University of Iowa Research Foundation

General Electric Company

Theophano Mitsa

Milan Sonka

Eric A Hoffman

Geoffrey McLennan

Renuka Uppaluri

Hui Hu

Robert A Buchanan

A method for automated analysis of textural differences present on an image volume. The image volume includes a plurality of volume elements, and each volume element has a gray level. The method includes defining a volume of interest (VOI); performing texture measures within the VOI; and classifying the VOI as belonging to a tissue pathology class based upon the texture measures. Computer readable media encoded with computer readable instructions for carrying out these functions. An apparatus that includes an image input adapted to receive a diagnostic medical image. The image includes a plurality of pixels, and each pixel has a particular gray level. The apparatus also includes a display for displaying a graphical user interface and the received image; and a processor adapted to perform texture measures on one or more groups of pixels within the image and classify each group of pixels to a tissue pathology class based upon the textures measures. The processor is further adapted to (1) associate a color to each group of pixels indicative of the group's tissue pathology class, (2) cause the display to display one or more of the colors on the image at the location of the associated group or groups of pixels, (3) permit a user to manually associate a tissue pathology class to a group of pixels, and (4) cause the display to display the manually-associated tissue pathology class.

Methods and apparatuses for analyzing images

A method and apparatus for analyzing CT images to determine the presence of pulmonary tissue pathology, such as in emphysema, IPF, sarcoid, etc. In accordance with one embodiment, a CT slice is selected to perform an automated, objective, and quantitative analysis of the slice. Initially, an image processing stage is performed, which includes segmentation and edgementation of the selected CT slice for preparation of a series of objective, quantitative measures to be performed on the slice. A region of interest (ROI) is selected on the CT slice in which these objective, quatitative measures are to be taken. The first set of objective, quantitative measures are first order texture measures that describe a frequency of occurrence of all gray levels assigned to pixels within the ROI of the image slice. The second set of objective, quantitative measures are second order texture measures that characterize the spatial interdependencies between particular pixels of the ROI. Fractal analysis could also be performed to provide additional objective, quantitative measures of the ROI. The ROI is classified to a particular tissue pathology class based upon an optimal subset of first or second order texture measures and fractal measures obtained. A color-coded output is displayed for visual presentation to a user indicating the different tissue pathology classes assigned to different regions of the CT slice.

Method and apparatus for analyzing CT images to determine the presence of pulmonary tissue pathology

The present invention, in one form, is a method for generating a calcification score in a CT image data using a scoring algorithm. In accordance with one embodiment of the algorithm, a calcium score is determined by identifying a scorable region in the image data, defining at least one region of interest in the scorable region, and determining a density score. After determining a calcium score for each region of interest, a total calcium score is determined. In addition, to correct for unequal and/or non-contiguous spacing of the slices of image data, a weighted total calcium score is determined.

Methods and apparatus for calcification scoring

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Waukesha, WI, United States of America

Theophano Mitsa