A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using texture spectrum
Pattern Recognition
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
Color and texture image segmentation using uniform local binary patterns
Machine Graphics & Vision International Journal
Texture Classification in Lung CT Using Local Binary Patterns
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Computers in Biology and Medicine
False positive reduction in mammographic mass detection using local binary patterns
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
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
Hi-index | 12.05 |
Wireless capsule endoscopy (WCE) opens a new stage for diagnosing gastrointestinal tract diseases since it enables direct visualization of the small intestine for the first time. However, it requires a clinician's long time inspection due to a great number of images produced by the procedure. Therefore, it may be beneficial to devise an automatic detection system to help clinicians identify problematic images. In this work, we attempt to design a computerized scheme aiming for polyp WCE image recognition though polyp in WCE images show great variations in appearance. This scheme utilizes a new texture feature to characterize WCE images, which integrates advantages of wavelet transform and uniform local binary pattern. With support vector machine (SVM) as a classifier, extensive experiments on our present image data, which consists of 600 normal WCE images and 600 polyp WCE images chosen from 10 patients, verify that it is promising to utilize the proposed scheme to detect polyp WCE images.