Image Processing for Volume Graphics and Analysis

Image Processing for
Volume Graphics and Analysis

IEEE Visualization 2000

Tutorial 6

Course Organizers	Lecturers
Terry S. Yoo NLM, NIH	Ioannis Kakadiaris University of Houston
Raghu Machiraju The Ohio State University	Ross Whitaker University of Utah

Abstract
Index to Course Materials
Index of Online Resources
Presenter Information
Speaker Biographies

This is a directory for the supplemental materials for the IEEE Visualization 2000 tutorial on Image Processing for Volume Graphics and Analysis. These materials were prepared for the Vis2000 CD-ROM proceedings. Additional information and any materials added after production of the CD-ROM can be found in the current online repository for the course at http://visual.nlm.nih.gov/tutorials/vis2000

Abstract

This course presents tools and techniques for processing volume data as part of a visualization framework. The conventional volume rendering pipeline has been effectively used to visualize volume data that is often considered a sampled density map. However, more and more people are looking at data that has noise, occluding surfaces, density fluctuations, limited resolution, etc. These factors require users to do more "processing". Advanced volume processing is what enables people to do 1) linear and nonlinear filtering, 2) interpolation, 3) reconstruction, 4) feature extraction, and 5) model fitting. In the first half of the course, we describe the problem as a pipeline from the reconstruction of the continuous model from the sampled data, through the application of transfer functions for shading and classification, to the transformation sampling and projection of the reconstructed values for visualization. The goal is to extract or locate structures hidden within the data. A tacit requirement is to do so without masking detail with unwanted artifacts. Thus, the emphasis will be on factors which affect final image quality.

Figure A. Idealized view of the volume rendering pipeline, adapted from Westover 1991.

In the second half of the course, presenters will examine advanced image processing methods such as multiscale analysis, segmentation techniques, and level set algorithms. These techniques are gaining acceptance among both researchers and practitioners to gain extensive understanding of structures inherently present in datasets. Also, the use of complex data, not easily represented as a density map, require the use of these sophisticated analysis techniques. Case-studies and applications will be presented to illustrate and demonstrate the techniques.

Index to Course Materials

Course Materials

Bibliography on Digital Sampling and Filtering, T. Yoo

Bibliography on Image Processing for Graphics and Visualization , R. Machiraju

Tutorial on Isosurfaces and Level Sets, including bibliography , R. Whitaker

Papers on Reconstruction, Transfer Functions, and Parameter Modeling

Marks, J., et al. 1997. Design Galleries: A General Approach to Setting Parameters for Computer Graphics and Animation, Proc. SIGGRAPH 1997. 389-400.

Machiraju, R. and R. Yagel. 1996. Reconstruction error characterization and control: a sampling theory approach. IEEE Trans. on Vis. and Comp. Graphics. 2(4). 364-378.

Moeller, T., R. Machiraju, K. Mueller, and R. Yagel. 1996. Classification and Local Error Estimation of Interpolation and Derivative Filters for Volume Rendering. Proc. 1996 ACM Symp. on Vol. Vis.. 71-78.

Moeller, T., R. Machiraju, K. Mueller, and R. Yagel. 1997. A Comparison Of Normal Estimation Schemes. Proc. IEEE Conf. on Visualization '97. 19-26.

Moeller, T., K. Mueller, Y. Kurzion, R. Machiraju, and R. Yagel. 1998. Design Of Accurate And Smooth Filters For Function And Derivative Reconstruction Proc. 1998 ACM Symp. on Vol. Vis.. 143-151.

Papers on Wavelets and Multiscale Analysis

Machiraju, R. Z. Zhu, B. Fry, and R. Moorhead. 1998. Structure-significant representation of structured datasets. IEEE Trans. on Vis. and Comp. Graphics. 4(2). 117-132.

Trott, A., R. Moorhead, and J. McGinley. 1996. Wavelets applied to lossless compression and progressive transmission of floating point data in 3-D curvilinear grids. Proc. IEEE Conf. on Visualization '96. 385-388.

Index of Online Resources

Online Tutorials and Materials (list under development)

a link to the online version of this page http://visual.nlm.nih.gov/tutorials/vis2000

Index to online conference pages, journals, and bibliographies

Interesting Computer Vision Links, I. Kakadiaris

Web Surveys of Computer Vision Conferences

at CMU - indexed by year Computer Vision Conferences and Symposia
at USC - with abstracts and deadlines Computer Vision Conferences
at Utrecht, the Netherlands - with deadlines.
concentrating on medical imaging and computer vision. Conferences: Medical Imaging and Computer Vision

Speaker Information

Ioannis A. Kakadiaris Assistant Professor Department of Computer Science University of Houston 516 PGH, MS CSC 3475 4800 Calhoun Houston, TX 77204-3475, USA Tel: (713) 743-1255 Fax: (713) 743 1198 ioannisk@uh.edu	Raghu Machiraju Assistant Professor Department of Computer and Information Science The Ohio State University 395 Dreese Laboratories 2014 Neil Avenue Columbus, OH 43210-1277, USA (614) 292-6730 fax: (614) 292-2911 raghu@cis.ohio-state.edu
Ross Whitaker Assistant Professor Department of Computer Science Merrill Engineering Building Rm. 3190 The University of Utah Salt Lake City, UT 84112 (801) 581-8224 fax: (801) 581-5843 rtw@cs.utah.edu	Terry S. Yoo Computer Scientist Office of High Performance Computing and Communications National Library of Medicine National Institutes of Health 8600 Rockville Pike Bethesda, MD 20894, USA (301) 435-3268 fax: (301) 402-4080 yoo@nlm.nih.gov

Speaker Biosketches

Terry S. Yoo is a Computer Scientist in the Office of High Performance Computing and Communications at the National Library of Medicine, the National Institutes of Health. His research interests include visualization of 3D medical data, interactive computer graphics and medical image processing. Prior to his appointment at the National Institutes of Health, Dr. Yoo developed a research program in Interventional Magnetic Resonance Imaging at the University of Mississippi Medical Center in partnership with the Engineering School at the University of Mississippi.

He received his A.B. in Biology from Harvard in 1985 and his M.S. and Ph.D. in Computer Science from the University of North Carolina at Chapel Hill in 1990 and in 1996, respectively. He worked for BBN Laboratories, Incorporated, AT\&T Technologies, and MCNC before beginning his doctoral studies. During an interim in his doctoral research, he served as site coordinator for the NSF/ARPA Science and Technology Center for Computer Graphics and Scientific Visualization.

Raghu Machiraju is currently an Assistant Professor of Computer and Information Science at the Ohio State University. His research interests include visualization, graphics, image analysis and high performance computing. More specifically his research focuses on the representation, analysis and display of large datasets. He obtained his Doctorate from The Ohio State University in 1996. Following his doctoral award he served as an Assistant Professor at the Mississippi State University in the Computer Science Department and as a research scientist at the NSF Engineering Research Center for Computational Field Simulation. He received the NSF Faculty Early Career Award for his proposal titled "On the Assessment of Volume Rendering Algorithms in Visual Computing" in the Fall of 1998. His Research is funded by DoD, DoE, NSF and Mistusbishi Electric Research Labs (MERL).

Ioannis A. Kakadiaris is an Assistant Professor in the Computer Science Department of the University of Houston since August 1997. From November 1996 to July 1997 he was a Post-Doctoral Fellow in the Computer and Information Science Department of the University of Pennsylvania the University of Pennsylvania. Dr. Kakadiaris received the Ptychion (B.S.) (1989) in Physics from the University of Athens, Greece, the M.Sc. (1991) in Computer Science from the Northeastern University, Boston, MA and the PhD (1997) in Computer Science from the University of Pennsylvania, Philadelphia, PA. Dr. Kakadiaris is the Director of the Visual Computing Lab and his expertise includes physics-based modeling, computer vision, computer graphics and medical imaging.

His research concentrates on developing algorithms, techniques and systems that increase our understanding on data interrogation and information extraction, and on data representations that facilitate these tasks in the medical field. He received the NSF Faculty Early Career Award for his proposal titled "An Integrated Framework for Data- Driven Representations and Algorithms in Visual Computing" in the Spring of 2000. His Research is funded by NSF, NASA, State of Texas, and American Honda.

Ross Whitaker received his B.S. degree in Electrical Engineering , and Engineering Physics from Princeton University in 1986, where he graduated summa cum laude and gained membership to Tau Beta Pi and Phi Beta Kappa. He worked from 1986 to 1988 as a management consultant for the Boston Consulting Group, where he consulted for a number of U.S. and international fortune 500 companies. In 1993 he received his Ph.D. in Computer Science from the University of North Carolina at Chapel Hill, where he also received the UNC Alumni Fellowship Award. In 1994 he joined the European Computer-Industry Research Centre in Munich, Germany as a research scientist in the User Interaction and Visualization Group where he conducted research in augmented reality and participated in several European research projects. In 1996 he joined the Department of Electrical and Computer Engineering at the University of Tennessee as an assistant professor and a member of the Imaging, Robotics, and Intelligent Systems (IRIS) laboratory. Dr. Whitaker is currently an assistant professor at the University of Utah, Department of Computer Science.

His research interests include: computer vision, image processing, medical imaging, and computer graphics/visualization. He has published papers on the following topics: image segmentation and feature extraction from nonlinear diffusion, interactive visualization of 3D medical data, and the use of implicit deformable models in segmentation and volume visualization. He has solicited, received, and managed research grants from NSF and the European Commission.

Last updated: Mon Sep 11 16:00:49 EDT 2000