University of Chicago

Arch Development Corporation

Hidetaka Arimura

Kunio Doi

Shigehiko Katsuragawa

Junji Morishita

Feng Li

Junji Shiraishi

A method, system, and computer program product for detecting at least one nodule in a medical image of a subject, including identifying, in the medical image, an anatomical region corresponding to at least a portion of an organ of interest; filtering the medical image to obtain a difference image; detecting, in the difference image, a first plurality of nodule candidates within the anatomical region; calculating respective nodule feature values of the first plurality of nodule candidates based on pixel values of at least one of the medical image and the difference image; removing false positive nodule candidates from the first plurality of nodule candidates based on the respective nodule feature values to obtain a second plurality of nodule candidates; and determining the at least one nodule by classifying each of the second plurality of nodule candidates as a nodule or a non-nodule based on at least one of the pixel values and the respective nodule feature values. True-positive nodules are identified using linear discriminant analysis and/or a Multi-MTANN.

Automated method and system for the detection of lung nodules in low-dose CT images for lung-cancer screening

A method, system and computer readable medium for a computer-automated method for identifying given image data, including obtaining template image data corresponding to said given image data; calculating correlation values between the given image data and said template image data; and identifying said image data based on the correlation values calculated in the calculating step.

Method, system and computer readable medium for identifying chest radiographs using image mapping and template matching techniques

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Clarendon Hills, IL, United States of America

Hidetaka Arimura