An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture analysis for ulcer detection in capsule endoscopy images
Image and Vision Computing
Identification of ulcers in wireless capsule endoscopy videos
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
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Wireless Capsule Endoscopy (WCE) is a painless and noninvasive technique that allows physicians to visualize the entire small bowel. Before WCE was introduced, examining the entire small bowel was impossible without surgical procedure. Although WCE is a technology breakthrough, the manual video diagnosis session is time consuming and is prone to human cognition errors. Therefore, gastroenterologists urge computer vision researchers to develop computer-aided diagnosis systems to assist the review session. In this paper, we would like to present an ulcer detection scheme that identifies ulcers in WCE video. A saliency map is created by means of channel mixer to highlight suspicious regions. Once the suspicious regions are identified, we extract textural features along the contour and classify them using a trained classifier for ulcer validation.