Abstract: This invention provides a system and method for assessing risk of a breast cancer diagnosis based upon imagery of tissue and (optionally) other patient-related factors. A CAD (or similar) system analyzes the imagery and generates a plurality of numerical feature values. An assessment module receives inputs from patient factors and history and computes the risk based upon the feature values and the patient factors and history. A masking module receives inputs from the patient factors and history, and computes the risk of having a cancer, which cancer is otherwise characterized by a low probability of detection, based upon the feature values and the patient factors and history. A recall module receives inputs from the assessment module and the masking assessment module, and generates a computer-aided indication of a clinical follow-up by the patient. Results of assessment(s) can be displayed to the clinician and/or patient using a graphical interface display.

Abstract: Lung segmentation and bone suppression techniques are helpful pre-processing steps prior to radiographic analyzes of the human thorax, as may occur during cancer screenings and other medical examinations. Autonomous lung segmentation may remove spurious boundary pixels from a radiographic image, as well as identify and refine lung boundaries. Thereafter, autonomous bone suppression may identify clavicle, posterior rib, and anterior rib bones using various image processing techniques, including warping and edge detection. The identified clavicle, posterior rib, and anterior rib bones may then be suppressed from the radiographic image to yield a segmented, bone suppressed radiographic image.

Abstract: Breast density measurements are used to perform Breast Imaging Reporting and Data System (BI-RADS) classification during breast cancer screenings. The accuracy of breast density measurements can be improved by quantitatively processing digital mammographic images. For example, breast segmentation may be performed on a mammographic image to isolate the breast tissue from the background and pectoralis tissue, while a breast thickness adjustment may be performed to compensate for decreased tissue thickness near the skin line of the breast. In some instances, BI-RADS density categorization may consider the degree to which dense tissue is dispersed throughout the breast. A breast density dispersion parameter can also be obtained using quantitative techniques, thereby providing objective BI-RADS classifications that are less susceptible to human error.

Abstract: Lung segmentation and bone suppression techniques are helpful pre-processing steps prior to radiographic analyses of the human thorax, as may occur during cancer screenings and other medical examinations. Autonomous lung segmentation may remove spurious boundary pixels from a radiographic image, as well as identify and refine lung boundaries. Thereafter, autonomous bone suppression may identify clavicle, posterior rib, and anterior rib bones using various image processing techniques, including warping and edge detection. The identified clavicle, posterior rib, and anterior rib bones may then be suppressed from the radiographic image to yield a segmented, bone suppressed radiographic image.

Abstract: This invention provides a system, method and computer-readable medium for detecting and localizing organs and other regions of interest in medical image data provided by a medical imaging procedure using gradient template information with respect to an example of the imaged organ and cross-correlation techniques to generate object detection information. In an embodiment, the detection and localization process/processor receives a plurality of gradient templates and employers a template with the closest matching cross-correlation to the gradient of the organ in the medical image.

Abstract: A system and a method are disclosed that forms a novel, synthetic, two-dimensional image of an anatomical region such as a breast. Two-dimensional regions of interest (ROIs) such as masses are extracted from three-dimensional medical image data, such as digital tomosynthesis reconstructed volumes. Using image processing technologies, the ROIs are then blended with two-dimensional image information of the anatomical region to form the synthetic, two-dimensional image. This arrangement and resulting image desirably improves the workflow of a physician reading medical image data, as the synthetic, two-dimensional image provides detail previously only seen by interrogating the three-dimensional medical image data.

Abstract: This invention provides an automated system and method for determining contrast media arrival in vessels near tissues of interest that have been imaged using a predetermined imaging system that produces a plurality of temporally phased images. The system and method reliably distinguishes between actual contrast arrival and potential false positives that can render basic automated techniques inoperable or unreliable. In an illustrative embodiment, the system and method for determining a contrast arrival phase in a plurality of temporal phases of a medical image dataset of tissue includes an image pre-processor or process that, for each of at least a subset of the temporal phases of the medical image dataset, with at least a subset of the signal intensity values respectively in each of the temporal phases, computes signal intensity frequency distributions.

Abstract: Disclosed herein are a computer system, an image processing method, and a computer-readable medium for interpolating volumetric medical image data. There is disclosed a hybrid interpolation procedure performed on the volumetric medical image data in accordance with spatial transforms. In the hybrid interpolation procedure, a different order of interpolation, respectively, is performed for at least two different dimensions of the volumetric medical image data. The different orders of interpolation can include bicubic interpolation and linear interpolation. Applications include registration of source volumetric medical images to target volumetric medical images and operations (e.g., rotation, re-scaling) invoking a transformation for medical image data visualization/display purposes.

Abstract: Disclosed are methods, and associated systems comprising processors, input devices and output devices, of detecting regions of interest in a tomographic breast image. The methods may comprise: acquiring tomographic breast image data; deriving a plurality of synthetic sub-volumes from the tomographic breast image data; wherein each subvolume is defined by parallel planar top and bottom surfaces; wherein planar top and bottom surfaces of successive subvolumes are parallel to each other; and wherein a top planar surface of a sub-volume is offset from a top planar surface of a prior sub-volume, such that successive sub-volumes overlap; for each sub-volume, deriving a two-dimensional image; for each two-dimensional image, identifying regions of interest therein; deriving at least one region of interest of potential clinical interest from a plurality of identified regions of interest; and outputting information associated with at least one derived region of interest of potential clinical interest.

Abstract: A system and a method are disclosed that forms a novel, synthetic, two-dimensional image of an anatomical region such as a breast. Two-dimensional regions of interest (ROIs) such as masses are extracted from three-dimensional medical image data, such as digital tomosynthesis reconstructed volumes. Using image processing technologies, the ROIs are then blended with two-dimensional image information of the anatomical region to form the synthetic, two-dimensional image. This arrangement and resulting image desirably improves the workflow of a physician reading medical image data, as the synthetic, two-dimensional image provides detail previously only seen by interrogating the three-dimensional medical image data.

Abstract: Systems and methods are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In certain embodiments, a quantitative measure of spiculation and quantitative measure of density are computed for each suspicious mass located. At least one classification scheme, developed using true and false positives with similar quantitative measures, is then selected for each suspicious mass according to both quantitative measures. In certain other embodiments, a measure of breast location is computed for each suspicious mass. In one embodiment, the location determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.

Abstract: Disclosed are methods, and associated systems comprising processors, input devices and output devices, of detecting regions of interest in a tomographic breast image. The methods may comprise: acquiring tomographic breast image data; deriving a plurality of synthetic sub-volumes from the tomographic breast image data; wherein each subvolume is defined by parallel planar top and bottom surfaces; wherein planar top and bottom surfaces of successive subvolumes are parallel to each other; and wherein a top planar surface of a sub-volume is offset from a top planar surface of a prior sub-volume, such that successive sub-volumes overlap; for each sub-volume, deriving a two-dimensional image; for each two-dimensional image, identifying regions of interest therein; deriving at least one region of interest of potential clinical interest from a plurality of identified regions of interest; and outputting information associated with at least one derived region of interest of potential clinical interest.

Abstract: This invention provides a system and method for fusing and synthesizing a plurality of medical images defined by a plurality of imaging parameters that allow the visual enhancements of each image data set to be selectively combined with those of other image datasets. In this manner, a user-defined parameter set can be generated in the final response image dataset. This final response image dataset displays visual data represents a form particularly useful to the clinician. In an illustrative embodiment, the system for fusing and synthesizing the plurality of medical images provides an image fusion process/processor that fuses a plurality of magnetic resonance imaging (MRI) datasets. A first image dataset of the MRI datasets is defined by apparent diffusion coefficient (ADC) values. A second image dataset of the MRI datasets is defined by at least one parameter other than the ADC values.

Abstract: Systems, computer-readable media, methods, and a medical imaging apparatus for improving the automated detection of suspicious regions of interest in x-ray images of anatomical organs under study are disclosed. Noise effects in x-ray images are suppressed to predetermined levels by filtering the original x-ray images and then combining the original images with the filtered images in such a way that the predetermined noise value is met. The resulting modified x-ray images then may be analyzed to automatically detect suspected breast microcalcifications or other suspicious regions of interest. In addition, three-dimensional digital images of anatomical organs may be computed from a plurality of such modified x-ray images of an anatomical organ taken from different angles, as in CT imaging, and the three-dimensional digital images may be processed to automatically detect suspicious regions of interest.

Abstract: A method for performing a high-resolution pharmacokinetic analysis for calculation of tissue parameters for a fast-enhancing tissue enables medical personnel to accurately determine pharmacokinetic parameters in fast-enhancing tissues. The method includes obtaining mask image data of the tissue when it is in a steady state condition, obtaining a time series of image data of the tissue when the contrast agent is flowing in the tissue, and increasing a spatial resolution of the time series of image data using the mask image data to obtain a time series of increased spatial resolution image data. The method further includes performing a pharmacokinetic analysis to obtain data including at least one parameter that characterizes the tissue, providing a multi-parameter look-up table derived from a combination of two or more parameters, and providing a display including one parameter or a parametric image, where the parametric image is derived from the look-up table.

Abstract: Systems, computer-readable media, and methods are presented that identify suspicious anomalies in a colon with higher sensitivity and at a lower false positive rate. A plurality of images of an anatomical colon is acquired. Candidate suspicious anomalies are identified in each image. The candidate suspicious anomalies across images are then compared using registration and matching. Features of candidate suspicious anomalies across images may be jointly evaluated to perform classification.

Abstract: Methods are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In certain embodiments, a quantitative measure of spiculation and quantitative measure of density are computed for each suspicious mass located. At least one classification scheme, developed using true and false positives with similar quantitative measures, is then selected for each suspicious mass according to both quantitative measures. In certain other embodiments, a measure of breast location is computed for each suspicious mass. In one embodiment, the location determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.

Abstract: Methods and systems are presented that improve a radiologist's ability to identify polyps by automatically and more accurately detecting and displaying colonic residue such as tagged or untagged stool or colonic fluid in medical images of the colorectal region. A virtual colonography imaging system obtains medical imagery of the colon. Improved computer-aided detection (CAD) algorithms identify colonic residue in the imagery by calculating feature vectors of and using statistical classification methods to classify regions of colonic residue to distinguish them from false positives.

Abstract: This discloses methods and systems for the processing of medical image data of a colon acquired with an imaging device, such as a computerized tomography (“CT”) scanner and more particularly, to methods and systems for the classification of structures or objects in said medical image data. The disclosed methods and systems analyze image data for objects such as rectal tubes or stools, or for clusters of suspicious regions, and may eliminate such objects from further analysis prior to presenting potential polyps to a user.

Abstract: A method, system and computer-readable medium of filtering noise pixels and other extraneous data, including saturated fat tissue and air data in image data is provided. Examples of image data may include but are not limited to magnetic resonance imaging data and computed tomography data. The method includes receiving pixel count for each signal intensity value of the image data; determining a signal intensity value, Ipeak, corresponding to a pixel count of a greatest number of pixels, Npeak; setting a noise threshold at a signal intensity value, Inoise, corresponding to a pixel count, NI, such that NI is determined based on Npeak; and filtering from the image data one or more pixels with signal intensity values below the noise threshold. NI may be determined such that NI=Npeak/3 or close to Npeak/3.