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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

Ross Whitaker

Segmentation
sarang

Sarang Joshi

Shape Statistics
Segmentation
Brain Atlasing
tolga

Tolga Tasdizen

Image Processing
Machine Learning
tom

Tom Fletcher

Shape Statistics
Diffusion Tensor Analysis
chris

Chris Johnson

Diffusion Tensor Analysis
     



Image Analysis Project Sites:


Publications in Image Analysis:




Geodesic Regression on Riemannian Manifolds
P.T. Fletcher. In Proceedings of the Third MICCIA International Workshop on Mathematical Foundations of Computational Anatomy (MFCA), Toronto, Canada, pp. 75--86. 2011.



Horoball hulls and extents in positive definite space
P.T. Fletcher, J. Moeller, J. Phillips, S. Venkatasubramanian. In Algorithms and Data Structures, Lecture Notes in Computer Science (LNCS), Vol. 6844/2011, pp. 386--398. 2011.
DOI: 10.1007/978-3-642-22300-6_33



WE-E-BRC-06: Comparison of Two Methods of Contouring Internal Target Volume on Multiple 4DCT Data Sets from the Same Subjects: Maximum Intensity Projection and Combination of 10 Phases
B. Salter, B. Wang, M. Sadinski, S. Ruhnau, V. Sarkar, J. Hinkle, Y. Hitchcock, K. Kokeny, S. Joshi. In Medical Physics, Vol. 38, No. 6, pp. 3820. 2011.



WE-E-BRC-05: Voxel Based Four Dimensional Tissue Deformation Reconstruction (4DTDR) Validation Using a Real Tissue Phantom
M. Szegedi, J. Hinkle, S. Joshi, V. Sarkar, P. Rassiah-Szegedi, B. Wang, B. Salter. In Medical Physics, Vol. 38, pp. 3819. 2011.



Statistical Growth Modeling of Longitudinal DT-MRI for Regional Characterization of Early Brain Development
N. Sadeghi, M.W. Prastawa, P.T. Fletcher, J.H. Gilmore, W. Lin, G. Gerig. In Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2011 Workshop on Image Analysis of Human Brain Development, pp. 1507--1510. 2011.
DOI: 10.1109/ISBI.2012.6235858

A population growth model that represents the growth trajectories of individual subjects is critical to study and understand neurodevelopment. This paper presents a framework for jointly estimating and modeling individual and population growth trajectories, and determining significant regional differences in growth pattern characteristics applied to longitudinal neuroimaging data. We use non-linear mixed effect modeling where temporal change is modeled by the Gompertz function. The Gompertz function uses intuitive parameters related to delay, rate of change, and expected asymptotic value; all descriptive measures which can answer clinical questions related to growth. Our proposed framework combines nonlinear modeling of individual trajectories, population analysis, and testing for regional differences. We apply this framework to the study of early maturation in white matter regions as measured with diffusion tensor imaging (DTI). Regional differences between anatomical regions of interest that are known to mature differently are analyzed and quantified. Experiments with image data from a large ongoing clinical study show that our framework provides descriptive, quantitative information on growth trajectories that can be directly interpreted by clinicians. To our knowledge, this is the first longitudinal analysis of growth functions to explain the trajectory of early brain maturation as it is represented in DTI.

Keywords: namic



Sample drift correction in 3D fluorescence photoactivation localization microscopy
M.J. Mlodzianoski, J.M. Schreiner, S.P. Callahan, K. Smolková, A. Dlasková, J. Šantorová, P. Ježek, J. Bewersdorf. In Optics Express, Vol. 19, No. 16, pp. 15009--15019. 2011.
DOI: 10.1364/OE.19.015009



Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes
A. Irimia, M.C. Chambers, J.R. Alger, M. Filippou, M.W. Prastawa, Bo Wang, D. Hovda, G. Gerig, A.W. Toga, R. Kikinis, P.M. Vespa, J.D. Van Horn. In Journal of Neurotrauma, Vol. 28, No. 11, pp. 2287--2306. November, 2011.
DOI: 10.1089/neu.2011.1920
PubMed ID: 21787171

Although neuroimaging is essential for prompt and proper management of traumatic brain injury (TBI), there is a regrettable and acute lack of robust methods for the visualization and assessment of TBI pathophysiology, especially for of the purpose of improving clinical outcome metrics. Until now, the application of automatic segmentation algorithms to TBI in a clinical setting has remained an elusive goal because existing methods have, for the most part, been insufficiently robust to faithfully capture TBI-related changes in brain anatomy. This article introduces and illustrates the combined use of multimodal TBI segmentation and time point comparison using 3D Slicer, a widely-used software environment whose TBI data processing solutions are openly available. For three representative TBI cases, semi-automatic tissue classification and 3D model generation are performed to perform intra-patient time point comparison of TBI using multimodal volumetrics and clinical atrophy measures. Identification and quantitative assessment of extra- and intra-cortical bleeding, lesions, edema, and diffuse axonal injury are demonstrated. The proposed tools allow cross-correlation of multimodal metrics from structural imaging (e.g., structural volume, atrophy measurements) with clinical outcome variables and other potential factors predictive of recovery. In addition, the workflows described are suitable for TBI clinical practice and patient monitoring, particularly for assessing damage extent and for the measurement of neuroanatomical change over time. With knowledge of general location, extent, and degree of change, such metrics can be associated with clinical measures and subsequently used to suggest viable treatment options.

Keywords: namic



Comparison of the endocranial ontogenies between chimpanzees and bonobos via temporal regression and spatiotemporal registration
S. Durrleman, X. Pennec, A. Trouvé, N. Ayache, J. Braga. In Journal of Human Evolution, pp. 74--88. January, 2011.
DOI: 10.1016/j.jhevol.2011.10.004
PubMed ID: 22137587

This paper aims at quantifying ontogenetic differences between bonobo (Pan paniscus) and chimpanzee (Pan troglodytes) endocrania, using dental development as a timeline. We utilize a methodology based on smooth and invertible deformations combined with a metric of "currents" that defines a distance between endocranial surfaces and does not rely on correspondence between landmarks. This allows us to perform a temporal surface regression that estimates typical endocranial ontogenetic trajectories separately for bonobos and chimpanzees. We highlight non-linear patterns of endocranial ontogenetic change and significant differences between species at local anatomical levels rather than considering the endocranium as a uniform entity. A spatiotemporal registration permits the quantification of inter-species differences decomposed into a morphological deformation (accounting for size and shape differences independently of age) and a time warp (accounting for changes in the dynamics of development). Our statistical simulations suggest that patterns of endocranial volume (EV) increase may differ significantly between bonobos and chimpanzees, with an earlier phase of a relatively rapid increase (preferentially at some endocranial subdivisions) in the former and a much later phase of relatively rapid increase in the latter. As a consequence, the chimpanzee endocranium appears to reach its adult size later. Moreover, the time warp indicates that juvenile bonobos develop much slower than juvenile chimpanzees, suggesting that inter-specific ontogenetic shifts do not only concern EV increase, but also the rate of shape changes over time. Our method provides, for the first time, a quantitative estimation of inter-specific ontogenetic shifts that appear to differentiate non-linearly.



A fast iterative method for solving the Eikonal equation on triangulated surfaces
Z. Fu, W.-K. Jeong, Y. Pan, R.M. Kirby, R.T. Whitaker. In SIAM Journal of Scientific Computing, Vol. 33, No. 5, pp. 2468--2488. 2011.
DOI: 10.1137/100788951
PubMed Central ID: PMC3360588

This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton–Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput., 30 (2008), pp. 2512–2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.



Synergy of image analysis for animal and human neuroimaging supports translational research on drug abuse
G. Gerig, I. Oguz, S. Gouttard, J. Lee, H. An, W. Lin, M. McMurray, K. Grewen, J. Johns, M.A. Styner. In Frontiers in Child and Neurodevelopmental Psychiatry, Vol. 2, Edited by Linda Mayes, pp. 9 pages. 2011.
ISSN: 1664-0640
DOI: 10.3389/fpsyt.2011.00053



Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs
L.K. Ha, M.W. Prastawa, G. Gerig, J.H. Gilmore, C.T. Silva. In International Journal of Biomedical Imaging, Special Issue in Parallel Computation in Medical Imaging Applications, Vol. 2011, Note: Article ID 572187, pp. 16 pages. 2011.
DOI: 10.1155/2011/572187



Multi-scale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs
L.K. Ha, J. Krüger, S. Joshi, C.T. Silva. Vol. 1, Ch. 10, Morgan Kaufmann, pp. 42. 2011.

In this chapter, we present a high performance multi-scale 3D image processing framework to exploit the parallel processing power of multiple graphic processing units (Multi-GPUs) for medical image analysis. We developed GPU algorithms and data structures that can be applied to a wide range of 3D image processing applications and efficiently exploit the computational power and massive bandwidth offered by modern GPUs. Our framework helps scientists solve computationally intensive problems which previously required super computing power. To demonstrate the effectiveness of our framework and to compare to existing techniques, we focus our discussions on atlas construction - the application of understanding the development of the brain and the progression of brain diseases.



Adaptive Riemannian Metrics for Improved Geodesic Tracking of White Matter
X. Hao, R.T. Whitaker, P.T. Fletcher. In Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science (LNCS), Vol. 6801/2011, pp. 13--24. 2011.
DOI: 10.1007/978-3-642-22092-0_2



Estimation of Smooth Growth Trajectories with Controlled Acceleration from Time Series Shape Data
J. Fishbaugh, S. Durrleman, G. Gerig. In Lecture Notes in Computer Science, LNCS 6892, Springer, pp. 401--408. 2011.
DOI: 10.1007/978-3-642-23629-7_49

Longitudinal shape analysis often relies on the estimation of a realistic continuous growth scenario from data sparsely distributed in time. In this paper, we propose a new type of growth model parameterized by acceleration, whereas standard methods typically control the velocity. This mimics the behavior of biological tissue as a mechanical system driven by external forces. The growth trajectories are estimated as smooth flows of deformations, which are twice differentiable. This differs from piecewise geodesic regression, for which the velocity may be discontinuous. We evaluate our approach on a set of anatomical structures of the same subject, scanned 16 times between 4 and 8 years of age. We show our acceleration based method estimates smooth growth, demonstrating improved regularity compared to piecewise geodesic regression. Leave-several-out experiments show that our method is robust to missing observations, as well as being less sensitive to noise, and is therefore more likely to capture the underlying biological growth.

Keywords: na-mic



Three-dimensional reconstruction of serial mouse brain sections using high-resolution large-scale mosaics
M.L. Berlanga, S. Phan, E.A. Bushong, S. Lamont, S. Wu, O. Kwon, B.S. Phung, M. Terada, T. Tasdizen, E. Martone, M.H. Ellisman. In Frontiers in Neuroscience Methods, Vol. 5, pp. (published online). March, 2011.
DOI: 10.3389/fnana.2011.00017



Detection of Neuron Membranes in Electron Microscopy Images using Multi-scale Context and Radon-like Features
M. Seyedhosseini, R. Kumar, E. Jurrus, R. Guily, M. Ellisman, H. Pfister, T. Tasdizen. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Lecture Notes in Computer Science (LNCS), Vol. 6891, pp. 670--677. 2011.
DOI: 10.1007/978-3-642-23623-5_84



A rapid 2-D centerline extraction method based on tensor voting
Z. Leng, J.R. Korenberg, B. Roysam, T. Tasdizen. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1000--1003. 2011.
DOI: 10.1109/ISBI.2011.5872570



Quantifying variability in radiation dose due to respiratory-induced tumor motion
S.E. Geneser, J.D. Hinkle, R.M. Kirby, Bo Wang, B. Salter, S. Joshi. In Medical Image Analysis, Vol. 15, No. 4, pp. 640--649. 2011.
DOI: 10.1016/j.media.2010.07.003



Fast AdaBoost training using weighted novelty selection
M. Seyedhosseini, A.R.C. Paiva, T. Tasdizen. In Proc. IEEE Intl. Joint Conf. on Neural Networks, San Jose, CA, USA pp. 1245--1250. August, 2011.

In this paper, a new AdaBoost learning framework, called WNS-AdaBoost, is proposed for training discriminative models. The proposed approach significantly speeds up the learning process of adaptive boosting (AdaBoost) by reducing the number of data points. For this purpose, we introduce the weighted novelty selection (WNS) sampling strategy and combine it with AdaBoost to obtain an efficient and fast learning algorithm. WNS selects a representative subset of data thereby reducing the number of data points onto which AdaBoost is applied. In addition, WNS associates a weight with each selected data point such that the weighted subset approximates the distribution of all the training data. This ensures that AdaBoost can trained efficiently and with minimal loss of accuracy. The performance of WNS-AdaBoost is first demonstrated in a classification task. Then, WNS is employed in a probabilistic boosting-tree (PBT) structure for image segmentation. Results in these two applications show that the training time using WNS-AdaBoost is greatly reduced at the cost of only a few percent in accuracy.



Point Set Registration Using Havrda–Charvat–Tsallis Entropy Measures
N.J. Tustison, S.P. Awate, G. Song, T.S. Cook, J.C. Gee. In IEEE Transactions on Medical Imaging, Vol. 30, No. 2, pp. 451--460. 2011.