Software agent with reinforcement learning approach for medical image segmentation
Journal of Computer Science and Technology
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Image segmentation still requires improvements although there have been research work since the last few decades. This is due to some factors. Firstly, most image segmentation solution is problem-based. Secondly, medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested objects. The goal of this work is to design a framework to extract simultaneously several objects of interest from Computed Tomography (CT) images. Our method does not need a large training set or priori knowledge. The learning phase is based on reinforcement learning (RL). The input image is divided into several sub-images, and each RL agent works on it to find the suitable value for each object in the image. Each state in the environment has associated defined actions, and a reward function computes reward for each action of the RL agent. Finally the valuable information is stored in a Q-Matrix, and the final result can be applied in segmentation of new similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 93%.