Adaptive remapping procedure for electronic cleansing of fecal tagging CT Colonography images

Fecal tagging preparations are attracting notable interest as a way to increase patients' compliance to virtual colonoscopy. Patient-friendly preparations, however, often result in less homogeneous tagging. Electronic cleansing algorithms should be capable of dealing with such preparations and yield good quality 2D and 3D images; moreover, successful electronic cleansing lays the basis for the application of Computer Aided Detection schemes. In this work, we present a cleansing algorithm based on an adaptive remapping procedure, which is based on a model of how partial volume affects both the air-tissue and the soft-tissue interfaces. Partial volume at the stool-soft tissue interface is characterized in terms of the local characteristics of tagged regions, in order to account for variations in tagging intensity throughout the colon. The two models are then combined in order to obtain a remapping equation relating the observed intensity to the that of the cleansed colon. The electronic cleansed datasets were then processed by a CAD scheme composed of three main steps: colon surface extraction, polyp candidate segmentation through curvature-based features, and linear classifier-based discrimination between true polyps and false alarms. Results obtained were compared with a previous version of the cleansing algorithm, in which a simpler remapping procedure was used. Performances are increased both in terms of the visual quality of the 2D cleansed images and 3D rendered volumes, and of CAD performances on a sameday FT virtual colonoscopy dataset.