Sean Ho

We develop a new method for automatic segmentation of anatomical structures from volumetric medical images. Driving application is tumor segmentation from 3-D MRIs, which is known to be a very challenging problem due to the variability of tumor geometry and intensity patterns. Level-set snakes offer significant advantages over conventional statistical classification and mathematical morphology, however snakes with constant propagation need careful initialization and can leak through weak or missing boundary parts. Our region competition method overcomes these problems by modulating the propagation term with a signed local statistical force, leading to a stable solution. A pre- vs. post-contrast difference image is used to calculate probabilities for background and tumor regions, with a mixture-modelling fit of the histogram. Preliminary results on five cases with significant shape and intensity variability demonstrate that the new method might become a powerful and efficient tool for the clinic. Validity is demonstrated by comparison with manual expert segmentation.

Active contour segmentation and its robust implementation using level sets have been studied thoroughly in the medical image analysis literature. Despite the availability of these powerful methods, clinical research still largely relies on manual slice-by-slice outlining for anatomical structure segmentation. To bridge the gap between methodological advances and clinical routine, we developed ITK-SNAP: an open source application intended to make level set segmentation easily accessible to a wide range of users with various levels of mathematical expertise. We briefly describe this new tool and report the results of a validation study in which ITK-SNAP was compared to manual segmentation of the caudate in the context of an ongoing child neuroimaging autism study.

This paper presents results for real-time visualization of out-of-core collections of 3D objects. This is a significant extension of previous methods and shows the generality of hierarchical paging procedures applied both to global terrain and any objects that reside on it. Applied to buildings, the procedure shows the effectiveness of using a screen-based paging and display criterion within a hierarchical framework. The results demonstrate that the method is scalable since it is able to handle multiple collections of buildings (e.g., cities) placed around the earth with full interactivity and without extensive memory load. Further the method shows efficient handling of culling and is applicable to larger, extended collections of buildings. Finally, the method shows that levels of detail can be incorporated to provide improved detail management.