Abstract: An apparatus, method and computer program product for performing computer aided diagnosis on temporal subtraction images of objects. A mode of a gray-level histogram is identified, and a gray-level threshold is established at a predefined fraction of this modal value. All pixels with gray levels below this threshold that lie within the lung regions of the temporal subtraction image remain “on,” while all other pixels are set to zero. Area and circularity requirements are imposed to eliminate false-positive regions. Areas of pathologic change identified in this manner may be presented as outlines in the subtraction image or as highlighted regions in the original radiographic image so that, in effect, temporal subtraction becomes a “background” process for computer-aided diagnosis. The present invention is also directed to method, apparatus, and computer program product for performing temporal subtraction on energy subtraction images, with or without subsequent computer aided diagnosis, of objects.

Abstract: An apparatus, method and computer program product for performing computer aided diagnosis on temporal subtraction images of objects. A mode of a gray-level histogram is identified, and a gray-level threshold is established at a predefined fraction of this modal value. All pixels with gray levels below this threshold that lie within the lung regions of the temporal subtraction image remain “on,” while all other pixels are set to zero. Area and circularity requirements are imposed to eliminate false-positive regions. Areas of pathologic change identified in this manner may be presented as outlines in the subtraction image or as highlighted regions in the original radiographic image so that, in effect, temporal subtraction becomes a “background” process for computer-aided diagnosis. The present invention is also directed to method, apparatus, and computer program product for performing temporal subtraction on energy subtraction images, with or without subsequent computer aided diagnosis, of objects.

Abstract: A method, system and computer readable medium for automated detection of lung nodules in computed tomography (CT) image scans, including generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from the CT image scans; generating three-dimensional segmented lung volume images by combining the two-dimensional segmented lung images; determining three-dimensional lung nodule candidates from the three-dimensional segmented lung volume images, including, identifying structures within the three-dimensional segmented lung volume images that meet a volume criterion; deriving features from the lung nodule candidates; and detecting lung nodules by analyzing the features to eliminate false-positive nodule candidates from the nodule candidates.

Abstract: A method, system, and computer program product for automated and semi-automated measurements of pleural space and/or pleural thickening in thoracic CT images to identify the presence and quantify the extent of pleura-based disease, including obtaining a CT image including the pleural space and/or the pleural thickening, segmenting lungs in the obtained image, obtaining an initial endpoint on a chest wall or a mediastinum boundar; determining a terminal endpoint on a boundary of the at least one segmented lung; and determining a distance between said initial endpoint and said the terminal endpoint as the extent of the pleural space and/or the pleural thickening. Exemplary embodiments include determining the extent of pleural space and/or pleural thickening based on linear distance, area, and volume of the space between the lung boundary and at least one of the chest wall and the mediastinum boundary.

Abstract: A method, system, and computer product for the automated segmentation of the lung fields and costophrenic angle (CP) regions in posteroanterior (PA) chest radiographs, wherein image segmentation based on gray-level threshold analysis is performed by applying an iterative global gray-level thresholding method to a chest image based on the features of a global gray-level histogram. Features of the regions in a binary image constructed at each iteration are identified and analyzed to exclude regions external to the lung fields. The initial lung contours that result from this global process are used to facilitate a local gray-level thresholding method. Individual regions-of-interest (ROIs) are placed along the initial contour. A procedure is implemented to determine the gray-level thresholds to be applied to the pixels within the individual ROIs. The result is a binary image, from which final contours are constructed. Smoothing processes are applied, including a unique adaptation of a rolling ball method.

Abstract: A method, system, and computer program product for automated measurements of pleural space and/or pleural thickening in thoracic CT images to identify the presence and quantify the extent of pleura-based disease, including obtaining a CT image including the pleural space and/or the pleural thickening, segmenting lungs in the obtained image, constructing a chest wall image from the obtained image using a lung boundary obtained in the segmenting step, identifying ribs in the chest wall image, mapping a location of the identified ribs back into the obtained image, and determining in the obtained image the extent of the pleural space and/or the pleural thickening between the identified ribs mapped back into the obtained CT image and at least one segmented lung. Exemplary embodiments include determining the extent of pleural space and/or pleural thickening based on linear distance, area, and volume of the space between the ribs and the lung boundary.

Abstract: A method, system, and computer program product for automated and semi-automated measurements of pleural space and/or pleural thickening in thoracic CT images to identify the presence and quantify the extent of pleura-based disease, including obtaining a CT image including the pleural space and/or the pleural thickening, segmenting lungs in the obtained image, obtaining an initial endpoint on a chest wall or a mediastinum boundary; determining a terminal endpoint on a boundary of the at least one segmented lung; and determining a distance between said initial endpoint and said the terminal endpoint as the extent of the pleural space and/or the pleural thickening. Exemplary embodiments include determining the extent of pleural space and/or pleural thickening based on linear distance, area, and volume of the space between the lung boundary and at least one of the chest wall and the mediastinum boundary.

Abstract: A method, system, and computer product for the automated segmentation of the lung fields and costophrenic angle (CP) regions in posteroanterior (PA) chest radiographs, wherein image segmentation based on gray-level threshold analysis is performed by applying an iterative global gray-level thresholding method to a chest image based on the features of a global gray-level histogram. Features of the regions in a binary image constructed at each iteration are identified and analyzed to exclude regions external to the lung fields. The initial lung contours that result from this global process are used to facilitate a local gray-level thresholding method. Individual regions-of-interest (ROIs) are placed along the initial contour. A procedure is implemented to determine the gray-level thresholds to be applied to the pixels within the individual ROIs. The result is a binary image, from which final contours are constructed. Smoothing processes are applied, including a unique adaptation of a rolling ball method.

Abstract: A system and method for digital imaging. A digital radiological image of a subject is obtained having at least one low density region. The image is processed using first weighting factors in the at least one low density region of the digital image and second weighting factors smaller than the first weighting factors in regions of the digital image other than the at least one low density region. A processed digital image is obtained and a representation of the processed digital image is produced. In the processing of the image, unsharp mask filtering is employed using a processing curve having maximum unsharp mask filtering in the at least one low density region of the digital image and a constant amount of unsharp mask filtering less than the maximum unsharp mask processing in the regions of the image other than the low density regions.

Abstract: A method, system, and computer program product for automated measurements of pleural space and/or pleural thickening in thoracic CT images to identify the presence and quantify the extent of pleura-based disease, including obtaining a CT image including the pleural space and/or the pleural thickening, segmenting lungs in the obtained image, constructing a chest wall image from the obtained image using a lung boundary obtained in the segmenting step, identifying ribs in the chest wall image, mapping a location of the identified ribs back into the obtained image, and determining in the obtained image the extent of the pleural space and/or the pleural thickening between the identified ribs mapped back into the obtained CT image and at least one segmented lung. Exemplary embodiments include determining the extent of pleural space and/or pleural thickening based on linear distance, area, and volume of the space between the ribs and the lung boundary.

Abstract: A method, system, and computer product for the automated segmentation of the lung fields and costophrenic angle (CP) regions in posteroanterior (PA) chest radiographs, wherein image segmentation based on gray-level threshold analysis is performed by applying an iterative global gray-level thresholding method to a chest image based on the features of a global gray-level histogram. Features of the regions in a binary image constructed at each iteration are identified and analyzed to exclude regions external to the lung fields. The initial lung contours that result from this global process are used to facilitate a local gray-level thresholding method. Individual regions-of-interest (ROIs) are placed along the initial contour. A procedure is implemented to determine the gray-level thresholds to be applied to the pixels within the individual ROIs. The result is a binary image, from which final contours are constructed. Smoothing processes are applied, including a unique adaptation of a rolling ball method.

Abstract: A system and method for digital imaging. A digital radiological image of a subject is obtained having at least one low density region. The image is processed using first weighting factors in the at least one low density region of the digital image and second weighting factors smaller than the first weighting factors in regions of the digital image other than the at least one low density region. A processed digital image is obtained and a representation of the processed digital image is produced. In the processing of the image, unsharp mask filtering is employed using a processing curve having maximum unsharp mask filtering in the at least one low density region of the digital image and a constant amount of unsharp mask filtering less than the maximum unsharp mask filtering in the regions of the image other than the low density regions.

Abstract: Automated Method and System for the Segmentation of Lung Regions in Computed Tomography Scans A method and system for the automated segmentation of the lung regions in thoracic CT scans includes construction of a cumulative gray level profile from pixels along the diagonal of each CT section image. The shape of this profile is used to identify a gray level threshold that is used to create a binary image. A contour detection algorithm generates a segmented thorax region. The trachea and main bronchi are segmented and eliminated from the segmented thorax region to prevent subsequent inclusion within the segmented lung regions. A gray level histogram is constructed to identify a second gray level threshold, which is applied to the segmented thorax region to create a binary image. If the two lungs regions are “fused,” the anterior junction is then delineated and turned “off” in the binary image to separate the two lungs.

Abstract: A method, system and computer readable medium for automated detection of lung nodules in computed tomography (CT) image scans, including generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from the CT image scans; generating three-dimensional segmented lung volume images by combining the two-dimensional segmented lung images; determining three-dimensional lung nodule candidates from the three-dimensional segmented lung volume images, including, identifying structures within the three-dimensional segmented lung volume images that meet a volume criterion; deriving features from the lung nodule candidates; and detecting lung nodules by analyzing the features to eliminate false-positive nodule candidates from the nodule candidates.

Abstract: A method and system for the automated segmentation of the lung regions in lateral chest radiographs. This is achieved according to the invention by providing an improved computerized, automated method for image segmentation based on gray-level threshold analysis. A unique method for identifying an approximate outer bounds on the extent of the lung fields in the image is performed to restrict the region further analyzed. An iterative global gray-level thresholding method is applied based on the features of a global gray-level histogram. Features of the regions in a binary image constructed at each iteration are identified and subjected to a modified analysis to exclude regions external to the lung field. The initial lung region contour that results from this global process is used to facilitate a novel adaptive local gray level thresholding method. Individual regions-of-interest (ROIs) are placed along the initial contour. The dimensions of the several ROIs are based upon the patient anatomy enclosed therein.

Abstract: A method, system, and computer product for the automated segmentation of the lung fields and costophrenic angle (CP) regions in posteroanterior (PA) chest radiographs, wherein image segmentation based on gray-level threshold analysis is performed by applying an iterative global gray-level thresholding method to a chest image based on the features of a global gray-level histogram. Features of the regions in a binary image constructed at each iteration are identified and analyzed to exclude regions external to the lung fields. The initial lung contours that result from this global process are used to facilitate a local gray-level thresholding method. Individual regions-of-interest (ROIs) are placed along the initial contour. A procedure is implemented to determine the gray-level thresholds to be applied to the pixels within the individual ROIs. The result is a binary image, from which final contours are constructed. Smoothing processes are applied, including a unique adaptation of a rolling ball method.

Abstract: A method, system, and computer product for the automated segmentation of the lung fields and costophrenic angle (CP) regions in posteroanterior (PA) chest radiographs wherein image segmentation based on gray-level threshold analysis is performed by applying an iterative global gray-level thresholding method to a chest image based on the features of a global gray-level histogram. Features of the regions in a binary image constructed at each iteration are identified and analyzed to exclude regions external to the lung fields. The initial lung contours that result from this global process are used to facilitate a local gray-level thresholding method. Individual regions-of-interest (ROIs) are placed along the initial contour. A procedure is implemented to determine the gray-level thresholds to be applied to the pixels within the individual ROIs. The result is a binary image, from which final contours are constructed. Smoothing processes are applied, including a unique adaptation of a rolling ball method.

Abstract: A method, system and computer readable medium configured for computerized detection of lung abnormalities, including obtaining a standard digital chest image and a soft-tissue digital chest image; generating a first difference image from the standard digital chest image and a second difference image from the soft-tissue digital chest image; identifying candidate abnormalities in the first and second difference images; extracting from the standard digital chest image and the first difference image predetermined first features of each of the candidate abnormalities identified in the first difference image; extracting from the soft-tissue digital chest image and the second difference images predetermined second features of each of the candidate abnormalities identified in the second difference image; analyzing the extracted first features and the extracted second features to identify and eliminate false positive candidate abnormalities respectively corresponding thereto; applying extracted features from remainin

Abstract: A method and system for displaying medical images and computer output from various CAD schemes on the images. Images are loaded into the display system and displayed in a main viewing area. The system can display various CAD schemes, such as the automated detection of lung nodules, interstitial infiltrates, and heart size, in the case of a chest image, on the image. The method and system also display interval change (temporal subtraction) between images. Individual abnormality from CAD schemes can be viewed by clicking buttons with minified (postage stamp size) images with CAD annotation rendered into them. The images and results are then shown on a high-speed monitor. Interval change between the current and a previous image can also be chosen by clicking buttons containing minified images. Interval change between any other pair of images for a patient can be selected by choosing from minified subtraction images presented in a two-dimensional array format.

Abstract: A method for the automated detection of gross abnormalities and asymmetries in chest images, including generating image data from radiographic images of the thorax. The image data are then analyzed in order to produce the boundaries of the aerated lung regions in the thorax. This analysis comprises location of the mediastinum and lung apices, iterative global thresholding with centroid testing of contours, local thresholding on regions along initial contours of the aerated lung, correction for regions near the costo- and cardiophrenic angles in the chest, analysis of the areas and density distribution within the aerated lung regions in the chest and determination of the likelihood of the presence of a gross abnormality or asymmetry. Final output could be the computer determined contours of the lungs or the likelihood for abnormality.