Abstract: A method of detecting blood vessel shadows in an anterior posterior x-ray radiograph comprising the steps of: generating candidate sub areas of the radiograph showing changes in contrast above a threshold level; supressing rib shadow edges; eliminating lung tissue shadow edges, and categorizing and eliminating nodule shadows.

Abstract: Detected lung nodules are presented in a chest radiographic sub-image. A curve is matched to pixels in the sub-image and confidence values for individual pixels is determined. A confidence image is generated consisting of the confidence values at the position of the respective pixel. Separated regions of pixels within the confidence image are identified which have a confidence value greater than a threshold confidence value. A filtered confidence image is generated consisting of the separated regions of the confidence image which are larger than a threshold area. A histogram of values characteristic for the matching of the curve is determined, wherein the filtered confidence image is used as a mask, such that only values are considered for the histogram which correspond to the separated regions of the filtered confidence image. A statistical measure of the histogram is determined and the lung nodules are verified based on the statistical measure.

Abstract: A method of detecting lung nodules in an anterior posterior x-ray radiograph comprising the steps of: generating candidate regions in image showing changes in contrast above a threshold level, and eliminating false positives by eliminating edges assignable to organs by: identifying edges; categorizing and eliminating rib edges; categorizing and eliminating lung tissue edges, and categorizing and eliminating blood vessels.

Abstract: A medical imaging system is used to recognize an internal structure from a three-dimensional image. The image includes image sub-volumes. An image sub-volume is selected using a non-linear search pattern. The selected image sub-volume is analyzed for the presence of the internal structure. The steps of selecting an image sub-volume using the non-linear search pattern and analyzing the selected sub-volume for the presence of the internal structure are repeated until the internal structure is found in an image sub-volume. Bounds of the internal structure are identified based on the location of the image sub-volume within which the internal structure is found.

Abstract: A method is described for the automatic analysis of image data of a structure. in at least one embodiment, the method includes: providing image data in the form of a three-dimensional voxel array, performing segmentation of the voxel array in order to determine a voxel subset, performing feature extraction at least for particular voxels of the voxel subset in order to generate a feature map, generating a scalar difference map on the basis of the feature map, performing classification with the aid of the difference map and identifying a structural anomaly in the image data on the basis of a classification result. A method for driving an image display device, an image processing system and an imaging system are furthermore described.

Abstract: Detected lung nodules are presented in a chest radiographic sub-image. A curve is matched to pixels in the sub-image and confidence values for individual pixels is determined. A confidence image is generated consisting of the confidence values at the position of the respective pixel. Separated regions of pixels within the confidence image are identified which have a confidence value greater than a threshold confidence value. A filtered confidence image is generated consisting of the separated regions of the confidence image which are larger than a threshold area. A histogram of values characteristic for the matching of the curve is determined, wherein the filtered confidence image is used as a mask, such that only values are considered for the histogram which correspond to the separated regions of the filtered confidence image. A statistical measure of the histogram is determined and the lung nodules are verified based on the statistical measure.

Abstract: A method of identifying nodules in radiological images, said method comprising: (a) obtaining a radiological image; (b) selecting a sub-image centered around a candidate location; (c) dividing the sub-image into a rectangular array of cells; (d) calculating absolute values of Intensity Differences id(k) according to a Fractional Brownian Motion (FBM) calculation equation: id ( k ) = [ ? x = 0 N - 1 ? ? y = 0 N - k - 1 ? ? I ? ( x , y ) - I ? ( x , y + k ) ? 4 ? N ? ( N - k ) + ? y = 0 N - 1 ? ? x = 0 N - k - 1 ? ? I ? ( x , y ) - I ? ( x + k , y ) ? 4 ? N ? ( N - k ) + ? x = 0 N - 1 - k ? ? y = 0 N - k - 1 ? ? I ? ( x , y ) - I ? ( x + k , y + k ) ? 4 ? ( N - k ) 2 + ? x = 0 N - 1 - k ? ? y = 0 N - k - 1 ? ? I ? ( x , N - y ) - I ? ( x + k , N - ( y + k ) ) ? 4 ? ( N - k ) 2 ] , for k=1

Abstract: A method of detecting lung nodules in an anterior posterior x-ray radiograph comprising the steps of: generating candidate regions in image showing changes in contrast above a threshold level, and eliminating false positives by eliminating edges assignable to organs by: identifying edges; categorizing and eliminating rib edges; categorizing and eliminating lung tissue edges, and categorizing and eliminating blood vessels.

Abstract: A method of detecting blood vessel shadows in an anterior posterior x-ray radiograph comprising the steps of: generating candidate sub areas of the radiograph showing changes in contrast above a threshold level; supressing rib shadow edges; eliminating lung tissue shadow edges, and categorizing and eliminating nodule shadows.

Abstract: A medical imaging system is used to recognize an internal structure from a three-dimensional image. The image includes image sub-volumes. An image sub-volume is selected using a non-linear search pattern. The selected image sub-volume is analyzed for the presence of the internal structure. The steps of selecting an image sub-volume using the non-linear search pattern and analyzing the selected sub-volume for the presence of the internal structure are repeated until the internal structure is found in an image sub-volume. Bounds of the internal structure are identified based on the location of the image sub-volume within which the internal structure is found.