Abstract: Values for ultrasound acquisition parameters are altered in a manifold space. The number of parameters to be set is reduced using a manifold. Virtual parameters different than the acquisition parameters are used to alter the greater number of acquisition parameters. In a further use, optimum image settings may be obtained in an automated system by measuring image quality for feeding back to virtual parameter adjustment.

Abstract: A needle is enhanced in a medical diagnostic ultrasound image. The image intensities associated with a needle in an image are adaptively increased and/or enhanced by compounding from a plurality of ultrasound images. Filtering methods and probabilistic methods are used to locate possible needle locations. In one approach, possible needles are found in component frames that are acquired at the same time but at different beam orientations. The possible needles are associated with each other across the component frames and false detections are removed based on the associations. In one embodiment of needle detection in an ultrasound component frame, lines are found first. The lines are then searched to find possible needle segments. In another embodiment, data from different times may be used to find needle motion and differences from a reference, providing the features in additional to features from a single component frame for needle detection.

Abstract: Values for ultrasound acquisition parameters are altered in a manifold space. The number of parameters to be set is reduced using a manifold. Virtual parameters different than the acquisition parameters are used to alter the greater number of acquisition parameters. In a further use, optimum image settings may be obtained in an automated system by measuring image quality for feeding back to virtual parameter adjustment.

Abstract: A needle is enhanced in a medical diagnostic ultrasound image. The image intensities associated with a needle in an image are adaptively increased and/or enhanced by compounding from a plurality of ultrasound images. Filtering methods and probabilistic methods are used to locate possible needle locations. In one approach, possible needles are found in component frames that are acquired at the same time but at different beam orientations. The possible needles are associated with each other across the component frames and false detections are removed based on the associations. In one embodiment of needle detection in an ultrasound component frame, lines are found first. The lines are then searched to find possible needle segments. In another embodiment, data from different times may be used to find needle motion and differences from a reference, providing the features in additional to features from a single component frame for needle detection.

Abstract: A fetal parameter or anatomy is measured or detected from three-dimensional ultrasound data. An algorithm is machine-trained to detect fetal anatomy. Any machine training approach may be used. The machine-trained classifier is a joint classifier, such that one anatomy is detected using the ultrasound data and the detected location of another anatomy. The machine-trained classifier uses marginal space such that the location of anatomy is detected sequentially through translation, orientation and scale rather than detecting for all location parameters at once. The machine-trained classifier includes detectors for detecting from the ultrasound data at different resolutions, such as in a pyramid volume.

Abstract: A method for detecting fetal anatomic features in ultrasound images includes providing an ultrasound image of a fetus, specifying an anatomic feature to be detected in a region S determined by parameter vector ?, providing a sequence of probabilistic boosting tree classifiers, each with a pre-specified height and number of nodes. Each classifier computes a posterior probability P(y|S) where y?{?1,+1}, with P(y=+1|S) representing a probability that region S contains the feature, and P(y=?1|S) representing a probability that region S contains background information. The feature is detected by uniformly sampling a parameter space of parameter vector ? using a first classifier with a sampling interval vector used for training said first classifier, and having each subsequent classifier classify positive samples identified by a preceding classifier using a smaller sampling interval vector used for training said preceding classifier.

Abstract: A method for segmenting and measuring anatomical structures in fetal ultrasound images includes the steps of providing a digitized ultrasound image of a fetus comprising a plurality of intensities corresponding to a domain of points on a 3-dimensional grid, providing a plurality of classifiers trained to detect anatomical structures in said image of said fetus, and segmenting and measuring an anatomical structure using said image classifiers by applying said elliptical contour classifiers to said fetal ultrasound image, wherein a plurality of 2-dimensional contours characterizing said anatomical structure are detected. The anatomical structure measurement can be combined with measurement of another anatomical structure to estimate gestational age of the fetus.

Abstract: A fetal parameter or anatomy is measured or detected from three-dimensional ultrasound data. An algorithm is machine-trained to detect fetal anatomy. Any machine training approach may be used. The machine-trained classifier is a joint classifier, such that one anatomy is detected using the ultrasound data and the detected location of another anatomy. The machine-trained classifier uses marginal space such that the location of anatomy is detected sequentially through translation, orientation and scale rather than detecting for all location parameters at once. The machine-trained classifier includes detectors for detecting from the ultrasound data at different resolutions, such as in a pyramid volume.

Abstract: A method for detecting fetal anatomic features in ultrasound images includes providing an ultrasound image of a fetus, specifying an anatomic feature to be detected in a region S determined by parameter vector ?, providing a sequence of probabilistic boosting tree classifiers, each with a pre-specified height and number of nodes. Each classifier computes a posterior probability P(y|S) where y?{?1,+1}, with P(y=+1|S) representing a probability that region S contains the feature, and P(y=?1|S) representing a probability that region S contains background information. The feature is detected by uniformly sampling a parameter space of parameter vector ? using a first classifier with a sampling interval vector used for training said first classifier, and having each subsequent classifier classify positive samples identified by a preceding classifier using a smaller sampling interval vector used for training said preceding classifier.

Abstract: A method for segmenting and measuring anatomical structures in fetal ultrasound images includes the steps of providing a digitized ultrasound image of a fetus comprising a plurality of intensities corresponding to a domain of points on a 3-dimensional grid, providing a plurality of classifiers trained to detect anatomical structures in said image of said fetus, and segmenting and measuring an anatomical structure using said image classifiers by applying said elliptical contour classifiers to said fetal ultrasound image, wherein a plurality of 2-dimensional contours characterizing said anatomical structure are detected. The anatomical structure measurement can be combined with measurement of another anatomical structure to estimate gestational age of the fetus.