based applications we need to segment an object of interest in digital images. For methods which rely on a learning process, the lack of sufficient number of training samples is usually an obstacle, especially when the samples need to be manually prepared by an expert. In addition, none of the existing methods uses online feedback from the user in order to evaluate the generated results and continuously improve them. Considering these factors, a new algorithm based on reinforcement learning is discussed in this chapter. The approach starts with a limited number of training samples and improves its performance in the course of time.
A potential obstacle when we apply reinforcement learning into image-based applications is the large number of state-action pairs. In such cases, it is usually difficult to evaluate the state-action information especially when the agent is in the exploration mode. The opposition-based leaning is one of the methods that can be applied to converge to a solution faster in spite of a large state space. Using opposite actions we can update the agent’s knowledge more rapidly. The experiments show the results for a medical application.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Betrounia, N., Vermandela, M., Pasquierc, D., Maoucheb, S., 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, 43–51 (2005)
Nanayakkara, N.D., Samarabandu, J., Fenster, A.: Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations. Physics in Medicine and Biology 51, 1831–1848 (2006)
Chiu, B., Freeman, G.H., Salama, M.M.A., Fenster, A.: Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour. Physics in Medicine and Biology 49, 4943–4960 (2004)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)
Holt, R.J., Netravali, A.N.: Using Line Correspondences In Invariant Signatures For Curve Recognition. Image and Vision Computing 11(7), 440–446 (1993)
Insana, M.F., Brown, D.G.: Acoustic scattering theory applied to soft biological tissues, Ultrasonic Scattering in biological tissues. CRC Press, Boca Raton (1993)
Ladak, H.M., Mao, F., Wang, Y., Downey, D.B., Steinman, D.A., Fenster, A.: Prostate boundary segmentation from 2D ultrasound images. Medical Physics 27, 1777–1788 (2000)
Noble, J.A., Boukerroui, D.: Ultrasound Image Segmentation: A Survey. IEEE Transactions on Medical Imaging 25, 987–1010 (2006)
Pathak, S.D.V., Chalana, D.R., Haynor, Kim, Y.: Edge-guided boundary delineation in prostate ultrasound images. IEEE Transactions on Medical Imaging 19, 1211–1219 (2000)
Prater, J.S., Richard, W.D.: Segmenting ultrasound images of the prostate using neural networks. Ultrasound Imaging 14, 159–185 (1992)
Rodriguez, W., Lastb, M., Kandel, A., Bunked, H.: 3-Dimensional curve similarity using string matching. Robotics and Autonomous Systems 49, 165–172 (2004)
Shen, D., Zhan, Y., Davatzikos, C.: Segmentation of Prostate Boundaries From Ultrasound Images Using Statistical Shape Model. IEEE Transactions on Medical Imaging 22, 539–551 (2003)
Sahba, F., Tizhoosh, H.R.: Filter Fusion for Image Enhancement Using Reinforcement Learning. In: Canadian Conference on Electrical and Computer Engineering, pp. 847–850 (2003)
Sahba, F., Tizhoosh, H.R., Salama, M.M.A.: Using Reinforcement Learning for Filter Fusion in Image Enhancement. In: The Fourth IASTED International Conference on Computational Intelligence, Calgary, Canada, pp. 262–266 (2005)
Sahba, F., Tizhoosh, H.R., Salama, M.M.A.: A Reinforcement Learning Framework for Medical Image Segmentation. In: The IEEE World Congress on Computational Intelligence (WCCI), Vancouver, Canada pp. 1238–1244 (2006)
Sahba, F., Tizhoosh, H.R., Salama, M.M.A.: Increasing object recognition rate using reinforced segmentation. In: The IEEE International Conference on Image Processing (ICIP), Atlanta, pp. 781–784 (2006)
Shokri, M., Tizhoosh, H.R.: Q(λ)-based Image Thresholding. In: Canadian Conference on Computer and Robot Vision, pp. 504–508 (2004)
Taylor, G.A.: A Reinforcement Learning Framework for Parameter Control in Computer Vision Applications. In: Proceedings of the First Canadian Conference on Computer and Robot Vision, pp. 496–503 (2004)
Tizhoosh, H.R., Taylor, G.W.: Reinforced Contrast Adaptation. International Journal of Image and Graphic 6, 377–392 (2006)
Singh, S., Norving, P., Cohn, D.: Introduction to Reinforcement Learning. Harlequin Inc. (1996)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)
Wang, Y., Cardinal, H., Downey, D., Fenster, A.: Semiautomatic three-dimensional segmentation of the prostate using two dimensional ultrasound images. Medical physics 30, 887–897 (2003)
Watkins, C.J.C.H., Dayan, P.: Q-Learning. Machine Learning 8, 279–292 (1992)
Brunelli, R., Mich, O.: Histogram analysis for image retrieval. Pattern Recognition 34, 1625–1637 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sahba, F., Tizhoosh, H.R. (2008). Opposite Actions in Reinforced Image Segmentation. In: Tizhoosh, H.R., Ventresca, M. (eds) Oppositional Concepts in Computational Intelligence. Studies in Computational Intelligence, vol 155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70829-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-70829-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70826-1
Online ISBN: 978-3-540-70829-2
eBook Packages: EngineeringEngineering (R0)