Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nodule Detection on Chest Helical CT Scans by Using a Genetic Algorithm
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
On clustering and retrieval of video shots through temporal slices analysis
IEEE Transactions on Multimedia
Evaluating retina image fusion based on quantitative approaches
WSEAS Transactions on Computers
Auto-detection of non-isolated pulmonary nodules in X-ray CT images
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
WSEAS Transactions on Information Science and Applications
Sensitivity improvement of automatic pulmonary nodules detection in chest X-ray CT images
Artificial Life and Robotics
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In this paper, we propose a new method for computer aided detection of pulmonary nodules in X-ray CT images to reduce false positive rate under high true positive rate conditions. An essential part of the method is to extract and combine two novel and effective features from the original CT images: One is orientation features of nodules in a region of interest (ROI) extracted by a Gabor filter, while the other is variation of CT values of the ROI in the direction along body axis. By using the extracted features, pattern recognition techniques can then be used to discriminate between nodule and non-nodule images. Simulation results show that discrimination performance using the proposed features is extremely improved compared to that of the conventional method.