Arch Development Corporation

University of Chicago

Junji Morishita

Kunio Doi

Shigehiko Katsuragawa

Xin-Wei Xu

Hidetaka Arimura

Shigehiko Katsuagawa

A method for determining whether a first medical image and a second medical image are medical images of the same patient, comprising selecting a first region in the first medical image; selecting a second region in the second medical image; determining a common region based on a boundary of the first region and a boundary of the second region; calculating a correlation coefficient based on image data from the first medical image in the common region and image data from the second medical image in the common region; and determining whether the first medical image and the second medical image are medical images of the same patient based on the correlation coefficient. Biological fingerprints from parts of chest radiographs such as thoracic fields, cardiac shadows, lung apices, superior mediastinum, and the right lower lung that includes the costophrenic angle, are used for the purpose of patient recognition and identification.

Automated method of patient recognition using chest radiographs

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

A computerized method for the detection and characterization of disease in an image derived from a chest radiograph, wherein an image in the chest radiograph is processed to determine the ribcage boundary, including lung top edges, right and left ribcage edges, and right and left hemidiaphragm edges. Texture measures including RMS variations of pixel values within regions of interest are converted to relative exposures and corrected for system noise existing in the system used to produce the image. Texture and/or geometric pattern indices are produced. A histogram(s) of the produced index (indices) is produced and values of the histograms) are applied as inputs to a trained artificial neural network, which classifies the image as normal or abnormal. In one embodiment, obviously normal and obviously abnormal images are determined based on the ratio of abnormal regions of interest to the total number of regions of interest in a rule-based method, so that only difficult cases to diagnose are applied to the artificial neural network.

Computer-aided method for automated image feature analysis and diagnosis

A computerized method for the detection and characterization of disease in an image derived from a chest radiograph, wherein an image in the chest radiograph is processed to determine the ribcage boundary, including lung top edges, right and left ribcage edges, and right and left hemidiaphragm edges. Texture measures including RMS variations of pixel values within regions of interest are converted to relative exposures and corrected for system noise existing in the system used to produce the image. Texture and/or geometric pattern indices are produced. A histogram(s) of the produced index (indices) is produced and values of the histogram(s) are applied as inputs to a trained artificial neural network, which classifies the image as normal or abnormal. In one embodiment, obviously normal and obviously abnormal images are determined based on the ratio of abnormal regions of interest to the total number of regions of interest in a rule-based method, so that only difficult cases to diagnose are applied to the artificial neural network.

Growing community of inventors

Chicago, IL, United States of America

Junji Morishita