Abstract: Driver insurance and risk management scores are calculated per driver based on vehicle and driver behavior data collected during driving sessions. Often one vehicle is shared by multiple drivers and one driver can drive multiple vehicles. The present disclosure securely tracks the individual drivers, stores, and retrieves associated driver data for analysis on the edge (vehicle) in near real time. Data collected is analyzed at various time intervals (each trip, daily, monthly) to produce the scores. The goal of the proposed solution minimizes cost associated with data transmission and cloud storage, tracks long term driver driving history on the edge for near real time analysis of driver behavior, minimizes driver distraction due to user device while driving, securely stores and retrieves driver driving data on the edge device associated with the driver, restricts access to the driver driving data, and restricts user device access to servers and telematic units.

Abstract: The present disclosure discloses systems and methods that can be implemented on the edge system in a vehicle for near real-time detection of collisions with multi-level confidence (i.e., low confidence, moderate confidence, high confidence), automated gathering and report generation of collision information required for post-accident analysis in a manner suitable for insurance company process workflow and storing the confidential collision details securely on the vehicle (edge) system. Confidence is determined by preconfigured adaptive thresholds measured by sensor data.

Abstract: Driver insurance and risk management scores are calculated per driver based on vehicle and driver behavior data collected during driving sessions. Often one vehicle is shared by multiple drivers and one driver can drive multiple vehicles. The present disclosure securely tracks the individual drivers, stores, and retrieves associated driver data for analysis on the edge (vehicle) in near real time. Data collected is analyzed at various time intervals (each trip, daily, monthly) to produce the scores. The goal of the proposed solution minimizes cost associated with data transmission and cloud storage, tracks long term driver driving history on the edge for near real time analysis of driver behavior, minimizes driver distraction due to user device while driving, securely stores and retrieves driver driving data on the edge device associated with the driver, restricts access to the driver driving data, and restricts user device access to servers and telematic units.

Abstract: Driver insurance and risk management scores are calculated per driver based on vehicle and driver behavior data collected during driving sessions. Often one vehicle is shared by multiple drivers and one driver can drive multiple vehicles. The present disclosure securely tracks the individual drivers, stores, and retrieves associated driver data for analysis on the edge (vehicle) in near real time. Data collected is analyzed at various time intervals (each trip, daily, monthly) to produce the scores. The goal of the proposed solution minimizes cost associated with data transmission and cloud storage, tracks long term driver driving history on the edge for near real time analysis of driver behavior, minimizes driver distraction due to user device while driving, securely stores and retrieves driver driving data on the edge device associated with the driver, restricts access to the driver driving data, and restricts user device access to servers and telematic units.

Abstract: Driver insurance and risk management scores are calculated per driver based on vehicle and driver behavior data collected during driving sessions. Often one vehicle is shared by multiple drivers and one driver can drive multiple vehicles. The present disclosure securely tracks the individual drivers, stores, and retrieves associated driver data for analysis on the edge (vehicle) in near real time. Data collected is analyzed at various time intervals (each trip, daily, monthly) to produce the scores. The goal of the proposed solution minimizes cost associated with data transmission and cloud storage, tracks long term driver driving history on the edge for near real time analysis of driver behavior, minimizes driver distraction due to user device while driving, securely stores and retrieves driver driving data on the edge device associated with the driver, restricts access to the driver driving data, and restricts user device access to servers and telematic units.

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 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: The present invention discloses a framework for monitoring workflow within an application having multiple levels of functionality, the framework being capable of combining a plurality of components from different sources and comprising a process level for selecting a set of defined process steps to be applied to a data set associated with a set of activities, a sub-process level including an aggregation of selected activities from the set of activities and facilitating navigation between the selected activities, and an activity level wherein an activity's property in the data set is modified as a result of the applied processing of the activity level to produce an output data set.

Abstract: A method for computer-aided detection of microcalcification clusters obtains digital mammography data for a single view image and normalizes and filters the image data to reduce noise. A first mask is generated and applied to the image data for defining the breast structure, forming a first cropped image. A second mask is generated and applied to the image data for defining muscle structure, forming a second cropped image. An artifact mask corresponding to vascular calcifications and known imaging artifacts is generated and applied to the first and second cropped images, defining first and second artifact-masked cropped images. In a repeated sequence, portions of each artifact-masked cropped image are processed using an enhancement algorithm and reducing edge effects to obtain a set of microcalcification cluster candidates and suspected microcalcification clusters.

Abstract: An apparatus and method for scanning a set of film images for a patient. A set of film images is assembled for scanning, and a case divider is provided at the beginning or end of the set of film images to serve as an indicator that differentiates the set of film images from any other film images to be scanned. The case divider is disposed on a separate sheet and bears a pattern that indicates its function as a case divider. The set of films is scanned along with the case divider to obtain scanned image data. The case divider pattern is detected in the scanned image data to identify the case divider.

Abstract: A computer aided detection (CAD) system for assigning CAD applications to digital images. The system comprises a case input system (CIS), image data repositories and an algorithm server. There is also provided a method for assigning CAD applications to digital images the method comprising the steps of selecting an acquisition model (AM) and selecting a CAD application using the selected AM.