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Volume Graphics (graduate course)

Volume Graphics (graduate course). Bong-Soo Sohn School of Computer Science and Engineering Chung-Ang University. Course Overview. Level : CSE graduate course No Textbook We will use lecture notes, recent papers, and several handouts. Lecture Format

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Volume Graphics (graduate course)

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  1. Volume Graphics(graduate course) Bong-Soo Sohn School of Computer Science and Engineering Chung-Ang University

  2. Course Overview • Level : CSE graduate course • No Textbook • We will use lecture notes, recent papers, and several handouts. • Lecture Format • Lectures by Instructor (half) + Student Presentation (half) • Topics • Scalar and Vector Volume Visualization Techniques • Point/Image Based Geometric Processing • Shape Analysis

  3. Course Information • Time: Thu7,8,9 • Place: 208-529 • Instructor Information • Office: 208-501 • email : bongbong@cau.ac.kr • Office Tel : 820-5843 • Office Hour: Thu 1pm-2pm or email appointment

  4. Image and Geometric Processing • Engineering Research • Scientific Research • Biomedical Research • Building/Plant Construction Filtering, Segmentation OB JEC T Laser Scanner Point Cloud Geometric Modeling Processing CT/MRI Visualization Electron Microscopy 3D/4D Image Quantification (Structure Analysis) OCT Simulation

  5. Input Biomedical Images • Rapid Advance of Imaging Techniques • Multiscale • Static(3D) vs time-varying(4D) X-ray Crystallography Electron Microscopy OCT (Optical Coherence Tomography) CT/MRI Cryo-EM Cellular and Tissue Level (Nano Scale) Molecular Level (Angstrom Scale) Organ Level (Micro Scale) Organ Level

  6. Building Information Modeling (BIM) • generation and management of a digital representation of physical and functional characteristics of a facility.

  7. Salient Feature Analysis • Salient Contour Extraction • Useful for segmentation, analysis and visualization of regions of interest • Can be applied to CAD(Computer Aided Diagnosis) for detecting suspicious regions breast boundary pectoral muscle dense tissue mass (tumor) dense tissue

  8. KISTI 수퍼컴퓨팅센터

  9. Cardiovascular Modeling Research Pipeline Rendering, Quantitative Visualization Simulation Geometric Modeling 3D Image Acquisition cardivascular disease research, medical device design, and surgical planning

  10. Sulcal Morphology Analysis(courtesy of Dr. J.-K. Seong) Reduced average sulcal curvature and depth in AD (Im et al. NeuroImage 2008)

  11. Biomedical OCT Visualization ( Journal of Korean Physical Society [SCI], 2007 ) • OCT(Optical Coherence Tomography) • Non-invasive optical tomographic imaging technique with micrometer scale resolution. • Widely accepted in biomedical applications • Contribution • Real-time volume visualization of 3-dimensional OCT images. 3D Volume Visualization

  12. Lecture Schedule • Visualization Overview (1 week) • Scalar Visualization Techniques (2~3 weeks) • Volume Rover • Volume Rendering • Ray casting, HW accelerated volume rendering • MIP (Maximum Intensity Projection) • Transfer function design • Isocontour Visualization • Marching Cubes + Accelerated method • Quantitative and Topological Analysis • Large Data Visualization (parallelism, out-of-core, compression) • Interactive Visualization Interface • Illustrative Visualization , NPR in Visualization

  13. Lecture Schedule • Vector Visualization Techniques (1 week) • Line Integral Convolution, Streamline • Image Based Geometric Modeling (1~2 weeks) • Filtering • Segmentation (Level Set Method) • Mesh Generation • Shape Analysis (2 weeks) • Voronoi Diagram, Delaunay Triangulation • Medial Axis Algorithms, Skeletonization • Shape Matching, Salient Feature Extraction • Surface Property (curvature, …) • Applications (Surface Reconstruction, Protein Docking, …)

  14. Volume Rendering, Isocontour • 3D World is modeled with a function (= image) • F(x,y,z) (e.g. CT : human body density) • Surface is modeled with a level set of a function • level set = isosurface = isocontour = implicit surface • { (x,y,z) | F(x,y,z) = w }( w is a fixed value, called isovalue ) • Level set may represent important features of a function • e.g. skin surface (w=skin density) or bone surface (w=bone density) in body CT

  15. Example (Volume Rendering, Isocontour) Level Set : F(x,y,z) = w w = skin density [ skin surface ] w = bone density [ volume image ] F(x,y,z) [ bone surface ]

  16. Hybrid Parallel Contour Extraction • Different from isocontour extraction • Divide contour extraction process into • Propagation • Iterative algorithm -> hard to optimize using GPU • multi-threaded algorithm executed in multi-core CPU • Triangulation • CUDA implementation executed in many-core GPU < performance of our hybrid parallel algorithm > < propagation >

  17. Interactive Interface with Quantitative Information • Geometric Property as saliency level • Gradient(color) + Area (thickness)

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