Software Agent with Reinforcement Learning Approach for Medical Image Segmentation

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%.