A novel computational intelligence-based approach for medical image artifacts detection
In this research, a novel computational intelligence-based algorithm to detect artifacts, specifically arrows, in medical images is presented. Image analyses techniques are developed to find the symbols and text automatically. Features are computed from the shape of arrow for the discrimination of arrows from other artifacts. We investigate a biologically-inspired reinforcement learning (RL) approach in an adaptive critic design (ACD) framework to apply Action Dependent Heuristic Dynamic Programming (ADHDP) for arrow discrimination based on the computed features. Experimental results for ADHDP are compared with feed forward multi-layer perception (MLP) back-propagation artificial neural networks (BP-ANN), particle swarm optimization (PSO) for training of a MLP neural network, genetic algorithm (GA) for training of a MLP neural network, k-nearest neighbor (KNN), and support vector machine (SVM).
B. Cheng et al., "A novel computational intelligence-based approach for medical image artifacts detection," Proceedings of the International Conference on Artificial Intelligence and Pattern Recognition (2010, Orlando, FL), pp. 113-120, International Society for Research in Science and Technology (ISRST), Jul 2010.
International Conference on Artificial Intelligence and Pattern Recognition (2010: Jul. 12-14, Orlando, FL)
Electrical and Computer Engineering
Keywords and Phrases
Action Dependent Heuristic Dynamic Programming; Adaptive Critic Designs; Back-propagation Artificial Neural Network; Biologically-inspired; K Nearest Neighbor (KNN); Medical Images; MLP Neural Networks; Multi-layer Perception; Neural Networks; Particle Swarm Optimization (PSO); Pattern Recognition; Reinforcement Learning; Support Vector Machines; Genetic Algorithms
International Standard Book Number (ISBN)
Article - Conference proceedings
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