A survey of image registration techniques
ACM Computing Surveys (CSUR)
Validation of registration accuracy
Handbook of medical imaging
Image Registration by Maximization of Combined Mututal Information and Gradient Information
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Color spaces for computer graphics
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Identifying Precursory Cancer Lesions Using Temporal Texture Analysis
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Cervical Cancer Detection Using Colposcopic Images: a Temporal Approach
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Registration of Challenging Image Pairs: Initialization, Estimation, and Decision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Aceto-White Temporal Patterns to Segment Colposcopic Images
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Computers in Biology and Medicine
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Fast parametric elastic image registration
IEEE Transactions on Image Processing
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Colposcopy is a well-established method to detect and diagnose intraepithelial lesions and uterine cervical cancer in early stages. During the exam color and texture changes are induced by the application of a contrast agent (e.g.3-5% acetic acid solution or iodine). Our aim is to densely quantify the change in the acetowhite decay level for a sequence of images captured during a colposcopy exam to help the physician in his diagnosis providing new tools that overcome subjectivity and improve reproducibility. As the change in acetowhite decay level must be calculated from the same tissue point in all images, we present an elastic image registration scheme able to compensate patient, camera and tissue movement robustly in cervical images. The image registration is based on a novel multi-feature entropy similarity criterion. Temporal features are then extracted using the color properties of the aligned image sequence and a dual compartment tissue model of the cervix. An example of the use of the temporal features for pixel-wise classification is presented and the results are compared against ground truth histopathological annotations.