A method for measuring distorted illumination patterns and correcting image artifacts in structured illumination microscopy. The method includes the steps of generating an illumination pattern by interfering multiple beams, modulating a scanning speed or an intensity of a scanning laser, or projecting a mask onto an object; taking multiple exposures of the object with the illumination pattern shifting in phase; and applying Fourier transform to the multiple exposures to produce multiple raw images. Thereafter, the multiple raw images are used to form and then solve a linear equation set to obtain multiple portions of a Fourier space image of the object. A circular 2-D low pass filter and a Fourier Transform are then applied to the portions. A pattern distortion phase map is calculated and then corrected by making a coefficient matrix of the linear equation set varying in phase, which is solved in the spatial domain.

Image analysis method for quantitative assessment of a degree of coordination of uterine motion, comprising: obtaining a recording of a uterus; tracking, in the recording, at least two propagation waves of uterine motion in at least two different regions of the uterus and/or on at least two different times; and determining the degree of coordination of the uterine motion based on a similarity measure of at least two characteristics of the at least two propagation waves.

This application provides a method for detecting images of testing object using hyperspectral imaging. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image and obtaining corresponded feature values for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.

Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem configured for removing one or more patterns in a specimen image that do not touch a defect detected in the specimen image thereby generating a modified specimen image. The computer subsystem is also configured for generating one or more hash codes for the modified specimen image. In addition, the computer subsystem is configured for assigning the specimen image to one of multiple groups based on a distance between the one or more hash codes and one or more other hash codes generated for a second modified specimen image generated for a second specimen image.

A method or system is disclosed to assess transmission errors in a visual media input. Domain knowledge is obtained from the visual media input by content analysis, codec analysis, distortion analysis, and human visual system modeling. The visual media input is divided into partitions, which are passed into deep neural networks (DNNs). The DNN outputs of all partitions are combined with the guidance of domain knowledge to produce an assessment of the transmission error. In one or more illustrative examples, transmission error assessment at a plurality of monitoring points in a visual media communication system is collected and assessed, followed by quality control processes and statistical performance assessment on the stability of the visual communication system.

An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.

There is provided an image processing device including: a plurality of light sources each emitting a plurality of lights; a camera outputting a first subject image obtained by photographing a subject; and a control unit controlling each of the plurality of light sources according to an optical coded pattern set based on a complex modulation transfer function during a shutter exposure time of the camera, and outputting a second subject image in which motion blur is removed from the first subject image according to point spread functions for each color channel modulated by the optical coded pattern set.

A system for analyzing biological specimens by spectral imaging includes a biosensor comprising at least one graphene layer on a substrate and a memory in communication with a processor. The biosensor is configured to acquire a biological specimen sample. The memory and the processor are configured to conduct Raman spectroscopy to obtain spectral data for the sample, transmit the spectral data to a hub for direct or indirect transmission to one or more servers, perform multivariate analysis on the spectral data, and deliver a report based on the multivariate analysis of the spectral data.

A method for delineating a ventricle from MRI data relating to the heart of a patient, the method comprising: a) providing a contrast-enhanced cine MRI dataset; b) providing one or more additional MRI datasets; c) segmenting one or more features on the additional MRI dataset or datasets; d) mapping the segmented features to the contrast-enhanced cine MRI dataset; and e) using the segmented features as mapped in step d) to assist segmentation of the ventricle on the contrast-enhanced cine MRI dataset.
A corresponding device and computer program are also disclosed.

A medical imaging system having at least one medical imaging device providing image data of a subject is provided. The medical imaging system further includes a processing system programmed to train a deep learning (DL) network using a plurality of training images to predict noise in input data. The plurality of training images includes a plurality of excitation (NEX) images acquired for each line of k-space training data. The processing system is further programmed to use the trained DL network to determine noise in the image data of the subject and to generate a denoised medical image of the subject having reduced noise based on the determined noise in the image data.