Abstract:
The present invention relates to lung measurement. In order to provide enhanced information about a patient that facilitates further assessment steps, 2D X-ray image data of a patient's chest is provided, and the image data is segmented to identify lung structures to provide segmented image data separated from un-segmented areas. Further, spatial lung volume information is extracted from the image data using the segmented image data derived from the image data. Still further, lungs symmetry information is determined using the extracted spatial lung volume information. Finally, the lungs symmetry information is provided to a user. For example, a 2D X-ray image data of a patient's chest is provided (84) and a lungs mask image is formed (86) after the step of segmenting the input image data. Then, the lungs mask image is used to define areas, within which a predetermined adaptation is applied (88) to the original 2D X-ray image data producing a thorax mask image. Next, left and right images are provided (90) showing the left and the right spatial lungs volume information of the regions defined originally by the lungs mask image. Finally, based on the spatial lungs volume information, lungs symmetry information or total lung volumes may be calculated and provided (92).
Abstract:
A digital image (40) comprises pixels with intensities relating to different energy levels. A method for processing the digital image (40) comprises the steps of: receiving first image data (42a) and second image data (42b) of the digital image (40), the first image data (42a) encoding a first energy level and the second image data (42b) encoding a second energy level; determining a regression model (44) from the first image data (42a) and the second image data (42b), the regression model (44) establishing a correlation between intensities of pixels of the first image data (42a) with intensities of pixels of the second image data (42b); and calculating residual mode image data (46) from the first image data (42a) and the second image data (42b), such that a pixel of the residual mode image data(46) has an intensity based on the difference of an intensity of the second image data(42b) at the pixel and a correlated intensity of the pixel of the first image data (42a), the correlated intensity determinate by applying the regression model to the intensity of pixel of the first image data (42a).
Abstract:
The present disclosure provides means for improved medical image post-processing. It utilizes an image data post-processing mechanism (122) comprising an encoder (122A) configured to encode input image data with input image properties and a decoder (122B) configured to decode the input image data to provide output image data with output image properties different to the input image properties. The post-processing mechanism (122) comprises a first post-processing setting applied to the encoder and/or the decoder and assigned to first output image properties. Further, the post-processing mechanism (122) is configured to predict a second post-processing setting applicable to the encoder and/or the decoder and assigned to second output image properties. The post-processing mechanism (122) is further configured to provide an image proposal comprising the input image data post-processed with the predicted second post-processing setting. Furthermore, the post-processing mechanism (122) is configured to receive a feedback signal assigned to the image proposal, and to evaluate the predicted second post-processing setting based on the received feedback signal.
Abstract:
an image processing system and related method. The system comprises an input interface (IN) configured for receiving an input image. A filter (FIL) of the system filters said input image to obtain a structure image from said input image, said structure image including a range of image values. A range identifier (RID) of the system identifies, based on an image histogram for the structure image, an image value sub-range within said range. The sub-range being associated with a region of interest. The system output through an output interface (OUT) a specification for said image value sub-range. In addition or instead, a mask image for the region of interest or for region or low information is output.
Abstract:
An X-ray apparatus for image acquisition and a related method. The apparatus comprises a field-of-view corrector (CS) configured to receive a scout image (SI) acquired by the imager with a tentative collimator setting in a pre-shot imaging phase where said imager operates with a low dosage radiation cone causing the detector to register the scout image. The low dosage cone has, in the detector's image plane, a first cross section smaller than the total area of the detector surface. The field-of-view corrector (CS) uses said scout image to establish field-of-view correction information for a subsequent imaging phase where the imager is to operate with a high dosage radiation cone, the high dosage higher than the low dosage.
Abstract:
A method is provided for adapting a 3D field of view (FOV) in ultrasound data acquisition so as to minimize the FOV volume in a manner that is controlled and precise. The method comprises defining a volumetric region across which 3D ultrasound data is desired, and then adapting the data acquisition field of view (FOV) in dependence upon the defined volumetric region, to encompass the region. This is achieved based on adapting a scan line length (or scan depth) of each individual scan line based on the defined volumetric region. In some embodiments, the volumetric region may be defined based on anatomical segmentation of a reference ultrasound dataset acquired in an initial step, and setting the volumetric region in dependence upon boundaries of an identified object of interest. The volumetric region may in a subset of embodiments be set as the region occupied by a detected anatomical object of interest.
Abstract:
The invention is related to a method for providing guidance data (30, 41) for positioning a region of interest (20, 31, 41, 56) of a subject (54), an X-ray source (52), and an X-ray detector (21, 34, 53). The method comprises: obtaining, by a processor (11), current positioning data of at least one palpable bony landmark (23, 33) derived from a palpation; obtaining current positioning data of the X-ray source (52) and of the X-ray detector (21, 34, 53) (S20); determining guidance data (30, 41) for positioning the region of interest (20, 31, 41, 56), the X-ray source (52) and the X-ray detector (21, 34, 53) and providing, by the processor (11), the guidance data (30, 41) (S40).
Abstract:
The disclosure relates to a system for analysis of medical image data, which represents a two-dimensional or three-dimensional medical image. The system is configured to read and/or determine, for the medical image, a plurality of image quality metrics and to determine a combined quality metrics based on the image quality metrics. The system is further configured so that the determination of the combined quality metrics takes into account an interaction between the image quality metrics in their combined effect on the combined quality metrics.
Abstract:
A computer-implemented method for positioning a subject in medical imaging, comprising: receiving a first image (20) of a region of interest (14, 16) of the subject (S10); determining first positioning data based on the first image (20), wherein the first positioning data indicates an alignment of the region of interest (14, 16) relative to a first image acquisition unit used to acquire the first image (20) (S20); determining guidance data based on the first positioning data, wherein the guidance data comprises a guidance for an alignment of the region of interest (14, 16) relative to a second image acquisition unit used to acquire a second image (60) from a current alignment to a target alignment, wherein the target alignment is to correspond to that derived from the first positioning data (S30); providing the guidance data for acquiring the second image (60) (S40).
Abstract:
A computer implemented method of making a measurement associated with a feature of interest in an image. The method comprises using (302) a model trained using a machine learning process to take the image as input and predict a pair of points between which to make the measurement of the feature of interest in the image. The method then comprises determining (304) the measurement, based on the predicted pair of points.