2747results about "Recognition of medical/anatomical patterns" patented technology

Systems and methods are provided for registering maps with images, involving segmentation of three-dimensional images and registration of images with an electro-anatomical map using physiological or functional information in the maps and the images, rather than using only location information. A typical application of the invention involves registration of an electro-anatomical map of the heart with a preacquired or real-time three-dimensional image. Features such as scar tissue in the heart, which typically exhibits lower voltage than healthy tissue in the electro-anatomical map, can be localized and accurately delineated on the three-dimensional image and map.

Digitized image data are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.

Methods of manufacturing a custom arthroplasty resection guide or jig are disclosed herein. For example, one method may include: generating MRI knee coil two dimensional images, wherein the knee coil images include a knee region of a patient; generating MRI body coil two dimensional images, wherein the body coil images include a hip region of the patient, the knee region of the patient and an ankle region of the patient; in the knee coil images, identifying first locations of knee landmarks; in the body coil images, identifying second locations of the knee landmarks; run a transformation with the first and second locations, causing the knee coil images and body coil images to generally correspond with each other with respect to location and orientation.

A payment card or other device (e.g., mobile telephone) is provided with a magnetic emulator operable to communicate data to a magnetic stripe read-head. A user can utilize buttons located on the card to perform activities that would otherwise be performed at an ATM, payment card reader, or by a waitress. A user can provide instructions on a card to accelerate a transaction. The information a user enters can be communicated to a point-of-sale device. For example, a user can enter into his / her card that the user desires $100 withdrawal from a checking account. The user can also enter his / her PIN into the card. The user can swipe his / her card into an ATM and instantly be provided with the desired $100.

The invention provides a multi-scale feature fusion ultrasonic image semantic segmentation method based on adversarial learning, and the method comprises the following steps: building a multi-scale feature fusion semantic segmentation network model, building an adversarial discrimination network model, carrying out the adversarial training and model parameter learning, and carrying out the automatic segmentation of a breast lesion. The method provided by the invention achieves the prediction of a pixel class through the multi-scale features of input images with different resolutions, improvesthe pixel class label prediction accuracy, employs expanding convolution for replacing partial pooling so as to improve the resolution of a segmented image, enables the segmented image generated by asegmentation network guided by an adversarial discrimination network not to be distinguished from a segmentation label, guarantees the good appearance and spatial continuity of the segmented image, and obtains a more precise high-resolution ultrasonic breast lesion segmented image.

A non-invasive, early stage method to obtain quantitative measures of mild cognitive impairment useful in diagnosing and following degenerative brain disease or closed head injuries by utilizing the image data from individual patient positron emission tomographic scans to construct a cognitive decline index that serves as a diagnostic and screening tool to reveal the onset of mild cognitive impairment and nervous system dysfunction which are sequelae of degenerative brain diseases and closed head injury. The method involves using weighted values of brain region intensities derived from comparing scans of normal subjects to a scan of the patient to calculate a cognitive decline index that is useful as a diagnostic tool for mild cognitive impairment. The weights for the intensity values for each region are derived from the differences of intensity values from regions of the brain of the patient selected by comparing the patient to normal control subjects.

A method and system for coronary artery detection in 3D cardiac volumes is disclosed. The heart chambers are segmented in the cardiac volume, and an initial estimation of a coronary artery is generated based on the segmented heart chambers. The initial estimation of the coronary artery is then refined based on local information in the cardiac volume in order to detect the coronary artery in the cardiac volume. The detected coronary artery can be extended using 3D dynamic programming.

An automated method of performing various processes and procedures includes central and / or distributed iris identification database servers that can be accessed by various stations. Each station may be equipped with a handheld staff-operated iris camera and software that can query the server to determine whether an iris image captured by the iris camera matches a person enrolled in the system. The station takes selective action depending on the identification of the person. In disclosed medical applications, the station may validate insurance coverage, locate and display a medical record, identify a procedure to be performed, verify medication to be administered, permit entry of additional information, history, diagnoses, vital signs, etc. into the patient's record, and for staff members may permit access to a secure area, permit access to computer functions, provide access to narcotics and other pharmaceuticals, enable activation of secured and potentially dangerous equipment, and other functions.

A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network.

A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and / or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.

A system for image registration and quantitative feature extraction for multiple image sets. The system includes an imaging workstation having a data processor and memory in communication with a database server in communication. The a data processor capable of inputting and outputting data and instructions to peripheral devices and operating pursuant to a software product and accepts instructions from a graphical user interface capable of interfacing with and navigating the imaging software product. The imaging software product is capable of instructing the data processor, to register images, segment images and to extract features from images and provide instructions to store and retrieve one or more registered images, segmented images, quantitative image features and quantitative image data and from the database server.

A biometrics authentication system having a small and simple configuration and being capable of implementing both of biometrics authentication and position detection is provided. A biometrics authentication system includes: a light source emitting light to an object; a microlens array section condensing light from the object; a light-sensing device obtaining light detection data of the object on the basis of the light condensed by the microlens array section; a position detection section detecting the position of the object on the basis of the light detection data obtained in the light-sensing device; and an authentication section, in the case where the object is a living body, performing authentication of the living body on the basis of the light detection data obtained in the light-sensing device.

The present invention generally relates to intravascular ultrasound (IVUS) image segmentation methods, and is more specifically concerned with an intravascular ultrasound image segmentation method for characterizing blood vessel vascular layers. The proposed image segmentation method for estimating boundaries of layers in a multi-layered vessel provides image data which represent a plurality of image elements of the multi-layered vessel. The method also determines a plurality of initial interfaces corresponding to regions of the image data to segment and further concurrently propagates the initial interfaces corresponding to the regions to segment. The method thereby allows to estimate the boundaries of the layers of the multi-layered vessel by propagating the initial interfaces using a fast marching model based on a probability function which describes at least one characteristic of the image elements.

An optical imaging system utilizes a three-dimensional (3D) light scanner to capture topography information, color reflectance information, and fluorescence information of a target object being imaged, such as a surgical patient. The system also utilizes the topography information of the target object to perform an image mapping process to project the captured fluorescence or other intraoperative images back onto the target object with enhanced definition or sharpness. Additionally, the system utilizes the topography information of the target object to co-register two or more images, such as a color image of the target object with a fluorescence image for presentation on a display or for projection back onto the target object.

A system including a memory to store image data corresponding to a three dimensional (3D) reconstructed image, and a processor that includes an automatic vessel tree extraction module. The automatic vessel tree extraction module includes a load data module to access the stored image data, a component identification module to identify those components of the image data deemed likely to belong to a vessel, a characteristic path computation module to compute one or more characteristic paths for an identified component having a volume greater than a specified threshold volume value and discard an identified component having a volume less than the specified threshold volume value, and a connection module to connect the computed one or more characteristic paths of the identified component to a characteristic path of another identified component or to a common vessel source until the identified components are connected or discarded.

A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and / or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.

A method for using computer-aided diagnosis (CAD) for digital tomosynthesis mammograms (DTM) including retrieving a DTM image file having a plurality of DTM image slices; applying a three-dimensional gradient field analysis to the DTM image file to detect lesion candidates; identifying a volume of interest and locating its center at a location of high gradient convergence; segmenting the volume of interest by a three dimensional region growing method; extracting one or more three dimensional object characteristics from the object corresponding to the volume of interest, the three dimensional object characteristics being one of a morphological feature, a gray level feature, or a texture feature; and invoking a classifier to determine if the object corresponding to the volume of interest is a breast cancer lesion or normal breast tissue.

Case images and report text models, each of which is a text model derived from a report text of each of the corresponding case images by making at least certain words / phrases within the report text changeable, are stored in association with each other in a case report storage unit. A case image which is similar to a diagnosis target image is retrieved from the case images stored in the case report storage unit by a similar image retrieval unit. Input of a word / phrase corresponding to the diagnosis target image is accepted in a changeable word / phrase section of the report text model by a report creation unit, thereby a report text of the diagnosis target image is created.

An algorithm is quickly scans a digital image volume to detect density nodules. A first stage is based on a transform to quickly highlight regions requiring further processing. The first stage operates with somewhat lower sensitivity than is possible with more detailed analyses, but operates to highlight regions for further analysis and processing. The transform dynamically adapts to various nodule sizes through the use of radial zones. A second stage uses a detailed gradient distribution analysis that only operates on voxels that pass a threshold of the first stage.

Pairs of stereo Xray radiographs are obtained from an X-ray imaging system and are digitized to form corresponding pairs of stereo images (602, 604). The pairs of stereo images (602, 604) are adjusted (410) to compensate for gray-scale illumination differences by grouping and processing pixel groups in each pair of images. Distortion in the nature of depth plane curvature and Keystone distortion due to the toed-in configuration of the X-ray imaging system are eliminated (412). A screen parallax for the pair of stereo images is adjusted (414) to minimize depth range so as to enable a maximum number of users to view the stereoscopic image, and particular features of interest.

A system, method, and computer program product for classifying a target structure in an image into abnormality types. The system has a scanning mechanism that scans a local window across sub-regions of the target structure by moving the local window across the image to obtain sub-region pixel sets. A mechanism inputs the sub-region pixel sets into a classifier to provide output pixel values based on the sub-region pixel sets, each output pixel value representing a likelihood that respective image pixels have a predetermined abnormality, the output pixel values collectively determining a likelihood distribution output image map. A mechanism scores the likelihood distribution map to classify the target structure into abnormality types. The classifier can be, e.g., a single-output or multiple-output massive training artificial neural network (MTANN).

Automatically determining image quality of a machine generated image may generate a local saliency map of the image to obtain a set of unsupervised features. The image is run through a trained convolutional neural network (CNN) to extract a set of supervised features from a fully connected layer of the CNN, the image convolved with a set of learned kernels from the CNN to obtain a complementary set of supervised features. The set of unsupervised features and the complementary set of supervised features are combined, and a first decision on gradability of the image is predicted. A second decision on gradability of the image is predicted based on the set of supervised features. Whether the image is gradable is determined based on a weighted combination of the first decision and the second decision.

The invention discloses an apparatus and method for automatic detection of pathogens within a sample. The apparatus and method are especially useful for high-throughput and / or low-cost detection of parasites with minimal need for trained personnel. The invention entails automated microscopic data acquisition, and performing image processing of images captured from a sample using classification algorithms. Visual classification features of structures are extracted from the image and compared to visual classification features associated with known pathogens. A determination is reached whether a pathogen is present in the sample; and if present, the pathogen may be identified to a pathogen species. Diagnosis is rapid and does not require medically trained personnel.

A structured light source comprising VCSEL arrays is configured in many different ways to project a structured illumination pattern into a region for 3 dimensional imaging and gesture recognition applications. One aspect of the invention describes methods to construct densely and ultra-densely packed VCSEL arrays with to produce high resolution structured illumination pattern. VCSEL arrays configured in many different regular and non-regular arrays together with techniques for producing addressable structured light source are extremely suited for generating structured illumination patterns in a programmed manner to combine steady state and time-dependent detection and imaging for better accuracy. Structured illumination patterns can be generated in customized shapes by incorporating differently shaped current confining apertures in VCSEL devices. Surface mounting capability of densely and ultra-densely packed VCSEL arrays are compatible for constructing compact on-board 3-D imaging and gesture recognition systems.

A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.

A method and system for physiological image registration and fusion is disclosed. A physiological model of a target anatomical structure in estimated each of a first image and a second image. The physiological model is estimated using database-guided discriminative machine learning-based estimation. A fused image is then generated by registering the first and second images based on correspondences between the physiological model estimated in each of the first and second images.

Methods for analyzing retinal tomography maps to detect patterns of optic nerve diseases such as glaucoma, optic neuritis, anterior ischemic optic neuropathy are disclosed in this invention. The areas of mapping include the macula centered on the fovea, and the region centered on the optic nerve head. The retinal layers that are analyzed include the nerve fiber, ganglion cell, inner plexiform and inner nuclear layers and their combinations. The overall retinal thickness can also be analyzed. Pattern analysis are applied to the maps to create single parameter for diagnosis and progression analysis of glaucoma and optic neuropathy.

Provided herein are portable imaging systems, for example, a digital processor-implemented system for the identification and / or classification of an object of interest on a body, such as a human or plant body. The systems comprise a hand-held imaging device, such as a smart device, and a library of algorithms or modules that can be implemented thereon to process the imaged object, extract representative features therefrom and classify the object based on the representative features. Also provided are methods for the identifying or classifying an object of interest on a body that utilize the algorithms and an automated portable system configured to implement the same.

The invention relates to a method for segmentation of a digital image being represented by picture element data for the purposes of visualization, segmentation and quantification, such as determining the volume of a segment. The method comprises the steps of performing a watershed transformation of the digital image by applying a watershed transformation method on the picture element data to provide one or more basins and post-processing by processing the picture element data belonging to at least one of the basins, where post-processing is one or a combination of the following steps: (i) gray-scale based segmentation, (ii) visualization using a transfer function, or (iii) volumetry using histogram analysis.