Colon segmentation for prepless virtual colonoscopy.

A novel segmentation framework for a prepless virtual colonoscopy (VC) is presented, which reduces the necessity for colon cleansing before the CT scan. The patient is injected rectally with a water-soluble iodinated contrast medium using manual insufflators and a small rectal catheter. Compared to the air-based contrast medium, this technique can better preserve the color lumen and reduce the partial volume effect.

Intra-patient supine-prone colon registration in CT colonography using shape spectrum.

CT colonography (CTC) is a minimally invasive screening technique for colorectal polyps and colon cancer. Since electronic colon cleansing (ECC) cannot completely remove the presence of pseudo-polyps, most CTC protocols acquire both prone and supine images to improve the visualization of the lumen wall and to reduce false positives. Comparisons between the prone and supine images can be facilitated by computerized registration between the scans.

Intrinsic geometric scale space by shape diffusion.

This paper formalizes a novel, intrinsic geometric scale space (IGSS) of 3D surface shapes. The intrinsic geometry of a surface is diffused by means of the Ricci flow for the generation of a geometric scale space. We rigorously prove that this multiscale shape representation satisfies the axiomatic causality property.

Geodesic distance-weighted shape vector image diffusion.

This paper presents a novel and efficient surface matching and visualization framework through the geodesic distance-weighted shape vector image diffusion. Based on conformal geometry, our approach can uniquely map a 3D surface to a canonical rectangular domain and encode the shape characteristics (e.g.

3D surface matching and registration through shape images.

In this paper, we present a novel and efficient surface matching framework through shape image representation. This representation allows us to simplify a 3D surface matching problem to a 2D shape image matching problem. Furthermore, we present a shape image diffusion-based method to find the most robust features to construct the matching and registration of surfaces.