Abstract: A device to correct an image blur within a medical image is described. An image analysis application executed by the device receives the medical image from a medical image provider. Next, the image blur is detected within the medical image by analyzing the medical image. The medical image is subsequently processed with a deep learning model to correct the image blur. In response to the processing, a de-blurred medical image is generated. The de-blurred medical image is provided for a presentation or a continued analysis.

Abstract: A system and method is disclosed to reduce variation of the clinical decision making process when an image reporting and data system (IRADS) for medical diagnosis is used. Image reporting and data systems provide guidelines for an operator to identify images as belonging to one of a number of categories and specific clinical actions are then recommended based upon such categories. Some clinical actions such as biopsies may be recommended by IRADS even when they are not necessary. The present inventive concept is configured to utilize a Computer-Assisted Diagnosis (CAD) system that is specifically programmed to minimize discrepancies between the recommended clinical actions of an individual or specific group of experts using the standard IRADS process and the optimum clinical actions based on correlation with biopsy proven data. The resulting CAD system reduces the number of unnecessary clinical actions such as biopsies based on the operator's error profile.

Abstract: A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

Abstract: A system and method is disclosed to reduce variation of the clinical decision making process when an image reporting and data system (IRADS) for medical diagnosis is used. Image reporting and data systems provide guidelines for an operator to identify images as belonging to one of a number of categories and specific clinical actions are then recommended based upon such categories. Some clinical actions such as biopsies may be recommended by IRADS even when they are not necessary. The present inventive concept is configured to utilize a Computer-Assisted Diagnosis (CAD) system that is specifically programmed to minimize discrepancies between the recommended clinical actions of an individual or specific group of experts using the standard IRADS process and the optimum clinical actions based on correlation with biopsy proven data. The resulting CAD system reduces the number of unnecessary clinical actions such as biopsies based on the operator's error profile.

Abstract: A system and method is disclosed to reduce variation of the clinical decision making process when an image reporting and data system (IRADS) for medical diagnosis is used. Image reporting and data systems provide guidelines for an operator to identify images as belonging to one of a number of categories and specific clinical actions are then recommended based upon such categories. Some clinical actions such as biopsies may be recommended by IRADS even when they are not necessary. The present inventive concept is configured to utilize a Computer-Assisted Diagnosis (CAD) system that is specifically programmed to minimize discrepancies between the recommended clinical actions of an individual or specific group of experts using the standard IRADS process and the optimum clinical actions based on correlation with biopsy proven data. The resulting CAD system reduces the number of unnecessary clinical actions such as biopsies based on the operator's error profile.

Abstract: A method and means to utilize machine learning to train a device to generate a confidence level indicator (CLI). The device is a CAD system that has been initially trained using initial machine learning to recommend classifications for image features presented to the device. Probabilistic classification is utilized to incorporate intermediate values given by a human operator to better indicate a level of confidence of the CAD system's recommendations as to what classes should be associated with certain image features.

Abstract: A system and method of processing an ultrasound signal may include, in response to receiving a set of complex frequency samples of the ultrasound signal inclusive of content data and noise at a first noise level and being used to image an anatomical region of a body, resampling multiple subsets of complex frequency samples from the set of complex frequency samples. The resampled subsets of complex frequency samples may be resampled from a first domain into a second domain. The transformed resampled subsets of complex frequency samples may be combined in the second domain to produce a result signal with a second noise level reduced from the first noise level. An image derived from the result signal may be displayed.

Abstract: The present invention relates to mitigating the effects of distortions in pulse shape and energy of spectrum from gamma ray and/or neutron detectors. In one embodiment, the present invention uses pattern recognition techniques to identify and separate various distortions in the pulse shape and energy to obtain a better estimate of the true number of pulses of specific energies which are characteristic of the radioactive materials present. Additionally, autoregressive models, pulse filtering and pattern recognition methods are used to obtain more accurate pulse characterization. In one embodiment, the present invention relates to a method of detecting a material comprising the steps of deriving an energy spectrum from nuclear radiation detected from the material, processing the energy spectrum for enhancing the energy spectrum, extracting one or more features from the enhanced energy spectrum, and classifying the material based on the one or more features.

Abstract: A method and apparatus for rapidly soliciting the help of nearby people in a crisis situation such as a kidnapping is disclosed. The invention allows a person such as a parent to alert people who are within a specified radius of the person to voluntarily assist in looking for a missing child, pet, elderly person or other party in the area immediately surrounding the site where the party was last seen. The system allows for this assistance to be solicited within seconds of recognition of the fact that the party is missing. The system operates over the public mobile phone network using available location based services (LBS) technology such as those mandated by the FCC for enhanced 911 mobile emergency calls. Users, once notified of the missing child, could log their locations into the system if they spot the child at a specific time after they receive the alert. The resultant child tracking record is reported to the initial caller periodically by mobile.

Abstract: Energy dispersive x-ray diffraction spectra are obtained from numerous volume elements within an object. A feature set such as a set of cepstrum coefficients is extracted from each spectrum and classified by a trained classifier such as a neural network to provide an indication of whether or not contraband such as explosives is present in the volume element. Indications for adjacent volume elements are evaluated in conjunction with one another, as by an erosion process, to suppress isolated indications and thereby suppress false alarms.

Abstract: The present invention relates to a robust speech processing method and system which models channel and noise variations with affine transforms to reduce mismatched conditions between training and testing. The affine transform relating the training vectors C.sub.k with the vectors for testing condition c.sub.k', is represented by the form:c'.sub.k.sup.T =Ac.sub.k.sup.T +bfor k=1 to N in which A is a matrix of predicator coefficients representing noise distortions and vector b represents channel distortions. Alternatively, an affine invariant cepstrum is generated during testing and training for modeling speech to account for noise and channel effects. From the improved speech processing, improved speaker recognition with channel and noise variations is obtained.