Chitsaz, M. and Woo, C.S. (2011) Software Agent with Reinforcement Learning Approach for Medical Image Segmentation. Journal of Computer Science and Technology, 26 (2). pp. 247-255. ISSN 1000-9000,
Full text not available from this repository.Abstract
Many image segmentation solutions are problem-based. Medical images have very similar grey level and texture among the interested objects. Therefore, medical image segmentation requires improvements although there have been researches done since the last few decades. We design a self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images. Our segmentation method has a learning phase that is based on reinforcement learning (RL) system. Each RL agent works on a particular sub-image of an input image to find a suitable value for each object in it. The RL system is define by state, action and reward. We defined some actions for each state in the sub-image. A reward function computes reward for each action of the RL agent. Finally, the valuable information, from discovering all states of the interest objects, will be stored in a Q-matrix and the final result can be applied in segmentation of similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 95%
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | biomedical image segmentation; multi-agent systems; reinforcement learning system; CT images; |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Depositing User: | Zanaria Saupi Udin |
| Date Deposited: | 26 Aug 2011 08:00 |
| Last Modified: | 12 Dec 2014 06:09 |
| URI: | http://eprints.um.edu.my/id/eprint/2072 |
Actions (login required)
 |
View Item |
