Abstract: A method for processing a radiographic image of a patient obtains radiographic image data and detects the position of inserted tubing or other foreign object in the obtained image and determines the tubing tip or object location. A region of interest in the neighborhood of the tubing tip or object location is defined and at least one anatomy structure within the region of interest is detected. The probability for mal-positioning of the tip or object is calculated by determining the position of the tip or object relative to the at least one anatomy structure.

Abstract: A method of processing a digital radiographic medical image. The digital radiographic medical image is accessed and a plurality of regions of interest is determined. For each of the plurality of regions of interest, steps are performed: determining at least one candidate region of interest (ROI) disease; identifying one ROI disease from the at least one candidate region of interest; determining a processing method appropriate to the identified one ROI disease; and applying the determined processing method to the region of interest to generate a disease enhanced region of interest. The digital radiographic medical image and one or more of the disease enhanced regions of interest can then be displayed.

Abstract: An apparatus and method calibration method, particularly to determine an x-ray generation technique to acquire a hand x-ray image using a mammography x-ray system for assessing Radiographic Absorptiometry (RA) based BMD (Bone Mineral Density).

Abstract: A method for consistent rendering of two or more diagnostic images according to a region of interest. Image data as input code values are obtained for each of the two or more diagnostic images. The background image content is segmented from tissue image content for each of the two or more diagnostic images. Corresponding structures are identified within the tissue image content, and the region of interest is identified within the tissue image content for each of the two or more diagnostic images. The input code values are remapped to output code values for each of the two or more diagnostic images.

Abstract: A computer aided tube and tip detection method for a radiographic image. Radiographic image data is obtained, and a region of interest in the image is determined. The image is processed to provide edge enhancements forming an edge-enhanced image. Edge segments in the edge-enhanced image are detected. Connected lines from the edge segments are formed to obtain a set of connected lines. A tube structure is identified by pairing one or more pairs of connected lines that are separated by a width dimension in a predetermined range. A tip is detected for the tube structure according to the convergence or divergence of paired connected lines.

Abstract: A pattern recognition method for automatic abnormal tissue detection and differentiation using contrast enhanced MRI images. The method includes the steps of acquiring a plurality of MRI breast image sets; aligning the plurality of MRI breast images with respect to spatial coordinates; differencing the plurality of MRI breast image sets with a reference MRI image set, producing a plurality of difference image sets; segmenting the plurality of difference image sets, producing a plurality of MRI breast images with segmented intensity pixels; applying dynamic system identification to the segmented intensity pixels, producing a plurality of dynamic system parameters; and classifying the plurality of system parameters augmented with other physical or non-physical factors into different classes.

Abstract: A method for supporting diagnostic workflow from a medical imaging apparatus. A set of at least two images are obtained from a patient and displayed according to a user-specified image display layout selected from a plurality of image display layouts. One or more markers are associated with a region of interest in the displayed images. A list of regions of interest is generated, each having an entry for each associated marker. A classification is assigned to each entry in the list of regions of interest according to health risk.

Abstract: A method to process medical images for a desired global and local contrast enhancement. A digital image is accessed and segmented, and a first histogram of the digital image is generated. An overall contrast enhanced histogram is generated using the first histogram. A local contrast enhanced histogram is generated using the first histogram. A combined histogram is generated using the overall contrast enhanced histogram and the local contrast enhanced histogram. A contrast-enhanced digital image is generated using the combined histogram.

Abstract: A cross-time and cross-modality inspection method for medical image diagnosis. A first set of medical images of a subject is accessed wherein the first set is captured at a first time period by a first modality. A second set of medical images of the subject is accessed, wherein the second set is captured at a second time period by a second modality. The first and second sets are each comprised of a plurality of medical image. Image registration is performed by mapping the plurality of medical images of the first and second sets to predetermined spatial coordinates. A cross-time image mapping is performed of the first and second sets. Means are provided for interactive cross-time medical image analysis.

Abstract: An apparatus and method calibration method, particularly to determine an x-ray generation technique to acquire a hand x-ray image using a mammography x-ray system for assessing Radiographic Absorptiometry (RA) based BMD (Bone Mineral Density).

Abstract: A cross-time inspection method for medical image diagnosis. A first set of medical images of a subject is accessed wherein the first set is captured at a first time period. A second set of medical images of the subject is accessed, wherein the second set is captured at a second time period. The first and second sets are each comprised of a plurality of medical image. Image registration is performed by mapping the plurality of medical images of the first and second sets to predetermined spatial coordinates. A cross-time image mapping is performed of the first and second sets. Means are provided for interactive cross-time medical image analysis.

Abstract: A method and system for determining the bone mineral density of a body extremity. An image of a body extremity is acquired using a mammography x-ray system whereby a bone mineral density can be performed on the image. The system for determining the bone mineral density of a body extremity includes: a support for supporting the body extremity; a detector for capturing an image of the body extremity; and an x-ray source adapted to project an x-ray beam through the body extremity toward the detector, the x-ray source having a voltage of no more than about 45 kVp and having a target/filter combination of rhodium/rhodium, molybdenum/molybdenum, molybdenum/rhodium, or tungsten/rhodium.

Abstract: A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists.

Abstract: A method for determining a candidate lesion region in a digital ultrasound medical image of anatomical tissue. The method includes the steps of: accessing the digital ultrasound medical image of anatomical tissue; applying an anisotropic diffusion filter to the ultrasound image to generate a filtered ultrasound image; performing a normalized cut operation on the filtered ultrasound image to partition the filtered ultrasound image into a plurality of regions; and selecting, from the plurality of regions, at least one region as a candidate lesion region.

Abstract: A method for detecting and analyzing candidate lesions in a magnetic resonance image of a breast. The method includes the steps of: accessing a plurality of temporal magnetic resonance images of the breast; identifying candidate lesions by performing a temporal pattern analysis of the plurality of images to produce temporal features based on an uptake phase and a washout phase; performing a morphological operation on the candidate lesions to produce morphological features; and classifying the candidate lesions using the morphological features and temporal features to produce classified candidate lesions.

Abstract: A method of calculating a disease assessment by analyzing a medical image, comprising (1) extracting at least one lesion feature value from the medical image; (2) extracting at least one risk feature value from the medical image; and (3) determining the disease assessment based on the at least one lesion feature value and the at least one risk feature value. The method employs lesion characterization for characterizing the lesion, and risk assessment based on the lesion's surroundings, i.e., the environment local and distal to the lesion. Computerized methods both characterize mammographic lesions and assess the breast parenchymal pattern on mammograms, resulting in improved characterization of lesions for specific subpopulations, combining the benefits of both techniques.

Abstract: A method for determining a candidate lesion region within an ultrasound image. The method includes the steps of: accessing a digital ultrasound image of anatomical tissue; segmenting spatially contiguous pixels in the ultrasound image into a plurality of regions in accordance with substantially similar intensity values and spatial smoothness constraints; and selecting, from the plurality of regions, one or more candidate lesion regions having an intensity value lower than a pre-determined intensity value. In one arrangement, the one or more candidate lesion region is classified into at least one of the following classes: benign, malignant, or unknown.

Abstract: A pattern recognition method for automatic abnormal tissue detection and differentiation using contrast enhanced MRI images. The method includes the steps of acquiring a plurality of MRI breast image sets; aligning the plurality of MRI breast images with respect to spatial coordinates; differencing the plurality of MRI breast image sets with a reference MRI image set, producing a plurality of difference image sets; segmenting the plurality of difference image sets, producing a plurality of MRI breast images with segmented intensity pixels; applying dynamic system identification to the segmented intensity pixels, producing a plurality of dynamic system parameters; and classifying the plurality of system parameters augmented with other physical or non-physical factors into different classes.

Abstract: A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists.

Abstract: A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists.