Colposcopic images from cervix uteri are subjected to a segmentation algorithm using a combination of an active contour model or snakes on multiresolution levels, using a Gaussian Pyramid (GP). The segmentation aims to outline a specific feature from the cervical images- the Transformation Zone (TZ), where a possible neoplasia (a pre-cancer or cancer tissue stage) can occur. The process includes an implementation of a new snake - the boundary-searching snake, based on both image gradient features and region features. The adaptive 'snake' is executed on a low image resolution level, aiming to avoid a specific artifact in the images-known as a specular reflection. Further, the snake coordinates are propagated to the highest level of the GP. The resulting algorithm segments one of the most complex and variable anatomical shapes as a biological structure in its normal and pre-cancerous stages of the cervix uteri
