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Image Analysis

SCI's imaging work addresses fundamental questions in 2D and 3D image processing, including filtering, segmentation, surface reconstruction, and shape analysis. In low-level image processing, this effort has produce new nonparametric methods for modeling image statistics, which have resulted in better algorithms for denoising and reconstruction. Work with particle systems has led to new methods for visualizing and analyzing 3D surfaces. Our work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs). Application areas include medical image analysis, biological image processing, defense, environmental monitoring, and oil and gas.


Ross Whitaker


Sarang Joshi

Shape Statistics
Brain Atlasing

Tolga Tasdizen

Image Processing
Machine Learning

Tom Fletcher

Shape Statistics
Diffusion Tensor Analysis

Chris Johnson

Diffusion Tensor Analysis

Image Analysis Project Sites:

Publications in Image Analysis:

Multi-object analysis of volume, pose, and shape using statistical discrimination
K. Gorczowski, M. Styner, J.Y. Jeong, J.S. Marron, J. Piven, H.C. Hazlett, S.M. Pizer, G. Gerig. In IEEE Trans Pattern Anal Mach Intell., Vol. 32, No. 4, pp. 652--661. April, 2010.
DOI: 10.1109/TPAMI.2009.92
PubMed ID: 20224121

Genetic and environmental contributions to neonatal brain structure: A twin study
J.H. Gilmore, J.E. Schmitt, R.C. Knickmeyer, J.K. Smith, W. Lin, M. Styner, G. Gerig, M.C. Neale. In Human Brain Mapping, Vol. 31, No. 8, Note: ePub 8 Jan 2010, pp. 1174--1182. 2010.
PubMed ID: 20063301

Principal neighborhood dictionaries for nonlocal means image denoising
T. Tasdizen. In IEEE Transactions on Image Processing, Vol. 18, No. 12, Note: Epub 2009 Jul 24, pp. 2649--2660. 2009.
PubMed ID: 19635697

Serial Neural Network Classifier for Membrane Detection using a Filter Bank
E. Jurrus, A.R.C. Paiva, S. Watanabe, R. Whitaker, E.M. Jorgensen, T. Tasdizen. In Proc. Workshop on Microscopic Image Analysis with Applications in Biology, Bethesda, MD, USA, 2009.

Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples
R.S. MacLeod, J.G. Stinstra, S. Lew, R.T. Whitaker, D.J. Swenson, M.J. Cole, J. Krüger, D.H. Brooks, C.R. Johnson. In Philosophical Transactions of The Royal Society A, Mathematical, Physical & Engineering Sciences, Vol. 367, No. 1896, pp. 2293--2310. 2009.

Microscopic Computed Tomography Based Virtual Histology for Visualization and Morphometry of Atherosclerosis in Diabetic Apolipoprotein E Mutant Mice
H.G. Martinez, S.I. Prajapati, C.A. Estrada, F. Jimenez, M.P. Quinones, I. Wu, A. Bahadur, A. Sanderson, C.R. Johnson, M. Shim, C. Keller, S.S. Ahuja. In Circulation: Journal of the American Heart Association, Vol. 120, No. 9, pp. 821--822. 2009.

Serial Neural Network Classifier for Membrane Detection using a Filter Bank.
SCI Technical Report, E. Jurrus, A.R.C. Paiva, S. Watanabe, R.T. Whitaker, E.M. Jorgensen, T. Tasdizen. No. UUSCI-2009-006, SCI Institute, University of Utah, 2009.

Automatic Markup of Neural Cell Membranes Using Boosted Decision Stumps
Kannan U.V., A.R.C. Paiva, E. Jurrus, T. Tasdizen. In Proceedings of the IEEE International Symposium on Biomedical Engineering (ISBI 2009), Boston, MA, pp. 1039--1042. 2009.
DOI: 10.1109/ISBI.2009.5193233

To better understand the central nervous system, neurobiologists need to reconstruct the underlying neural circuitry from electron microscopy images. One of the necessary tasks is to segment the individual neurons. For this purpose, we propose a supervised learning approach to detect the cell membranes. The classifier was trained using AdaBoost, on local and context features. The features were selected to highlight the line characteristics of cell membranes. It is shown that using features from context positions allows for more information to be utilized in the classification. Together with the nonlinear discrimination ability of the AdaBoost classifier, this results in clearly noticeable improvements over previously used methods.

Keywords: crcns, neural networks

Multimaterial Meshing of MRI Head Data for Bioelectric Field Simulations
R.T. Whitaker, R.M. Kirby, J.G. Sinstra, M.D. Meyer. In Proceedings of the 17th International Meshing Roundtable, 2008.

The problem of body fitting meshes that are both adaptive and geometrically accurate is important in a variety of biomedical applications in a multitude of clinical settings, including electrocardiology, neurology, and orthopedics. Adaptivity is necessary because of the combination of large-scale and smallscale structures (e.g. relatively small blood vessels spanning a human head). Geometric accuracy is important for several reasons. In some cases, such as computational fluid dynamics, the fine-scale structure of the fluid domain is important for qualitative and quantitative accuracy of the solutions. More generally, finite element approximations of elliptic problems with rough coefficients require increased spatial resolution normal to material boundaries [3]. The problem of constructing meshes from biomedical images is particularly difficult because of the complexity and irregularity of the structures, and thus tuning or correcting meshes by hand is quite difficult and time consuming. Many researchers and, indeed, commercial products simply subdivide the underlying hexahedral image grid and assign material properties to tetrahedra based on standard decomposition of each hexahedron into tetrahedra.

This paper presents a small case study of the results of a recently developed method for multimaterial, tetrahedral meshing of biomedical volumes [6]. The method uses an iterative relaxation of surface point point positions that are constrained to subsets of the volume that correspond to boundaries between different materials. In this paper we briefly review the method and present results on a set of MRI head images for use in bioelectric field simulation and source localization.

Dynamic Particle Systems for Adaptive Sampling of Implicit Surfaces
M.D. Meyer. School of Computing, University of Utah, 2008.

A ubiquitous requirement in many mathematical and computational problems is a set of well-placed point samples. For producing very even distributions of samples across complex surfaces, a dynamic particle system is a controllable mechanism that naturally accommodates strict sampling requirements. The systemfirst constrains particles to a surface, and then moves the particles across the surface until they are arranged in minimal energy configurations. Adaptivity is added into the system by scaling the distance between particles, causing higher densities of points around surface features. In this dissertation we explore and refine the dynamics of particle systems for generating efficient and adaptive point samples of implicit surfaces.

Throughout this dissertation, we apply the adaptive particle system framework to several application areas. First, efficient visualizations of high-order finite element datasets are generated by developing adaptivity metrics of surfaces that exist in the presence of curvilinear coordinate transformation. Second, a framework is proposed that meets fundamental sampling constraints of Delaunay-based surface reconstruction algorithms. In meeting these constraints, the particle distributions produce nearly-regular, efficient isosurface tessellation that are geometrically and topologically accurate. And third, a novel analytic representation of material boundaries in multimaterial volume datasets is developed, as well as a set of projection operators, that allow for explicit sampling of nonmanifold material intersections. Using a tetrahedral labeling algorithm, the material intersections are extracted as watertight, nonmanifold meshes that are well-suited for simulations.

Particle-based Sampling and Meshing of Surfaces in Multimaterial Volumes
M.D. Meyer, R.T. Whitaker, R.M. Kirby, C. Ledergerber, H. Pfister. In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1539--1546. 2008.

An Optimal-Path Approach for Neural Circuit Reconstruction
E. Jurrus, R.T. Whitaker, B. Jones, R. Marc, T. Tasdizen. In Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1609--1612. 2008.
PubMed ID: 19172170

Realizing the Potential of Positron Emission Tomography with 18F-Fluorodeoxyglucose to Improve the Treatment of Alzheimer
N.L. Foster, A.Y. Wang, T. Tasdizen, P.T. Fletcher, J.M. Hoffman, R.A. Koeppe. In Journal of the Alzheimer, Vol. 4, No. 1, Suppl. 1, pp. S29--36. 2008.
PubMed ID: 18631997

Automatic Classification of Alzheimer
N. Sadeghi, N.L. Foster, A.Y. Wang, S. Minoshima, A.P. Lieberman, T. Tasdizen. In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI 2008): From Nano to Macro, pp. 408--411. 2008.
DOI: 10.1109/ISBI.2008.4541019

Principal Components for Non-Local Means of Image Denoising
T. Tasdizen. In Proceedings of the International Conference on Image Processing (ICIP 2008), pp. 1728--1731. 2008.
PubMed ID: 19180227

Fast Isosurface Extraction Methods for Large Image Data Sets
Y. Livnat, S.G. Parker, C.R. Johnson. In Handbook of Medical Image Processing and Analysis, 2nd edition, Ch. 47, Note: (to appear), Edited by Isaac N. Bankman, Elsevier, pp. 801--816. 2008.

A Structural MRI Study of Human Brain Development from Birth to Two Years
R.C. Knickmeyer, S. Gouttard, C. Kang, D. Evans, K. Wilber, K.J. Smith, R.M. Hamer, W. Lin, G. Gerig, J.H. Gilmore. In The Journal of Neuroscience, Vol. 28, No. 47, pp. 12176--12182. Nov, 2008.
PubMed ID: 19020011

CRA-NIH Computing Research Challenges in Biomedicine Workshop Recommendations
D. Reed, C.R. Johnson. Note: Computing Research Association (CRA), 2007.

Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmaps
S. Gerber, T. Tasdizen, R.T. Whitaker. In Proceedings of the 2007 International Conference on Machine Learning (ICML), pp. 281--288. 2007.

Particle Systems for Efficient and Accurate Finite Element Visualization
M.D. Meyer, B. Nelson, R.M. Kirby, R.T. Whitaker. In IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 5, pp. 1015--1026. 2007.