Medical Image Segmentation by Using Reinforcement Learning Agent

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%.