A Reinforcement Learning Framework for Medical Image Segmentation
- 1 January 2006
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in The 2006 IEEE International Joint Conference on Neural Network Proceedings
Abstract
This paper introduces a new method to medical image segmentation using a reinforcement learning scheme. We use this novel idea as an effective way to optimally find the appropriate local thresholding and structuring element values and segment the prostate in ultrasound images. Reinforcement learning agent uses an ultrasound image and its manually segmented version and takes some actions (i.e., different thresholding and structuring element values) to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal determined objectively. The agent uses these objective reward/punishment to explore/exploit the solution space. The values obtained using this way can be used as valuable knowledge to fill a Q-matrix. The reinforcement learning agent can use this knowledge for similar ultrasound images as well. The results demonstrate high potential for applying reinforcement learning in the field of medical image segmentation.Keywords
This publication has 10 references indexed in Scilit:
- REINFORCED CONTRAST ADAPTATIONInternational Journal of Image and Graphics, 2006
- A coarse-to-fine approach to prostate boundary segmentation in ultrasound imagesBioMedical Engineering OnLine, 2005
- Filter fusion for image enhancement using reinforcement learningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Survey over image thresholding techniques and quantitative performance evaluationJournal of Electronic Imaging, 2004
- Prostate segmentation from 2D ultrasound imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- American cancer society national prostate cancer detection project. Goals and current statusCancer, 1995
- Using line correspondences in invariant signatures for curve recognitionImage and Vision Computing, 1993
- Q-learningMachine Learning, 1992
- The Signature of a Plane CurveSIAM Journal on Computing, 1986
- Error measures for scene segmentationPattern Recognition, 1977