A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On combining classifiers using sum and product rules
Pattern Recognition Letters
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers in Biology and Medicine
Texture analysis for ulcer detection in capsule endoscopy images
Image and Vision Computing
Analysis of Crohn's disease lesions in capsule endoscopy images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Score normalization in multimodal biometric systems
Pattern Recognition
Computer-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine
Mean shift-based lesion detection of gastroscopic images
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Artificial Intelligence in Medicine
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Objective: Capsule endoscopy is useful in the diagnosis of small bowel diseases. However, the large number of images produced in each test is a tedious task for physicians. To relieve burden of physicians, a new computer-aided detection scheme is developed in this study, which aims to detect small bowel tumors for capsule endoscopy. Methods and materials: A novel textural feature based on multi-scale local binary pattern is proposed to discriminate tumor images from normal images. Since tumor in small bowel exhibit great diversities in appearance, multiple classifiers are employed to improve detection accuracy. 1200 capsule endoscopy images chosen from 10 patients' data constitute test data in our experiment. Results: Multiple classifiers based on k-nearest neighbor, multilayer perceptron neural network and support vector machine, which are built from six different ensemble rules, are experimented in three different color spaces. The results demonstrate an encouraging detection accuracy of 90.50%, together with a sensitivity of 92.33% and a specificity of 88.67%. Conclusion: The proposed scheme using color texture features and classifier ensemble is promising for small bowel tumor detection in capsule endoscopy images.