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
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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
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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
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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
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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
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Building Information Modeling (BIM)• generation and management of a digital representation of
physical and functional characteristics of a facility.
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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
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KISTI 수퍼컴퓨팅센터
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Cardiovascular Modeling Research Pipeline
3D Image Acquisition
Geometric Modeling
Simulation
Rendering,QuantitativeVisualization
cardivascular disease research, medical device design, and surgical planning
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Sulcal Morphology Analysis(courtesy of Dr. J.-K. Seong)
Reduced average sulcal curvature and depth in AD (Im et al. NeuroImage 2008)
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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
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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
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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, …)
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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
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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
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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 >
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Interactive Interface with Quantitative Information
• Geometric Property as saliency level– Gradient(color) + Area (thickness)
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