Automatic colposcopy video tissue classification using higher order entropy-based image registration.

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.