Tolga Tasdizen

Associate Professor, Electrical and Computer Engineering Department
Adjunct Associate Professor, School of Computing

Curriculum Vitae
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I am an Associate Professor in the Electrical and Computer Engineering Department at the University of Utah. I am also a member of the Scientific Computing and Imaging Institute. My research interests are in the general areas of image processing, computer vision and pattern recognition. More specifically, we study pattern recognition, supervised learning and variational methods for image restoration, segmentation and analysis. Over the last five years, the main driving application of my research has been neural circuit reconstruction (connectomics) from large-scale electron and confocal microscopy image datasets. This application poses challenges in image registration, pattern recognition and 3D segmentation, which are very well aligned with my research interests. As of January 2015, my research group (SCI) is in the leading positions of the Segmentation of neuronal structures in EM stacks and 3D segmentation of neurites in EM images challenge leaderboards.

Image registration: My research group has developed algorithms for assembling 3D volumes from hundreds of thousands of 2D images of serial sections. In electron microscopy, our approach uses the well-known shift property of the Fourier transform for a computationally feasible solution to correcting distortions between sections due to the cutting process as well as the imaging optics. For confocal microscopy of neurons, we have recently developed a novel algorithm that combines axon tracing and a landmark based alignment approach that is well suited to the sparse nature of these images. These tools are combined in our NCR Toolset, which is freely available along with an easy to use graphical interface. Some related publications in this area are:

L. Hogrebe, A. R.C. Paiva, E. Jurrus, C. Christensen, M. Bridge, J.R. Korenberg, P. R. Hof, B. Roysam, T. Tasdizen, Serial Section Registration of Axonal Confocal Microscopy Datasets for Long Range Neural Circuit Reconstruction, Journal of Neuroscience Methods 207, pp. 200-210, 2012.

M. L. Berlanga, S. Phan, E. A. Bushong, S. Lamont, S. Wu, O. Kwon, B. S. Phung, M. Terada, T. Tasdizen, E. Martone and M. H. Ellisman, Three-dimensional reconstruction of serial mouse brain sections using high-resolution large-scale mosaics, Frontiers in Neuroscience Methods, March 2011.

T. Tasdizen, P. Koshevoy, B. C. Grimm, J. R. Anderson, B. W. Jones, C. B. Watt, R. T. Whitaker and R. E. Marc, Automatic mosaicking and volume assembly for high-throughput serial-section transmission electron microscopy, Journal of Neuroscience Methods, 193(1), 132-44, 2010.

J.R. Anderson, B.W. Jones, J-H Yang, M.V. Shaw, C.B. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, Kannan U V, R. Whitaker, D. Mastronarde , T. Tasdizen, R.E. Marc. A Computational Framework for Ultrastructural Mapping of Neural Circuitry, PLoS Biology, March, vol. 7, no. 3, pp. e74, 2009.

Pattern Recognition: Electron microscopy images of neural tissue are typically hard to segment due to their properties. The staining used in electron microscopy is non-selective, i.e. it highlights all cellular and intracellular membranes present in the tissue sample. Therefore, a successful segmentation of cells first requires the differentiation of cellular membranes from intracellular membranes, which look similar at the local scale but are differentiable using non-local contextual cues. We have developed multi-scale and hierarchical contextual classifier for this problem. See the following papers and Mojtaba Seyedhosseini and Ting Liu's webpages for more details:

SM Seyedhosseini, M Sajjadi and T Tasdizen, Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks, ICCV 2013.

E Jurrus, S Watanabe, ARC Paiva, MH Ellisman, EM Jorgensen and T Tasdizen, Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images, Neuroinformatics. 2013 Jan;11(1):5-29

SM Seyedhosseini, MH Ellisman and T Tasdizen, Multi-Class Multi-Scale Series Contextual Model for Image Segmentation, IEEE Trans Image Processing, 22:11 pp. 4486–4496, November 2013.

S. M. Seyedhosseini, R. Kumar, E. Jurrus, R. Guily, M. Ellisman, H. Pfister and T. Tasdizen, Detection of Neuron Membranes in Electron Microscopy Images using Multi-scale Context and Radon-like Features, MICCAI 2011.

E. Jurrus, A. R. C. Paiva, S. Watanabe, J. R. Anderson, B. W. Jones, R. T. Whitaker, E. M. Jorgensen, R. E. Marc and T. Tasdizen, Detection of neuron membranes in electron microscopy images using a serial neural network architecture, Medical Image Analysis, 14:6, 770-83, 2010

Dimensionality reduction, image restoration: Also, for problems ranging from filtering to recognition, the conventional wisdom in image processing is to use a filter bank hand designed to match the problem at hand. However, filters are functions of image neighborhoods and evidence suggests they can be used directly as a feature representation. For instance, we have used image neighborhoods directly to classify brain tissues in magnetic resonance images and to segment general textured images. Unfortunately, a problem with representing context from large areas using image neighborhoods is the curse of dimensionality. We have used principal component analysis of image neighborhoods to develop a novel version of the non-local means image restoration algorithm that outperforms the original algorithm. Similarly, for cell membrane detection, we are currently utilizing a multi-scale image neighborhood approach to develop a method that will surpass the accuracy of state-of-the-art approaches.

T. Tasdizen, ”Principal Neighborhood Dictionaries for Non-local Means Image Denoising,” IEEE Transactions on Image Processing, vol. 18, no. 12., pp. 2649-60, December 2009. Top third accessed paper in IEEE Xplore in November 2009.

S. Gerber, T. Tasdizen and R. T. Whitaker, ”Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds,” ICCV 2009.

S. Gerber, T. Tasdizen, S. Joshi and R. T. Whitaker, ”On the Manifold Structure of the Space of Brain Images” MICCAI 2009.

S. Gerber, T. Tasdizen, T. Fletcher, S. Joshi and R. T. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Analysis, Volume 14, Issue 5, Special Issue on the 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), October 2010, Pages 643-653. Best paper of the special issue award.

3D segmentation: Even state-of-the art approaches can’t provide perfect cell membrane detection results. Gaps may exist between adjacent cells or a single cell can have false positive membranes inside, resulting in over-segmentation. Combining the detected membranes with shape-based regularization can further reduce these errors. We developed a novel three-dimensional surface processing method that is based on optimization of energy function defined on the field of surface normal vectors. Our approach couples a partial differential equation (PDE) that minimizes the surface normal energy and another PDE that links the surface to the changes in the surface normal vectors resulting from the first PDE.  We have applied this approach to smoothing and enhancement of geometrical models, and reconstruction of objects from laser range finder data. While our original methods were developed for models represented as level-set functions, other researchers have extended our methods to surface meshes. We are also working on a novel implicit and parametric shape model which we have named Disjunctive Normal Shape Model. Some related publications are:

N Ramesh, F Mesadi, M Cetin and T Tasdizen, Disjunctive Normal Shape Model, ISBI 2015.

T Liu, C Jones, SM Seyedhosseini and T Tasdizen, A Modular Hierarchical Approach to 3D Electron Microscopy Image Segmentation, J Neuroscience Methods, 226, pp. 88-102, 2014.

T. Tasdizen and R. T. Whitaker, “Higher-order Nonlinear Priors for Surface Reconstruction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, Num. 7, Pages 878-891, July 2004.

T. Tasdizen, R. T. Whitaker, Paul Burchard and Stanley Osher, “Geometric Surface Processing via Normal Maps,” ACM Transactions on Graphics, Vol. 22, Num. 4, Pages1012-1033, October 2003.


Contact Information

Best way to get in touch with me is via email.
Office  WEB 3887
Phone  (801) 581-3539
Fax      (801) 585-6513
Mail     Scientific Computing and Imaging Institute
            University of Utah
            72 S. Central Campus Drive, 3750 WEB
            Salt Lake City, 84112, USA