A radial structure tensor and its use for shape-encoding medical visualization of tubular and nodular structures.

The concept of curvature and shape-based rendering is beneficial for medical visualization of CT and MRI image volumes. Color-coding of local shape properties derived from the analysis of the local Hessian can implicitly highlight tubular structures such as vessels and airways, and guide the attention to potentially malignant nodular structures such as tumors, enlarged lymph nodes, or aneurysms. For some clinical applications, however, the evaluation of the Hessian matrix does not yield satisfactory renderings, in particular for hollow structures such as airways, and densely embedded low contrast structures such as lymph nodes. Therefore, as a complement to Hessian-based shape-encoding rendering, this paper introduces a combination of an efficient sparse radial gradient sampling scheme in conjunction with a novel representation, the radial structure tensor (RST). As an extension of the well-known general structure tensor, which has only positive definite eigenvalues, the radial structure tensor correlates position and direction of the gradient vectors in a local neighborhood, and thus yields positive and negative eigenvalues which can be used to discriminate between different shapes. As Hessian-based rendering, also RST-based rendering is ideally suited for GPU implementation. Feedback from clinicians indicates that shape-encoding rendering can be an effective image navigation tool to aid diagnostic workflow and quality assurance.

Informatics in radiology: Hesse rendering for computer-aided visualization and analysis of anomalies at chest CT and breast MR imaging.

A volume-rendering (VR) technique known as Hesse rendering applies image-enhancement filters to three-dimensional imaging volumes and depicts the filter responses in a color-coded fashion. Unlike direct VR, which makes use of intensities, Hesse rendering operates on the basis of shape properties, such that nodular structures in the resulting renderings have different colors than do tubular structures and thus are easily visualized. The renderings are mouse-click sensitive and can be used to navigate to locations of possible anomalies in the original images. Hesse rendering is meant to complement rather than replace conventional section-by-section viewing or VR. Although it is a pure visualization technique that involves no internal segmentation or explicit object detection, Hesse rendering, like computer-aided detection, may be effective for quickly calling attention to points of interest in large stacks of images and for helping radiologists to avoid oversights.

Automated model-based rib cage segmentation and labeling in CT images.

We present a new model-based approach for an automated labeling and segmentation of the rib cage in chest CT scans. A mean rib cage model including a complete vertebral column is created out of 29 data sets. We developed a ray search based procedure for rib cage detection and initial model pose. After positioning the model, it was adapted to 18 unseen CT data. In 16 out of 18 data sets, detection, labeling, and segmentation succeeded with a mean segmentation error of less than 1.3 mm between true and detected object surface. In one case the rib cage detection failed, in another case the automated labeling.

Spherical diffusion for 3D surface smoothing.

A diffusion-based approach to surface smoothing is presented. Surfaces are represented as scalar functions defined on the sphere. The approach is equivalent to Gaussian smoothing on the sphere and is computationally efficient since it does not require iterative smoothing. Furthermore, it does not suffer from the well-known shrinkage problem. Evolution of important shape features (parabolic curves) under diffusion is demonstrated. A nonlinear modification of the diffusion process is introduced in order to improve smoothing behavior of elongated and poorly centered objects.