Robust 3-D modeling of vasculature imagery using superellipsoids.

This paper presents methods to model complex vasculature in three-dimensional (3-D) images using cylindroidal superellipsoids, along with robust estimation and detection algorithms for automated image analysis. This model offers an explicit, low-order parameterization, enabling joint estimation of boundary, centerlines, and local pose. It provides a geometric framework for directed vessel traversal, and extraction of topological information like branch point locations and connectivity. M-estimators provide robust region-based statistics that are used to drive the superellipsoid toward a vessel boundary. A robust likelihood ratio test is used to differentiate between noise, artifacts, and other complex unmodeled structures, thereby verifying the model estimate. The proposed methodology behaves well across scale-space, shows a high degree of insensitivity to adjacent structures and implicitly handles branching. When evaluated on synthetic imagery mimicking specific structural complexities in tumor microvasculature, it consistently produces ubvoxel accuracy estimates of centerlines and widths in the presence of closely-adjacent vessels, branch points, and noise. An edit-based validation demonstrated a precision level of 96.6% at a recall level of 95.4%. Overall, it is robust enough for large-scale application.

Efficient migration of complex off-line computer vision software to real-time system implementation on generic computer hardware.

This paper addresses the problem of migrating large and complex computer vision code bases that have been developed off-line, into efficient real-time implementations avoiding the need for rewriting the software, and the associated costs. Creative linking strategies based on Linux loadable kernel modules are presented to create a simultaneous realization of real-time and off-line frame rate computer vision systems from a single code base. In this approach, systemic predictability is achieved by inserting time-critical components of a user-level executable directly into the kernel as a virtual device driver. This effectively emulates a single process space model that is nonpreemptable, nonpageable, and that has direct access to a powerful set of system-level services. This overall approach is shown to provide the basis for building a predictable frame-rate vision system using commercial off-the-shelf hardware and a standard uniprocessor Linux operating system. Experiments on a frame-rate vision system designed for computer-assisted laser retinal surgery show that this method reduces the variance of observed per-frame central processing unit cycle counts by two orders of magnitude. The conclusion is that when predictable application algorithms are used, it is possible to efficiently migrate to a predictable frame-rate computer vision system.