Conference Proceeding

# Improved Registration for Large Electron Microscopy Images.

Med. Sch., Comput. Radiol. Lab., Harvard Univ., Boston, MA, USA

Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2009; DOI:10.1109/ISBI.2009.5193077 In proceeding of: Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009 Source: DBLP

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**ABSTRACT:**Log-euclidean polyaffine transforms have recently been introduced to characterize the local affine behavior of the deformation in principal anatomical structures. The elegant mathematical framework makes them a powerful tool for image registration. However, their application is limited to large structures since they require the pre-definition of affine regions. This paper extends the polyaffine registration to adaptively fit a log-euclidean polyaffine transform that captures deformations at smaller scales. The approach is based on the sparse selection of matching points in the images and the formulation of the problem as an expectation maximization iterative closest point problem. The efficiency of the algorithm is shown through experiments on inter-subject registration of brain MRI between a healthy subject and patients with multiple sclerosis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 2):590-7. - [show abstract] [hide abstract]

**ABSTRACT:**We describe a computationally efficient and robust, fully-automatic method for large-scale electron microscopy image registration. The proposed method is able to construct large image mosaics from thousands of smaller, overlapping tiles with unknown or uncertain positions, and to align sections from a serial section capture into a common coordinate system. The method also accounts for nonlinear deformations both in constructing sections and in aligning sections to each other. The underlying algorithms are based on the Fourier shift property which allows for a computationally efficient and robust method. We demonstrate results on two electron microscopy datasets. We also quantify the accuracy of the algorithm through a simulated image capture experiment. The publicly available software tools include the algorithms and a Graphical User Interface for easy access to the algorithms.Journal of neuroscience methods 10/2010; 193(1):132-44. · 2.30 Impact Factor - [show abstract] [hide abstract]

**ABSTRACT:**Abdominal image non-rigid registration is a particularly challenging task due to the presence of multiple organs, many of which move independently, contributing to independent deformations. Local-affine registration methods can handle multiple independent movements by assigning prior definition of each affine component and its spatial extent which is less suitable for multiple soft-tissue structures as in the abdomen. Instead, we propose to use the local-affine assumption as a prior constraint within the dense deformation field computation. Our method use the dense correspondences field computed using the optical flow equations to estimate the local-affine transformations that best represent the deformation associated with each voxel with Gaussian regularization to ensure the smoothness of the deformation field. Experimental results from both synthetic and 400 controlled experiments on abdominal CT images and Diffusion Weighted MRI images demonstrate that our method yields a smoother deformation field with superior registration accuracy compared to the demons and diffeomorphic demons algorithms.01/2012: pages 116-124; , ISBN: 9783642285561

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