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Nested or intersecting surfaces are proven techniques for visualizing shape differences between static 3D objects (Weigle and Taylor II, IEEE Visualization, Proceedings, pp. 503–510, 2005). In this paper we present an image-based formulation for these techniques that extends their use to dynamic scenarios, in which surfaces can be manipulated or even deformed interactively. The formulation is based on our new layered rendering pipeline, a generic image-based approach for rendering nested surfaces based on depth peeling and deferred shading. We use layered rendering to enhance the intersecting surfaces visualization. In addition to enabling interactive performance, our enhancements address several limitations of the original technique. Contours remove ambiguity regarding the shape of intersections. Local distances between the surfaces can be visualized at any point using either depth fogging or distance fields: Depth fogging is used as a cue for the distance between two surfaces in the viewing direction, whereas closest-point distance measures are visualized interactively by evaluating one surface’s distance field on the other surface. Furthermore, we use these measures to define a three-way surface segmentation, which visualizes regions of growth, shrinkage, and no change of a test surface compared with a reference surface. Finally, we demonstrate an application of our technique in the visualization of statistical shape models. We evaluate our technique based on feedback provided by medical image analysis researchers, who are experts in working with such models., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

Nested or intersecting surfaces are proven techniques for visualizing shape differences between static 3D objects (Weigle and Taylor II, IEEE Visualization, Proceedings, pp. 503–510, 2005). In this paper we present an image-based formulation for these techniques that extends their use to dynamic scenarios, in which surfaces can be manipulated or even deformed interactively. The formulation is based on our new layered rendering pipeline, a generic image-based approach for rendering nested surfaces based on depth peeling and deferred shading. We use layered rendering to enhance the intersecting surfaces visualization. In addition to enabling interactive performance, our enhancements address several limitations of the original technique. Contours remove ambiguity regarding the shape of intersections. Local distances between the surfaces can be visualized at any point using either depth fogging or distance fields: Depth fogging is used as a cue for the distance between two surfaces in the viewing direction, whereas closest-point distance measures are visualized interactively by evaluating one surface’s distance field on the other surface. Furthermore, we use these measures to define a three-way surface segmentation, which visualizes regions of growth, shrinkage, and no change of a test surface compared with a reference surface. Finally, we demonstrate an application of our technique in the visualization of statistical shape models. We evaluate our technique based on feedback provided by medical image analysis researchers, who are experts in working with such models., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

This work investigates knowledge driven segmentation of cardiac MR perfusion sequences. We build upon previous work on multi-band AAMs to integrate into the segmentation both spatial priors about myocardial shape as well as temporal priors about characteristic perfusion patterns. Different temporal and spatial features are developed without a strict need for temporal correspondence across the image sequences. We also investigate which combination of spatial and temporal features yields the best segmentation performance. Our evaluation criteria were boundary errors wrt manual segmentations, area overlap, and convergence envelope. From a quantitative evaluation on 19 perfusion studies, we conclude that a combination of the maximum intensity projection feature and gradient orientation map yields the best segmentation performance, with an average point-to-curve error of 0.9-1 pixel wrt manual contours. We also conclude that addition of different temporal features does not necessarily increase performance., Mediamatics, Electrical Engineering, Mathematics and Computer Science

This work investigates knowledge driven segmentation of cardiac MR perfusion sequences. We build upon previous work on multi-band AAMs to integrate into the segmentation both spatial priors about myocardial shape as well as temporal priors about characteristic perfusion patterns. Different temporal and spatial features are developed without a strict need for temporal correspondence across the image sequences. We also investigate which combination of spatial and temporal features yields the best segmentation performance. Our evaluation criteria were boundary errors wrt manual segmentations, area overlap, and convergence envelope. From a quantitative evaluation on 19 perfusion studies, we conclude that a combination of the maximum intensity projection feature and gradient orientation map yields the best segmentation performance, with an average point-to-curve error of 0.9-1 pixel wrt manual contours. We also conclude that addition of different temporal features does not necessarily increase performance., Mediamatics, Electrical Engineering, Mathematics and Computer Science