A method of tumor detection and segmentation accepts a first Whole Slide Image (WSI) having a first resolution; creates a corresponding second WSI having a second resolution lower than the first resolution; applies an adaptive thresholding technique to the second WSI to create a background removal mask background; applies the mask to the first WSI to provide a third WSI with extracted patches, characterized by a third resolution, greater than the second resolution and lower than the first resolution; uses a first machine learning system on the third WSI to create a heat map at the third resolution, indicating a subset of the patches likely to include one or more clusters of tumor cells; and uses a second machine learning system on the first WSI and the heat map to segment each patch in a corresponding output image at the first resolution, outlining one or more corresponding clusters.

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vision, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

A method of determining whether a substrate is properly polished includes obtaining an image of the substrate, obtaining intensity values of a luminance plane for the image, generating an intensity histogram from the intensity values of the luminance plane, and analyzing the intensity histogram to determine whether the intensity histogram meets one or more criteria.

A dynamic image enhancement method and device using backlight adjustment, and a computer apparatus. The method comprises: acquiring coding information carried by display content; acquiring an optical indicator parameter of a display; parsing the display content according to the coding information and the optical indicator parameter, and acquiring information of current display content; performing analysis and computation according to the information of the display content, and acquiring analysis data; acquiring a first detail statistics weight and a second detail statistics weight according to the analysis data; acquiring a backlight control parameter and a signal control curve according to the first detail statistics weight and the second detail statistics weight; and adjusting the detail of a current image according to the backlight control parameter and the signal control curve.

A three-dimensional scanning system includes a projection light source, an image capturing apparatus, and a signal processing apparatus. The projection light source is configured to project a two-dimensional light to a target, where the two-dimensional light has a spatial frequency. The image capturing apparatus captures an image of the target illuminated with the two-dimensional light. The signal processing apparatus is coupled to the projection light source and the image capturing apparatus, to analyze a definition of the image of the two-dimensional light, where if the definition of the image of the two-dimensional light is lower than a requirement standard, the spatial frequency of the two-dimensional light is reduced.

Disclosed herein is a method of generating augmented images of tissue of a patient undergoing open treatment, in particular open surgery, wherein each augmented image associates at least one tissue parameter with a region or pixel of the image of the tissue, said method comprising the following steps: estimating a spectral composition of light illuminating a region of interest of the tissue, obtaining one or more multispectral images of the region of interest, applying a machine learning based regressor or classifier to the one or more multispectral images, or an image derived from said multispectral image, to thereby derive one or more tissue parameters associated with image regions or pixels of the corresponding multispectral image, wherein said regressor or classifier has been trained to predict the one or more tissue parameters from a multispectral image under a given spectral composition of illumination, wherein the regressor or classifier employed is made to match the estimated spectral composition of light illuminating said region of interest of the tissue.

A method and system for personalization of digital pathology analysis, may include: an image-analysis based diagnostics module, configured to extract at least one feature of a digital scan of a pathology slide of a patient, a human-machine interface module, configured to present the digital scan to a user for examination and at least one machine learning module, configured to produce at least one personalized suggestion according to the at least one extracted slide feature.

An image processing method, an image processing device, an electronic device, and a non-transitory computer readable storage medium are provided. The image processing method includes: obtaining an input image which includes M character rows; performing global correction processing on the input image to obtain an intermediate corrected image; determining the M character row lower boundaries corresponding to the M character rows according to the intermediate corrected image; and determining the local adjustment reference line and M retention coefficient groups based on the intermediate corrected image and the M character row lower boundaries; determining M local adjustment offset groups corresponding to the M character rows according to the M character row lower boundaries, the local adjustment reference line and the M retention coefficient groups; performing local adjustment on the M character rows in the intermediate corrected image according to the M local adjustment offset groups to obtain the target corrected image.

An image processing method, an image processing device, an electronic device, and a storage medium are provided. The image processing method includes: obtaining an input image, wherein the input image includes M character rows; performing global correction processing on the input image to obtain an intermediate corrected image; determining the M character row lower boundaries; determining the relative offset of all pixels in the intermediate corrected image according to the M character row lower boundaries, the first image boundary and the second image boundary of the intermediate corrected image; determining the local adjustment offset of all pixels in the intermediate corrected image according to the relative offsets of all pixels in the intermediate corrected image; and performing local adjustment on the intermediate corrected image according to the local adjustment offsets of all pixels in the intermediate corrected image to obtain the target corrected image.

For medical imaging such as MRI, machine training is used to train a network for segmentation using both the imaging data and protocol data (e.g., meta-data). The network is trained to segment based, in part, on the configuration and/or scanner, not just the imaging data, allowing the trained network to adapt to the way each image is acquired. In one embodiment, the network architecture includes one or more blocks that receive both types of data as input and output both types of data, preserving relevant features for adaptation through at least part of the trained network.