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 : Thu 7,8,9 Place : 208-529 Instructor Information –Office : 208-501 –email : bongbong@cau.ac.krbongbong@cau.ac.kr –Office Tel : 820-5843 –Office Hour : Thu 1pm-2pm or email appointment

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

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

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 7 mass (tumor) dense tissue breast boundarypectoral muscle

8. KISTI 수퍼컴퓨팅센터

9. Cardiovascular Modeling  Research Pipeline 3D Image Acquisition Geometric Modeling Simulation Rendering, Quantitative Visualization 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  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. ( Journal of Korean Physical Society [SCI], 2007 ) 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) [ volume image ] [ skin surface ] [ bone surface ] F(x,y,z) Level Set : F(x,y,z) = w w = skin density w = bone density

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 16

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