Small bowel image classification based on Fourier-Zernike moment features and canonical discriminant analysis

Authors:
Guangyi Chen;Tien D. Bui;Adam Krzyzak;Sridhar Krishnan
Affiliations:
Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada H3G 1M8;Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada H3G 1M8;Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada H3G 1M8;Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada M5B 2K3
Venue:
Pattern Recognition and Image Analysis
Year:
2013

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Abstract

In this paper, we propose a novel method for the classification of small bowel images into normal or abnormal class for automatic detection of cancers. We extract the Fourier features from the input small bowel image, and then the Zernike moment features are computed from the Fourier features. We then use the canonical discriminant analysis (CDA) to classify the small bowel images to normal or abnormal class. Experimental results show that the proposed method achieves the highest correct classification rate 100% for this problem. Our method is computationally very efficient. It can be used to automate the classification of capsule endoscopic images and to reduce the cost of interpreting those images that are acquired in clinical setting.