@article {2165,
	title = {Accelerating feature based registration using the Johnson-Lindenstrauss Lemma.},
	journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
	volume = {12},
	year = {2009},
	month = {2009},
	pages = {632-9},
	abstract = {<p>We introduce an efficient search strategy to substantially accelerate feature based registration. Previous feature based registration algorithms often use truncated search strategies in order to achieve small computation times. Our new accelerated search strategy is based on the realization that the search for corresponding features can be dramatically accelerated by utilizing Johnson-Lindenstrauss dimension reduction. Order of magnitude calculations for the search strategy we propose here indicate that the algorithm proposed is more than a million times faster than previously utilized naive search strategies, and this advantage in speed is directly translated into an advantage in accuracy as the fast speed enables more comparisons to be made in the same amount of time. We describe the accelerated scheme together with a full complexity analysis. The registration algorithm was applied to large transmission electron microscopy (TEM) images of neural ultrastructure. Our experiments demonstrate that our algorithm enables alignment of TEM images with increased accuracy and efficiency compared to previous algorithms.</p>
},
	keywords = {Algorithms, Animals, Artificial Intelligence, Automated, Computer-Assisted, Electron, Ferrets, Geniculate Bodies, Image Enhancement, Image Interpretation, Imaging, Microscopy, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Three-Dimensional},
	author = {Akselrod-Ballin, Ayelet and Bock, Davi and Reid, R Clay and Warfield, Simon K}
}
@article {2167,
	title = {Alignment of large image series using cubic B-splines tessellation: application to transmission electron microscopy data.},
	journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
	volume = {10},
	year = {2007},
	month = {2007},
	pages = {710-7},
	abstract = {<p>3D reconstruction from serial 2D microscopy images depends on non-linear alignment of serial sections. For some structures, such as the neuronal circuitry of the brain, very large images at very high resolution are necessary to permit reconstruction. These very large images prevent the direct use of classical registration methods. We propose in this work a method to deal with the non-linear alignment of arbitrarily large 2D images using the finite support properties of cubic B-splines. After initial affine alignment, each large image is split into a grid of smaller overlapping sub-images, which are individually registered using cubic B-splines transformations. Inside the overlapping regions between neighboring sub-images, the coefficients of the knots controlling the B-splines deformations are blended, to create a virtual large grid of knots for the whole image. The sub-images are resampled individually, using the new coefficients, and assembled together into a final large aligned image. We evaluated the method on a series of large transmission electron microscopy images and our results indicate significant improvements compared to both manual and affine alignment.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Electron, Image Enhancement, Image Interpretation, Imaging, Microscopy, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Three-Dimensional, Transmission},
	author = {Dauguet, Julien and Bock, Davi and Reid, R Clay and Warfield, Simon K}
}
