Software Agent with Reinforcement Learning Approach for Medical Image Segmentation |
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Authors: | Mahsa Chitsaz Chaw Seng Woo |
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Affiliation: | (1) Medical Instrument Analysis and Machine Intelligence Group, University of Waterloo, Waterloo, Canada;(2) Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada;(3) Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada |
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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%. |
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Keywords: | |
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