University of Utah Research Foundation

Siemens Medical Solutions Usa, Inc.

Tolga Tasdizen

Leo John Grady

Erik Jorgensen

Steven P Callahan

Ross Whitaker

John M Schreiner

Stan Kanarowski

Josh Cates

Shigeki Watanabe

Antonio Rafael Paiva

Bryan W Jones

Greg M Jones

A method is described for correlating microscopy images from a number of modalities in a sub diffraction resolution environment. The method may include receiving a number of datasets that may represent microscopy captures from a number of different modalities. The microscopy captures may contain feature markers that may be used to register a number of data points contained in a dataset with data points from another dataset. Upon registering the data points of the datasets, a combined dataset may be produced and a visual image of the combined dataset may be provided.

Microscopy visualization

A method, a system, and a computer-readable medium are provided for characterizing a dataset. A representative dataset is defined from a dataset by a computing device. The representative dataset includes a first plurality of data points and the dataset includes a second plurality of data points. The number of the first plurality of data points is less than the number of the second plurality of data points. The data point is added to the representative dataset if a minimum distance between the data point and each data point of the representative dataset is greater than a sampling parameter. The data point is added to a refinement dataset if the minimum distance between the data point and each data point of the representative dataset is less than the sampling parameter and greater than half the sampling parameter. A weighting matrix is defined by the computing device that includes a weight value calculated for each of the first plurality of data points based on a determined number of the second plurality of data points associated with a respective data point of the first plurality of data points. The weight value for a closest data point of the representative dataset is updated if the minimum distance between the data point and each data point of the representative dataset is less than half the sampling parameter. A machine learning algorithm is executed by the computing device using the defined representative dataset and the defined weighting matrix applied in an approximation for a computation of a full kernel matrix of the dataset to generate a parameter characterizing the dataset.

Characterizing datasets using sampling, weighting, and approximation of an eigendecomposition

A method of segmenting a digitized image includes marking a subset of pixels in an image, defining edge conductances between each pair of adjacent pixels in the image based on the intensity difference of each said pixel pair, associating a probability potential with each unmarked pixel, and using a multigrid method to solve for the probability potentials for each unmarked pixel, wherein a restriction operator from the image grid to a coarse grid is calculated from a conductance-weighted average of the conductances on the image grid, the coarse grid conductances are calculated from the image grid conductances using a &#x394;-Y conversion, and the multigrid prolongation operator is calculated using a conductance-weighted interpolation of the coarse grid conductances.

System and method for image segmentation by solving an inhomogenous dirichlet problem

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Salt Lake City, UT, United States of America

Tolga Tasdizen