Fundamentals of digital image processing
Fundamentals of digital image processing
Technical Note: \cal Q-Learning
Machine Learning
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation
Computing in Science and Engineering
A reinforcement agent for threshold fusion
Applied Soft Computing
A reinforcement agent for object segmentation in ultrasound images
Expert Systems with Applications: An International Journal
Medical Image Segmentation by Using Reinforcement Learning Agent
ICDIP '09 Proceedings of the International Conference on Digital Image Processing
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Delayed reinforcement learning for adaptive image segmentation andfeature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive integrated image segmentation and object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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%.