The nature of statistical learning theory
The nature of statistical learning theory
The computation of optical flow
ACM Computing Surveys (CSUR)
Feature comparisons of vector fields using earth mover's distance
Proceedings of the conference on Visualization '98
Compression of medical volumetric data in a video-codec framework
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Interactive 3-D virtual colonoscopy system
IEEE Transactions on Information Technology in Biomedicine
Lines of Curvature for Polyp Detection in Virtual Colonoscopy
IEEE Transactions on Visualization and Computer Graphics
Current concepts in computer-aided detection for CT colonography
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
An efficient and scalable deformable model for virtual reality-based medical applications
Artificial Intelligence in Medicine
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Since the introduction of Computed Tomographic Colonography (CTC), research has mainly focused on visualization and navigation techniques. Recently, efforts have shifted towards computer aided detection (CAD) of polyps. We propose a new approach to CAD in CT images that attempts to model the way a radiologist recognizes a polyp using optical flow fields (OFF). Features extracted from OFFs are used by a linear classifier for polyp detection. An initial validation of our technique resulted in an average of 75% specificity at 100% sensitivity in a 10-fold cross validation study on a set of 220 polyp-like structures, 20 of which were true polyps.