International Journal of Computer Vision
Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of MPEG-7 shape descriptors against other shape descriptors
Multimedia Systems
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
Pattern Recognition and Image Analysis
Hi-index | 0.00 |
Capsule endoscopy (CE) has gradually seen its wide application since it can directly view the small bowel in human body for the first time. However, a challenging problem is that too many images produced in each examination pose a tough workload to physicians. In this paper, we propose a new scheme aiming for intestinal polyp detection for CE images in order to partially solve this problem. This new scheme utilizes color and shape information, also powerful clues employed by physicians, to recognize polyp images. An illumination invariant color feature based on chromaticity histogram is first proposed. Integrating it with Zernike moments which are scale, translation and rotation invariant, we exploit the combined information as color and shape features to discriminate polyp from normal regions. Experiments on our present image data verify that it is promising to employ the proposed features to recognize polyp images.