82 results about "Mammographic image" patented technology

Disclosed is a mammographic imaging system and tools for processing mammographic images to enhance image acquisition and review workflow. The systems and tools enable rapid access to digital image information providing for improved workflow management and identification of images, upon initial review. The system and tools also allow for background processing of image information to reduce workflow delay. Specifically, digital images are processed in the background, i.e., they are automatically processed, free from specific task-orientated direction by a user, using resources that are not otherwise occupied addressing user-directed tasks. Background processing that may be supported includes preprocessing and interim processing. Preprocessing refers to processing of an image that occurs prior to initial review of that image by a physician. Interim processing refers to background processing that occurs during a review session, e.g., in a time period between initial review and a subsequent review of an image.

Mastopexy and breast reconstruction prostheses and implantation method that allow for radiographic imaging of the breast tissue. The prostheses are arcuate and elongate optionally meshed to conform with breast tissue when implanted. Prostheses are made from naturally occurring extracellular matrix, primarily collagen, that, allows for mammographic imaging without interference as is expected from synthetic materials.

An image processing system and method visually documents and displays changes between historical and later mammographic images, preferably in three dimensions. A composite image is created which visually emphasizes temporal differences between the historical and later images. Preferably three-dimensional, digitized images, displayable in various projections, are stored for archival purposes on computer readable media. An image processor preferably exploits an optical correlator to register the historical and later images accurately and provide correlation values as temporal scalars of the differences. The registered images are then compared, voxel-by-voxel, to detect temporal differences. The composite image is displayed with synthetic colors or other visual clues to emphasize apparent changes (for example, tumor growth or shrinkage).

Methods and related systems are described for detection of breast cancer in 3D ultrasound imaging data. Volumetric ultrasound images are obtained by an automated breast ultrasound scanning (ABUS) device. In ABUS images breast cancers appear as dark lesions. When viewed in transversal and sagittal planes, lesions and normal tissue appear similar as in traditional 2D ultrasound. However, architectural distortion and spiculation are frequently seen in the coronal views, and these are strong indicators of the presence of cancer. The described computerized detection (CAD) system combines a dark lesion detector operating in 3D with a detector for spiculation and architectural distortion operating on 2D coronal slices. In this way a sensitive detection method is obtained. Techniques are also described for correlating regions of interest in ultrasound images from different scans such in different scans of the same breast, scans of a patient's right versus left breast, and scans taken at different times. Techniques are also described for correlating regions of interest in ultrasound images and mammography images. Interactive user interfaces are also described for displaying CAD results and for displaying corresponding locations on different images.

A system and method are provided for the measurement and display of attributes of an object of interest in a medical image. An object of interest is identified, such as a lesion or cluster of lesions in a breast area on a mammography image. At least one attribute of the lesion is then automatically measured, such as the area of the lesion, the width and height of a cluster of lesions, the number of lesions in a cluster, or the distance from one or more lesions to an anatomical feature such as the nipple, skin line or chest wall. The measurements are then displayed to a user, for example by displaying the measurements on the mammography image. Additionally, anatomical zones, such as standard, quadrant and clock zones of the breast area may be determined and displayed on the mammography image or diagram to display the location of the lesion as it corresponds to the zones.

A method for displaying a mammography image. Digital data of the mammography image is obtained. The mammography image is segmented to identify at least a first diagnostically relevant region comprising an image of the breast tissue and a second diagnostically relevant region. A view type is assigned for the image, either cranio-caudal or medio-lateral oblique view, according to a symmetry index calculated from the segmented first diagnostically relevant region. Right or left laterality is assigned to the image according to a laterality feature calculated according to the relative position of at least the second diagnostically relevant region within the image.

The invention discloses a preprocessing method and a preprocessing system used for extracting breast regions in mammographic images. The method includes steps: subjecting the mammographic images to automatic threshold segmentation to obtain binary images; subjecting communicated regions in the binary images to filling processing to remove labels and holes in the mammographic images; dividing the binary images into a plurality of subregions, and binarizing each subregion according to local threshold value to position breast contour regions; subjecting the binary images and the breast contour regions to set 'and' operation, searching number of pixel points in each subregion containing difference in the binary images along a reference line from top to bottom, and positioning the subregion with maximum number of pixel points in regions containing difference to be nipple regions. By overall automatic threshold segmentation and local threshold analysis, the breast regions and the nipple regions in the mammographic images can be accurately positioned, the preprocessing process is low in computation complexity, and execution efficiency is high.

A method and system for converting low-dose mammographic images with much noise into higher quality, less noise, higher-dose-like mammographic images, using of a trainable nonlinear regression (TNR) model with a patch-input-pixel-output scheme, which can be called a call pixel-based TNR (PTNR). An image patch is extracted from an input mammogram acquired at a reduced x-ray radiation dose (lower-dose), and pixel values in the patch are entered into the PTNR as input. The output of the PTNR is a single pixel that corresponds to a center pixel of the input image patch. The PTNR is trained with matched pairs of mammograms, inputting low-dose mammograms together with corresponding desired standard x-ray radiation dose mammograms (higher-dose), which are ideal images for the output images. Through the training, the PTNR learns to convert low-dose mammograms to high-dose-like mammograms. Once trained, the trained PTNR does not require the higher-dose mammograms anymore. When a new reduced x-ray radiation dose (low dose) mammogram is entered, the trained PTNR would output a pixel value similar to its desired pixel value, in other words, it would output high-dose-like mammograms or “virtual high-dose” mammograms where noise and artifacts due to low radiation dose are substantially reduced, i.e., a higher image quality. With the “virtual high-dose” mammograms, the detectability of lesions and clinically important findings such as masses and microcalcifications can be improved.

A method for classifying tissue density of a breast includes obtaining mammography image data and segmenting the mammography image to identify the region representing the breast tissue. A plurality of regions within the breast tissue region are identified for obtaining image features therefrom. A plurality of image features are computed from the identified plurality of regions. The breast tissue density is classified using the computed plurality of image features.

A distributed architecture allows for decoupling of mammographic image acquisition and review, thereby enabling more efficient use of resources and enhanced processing. In one embodiment, the system (100) includes a number of image acquisition stations (102) and a number of image review stations (110) all associated with a central server (104). The server (104) is operative to access an image repository (106), a patient information data base (108) and a number of DICOM tools (112). The invention allows for more efficient and/or more convenient use of the image acquisition equipment and image processing stations. Moreover, the distributed architecture including the central image repository provides certain processing and analysis advantages. The invention also provides certain processing and workflow enhancements that allow for a more full realization of potential digital mammography advantages.

The invention presents a way to automatically assess the quality of acquired (S1, S2) digital mammographic images with respect to the image positioning of a patient's breast. The automated digital quality assessment (S3) is executed in real time and preferably notifies (S4) the technologist instantly if the image positioning quality of a mammographic image is inadequate. This makes it possible to retake the image while the patient is still present at the examination facility. The quality assessment notification includes information to the technologist, preferably both visually and statistically of land-mark positioning measurements. Alternatively, the digital quality assessment is accompanied by a computerized decision of whether the mammogram needs to be retaken, requiring a minimum of involvement, if at all, from the technologist. The invention hence provides a set of quality-assured mammographic images that can be stored (S5) and later accessed by a radiologist for review and diagnosis (S6).

At least one metric for a mammographic image is computed by a workstation, and provided on a workstation display along with the mammographic image. A clinician can select one or more different metrics to be computed for an image, as well as where they are to be shown and the manner in which they are to be shown on the display. Speech (may also be an audible sound which is not speech) may also be used in the workstation to audibly provide metrics information to the clinician.

A method and apparatus for processing digital mammographic images. The method and apparatus providing comparable mammographic images regardless of the imaging device generating the raw mammographic images. The processed mammographic images may be displayed with optimal global and local contrast, enhanced sharpness, and without the need to apply window level settings or data from lookup tables. A second mammographic image is generated out of a processed first mammographic image in such a way that the projected object, the breast, is more perceptible and obvious for a physician or other medical professional reviewing the mammographic image.

A method for detecting architectural distortion within mammographic image data. The method identifies breast tissue within the image data, generates an orientation field and a corresponding magnitude field within the identified breast tissue and generates a feature map by processing the orientation field with a phase portrait model at one or more image scales. The method identifies one or more architectural distortion features according to the generated feature map and displays the one or more identified architectural distortion features.

A method of processing a radiological image of an organ. The method may comprise generating a recombined image (R) from at least one previously acquired low energy image (L) and one high energy image (H) of a portion of the organ. The recombined image comprises a background and one or more areas corresponding to the presence of a contrast medium in the organ. The method may further comprise correcting the recombined image (R) by compensating (C) for the nonuniformities present in the background to obtain a compensated recombined image in which the background has a substantially uniform grey level.

A method for linear structure detection in mammographic images, comprising: locating a plurality of microcalcification candidate clusters in digital mammographic images; extracting a region of interest that encloses each of the candidate clusters; processing the region of interest to generate feature points that reveal geometric properties in the region; applying a line detection algorithm to the feature points to produce a line model; and analyzing the line model to determine whether a true linear structure is present in the first region of interest.

A process for deploying an anti-scattering grid in a mammograph is provided. The mammograph comprises a radiation source configured to emit radiation for taking mammographic images of a patient, a radiation detector comprising a network of sensors arranged periodically with a first pitch, and an anti-scattering grid arranged between the source and the detector, the anti-scattering grid comprising absorption laminates of radiations arranged parallel to each other and distributed periodically with a second pitch. The process comprises: displacing the anti-scattering grid relative to the detector or displacing the detector relative to the anti-scattering grid during emission of radiation; and adapting the second pitch to the first pitch, wherein displacement is perpendicular to the direction of the laminates of the anti-scattering grid, the laminates being arranged parallel to a side of the anti-scattering grid positioned against the patient.

An exemplary system, method and computer-accessible medium for classifying a breast tissue(s) a patient(s) can include, for example, receiving an image(s) of an internal portion(s) of a breast of the patient(s), and automatically classifying the breast tissue(s) of the breast by applying a neural network(s) to the image(s). The automatic classification can include a classification as to whether the breast tissue(s) is atypical ductal hyperplasia or ductal carcinoma. The automatic classification can include a classification as to whether the breast tissue(s) is a cancerous tissue or a non-cancerous tissue. The image(s) can be a mammographic image or an optical coherence tomography image.

Methods, systems, and computer program products for facilitating temporal comparison of medical images is provided, with one exemplary application being for breast mammograms. In one embodiment, prior and subsequent mammographic images of a breast acquired using at least partially different mammogram acquisition systems are displayed for simultaneous viewing on a same mammogram display at an identical tissue distance per unit display distance without requiring a scale-adjusting viewer input. Also described are other embodiments for optimally scaling, windowing and/or otherwise advantageously processing and/or displaying prior and subsequent mammographic image sets in manners that facilitate temporal comparison therebetween.

A method is disclosed for verifying linear structures in a digital mammographic image, comprising providing a configurable linear structure verifier in mammography computer assisted diagnosis system; optionally using an microcalcification candidate cluster driven linear structure verification methodology; selecting parameters for the linear structure verifier from a plurality of different parameter generating sources, at least one of which is controllable by human input; configuring the verifier according to selected parameters; and verifying linear structure using cascade rules.