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).

Meeting Name

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)


Document Type

Article - Conference proceedings

Document Version


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© 2010 International Society for Research in Science and Technology (ISRST), All rights reserved.

Publication Date

01 Jul 2010

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