GPU, How It Works? GRAPHICS PROCESSING UNITS Hidden Surfaces Determine which surfaces should be displayed Texturing Modify each pixel colour for added realism Rasterization Determine the overlapped pixels of some triangles Camera Simulation Projects the coloured 3D triangle onto the virtual camera’s film plane Lighting Compute the colour of the triangle based on the lights in the scene Model Transformation Especify each logical object in a scene in its own locally defined coordinate system 3D Geometric Primitives Description of the lights illumination the scene, the way each object reflects light, viewer’s position and orientation, etc.

AIRWC Accelerated Image Registration With CUDA

Medical Image Registration Process that aligns geometrically two images: the “source” or reference image (which remains unaltered) and target image (which suffers a geometric transformation in order to be aligned with the reference image).

Faster than conventional software running on a single CPU Program which makes possible fast medical image registration that uses affine transformations. AIRWC Accelerated Image Registration With CUDA

Benchmarking ExperimentCPUGPUSpeed Up 12 Parametre Affine Registration 8.5 minutes6 seconds98% 6 parameter registration 270 seconds2.39 seconds99%

Fast Level Segmentation Of Biomedical Images

Image Segmentation Splitting a digital image into one or more regions of interest. Discussed in a medical imaging context

Parallel algorithm executed on the GPU. Great interest segmenting three dimensional anatomical structures Disadvantage: Using level sets are relatively slow to compute Biomedical Image Segmentation

Level Set Method Ф(x,t) Contour (2D) or Surface (3D)

F Velocity Manipulating we can guide the level set to differents areas or shapes Dependent on the pixel intensity or curvature values of the level set No curvature term ( α=1)

Positive: desired regions Negative: underised regions D Describes the central intensity value of the region to be segmented. T Describes intensity derivation around T. ε

Level Set segmentation Inicial Mask Feature Images

Benchmarking 2D Segmentation Initial Mask Input Feature Image Input - Liver Output of Segmentation Liver segmentation T= 180; Є = 45; α = 0:003 T= 180; Є = 45; α = 0:003

The brain in sagittal view Feature Image Input – brain Initial Mask Input Output of Segmentation T = 45; Є = 30; α = 0:003 T = 45; Є = 30; α = 0:003

3D Segmentations The cerebral hemispheres T = 150; Є= 50; α = 0:03

Ansorge, Richard E.; Sawiak, Steve J.; Williams, Guy B.: Exceptionally Fast Non-Linear 3D Image Registration Using GPUs. University of Cambridge, November 2009 Chang, Wei-Hung; Lu, Cheng-Chang: Acceleration of Medical Image Registration Using Graphics Process Units in Computing Normalized Mutual Information. Department of Computer Science of Kent State University. Mike Giles. Jacobi iteration for a laplace discretisation on a 3d structured grid Chris A. Cocosco, Vasken Kollokian, Remi K.-S. Kwan, G. Bruce Pike, and Alan C. Evans. Brainweb: Online interface to a 3D MRI Simulated Brain Database. NeuroImage, 5:425, Luebke, David; Humphreys, Greg: How GPUs Work. NVIDIA Research and University of Virginia, 2007 C.L.C.X.C. Gui and MD Fox. Level set evolution without re-initialization: a new variational formulation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, volume 1, J.E. Cates, A.E. Lefohn, and R.T. Whitaker. GIST: an interactive, GPU-based level set segmentation tool for 3D medical images. Medical Image Analysis, 8(3):217{231, Ansorge, Richard E.: AIRWC: Accelerated Image Registration With CUDA. Cavendish Laboratory of University of Cambridge. August C. L. Badaj. Data Visualization Techniques. John Wiley & Sons, 1999 S. Marschner, R. Lobb. An evaluation of reconstruction filters for volume rendering. IEEE Vis., , High-Quality Rendering of Varying Isosurfaces with Cubic Trivariate C1-continous Splines Accelerating Advanced MRI Reconstructions On GPUs Ansorge, Richard E.; Sawiak, Steve J.; Williams, Guy B.: Exceptionally Fast Non-Linear 3D Image Registration Using GPUs. University of Cambridge, November 2009 Chang, Wei-Hung; Lu, Cheng-Chang: Acceleration of Medical Image Registration Using Graphics Process Units in Computing Normalized Mutual Information. Department of Computer Science of Kent State University. Mike Giles. Jacobi iteration for a laplace discretisation on a 3d structured grid Chris A. Cocosco, Vasken Kollokian, Remi K.-S. Kwan, G. Bruce Pike, and Alan C. Evans. Brainweb: Online interface to a 3D MRI Simulated Brain Database. NeuroImage, 5:425, Luebke, David; Humphreys, Greg: How GPUs Work. NVIDIA Research and University of Virginia, 2007 C.L.C.X.C. Gui and MD Fox. Level set evolution without re-initialization: a new variational formulation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, volume 1, J.E. Cates, A.E. Lefohn, and R.T. Whitaker. GIST: an interactive, GPU-based level set segmentation tool for 3D medical images. Medical Image Analysis, 8(3):217{231, Ansorge, Richard E.: AIRWC: Accelerated Image Registration With CUDA. Cavendish Laboratory of University of Cambridge. August C. L. Badaj. Data Visualization Techniques. John Wiley & Sons, 1999 S. Marschner, R. Lobb. An evaluation of reconstruction filters for volume rendering. IEEE Vis., , High-Quality Rendering of Varying Isosurfaces with Cubic Trivariate C1-continous Splines Accelerating Advanced MRI Reconstructions On GPUs References