volume graphics (graduate course) bong-soo sohn school of computer science and engineering chung-ang...
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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– Lectures by Instructor (half) + Student Presentation (half)
• Topics– Scalar and Vector Volume Visualization Techniques– Point/Image Based Geometric Processing– Shape Analysis
Course Information
• Time : Thu 7,8,9
• Place : 208-529
• Instructor Information– Office : 208-501– email : [email protected]– Office Tel : 820-5843– Office Hour : Thu 1pm-2pm
or email appointment
Image and Geometric Processing
3D/4DImage
CT/MRI
ElectronMicroscopy
OCT
Simulation
Geometric Modeling
Processing
Filtering,Segmentation
Visualization
Quantification(Structure Analysis)
Laser Scanner
PointCloudO
B JEC T
• Engineering Research• Scientific Research• Biomedical Research• Building/Plant Construction
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
Building Information Modeling (BIM)• generation and management of a digital representation of
physical and functional characteristics of a facility.
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
7mass (tumor) dense tissue dense tissuebreast boundary pectoral muscle
KISTI 수퍼컴퓨팅센터
Cardiovascular Modeling Research Pipeline
3D Image Acquisition
Geometric Modeling
Simulation
Rendering,QuantitativeVisualization
cardivascular disease research, medical device design, and surgical planning
Sulcal Morphology Analysis(courtesy of Dr. J.-K. Seong)
Reduced average sulcal curvature and depth in AD (Im et al. NeuroImage 2008)
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 VolumeVisualization
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
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, …)
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
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
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< propagation > < performance of our hybrid parallel algorithm >
Interactive Interface with Quantitative Information
• Geometric Property as saliency level– Gradient(color) + Area (thickness)
17