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
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet-Based Texture Classification of Tissues in Computed Tomography
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Texture analysis for ulcer detection in capsule endoscopy images
Image and Vision Computing
Computer-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine
MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy
IEEE Transactions on Circuits and Systems for Video Technology
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Pattern Recognition and Image Analysis
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Wireless capsule endoscopy (WCE) has been gradually applied in hospitals due to its great advantage that it can directly view the entire small bowel in human body compared with traditional endoscopies and other imaging techniques for gastrointestinal diseases. However, a challenging problem with this new technology is that too many images produced by WCE causes a tough task to doctors, so it is very significant to help and relief the clinicians if we can develop computer based automatic detection system to prescreen the collected large amount of images and identify the images with potential problems. In this paper, we propose a new scheme aimed for small bowel tumor detection of WCE images. This new scheme utilizes texture feature, also a powerful clue used by physicians, to detect tumor images with support vector machine. We put forward a new idea of wavelet based local binary pattern as the textural features to discriminate tumor regions from normal regions, which take advantage of wavelet transform and uniform local binary pattern. With support vector machine as the classifier, three-fold cross validation experiments on our present image data verify that it is promising to employ the proposed texture features to recognize the small bowel tumor regions.