Abstract: An image classifier comprises a first classifier and a second classifier. The first classifier comprises L individual classifiers, which are trained at different, respective image resolutions from a first full-resolution level to a lowest-resolution level. Outputs of the first set of classifiers are used to train the second classifier at the full-resolution level. Accordingly, the second classifier exploits contextual information at multiple different image resolutions. The classifiers may be trained to optimize a joint posterior probability at multiple resolutions.

Abstract: 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.

Abstract: 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.

Abstract: A method of identifying individual cells in an image of a cytological preparation. The method includes the steps of obtaining an image of a cytological preparation including a plurality of cells; identifying a first region of the image, the first region having a region boundary encompassing at least one lobe, wherein the first region includes at least one cell; detecting at least one circle within the first region, where the at least one circle substantially covers the at least one lobe of the first region; and if the first region has more than one circle, splitting the region into at least two subregions.

Abstract: 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.

Abstract: 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.

Abstract: Image pattern recognition is described. In accordance with one embodiment, a method for image recognition includes dividing an image into blocks in preparation for separating a region of interest of the image from the remainder of the image. The blocks can be analyzed to determine whether a two dimensional projection of data from one or more blocks has a circular shape. The region of interest can be identified by identifying the blocks with circular shaped projections.

Abstract: 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 ?-Y conversion, and the multigrid prolongation operator is calculated using a conductance-weighted interpolation of the coarse grid conductances.

Abstract: 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 ?-Y conversion, and the multigrid prolongation operator is calculated using a conductance-weighted interpolation of the coarse grid conductances.