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 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: 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.