In this paper we propose a principled approach for shape comparison. Given two surfaces, one to one correspondences are determined using the Laplace equation. The distance between corresponding points is then used to define both global and local dissimilarity statistics between the surfaces. This technique provides a powerful method to compare shapes both locally and globally for the purpose of segmentation, registration or shape analysis. For improved accuracy, we propose a Boundary Element Method. Our approach is applicable to datasets of any dimension and offers subpixel resolution. We illustrate the usefulness of the technique for validation of segmentation, by defining global dissimilarity statistics and visualizing errors locally on color-coded surfaces. We also show how our technique can be applied to multiple shapes comparison.
Although Pulmonary Embolism (PE) is one of the most common causes of unexpected death in the U.S., it may also be one of the most preventable. Images acquired from 16-slice Computed Tomography (CT) machines of contrast-injected patients provide sufficient resolution for the localization and analysis of emboli located in segmental and sub-segmental arteries. After a PE is found, it is difficult to assess the local characteristics of the affected arterial tree without automation. We propose a method to compute characteristics of the local arterial tree given the location of a PE. The computed information localizes the portion of the arterial tree that is affected by the embolism. Our method is based on the segmentation of the arteries and veins followed by a localized tree computation at the given site. The method determines bifurcation points and the remaining arterial tree. A preliminary segmentation method is also demonstrated to locally eliminate over-segmentation of the arterial tree. The final result can then be used assess the affected lung volume and arterial supply. Initial tests revealed a good ability to compute local tree characteristics of selected sites.
Pulmonary Embolism (PE) is one of the most common causes of unexpected death in the US. The recent introduction of 16-slice Computed Tomography (CT) machines allows the acquisition of very high-resolution datasets. This has made CT a more attractive means for diagnosing PE, especially for previously difficult to identify small subsegmental peripheral emboli. However, the large size of these datasets makes it desirable to have an automated method to help radiologists focus directly on potential candidates that might otherwise be overlooked. We propose a novel method to highlight potential PEs on a 3D representation of the pulmonary arterial tree. First lung vessels are segmented using mathematical morphology techniques. The density values inside the vessels are then used to color the outside of a Shaded Surface Display (SSD) of the vessel tree. As PEs are clots of significantly lower Hounsfield Unit (HU) values than surrounding contrast-enhanced blood, they appear as salient contrasted patches in this 3D rendering. During preliminary testing on 6 datasets 19 PEs out of 22 were detected (sensitivity 86%) with 2 false positives for every true positive (Positive Predictive Value 33%).
Utilizing off the shelf low cost parts, we have constructed a robot that is small, light, powerful and relatively inexpensive (< $3900). The system is constructed around the Beowulf concept of linking multiple discrete computing units into a single cooperative system. The goal of this project is to demonstrate a new robotics platform with sufficient computing resources to run biologically-inspired vision algorithms in real-time. This is accomplished by connecting two dual-CPU embedded PC motherboards using fast gigabit Ethernet. The motherboards contain integrated Firewire, USB and serial connections to handle camera, servomotor, GPS and other miscellaneous inputs/outputs. Computing systems are mounted on a servomechanism-controlled off-the-shelf “Off Road” RC car. Using the high performance characteristics of the car, the robot can attain relatively high speeds outdoors. The robot is used as a test platform for biologically-inspired as well as traditional robotic algorithms, in outdoor navigation and exploration activities. Leader following using multi blob tracking and segmentation, and navigation using statistical information and decision inference from image spectral information are discussed. The design of the robot is open-source and is constructed in a manner that enhances ease of replication. This is done to facilitate construction and development of mobile robots at research institutions where large financial resources may not be readily available as well as to put robots into the hands of hobbyists and help lead to the next stage in the evolution of robotics, a home hobby robot with potential real world applications.