Abstract: An image processing apparatus obtains image data obtained by reading an original, specifies a type of the original using obtained image data, and displays, on a display unit, character strings used for a name of a file of the obtained image data, as selectable candidates of character strings. When a user selects a character string of the candidates of character strings among the candidates of character strings, the image processing apparatus sets the selected character string as a character string used for the name of the file of the image data. The candidates of the character strings displayed on the display unit are selected in accordance with the specified type of the original.

Abstract: In one embodiment, a system for biomedical object segmentation includes one or more processors; one or more memory modules communicatively coupled to the one or more processors, and machine readable instructions stored on the one or more memory modules. The machine readable instructions cause the system to perform the following when executed by the one or more processors: receive image data of one or more biological constructs; analyze the image data to generate processed image data via a data analytics module to recognize biomedical objects; and automatically annotate the processed image data to indicate a location of one or more biological objects within the one or more biological constructs.

Abstract: In one embodiment, a method includes locating a plurality of part patches from an image, wherein each part patch comprises at least a portion of the image corresponding to a recognized human body portion or pose, and wherein each part patch is associated with a respective detection score larger than a threshold score, wherein the detection score is determined based on a comparison between the part patch with multiple training patches, generating a plurality of sets of feature data by processing each of the plurality of part patches with a plurality of convolutional neural networks, respectively, and determining whether a human attribute exists in the image based on the plurality of sets of feature data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining the identity of a user. In one aspect, a method comprises: obtaining at least one image depicting a reflection of light from a lens that is included in a set of eyeglasses; determining, based on the reflection of light from the lens, a set of features characterizing the lens; and determining an identity of a corresponding user based at least in part on the set of features characterizing the lens.

Abstract: The method comprises storing a set of images captured of the anterior chamber of various eyes, and using a processor for: a) processing some of said stored images by implementing an homogenization process that adjusts an horizontal and a vertical spatial resolution of each image of the set to be the same, and a centering and aligning process that computes statistical properties of the images, and uses said computed statistical properties to compute a centroid and a covariance matrix of each image; b) performing pair-wise distance measures between images of said processed images providing a pair-wise distance matrix; c) analyzing said pair-wise distance matrix by executing a nonlinear dimensionality reduction algorithm that assigns a point in an n-dimensional space associated to each analyzed image; and d) outputting the results of said analysis in a visual way enabling being usable to detect if said eyes suffer from glaucoma.

Abstract: A model-adaptive multi-source large-scale mask projection 3D printing system configured to conduct the following steps: projecting pure-color images of first and second colors having identical attributes, capturing an image of an overlapping portion and calculating height and width information of the overlapping portion; splitting a pre-processed slice and respectively recording width and height information of two slices resulting from the splitting and generating two gray scale images having identical attributes thereto; counting power values of identical positions of slices in different gray scale values, performing a further calculation to obtain a projection mapping function, using the projection mapping function as a basis for performing optimization on gray scale interpolation of the generated images; and fusing the processed gray scale images and the originally split two slices to obtain a mask projection 3D printing slice having a uniform shaping brightness, and forming a final product.

Abstract: According to various embodiments, the present disclosure provides a method of imaging reconstruction. The method of imaging reconstruction includes providing a target object, a detector, and a mask disposed between the target object and the detector; acquiring a measured image of the target object by the detector; providing an estimated image of the target object; partitioning the mask into multiple regions; for each of the regions, deriving a forward projection from the estimated image of the target object and the respective region, thereby acquiring multiple forward projections; comparing the measured image of the target object with the forward projections; and updating the estimated image of the target object based on a result of the comparing.

Abstract: Methods and systems for determining a region of interest (ROI) in an image associated with a subject are provided. Slice information of multiple slices of an image are identified and multiple slice ranges are determined based on the slice information. Based on the slice ranges, multiple sub-images of the image are then determined. For each sub-image, a template and a corresponding probability graph are acquired, a registration result is determined by registering the sub-image with the template, and a ROI in the sub-image is identified based on the registration result. A ROI of the image can be determined by combining the ROIs of the sub-images.

Abstract: Methods and systems are provided for independently removing streak artifacts and noise from medical images, using trained deep neural networks. In one embodiment, streak artifacts and noise may be selectively and independently removed from a medical image by receiving the medical image comprising streak artifacts and noise, mapping the medical image to a streak residual and a noise residual using the trained deep neural network, subtracting the streak residual from the medical image to a first extent, and subtracting the noise residual from the medical image to a second extent, to produce a de-noised medical image, and displaying the de-noised medical image via a display device.

Abstract: In order to assess, with high accuracy, a degree of a change having occurred in a structure, the present invention includes: a Betti number calculating section (42) configured to (I) generate, with respect to a single captured image obtained by capturing an image of a structure, a plurality of binarized images having respective binarization reference values different from each other and (II) calculate, for each of the plurality of binarized images, a first characteristic numerical value indicative of the number of connected regions each of which is obtained by connecting pixels each having one of pixel values obtained by binarization; and a prediction score determining section (44) configured to determine, in accordance with a result of a comparison, information on a change having occurred in the structure, the comparison having been made between (i) a pattern indicative of a relationship between (a) the respective binarization reference values and (b) the first characteristic numerical value and (ii) a prede

Abstract: The present disclosure relates to techniques to expand the dynamic range of a detector and achieve smooth transition in images used for X-ray image processing. The image finally obtained can fully retain useful information.

Abstract: Technology is disclosed herein for detecting motion in video using motion vectors. In an implementation, a frame of video is divided into regions and a vector score is identified for each of the regions. A selection is then made of a subset of the regions based on the identified vector scores, i.e. at least some of the regions may be excluded from further analysis based on their score. The selected subset is divided into or grouped in clusters. Motion may then be identified in response to at least one of the clusters appearing in at least one other frame of the video.

Abstract: A method of registering features in a repeating pattern can include (a) providing an object having a repeating pattern of features and a fiducial; (b) obtaining a target image of the object, wherein the target image includes the repeating pattern of features and the fiducial; (c) comparing the fiducial in the target image to reference data, wherein the reference data includes xy coordinates for a virtual fiducial; and (d) determining locations for the features in the target image based on the comparison of the virtual fiducial in the reference data to the fiducial in the data from the target image. The fiducial can have at least concentric circles that produce three different signal levels. The locations of the features can be determined at a variance of less than 5 ?m.

Abstract: A method, computer system, and a computer program product for dynamically altering at least one image is provided. The present invention may include receiving a plurality of data, wherein the received plurality of data includes at least one existing medical image. The present invention may also include determining that one or more user instructions for the received existing image were received. The present invention may then include implementing the one or more user instructions on the received existing medical image. The present invention may also include altering the received existing medical image based on the one or more implemented user instructions and a medical knowledge base.

Abstract: A system and method for generating contours in medical imaging scans. In some embodiments, the method includes calculating a first threshold, based on a first target point in a normalized scan data array, and generating a first contour. The contour may be a boundary of a first region, the first region being a region within which the elements of the normalized scan data array are less than the first threshold.

Abstract: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.

Abstract: A Convolutional Neural Network (CNN) assisted dielectric imaging method is provided. The method used CNN to incorporate the abundant image information from Magnetic Resonance (MR) images into the inverse scattering model-based microwave imaging process and generate high-fidelity dielectric images. A CNN is designed and trained to learn the complex mapping function from MR T1 images to dielectric images. Once trained, the new patients' MR T1 images are fed into the CNN to generate predicted dielectric images, which are used as the starting image for the microwave inverse scattering imaging. The CNN-predicted dielectric image significantly reduces the non-linearity and ill-posedness of the inverse scattering problem. The application of the proposed method to recover human brain dielectric images at 4 mm and 2 mm resolution with single-frequency and multi-frequency microwave measurements is provided.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes processing circuitry. The processing circuitry acquires at least one piece of spatial analysis information obtained by analyzing medical image data of an object, the medical image data being generated by another medical image diagnostic apparatus of modality different from an own apparatus. The processing circuitry then generates a superimposed image by superimposing an image indicating the at least one piece of spatial analysis information on a first medical image of the object generated by the own apparatus, and causes a display to display the superimposed image.

Abstract: An image processing device (100) includes a change detection unit (101) that detects a target state change in a person on the basis of an input image, and a determination unit (102) that determines an abnormal state in accordance with a detection obtained by detecting occurrences of the target state change in a plurality of persons.

Abstract: A method and a system for self-checkout. The system includes a scanner, an imaging device, and a computing device. The computing device is configured to: initiate a self-checkout event; instruct the imaging device to capture video frames of a region of interest (ROI); track the product; record scanning status and location status of the product; in response to receive a scanning signal from the scanner, record scanning status of the product as scanned; calculate a shoplifting risk score based on a number of the product having the scanning status of unscanned and disappears from the table region or ROI; and provide a shoplifting warning when the shoplifting score is large.