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GPU Brainstorming

This document outlines key GPU algorithms and classes to focus on for enhancing intraoperative imaging registration and processing. Priorities include techniques such as the Parallel Sparse Field Solver, Narrow Band Level Set, and various image metrics like 3D Mattes Mutual Information. We emphasize algorithms that are computationally efficient and scalable, such as Anisotropic Diffusion and Demons, while also exploring the importance of streamable and resampling methods. Additional considerations include basic filtering, registration strategies, and performing image processing with a focus on speed and accuracy.

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GPU Brainstorming

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Presentation Transcript

  1. GPU Brainstorming What Classes to focus on

  2. Top Priorities • Level Sets • (1) ParallelSparseFieldSolver (look at link from Paul) • (?) NarrowBandLevelSet • Must be Streamable • Registration • Image Metrics • (13) Mattes (may not be a good candidate), Mean • Resampling • (6) Bspline Transform (?) • (5) WindowingSinc Interpolator (only for final resampling) • FiniteDifferenceFilter • (4) Anisotropic Diffusion (…it must stream…) • (2) Demons • (7) Level Sets (shape detection, geodesic active contours)

  3. Top Priorities • (10) Geodesic Morphology • Richard Beare • (11) Region Growing • ConfidenceConnected (FloodFill iterator) • (5) FFT Transform(…if there is already a lib out there.. Apple ?) • (7) BinaryFunctorImageFilter • (7) UnaryFunctorFilter • (7) ConvolutionFilters • (15) Iterative Conditional Modes (statistics) • (7) Basic filters (Gaussian, Median)

  4. Criteria • Focus on algorithms that take several minutes(instead of making interactive the ones that take seconds). • Scalability ?linear with number of cores ? • Intraoperative imaging registration

  5. Good Targets • 3D Mattes Mutual Information, Deformable Registration (with Bsplines) • Rigid Registration • Mutual Information (Greg Sharp) • (histogram based)

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