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 : bongbong@cau.ac.kr– 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