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 for pharmacokinetic analysis, including: receiving time-series medical image data of a patient introduced with a contrast agent; identifying a reference region in the medical image data; identifying a plurality of points of interest in the medical image data; measuring an intensity of voxels in the reference region; and for each point of interest, measure an intensity of voxels therein, use the measured reference region and point of interest intensities to obtain an expression relating the point of interest's voxel concentration to that of the reference region, wherein the expression is a five-parameter nonlinear model with no reference to an arterial input function; and obtain values for each of the five-parameters by solving the expression and use the obtained values to determine whether the point of interest is malignant.

Abstract: A method for computer assisted lesion detection in magnetic resonance (MR) images includes acquiring dynamic contrast enhanced (DCE) MR images. The images are processed to produce a subtraction image illustrating change in voxel enhancement between the images. A Gaussian low-pass filter is applied to the subtraction image. An elimination mask is created from the filtered subtraction image by removing voxels with enhancement values below a threshold value. The elimination mask is used to remove noise from the subtraction image. One or more regions of suspicion are automatically detected from the noise-removed subtraction image. To produce the subtraction image, DCE-MR images are divided into first and second sub-sets. Positive-signed enhancement values of voxels of the MR images from the first sub-set are added to a combined subtraction image along with absolute values of all enhancement values of voxels of the MR images from the second sub-set.

Abstract: A method for segmenting organs in digitized medical images includes providing a set of segmented training images of an organ, computing a surface mesh having a plurality of mesh cells that approximates a border of the organ, extracting positive examples of all mesh cells and negative examples in the neighborhood of each mesh cell which do not belong to the organ surface, training from the positive examples and negative examples a plurality of classifiers for outputting a probability of a point being a center of a particular mesh cell, computing an active shape model using a subset of center points in the mesh cells, generating a new shape by iteratively deforming the active shape model to fit a test image, and using the classifiers to calculate a probability of each center point of the new shape being a center of a mesh cell which the classifier was trained to recognize.

Abstract: A method for analyzing a shape of a region of interest in a medical image of a body part, including: finding a region of interest in the medical image; calculating a Reeb graph of the region of interest, and determining whether the region of interest is a malignant lesion candidate based on a shape characteristic of the Reeb graph.

Abstract: A computer system for automatic selection of a computer-aided detection (CAD) algorithm including a database storing image data, a browser for navigating the data and selecting image data, an application receiving image data selected by the browser, and a selector selecting a CAD algorithm for processing the image data according to at least one of fixed attributes of the image data and an indication of a subject of the image data.

Abstract: A method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of images, each image including one or more candidate regions that have been identified as suspicious by a computer aided diagnosis system. Each image has been manually annotated to identify malignant regions. Multiple instance learning is applied to train a classifier to classify suspicious regions in a new image as malignant or benign by identifying those candidate regions that overlap a same identified malignant region, grouping each candidate region that overlaps the same identified malignant region into a same bag, and maximizing a probability P = ? i = 1 N ? p i y i ? ( 1 - p i ) 1 - y i , wherein N is a number of bags, pi is a probability of bag i containing a candidate region that overlaps with an identified malignant region, and yi is a label where a value of 1 indicates malignancy and 0 otherwise.

Abstract: A system for automatically detecting pulmonary emboli from medical image data includes receiving image data, automatically detecting one or more pulmonary embolism candidates from the image data, segmenting an airway tract from the image data, segmenting an artery structure from the image data, calculating a distance between each of the candidates and a nearest portion of the segmented airway, determining whether each of the candidates is within or outside of the segmented artery structure, rejecting candidates based on the calculated distance between each of the candidates and the nearest portion of the segmented airway and the determination as to whether each of the candidates is within or outside of the segmented artery structure, and indicating the location of the non-rejected candidates within the image data.

Abstract: A method for aligning medical images, including: generating a learned joint distribution of intensities of corresponding voxels of images acquired by a first modality and a second modality, wherein the first and second modalities are different from each other; acquiring a first image of a patient using the first modality; acquiring a second image of the patient using the second modality; and aligning the first and second images based on the learned joint distribution.

Abstract: A system for computer aided detection and decision support includes an ontology of image representations for injecting meaning into and adding relationships among image contents, an image understanding and parsing module in communication with the ontology of image representations for extracting structures from an image including the image contents, and a reasoning engine based in communication with the ontology and the image understanding and parsing module for classifying the image contents, wherein the system receives the image and corresponding descriptive information.

Abstract: A method for registering a medical image includes acquiring a first medical image of a subject. One or more simulated medical images are synthesized based on the acquired first medical image. One or more matching functions are trained using the first medical image and the simulated medical images. A second medical image of the subject is acquired. The first medical image and the second medical image are registered using the one or more trained matching functions.