Abstract: According to a first aspect, the invention relates to a method to support clinical decision by characterizing images acquired in sequence through a video medical device. The method comprises defining at least one image quantitative criterion, storing sequential images in a buffer, for each image (10) in the buffer, automatically determining, using a first algorithm, at least one output based on said image quantitative criterion and attaching said output to a timeline (11).

Abstract: A system and method are provided for medical imaging that includes acquiring, during a common imaging acquisition process, rotational, x-ray volume image data and x-ray tomosynthesis image data from a subject. The method includes reconstructing a time-resolved three-dimensional (3D) image volume from the rotational, x-ray volume image data and producing a four-dimensional (4D) image series of the subject with resolved overlapping features by selectively combining the time-resolved 3D image volume and the x-ray tomosynthesis imaging data.

Abstract: A method for generating a virtual jaw image includes performing a movement recording method to obtain a movement record which comprises producing at least one position data record over a defined time. A position data record describes a spatial position of a lower jaw to an upper jaw at a specific time. Surface sections of the upper and lower jaw are scanned at the defined time during the movement recording method to obtain a spatial relation of the surface sections during the defined time. Digital upper and lower jaw images are recorded. A position data record of a virtual position of the digital lower jaw image is assigned to the digital upper jaw image. A position data record is selected to obtain a selected position data record. The digital lower and upper jaw images are virtually aligned based on the selected position data record to produce the virtual jaw image.

Abstract: A system (100) is provided for performing a model-based segmentation of an anatomical structure in a medical image. The system comprises a processor (140) configured for performing a model-based segmentation of the anatomical structure by applying a deformable model to image data (042). Moreover, definition data (220) is provided which defines a geometric relation between a first part and a second part of the deformable model of which a corresponding first part of the anatomical structure is presumed to be better visible in the image data than a corresponding second part of the anatomical structure. The definition data is then used to adjust a fit of the second part of the deformable model. As a result, a better fit of the second part of the deformable model to the second part of the anatomical structure is obtained despite said part being relatively poorly visible in the image data.

Abstract: A fluid-borne microorganism particle detecting device having a heating flow path wherein flows a liquid that includes a microorganism particle; a microwave emitting device for irradiating, with microwave radiation, the liquid that includes the microorganism particles within the heating flow path; an inspection flow path that is connected to the heating flow path; an excitation beam light source for emitting an excitation beam toward the inspection flow path; and a fluorescence detector for detecting fluorescent light emitted by microorganism particles in the inspection flow path that has been illuminated by the excitation beam.

Abstract: An image processing method for diagnosing a disease using a captured image of an affected area, includes separating the captured image memorized into a brightness component and a color information component; separating the brightness component into a base component and a detail component; performing a highlighting process on the detail component; and restoring a brightness component from the base component and a highlighted detail component, and then generating a highlighted image using a restored brightness component and the color information component. The highlighting process includes highlighting the detail component depending on likelihood of vessel of a region to be diagnosed.

Abstract: A method and system for performing rendering at a given viewpoint of at least one portion of visual areas blinded to a perspective rendering at the given viewpoint are disclosed, the method comprising providing a projection model comprising a first part used for rendering far view and a second part for rendering near view such that at least one portion of the visual areas is rendered using the second part of the projection model and further wherein a transition between the first part of the projection model and the second part of the projection model is characterized by projection vectors substantially similar, obtaining image data, performing a first projection of at least one part of the image data according to the first part of the projection model to generate first projected data, performing a second projection of another part of the image data according to the second part of the projection model to generate second projected data and displaying projected data using the first projected data and the second

Abstract: Methods and apparatus are disclosed that assist a user such as a doctor to track changes that occur in features of a subject's skin as the skin features evolve over time. Such a tracking can be done for images captured under different imaging/lighting modalities, by different image capture devices, and/or at different points in time. Methods and apparatus to automatically identify and track the unconstrained appearance/disappearance of skin features are disclosed.

Abstract: A method of processing a medical image comprising an organ, the method comprising: obtaining a medical image; automatically estimating one or more organ intensity characteristics in the image, from contents of a region of the image that appears to correspond, at least in part, to at least a portion of the organ; providing a plurality of sets of organ intensity characteristics, each set a different example of possible intensity characteristics of the organ; choosing one of the plurality of sets, that has organ intensity characteristics that provide a better match than one or more other sets to the estimated organ intensity characteristics of the image; setting values of one or more image processing parameters based on the organ intensity characteristics of the chosen set; and automatically processing the image using said values of image processing parameters.

Abstract: A magnetic resonance scan sequence is executed in which nuclear spins are prepared in a preparation region with preparation parameters. The scan sequence provides first image data that image a scan region. The first image data are based on magnetic resonance data acquired with ultrashort echo times. The first image data are combined with reference image data that map the scan region, in order to obtain a resultant image.

Abstract: An apparatus and method for supporting computer aided diagnosis (CAD). The apparatus includes: a control processor configured to determine a duration during which a remaining image of a first region of interest (ROI) detected from a first image frame is displayed, based on a characteristic of measuring the first ROI; and a display configured to mark a remaining image of a second ROI of a second image frame in the first image frame and display the marked image on a screen, in response to the first image frame being acquired during a duration set to display the remaining image of the second ROI. the first image frame is obtained subsequent to the second image frame.

Abstract: A method of modeling a structure of a coronary artery of a subject may include: forming a learning-based shape model of the structure of the artery, based on positions of landmarks acquired from three-dimensional images; receiving a target image; and/or modeling the artery structure included in the target image, using the model. An apparatus for modeling a structure of a coronary artery may include: a memory configured to store a learning-based shape model of the artery, the learning-based shape model being formed based on positions of a plurality of landmarks acquired from three-dimensional images, the plurality of the landmarks corresponding to the artery; a communication circuit configured to receive a target image; and/or a processing circuit configured to model the artery structure included in the target image, using the model.

Abstract: The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

Abstract: An image processing apparatus includes a divider that generates a plurality pieces of third image information on the basis of a plurality of pieces of first image information and a plurality of pieces of second image information, a determiner that determines, on the basis of information regarding a sample, a filter to be used for the plurality of pieces of third image information, and a processor that deconvolutes each of the plurality of pieces of third image information using the determined filter. An image sensor that has received first resulting light emitted from a sample that has received first light emitted from a first angle outputs the plurality of pieces of first image information. The image sensor that has received second resulting light emitted from the sample that has received second light emitted from a second angle outputs the plurality of pieces of second image information.

Abstract: Disclosed are systems and methods for automated monitoring of the size, area or boundary of chronic wound images. The disclosure includes use of a probability map that measures the likelihood of wound pixels belonging to granulation, slough or eschar, which can then be segmented using any standard segmentation techniques. Measurement of the wound size, area or boundary occurs automatically and without user input related to outlining, filling in, or making measurement lines over the image on a display.

Abstract: An apparatus and method for computing optima of a function of a digital image. The apparatus includes circuitry configured to initialize a plurality of candidate points that lie in a solution space of the function, and computes one or more stationary points of the function. The circuitry deflates a gradient of the function at each of the one or more computed stationary points, and repeats the computing and the deflating until a first criteria is satisfied. The circuitry selects a predetermined number of fit points, recombines the selected fit points to generate a set of new candidate points, and repeats, for the set of new candidate points, the computing, the deflating, the first repeating, the selecting, and the recombining, until a second criteria is satisfied. The circuitry obtains the optima of the function upon the second criteria being satisfied, and processes the digital image based on the obtained optima.

Abstract: An example embodiment relates to a method for automatically stipulating a spectral distribution of the X-ray radiation of a number of X-ray sources of a computed tomography system. This involves firstly stipulating start control parameters of an X-ray source, which determine the dose and spectral distribution of X-ray radiation. On the basis of an expected attenuation of the X-ray radiation by an examination object, an examination-object-specific basic control parameter is then ascertained proceeding from the start control parameters. Afterwards, on the basis of the expected attenuation of the X-ray radiation by the examination object and the basic control parameter, first target control parameters and second target control parameters are ascertained for setting the spectral distribution of the X-ray radiation in a subsequent multi-energy measurement on the examination object. Moreover, a method, a control device suitable, and a computed tomography system comprising such a control device are described.

Abstract: An apparatus (MDS) and related method for image processing. The apparatus includes a multi-modal matcher (M). The matcher (M) operates to match a first reference (RF1) image against a stream (LF) of image frames (F1, F2). Once a match is found, the current reference image (RF1) is exchanged for a matching frame from the stream. The matcher (M) then attempts matching against subsequent frames in the stream by now using said matched frame as a new reference image. Once a further frame (Fm) is found that matches said new reference image, said further frame (Fm) is output as a best match.

Abstract: Method and system is disclosed for image segmentation. The method includes acquiring a digital image, constructing a directed graph from the digital image, calculating a plurality of cost functions, constructing an electrical network based upon the constructed directed graph and the plurality of calculated cost functions, simulating the electrical network using fixed-point linearization, and thresholding the voltages in the simulated electrical network to produce image segmentation. Simulation may be executed in parallel to achieve desirable computational efficiencies.

Abstract: Techniques for assessing a tissue condition and diagnosing, assessing the prognosis of, or the risk for pathological conditions are disclosed. The technique may include an image acquiring module adapted to receive an image comprising at least a portion of animal or human tissue, a delineation module adapted to indicate an analysis zone in said acquired image, a feature extraction module adapted to extract quantitative information from said analysis zone and a machine learning module adapted to receive said extracted information and apply at least one detection algorithm to assess a condition of said tissue. The feature extractor module may have at least a rotation compensation module to compensate for the rotation of the analysis zone.

Abstract: A voice controlled system uses context-sensitive interpretation of voice comments received by a voice recognition system to identify a region of patient image data identified by a verbal comment.

Abstract: The present invention relates to an image filtering method and a CT (Computed Tomography) system.

Abstract: Methods and apparatus distinguish invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS). One example apparatus includes a set of circuits, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of circuits includes a classification circuit that generates an invasiveness classification for a diagnostic 3D radiological image, a training circuit that trains the classification circuit to identify a texture feature associated with IA, an image acquisition circuit that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification circuit, and a prediction circuit that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training circuit trains the classification circuit using a set of 3D histological reconstructions combined with the set of 3D radiological images.

Abstract: Methods and systems are provided for reconstructing and automatically segmenting an image. In one embodiment, a method comprises acquiring projection data, the projection data comprising higher energy projection data and lower energy projection data, generating a first image from the projection data, generating a second image from the projection data, segmenting the second image to generate segments, and segmenting the first image based on the segments of the second image. In this way, an image which may otherwise prove challenging for an automatic segmentation process may be accurately segmented without sacrificing textural details of the image.

Abstract: Vascular structures are segmented by a locally adaptive method based on iterative region growing and morphological operations.

Abstract: Embodiments of the present invention provide a method for obtaining vital sign data of a target object, including: obtaining a 3D depth image of a target object; obtaining, according to depth values of pixels in the 3D depth image of the target object, framework parameters of the target object and a graphic contour of the target object, where the depth value, is obtained according to the distance information, indicates a distance between a point on the target object and the imaging device; retrieving a 3D model matching the framework parameters of the target object and the graphic contour of the target object from a 3D model library, and obtaining a parameter ratio of the 3D model; obtaining at least one real size of the target object; and obtaining vital sign data of the target object according to the parameter ratio of the 3D model and the at least one real size.

Abstract: The invention provides methods and apparatus for image processing that perform image segmentation on data sets in two- and/or three-dimensions so as to resolve structures that have the same or similar grey values (and that would otherwise render with the same or similar intensity values) and that, thereby, facilitate visualization and processing of those data sets.

Abstract: A method of generating a personalized parameter value for generating a medical image includes acquiring a first medical image of an object, which is imaged according to a predetermined parameter value set in a medical imaging apparatus, and a second medical image changed from the first medical image, determining a first standard parameter value corresponding to the first medical image and a second standard parameter value corresponding to the second medical image, and generating a personalized parameter value corresponding to the object on the basis of the first standard parameter value and the second standard parameter value.

Abstract: A system for quality assessment of optical colonoscopy images includes an input device configured to acquire a series of images during an optical colonoscopy. A computing device is coupled in communication with the input device and configured to acquire from the input device an input image from the series of images captured during the optical colonoscopy; form a cell grid including a plurality of cells on the input image; perform an image transformation onto the input image with each cell of the plurality of cells within the cell grid; reconstruct each cell to form a reconstructed image; compute a difference image of a sum of a plurality of differences between the input image and the reconstructed image; compute a histogram of the difference image; and apply a probabilistic classifier to the histogram to calculate an informativeness score for the input image.

Abstract: Embodiments that relate to presenting a mixed reality environment via a mixed reality display device are disclosed. For example, one disclosed embodiment provides a method for presenting a mixed reality environment via a head-mounted display device. The method includes using head pose data to generally identify one or more gross selectable targets within a sub-region of a spatial region occupied by the mixed reality environment. The method further includes specifically identifying a fine selectable target from among the gross selectable targets based on eye-tracking data. Gesture data is then used to identify a gesture, and an operation associated with the identified gesture is performed on the fine selectable target.

Abstract: A method and apparatus to select from an image (30) of a first sample (28), at least one region (44) digitally captured at a first resolution based upon how a counterfeit identification performance attribute of each region (44) digitally captured at the first resolution correlate to the counterfeit identification performance attribute of the region (44) digitally captured at a second resolution higher than the first resolution. The selected region (44) is used to determine whether the image (30) on a second sample is a counterfeit.

Abstract: Systems and methods are provided for constructing a statistical shape model from a set of training shapes. In one embodiment, two shapes in the training set can be parameterized to a common base domain. Correspondence between the shapes can be evaluated using shape-specific data, such as, for the case of anatomical shapes, anatomical curves and/or anatomical landmarks. In evaluating correspondence, the shape-specific data about the second shape can be mapped to the shape-specific data about the first shape, and the mapping can be optimized based at least in part on a deformation energy of the mapping.

Abstract: A biological information measurement apparatus includes: a plurality of first pixels configured to generate a first imaging signal based on received light; a plurality of second pixels configured to generate a second imaging signal based on the received light; a time series signal generation unit configured to generate a first time series signal by connecting representative values of first imaging signals in time series and generate a second time series signal by connecting representative values of second imaging signals in time series; a signal component separation unit configured to separate a plurality of signal components from each of the first and second time series signals; and a biological information component selector configured to select a signal component in accordance with the biological information, among the plurality of signal components separated by the signal component separation unit.

Abstract: The system and method of the invention pertains to an MR-guided breast biopsy procedure, specifically as to quickly identifying the biopsy location. More particularly, the system utilizes a diagnostic imaging modality such as magnetic resonance imaging (MRI) to locate one or more lesions in a human breast. Non-rigid registration between uncompressed screening images (where the lesion has been previously identified) and the compressed biopsy images enables easier identification of the biopsy site, hence shortening the biopsy procedure.

Abstract: An atlas-free magnetic resonance imaging method images at least one part of a brain. An MRI sequence configured for acquiring two image volumes of the part at different inversion times within a single acquisition is combined to a fat-water separation method for acquiring a fat-water separated image. For each echo time two image volumes are acquired, respectively a first image volume and a second image volume at the first echo time, and a first image volume and a second image volume at the second echo time, and combined to a uniform image. The acquired images are combined to form a final uniform image, a final fat-water separated image, and a final second image volume that are fed into a multichannel image segmentation algorithm using a Markov random field model for segmenting the part into multiple classes of cranial tissues, in order to obtain a segmented image of said part.

Abstract: Apparatus, methods, and computer-readable media are provided for segmentation, processing (e.g., preprocessing and/or postprocessing), and/or feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm. Tissue images processed by various embodiments described herein may be generated by Hematoxylin and Eosin (H&E) staining, immunofluorescence (IF) detection, immunohistochemistry (IHC), similar and/or related staining processes, and/or other processes. Predictive features described herein may be provided for use in, for example, one or more predictive models for treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.

Abstract: An imaging system includes an identification module, an analysis module, and a determination module. The identification module is configured to identify a scanning mode of operation. The analysis module is configured to determine an attenuation for an object for a scan to be performed on the object. The determination module is configured to determine an image contrast for each of plural setting combinations, determine a corresponding tolerable noise for the image contrast for each of the setting combinations based on the scanning mode of operation, and determine a corresponding diagnostic dosage for each setting combination, the diagnostic dosages corresponding to the image contrast and tolerable noise for the corresponding setting combination. The determination module is also configured to select an operational setting for the scan to be performed using the dosages determined.

Abstract: An ultrasound diagnostic apparatus according to an embodiment includes an image acquirer, a volume information calculator, a wall motion information calculator, a time change rate calculator, an extremum detector, and an index calculator. The image acquirer acquires ultrasonic image data including data for a left ventricle. The volume information calculator calculates time-series data of volume information of the left ventricle. The wall motion information calculator calculates time-series data of wall motion information of the left ventricle. The time change rate calculator calculates time-series data of the time change rate of volume information (first time-series data) and time-series data of the time change rate of wall motion information (second time-series data). The extremum detector detects extremums in early diastole of the first time-series data and the second time-series data (first extremum and second extremum).

Abstract: The embodiments relate to generating a 2D projection image of a vascular system of a body region of interest, including: (1) acquiring a 3D dataset of the body region of interest, (2) acquiring at least one 2D projection image of the body region of interest (S3), (3) generating a modified 3D dataset by eliminating vessels whose size exceeds a predetermined limit value, (4) normalizing the 2D projection image using projection data of the modified 3D dataset, (5) eliminating vessel projections in the normalized 2D projection image whose size exceeds a predetermined limit value, (6) interpolating the areas of the 2D projection image in which the vessel projections have been eliminated, and (7) denormalizing the normalized and interpolated 2D projection image using projection data of the modified 3D dataset.

Abstract: A medical imaging device and method includes a touch-sensitive display, a medical image acquisition subsystem, and a processor. The processor is configured to display a first UI object and a second UI object on the touch-sensitive display, where the first UI object includes a first selection point and a first boundary. The processor is configured to adjust a position of the first UI object in response to a touch-based user input and to automatically adjust a position of the first selection point with respect to the first boundary in response to the touch based input when the first UI object is within a threshold distance from one of the second UI object and an edge of the touch-sensitive display.

Abstract: Provided content is determined to contain an asset represented by reference content by comparing digital fingerprints of the provided content and the reference content. The fingerprints of the reference content and the provided content are generated using a convolutional neural network (CNN). The CNN is trained using a plurality of frame triplets including an anchor frame representing the reference content, a positive frame which is a transformation of the anchor frame, and a negative frame representing content that is not the reference content. The provided content is determined to contain the asset represented by the reference content based on a similarity measure between the generated fingerprints. If the provided content is determined to contain the asset represented by the reference content, a policy associated with the asset is enforced on the provided content.

Abstract: A method for automatically detecting a region of interest in a digital medical image, comprising over-segmenting the image into a plurality of superpixels through use of an over-segmentation algorithm; for each pair of neighboring superpixels in the plurality of superpixels, computing, through a machine learning algorithm, the probability of each pair being in one of three predetermined classes; for each superpixel in the plurality of superpixels, computing a probability of the superpixel being in the region of interest; generating an edge map from computing each pixel's value based on the computed superpixel probabilities; applying an extended Hough transform to the generated edge map to generate a Hough parameter counting space; determining the optimal quadrilateral in the Hough parameter counting space by excluding false positive edges; and designating the region of interest as being within the boundary of the determined optimal quadrilateral.

Abstract: A method for automatically identifying and localizing anatomical regions of a left ventricle of a heart using a segment model, the method including receiving a volumetric MR image series of one or more time-points in a heart cycle with associated myocardial boundaries, representing the heart, performing a long-axis partitioning on each image of the volumetric image series, performing a short-axis partitioning volumetric image series, generating a polar map, wherein each image of the volumetric image series is mapped to a location on the polar map, wherein the location is characterized using the long-axis partitioning and the short-axis partitioning, and generating a mapping from the polar map to a voxel in an associated image of the volumetric image series representing an anatomical location.

Abstract: When binary labeling is performed, an outline specification unit specifies a first outline present toward a target region and a second outline present toward a non-target region, and which have shapes similar to an outline of the target region. A voxel selection unit selects an N number of voxels constituting all of the first outline and the second outline. The energy setting unit sets N-order energy when a condition that all of the voxels of the first outline belong to the target region and all of the voxels of the second outline belong to the non-target region is satisfied smaller than the N-order energy when the condition is not satisfied. After then, labeling is performed by minimizing energy.

Abstract: Disclosed is a sample similarity measurement method for a digital pathology system. The sample similarity measurement method during a process of a digital pathology system includes extracting a first main area and a second main area of each of a first digital slide image and a second digital slide image; aligning the first main area and the second main area; classifying sample types of the first main area and the second main area when the alignment is successful; and measuring similarity of the first main area and the second main area when the sample types are the same.

Abstract: An image processing apparatus for obtaining a shape of a region of a processing target included in an image, includes: an attribute obtaining unit configured to obtain a intensity distribution attribute of the region of the processing target; and a shape obtaining unit configured to obtain the shape of the region by a calculation method selected in accordance with the intensity distribution attribute.

Abstract: A medical image processing apparatus includes a core line identifying unit that identifies a core line of a predetermined region of a tubular structure from medical three-dimensional image data, a center position identifying unit that identifies a center position of the tubular structure from a plurality of points forming the core line identified by the core line identifying unit, an obtaining unit that obtains positions where straight lines intersecting at the center position and the tubular structure in the medical three-dimensional image data are in contact with each other, and a calculating unit that calculates a length of the tubular structure based on the positions obtained by the obtaining unit.

Abstract: High quality, high contrast images of an iris and the face of a person are acquired in rapid succession in either sequence by a single sensor and one or more illuminators, preferably within less than one second of each other, by changing the data acquisition settings or illumination settings between each acquisition.

Abstract: An image processing apparatus includes: a detecting unit configured to detect images of interest including regions of interest that are estimated as an object to be detected, from a group of a series of images acquired by sequentially imaging a lumen of a living body; a global similarity calculating unit configured to calculate a global similarity that is a similarity between regions including at least regions other than the regions of interest, between the images of interest different from one another; an image-of-interest group extracting unit configured to extract an image-of-interest group including identical regions of interest, in accordance with comparison between a threshold and the global similarity or a determination parameter based on the global similarity; and a representative image extracting unit configured to extract a representative image from the image-of-interest group.

Abstract: A medical image processing apparatus and a breast image processing method thereof are provided. The processing method at least contains but not limited to the following steps. At least one slice of breast image is obtained. Mammary glandular tissue in each breast image is detected through a mammary glandular tissue detector. The mammary glandular tissue detector is based on texture characteristic analysis. Therefore, the embodiments of the present disclosure would assist density analysis of the mammary glandular tissue and efficiently reduce false positive of computer-aided detection. In addition, based on a result of the density analysis of the mammary glandular tissue, the embodiment would further determine lactation yield and present density diagrams of mammary glandular tissue of left and right breasts. A breast region may also be separated from the breast image based on rib information according to the embodiments of the present disclosure.