Abstract: In accordance with the present disclosure, the present technique finds a diagnostic scan timing for a non-static object (e.g., a heart or other dynamic object undergoing motion) from raw scan data, as opposed to reconstructed image data. To find the scan timing, a monitoring scan of a patient's heart is performed. In the monitoring scan, the patient dose may be limited or minimized. As the projection data is acquired during such a monitoring scan, the projection data may be subjected to sinogram analysis in a concurrent or real-time manner to determine when to start (or trigger) the diagnostic scan.

Abstract: In accordance with the present disclosure, the present technique finds a diagnostic scan timing for a non-static object (e.g., a heart or other dynamic object undergoing motion) from raw scan data, as opposed to reconstructed image data. To find the scan timing, a monitoring scan of a patient's heart is performed. In the monitoring scan, the patient dose may be limited or minimized. As the projection data is acquired during such a monitoring scan, the projection data may be subjected to sinogram analysis in a concurrent or real-time manner to determine when to start (or trigger) the diagnostic scan.

Abstract: A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.

Abstract: A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.

Abstract: A computationally efficient dictionary learning-based term is employed in an iterative reconstruction framework to keep more spatial information than two-dimensional dictionary learning and require less computational cost than three-dimensional dictionary learning. In one such implementation, a non-local regularization algorithm is employed in an MBIR context (such as in a low dose CT image reconstruction context) based on dictionary learning in which dictionaries from different directions (e.g., x,y-plane, y,z-plane, x,z-plane) are employed and the sparse coefficients calculated accordingly. In this manner, spatial information from all three directions is retained and computational cost is constrained.

Abstract: The present approach relates to the use of a database (i.e., a dictionary) of image patterns to be avoided or de-emphasized during an image reconstruction process, such as an iterative image reconstruction process. Such a dictionary may be characterized as a negative or “bad” dictionary. The negative dictionary may be used to constrain an image reconstruction process to avoid or minimize the presence of the patterns present in the negative dictionary.

Abstract: A computationally efficient dictionary learning-based term is employed in an iterative reconstruction framework to keep more spatial information than two-dimensional dictionary learning and require less computational cost than three-dimensional dictionary learning. In one such implementation, a non-local regularization algorithm is employed in an MBIR context (such as in a low dose CT image reconstruction context) based on dictionary learning in which dictionaries from different directions (e.g., x,y-plane, y,z-plane, x,z-plane) are employed and the sparse coefficients calculated accordingly. In this manner, spatial information from all three directions is retained and computational cost is constrained.

Abstract: Approaches related to performing calibration of a CT scanner or of processes (e.g., correction and/or reconstruction) performed on acquired CT scan data are described. In certain described approaches, calibration is attained without performing a calibration scan using a dedicated calibration phantom. In certain embodiments, calibration is performed using a feature intrinsic to the imaged object, such as a jacket disposed about a drilled core sample.

Abstract: An apparatus usable in an image encoding and/or decoding system includes a segmentation unit to convert a first image of a first resolution into a second image of a second resolution, to segment the second image of the second resolution with one or more blocks of a binary mask layer having a foreground and a background, and to convert the segmented second image into a third image of a third resolution as a segmented image.

Abstract: This disclosure describes techniques for initially calibrating an image capture device. According to one aspect, the calibration includes calibrating parameters of an image filter for resolving the captured image. The method includes receiving a captured image of a test target and resolving the captured image using a set of initial parameters. A sharpness index of the resolved image is determined without matching the captured image to the test target. The sharpness index is used to minimize a cost function for determining parameters for use by the image capture device.

Abstract: An image processing method of an image processing apparatus, the method includes: discerning pixels to be processed on the basis of a preset reference pixel value in a scanned image that comprises plural pixels; drawing a pixel area including the pixels to be processed from the scanned image on the basis of a preset condition; and selecting whether to apply an image process to the pixels to be processed or the drawn pixel area on the basis of a percentage of the pixels to be processed in the drawn pixel area.

Abstract: This disclosure describes techniques for initially calibrating an image capture device. According to one aspect, the calibration includes calibrating parameters of an image filter for resolving the captured image. The method includes receiving a captured image of a test target and resolving the captured image using a set of initial parameters. A sharpness index of the resolved image is determined without matching the captured image to the test target. The sharpness index is used to minimize a cost function for determining parameters for use by the image capture device.

Abstract: An apparatus usable in an image encoding and/or decoding system includes a segmentation unit to convert a first image of a first resolution into a second image of a second resolution, to segment the second image of the second resolution with one or more blocks of a binary mask layer having a foreground and a background, and to convert the segmented second image into a third image of a third resolution as a segmented image.

Abstract: An apparatus usable in an image encoding and/or decoding system includes a segmentation unit to convert a first image of a first resolution into a second image of a second resolution, to segment the second image of the second resolution with one or more blocks of a binary mask layer having a foreground and a background, and to convert the segmented second image into a third image of a third resolution as a segmented image.

Abstract: An apparatus usable in an image encoding and decoding system includes segmentation unit to divide an image into one or more blocks, to segment the blocks into a binary mask layer of a foreground and a background according to a cost optimized function and a feature vector to generate a segmentation image according to the segmented blocks.

Abstract: An image processing method of an image processing apparatus, the method includes: discerning pixels to be processed on the basis of a preset reference pixel value in a scanned image that comprises plural pixels; drawing a pixel area including the pixels to be processed from the scanned image on the basis of a preset condition; and selecting whether to apply an image process to the pixels to be processed or the drawn pixel area on the basis of a percentage of the pixels to be processed in the drawn pixel area.

Abstract: An apparatus usable in an image encoding and decoding system includes segmentation unit to divide an image into one or more blocks, to segment the blocks into a binary mask layer of a foreground and a background according to a cost optimized function and a feature vector to generate a segmentation image according to the segmented blocks.

Abstract: An apparatus usable in an image encoding and/or decoding system includes a segmentation unit to convert a first image of a first resolution into a second image of a second resolution, to segment the second image of the second resolution with one or more blocks of a binary mask layer having a foreground and a background, and to convert the segmented second image into a third image of a third resolution as a segmented image.