@proceedings {3305,
	title = {Joint nonparametric alignment for analyzing spatial gene expression patterns of Drosophila imaginal discs.},
	volume = {2},
	year = {2005},
	month = {2005 Jun 20},
	pages = {755-60},
	abstract = {<p>To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing images of corresponding tissues into alignment is a critical first step in making a meaningful comparative analysis of these spatial patterns. Significant image noise and variability in the shapes make it hard to pick a canonical shape model. In this paper, we address these problems by combining segmentation and unsupervised shape learning algorithms. We first segment images to acquire structures of interest, then jointly align the shapes of these acquired structures using an unsupervised nonparametric maximum likelihood algorithm along the lines of congealing, while simultaneously learning the underlying shape model and associated transformations. The learned transformations are applied to corresponding images to bring them into alignment in one step. We demonstrate the results for images of various classes of Drosophila imaginal discs and discuss the methodology used for a quantitative analysis of spatial gene expression patterns.</p>
},
	author = {Ahammad, Parvez and Harmon, C and Hammonds, Ann S and Sastry, S and Rubin, Gerald M}
}
@conference {3306,
	title = {Joint MRI bias removal using entropy minimization across images.},
	booktitle = {Neural Information Processing Systems Conference},
	year = {2004},
	month = {2004},
	abstract = {<p>The correction of bias in magnetic resonance images is an important problem in medical image processing. Most previous approaches have used a maximum likelihood method to increase the likelihood of the pixels in a single image by adaptively estimating a correction to the unknown image bias field. The pixel likelihoods are defined either in terms of a pre-existing tissue model, or non-parametrically in terms of the image{\textquoteright}s own pixel values. In both cases, the specific location of a pixel in the image is not used to calculate the likelihoods. We suggest a new approach in which we simultaneously eliminate the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different images, rather than within an image, to eliminate bias fields from all of the images simultaneously. The method builds a multi-resolution non-parametric tissue model conditioned on image location while eliminating the bias fields associated with the original image set. We present experiments on both synthetic and real MR data sets, and present comparisons with other methods.</p>
},
	author = {Learned-Miller, EG and Ahammad, Parvez}
}
