Abstract: This invention provides a simple user interface and sequencing viewing method for a mammography interpretation workstation. In particular, the invention includes the method and apparatus that moves the source images and the associated data using two-level of pre-fetching and caching mechanism, sequences the reading workflow (including worklist, hanging protocol, viewing sequencing), draws markup using electronic grease pan, and automatically generates the recall forms and screening reports. The user interface operates on single button and mouse wheel style to maximize the radiologists' efficiency.

Abstract: A method and apparatus are disclosed for an image preprocessing device that automatically detects chestwall laterality; removes border artifacts; and segments breast tissue and pectoral muscle from digital mammograms. The algorithms in the preprocessing device utilize the computer cache, a vertical Sobel filter and a probabilistic Hough transform to detect curved edges. The preprocessing result, along with a pseudo-modality normalized image, can be used as input to a CAD (computer-aided detection) server or to a mammography image review workstation. In the case of workstation input, the preprocessing results improve the protocol for chestwall-to-chestwall image hanging, and support optimal image contrast display of each segmented region.

Abstract: A method of reviewing medical images and clinical data to generate a diagnosis or treatment decision is provided. The method includes receiving, at a computer-aided detection (CAD) system, the medical images and clinical data, processing the medical images and clinical data; to generate initial finding candidates and clustering the initial finding candidates into a plurality of groups. The method further includes classifying the initial finding candidates using machine learning algorithms integrated into the CAD system into one or more categories one or more categories of the initial finding candidates using type 2 fuzz logic, and determining detection and assessment statistics based on at least the assessed categories and classified findings using Bayesian probability analysis.

Abstract: A method of reviewing medical images and clinical data to generate a diagnosis or treatment decision is provided. The method includes receiving the medical images and clinical data and processing the medical images and clinical data. The method also includes receiving concurrent data resulting from additional processing of the medical images and clinical data and processing the concurrent data using integrated machine learning algorithms to generate a diagnosis or treatment decision based on the processed concurrent data and processed medical images and clinical data.

Abstract: The present invention provides an algorithm to detect and trace the spicules of a mass density in digital mammograms using an adaptive threshold edge algorithm and a flood-fill segmentation algorithm. Elongation criteria are used to remove false edges that do not radiate from a central mass margin. The algorithm works on a central mass border and spicules feature map that contains a subset of the pixels from the source image, so processing time is fast enough for use in a mammography CAD server and for real-time computation within a digital mammography workstation.

Abstract: The present invention provides a method and system using computer-aided detection (CAD) algorithms to aid diagnosis and visualization of tomosynthesis mammography data. The proposed CAD algorithms process two-dimensional and three-dimensional tomosynthesis mammography images and identify regions of interest in breasts. The CAD algorithms include the steps of preprocessing; candidate detection of potential regions of interest; and classification of each region of interest to aid reading by radiologists. The detection of potential regions of interest utilizes two dimensional projection images for generating candidates. The resultant candidates in two dimensional images are back-projected into the three dimensional volume images. The feature extraction for classification operates in the three dimensional image in the neighborhood of the back-projected candidate location.

Abstract: The present invention provides a method for selecting mass density candidates from digital image, for example mammograms, for computer-aided lesion detection, review and diagnosis. A method of selecting mass density candidates from a digital image for computer-aided cancer detection, review and diagnosis includes down-sampling the digital image to a low resolution; smoothing an edge along a skinline; applying a Gaussian difference filter to enhance intensity to form a filtered image; masking the filtered image using a breast mask; using a Canny detector to find potential mass density contours; and generating a mass density candidate list from Canny contours produced in the Canny detector.

Abstract: The present invention provides a method for enhancing microcalcifications for computer-aided lesion detection, review and diagnosis. The method includes two steps: partitioning of the breast tissue area and filtering with a convolution kernel. The partitioning process delineates: breast glandular tissue area; fat tissue sub-area and dense tissue sub-area. The 2D or 3D convolution kernels are designed to highlight small spot regions of rapid intensity changes on 2D mammograms or 3D tomosynthesis mammography images. The size of such a kernel is calculated based on the resolution of the mammographic images that are produced from each manufacturer's digital radiography device.

Abstract: The present invention provides a method and system using computer-aided detection (CAD) algorithms to aid diagnosis and visualization of tomosynthesis mammography data. The proposed CAD algorithms process two-dimensional and three-dimensional tomosynthesis mammography images and identify regions of interest in breasts. The CAD algorithms include the steps of preprocessing; candidate detection of potential regions of interest; and classification of each region of interest to aid reading by radiologists. The detection of potential regions of interest utilizes two dimensional projection images for generating candidates. The resultant candidates in two dimensional images are back-projected into the three dimensional volume images. The feature extraction for classification operates in the three dimensional image in the neighborhood of the back-projected candidate location.

Abstract: This invention provides a computational intelligence method and system that can be used interactively by a radiologist in a “concurrent read” model to aid diagnosis from medical images. In particular, the invention operates more like a very patient, indefatigable knowledge accumulating and communicating companion for the radiologist, rather than a second “expert” whose advice is sought after a normal review. The system works interactively with the radiologist during image reading, prompting areas to review in more detail, providing computer generated features and interpretation, and suggesting potential diagnoses for areas of suspicion that are identified either by the machine or the human. In addition, the human can obtain more information from the system—the radiologist can query as to why a particular region is highlighted, or why a particular diagnosis is postulated for an area.

Abstract: A method and apparatus for processing medical images from one of a plurality of digital acquisition modalities or manufacturers with different imaging condition is proposed for creating consistent appearance of the images. The method comprises (1) tissue segmentation to isolate the region of interest; (2) dynamic extraction of the optimal parameters for image transformation from the segmented region; (3) generation of a transformation function from the individual image optimized parameters; and (4) use of the transformation function to produce images that have consistent image characteristics. This method also applies to multiple images from a single study or multiple studies. The transformed images can be used for computer-aided lesion detection, review and diagnosis.

Abstract: A method and apparatus are disclosed for an image preprocessing device that automatically detects chestwall laterality; removes border artifacts; and segments breast tissue and pectoral muscle from digital mammograms. The algorithms in the preprocessing device utilize the computer cache, a vertical Sobel filter and a probabilistic Hough transform to detect curved edges. The preprocessing result, along with a pseudo-modality normalized image, can be used as input to a CAD (computer-aided detection) server or to a mammography image review workstation. In the case of workstation input, the preprocessing results improve the protocol for chestwall-to-chestwall image hanging, and support optimal image contrast display of each segmented region.

Abstract: This invention provides a simple user interface and sequencing viewing method for a mammography interpretation workstation. In particular, the invention includes the method and apparatus that moves the source images and the associated data using two-level of pre-fetching and caching mechanism, sequences the reading workflow (including worklist, hanging protocol, viewing sequencing), draws markup using electronic grease pan, and automatically generates the recall forms and screening reports. The user interface operates on single button and mouse wheel style to maximize the radiologists' efficiency.

Abstract: This invention utilizes a number of Computational Intelligence (CI) techniques with different learning methods in a computer-aided detection, review and diagnosis (CAD) device. Specifically, an unsupervised learning method is used for clustering of types of abnormal findings. Then a number of classifiers for each type of findings are trained with appropriate learning algorithms; and combined in three different manners to produce one classifier that can be operated at three different operating points. A fuzzy system is used for mapping the findings to diagnostic reports constructed using a formal language. Finally, the finding statistics is calculated based on Bayesian probability. During image review, the device provides the readers some insight as to how it derives its outputs. The output of the device can be updated in an interactive and progressive manner by a human reader (radiologist). The output from classification can be updated by the human, and is fed as input to the assessment task.

Abstract: A system and method for optimal scheduling of image processing jobs is provided. Requests for processing originate either from a DICOM service that receives images sent to the system, and forwards those for batch processing, or from an interactive workstation application, which requests interactive CAD processing. Each request is placed onto a queue which is sorted first by priority, and second by the time that the request is added to the queue. Requests for interactive processing from a workstation application are added to the queue with the highest priority, whereas requests for batch processing are added at a low priority. The algorithm service takes the top-most item from the queue and passes the request to the algorithms which it hosts, and when that processing is completed, it sends a message to one or more output queues.

Abstract: This invention provides a dual-magnify-glass viewing method for a mammography workstation. In particular, the invention includes a method that registers regions of interest on a pair of mammography images and provides a user interface to visualize the paired full resolution or magnified images in a synchronized style. The paired images are two views that can be either left and right bilateral mammogram views; two alternative projection mammogram views, or corresponding current and prior same mammogram views. The paired images can also be two views from different modalities, such as, X-ray and ultrasound images. This invention can be used to aid the radiologist to more effectively view mammography or other breast images.

Abstract: The present invention provides an algorithm to detect and trace the spicules of a mass density in digital mammograms using an adaptive threshold edge algorithm and a flood-fill segmentation algorithm. Elongation criteria are used to remove false edges that do not radiate from a central mass margin. The algorithm works on a central mass border and spicules feature map that contains a subset of the pixels from the source image, so processing time is fast enough for use in a mammography CAD server and for real-time computation within a digital mammography workstation.

Abstract: The present invention provides a method for selecting mass density candidates from mammograms for computer-aided lesion detection, review and diagnosis. The method has two steps: a Gaussian difference filter to enhance the intensity and a Canny detector to find potential mass density contours. For circumscribed masses, an additional Hough circle detector is used. This invention makes use of both intensity and morphology information and only processes each image at a single gray-level, so both sensitivity and processing time are improved. The selection algorithm can be also used to select mass candidates from ultrasound images, from 3D tomosynthesis mammography images and from breast MRI images.