Shape Priors for Segmentation of the Cervix Region Within Uterine Cervix Images

The work focuses on a unique medical repository of digital uterine cervix images (“cervigrams”) collected by the National Cancer Institute (NCI), National Institute of Health, in longitudinal multiyear studies. NCI together with the National Library of Medicine is developing a unique web-based database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for the automated analysis of the cervigram content to support the cancer research. In recent works, a multistage automated system for segmenting and labeling regions of medical and anatomical interest within the cervigrams was developed. The current paper concentrates on incorporating prior-shape information in the cervix region segmentation task. In accordance with the fact that human experts mark the cervix region as circular or elliptical, two shape models (and corresponding methods) are suggested. The shape models are embedded within an active contour framework that relies on image features. Experiments indicate that incorporation of the prior shape information augments previous results

Contours, Optic Flow, and Prior Knowledge: Cues for Capturing 3D Human Motion in Videos

Human 3D motion tracking from video is an emerging research field with many applications demanding highly detailed results. This chapter surveys a high quality generative method, which employs the person’s silhouette extracted from one or multiple camera views for fitting an a-priori given 3D body surface model. A coupling between pose estimation and contour extraction allows for reliable tracking in cluttered scenes without the need of a static background. The optic flow computed between two successive frames is used for pose prediction. It improves the quality of tracking in case of fast motion and/or low frame rates. In order to cope with unreliable or insufficient data, the framework is further extended by the use of prior knowledge on static joint angle configurations

Variational methods in shape analysis

The analysis of shapes as elements in a frequently infinite-dimensional space of shapes has attracted increasing attention over the last decade. There are pioneering contributions in the theoretical foundation of shape space as a Riemannian manifold as well as path-breaking applications to quantitative shape comparison, shape recognition, and shape statistics. The aim of this chapter is to adopt a primarily physical perspective on the space of shapes and to relate this to the prevailing geometric perspective. Indeed, we here consider shapes given as boundary contours of volumetric objects, which consist either of a viscous fluid or an elastic solid. In the first case, shapes are transformed into each other via viscous transport of fluid material, and the flow naturally generates a connecting path in the space of shapes. The viscous dissipation rate—the rate at which energy is converted into heat due to friction—can be defined as a metric on an associated Riemannian manifold. Hence, via the computation of shortest transport paths one defines a distance measure between shapes