Local image enhancement processing is executed on an image obtained by imaging a transcription material. The enhanced image is divided into a plurality of patch images and input to a machine learning identifier. The patch images after segmentation output from the machine learning identifier are combined to generate a likelihood map image of skin ridges from the whole image based on a result of the segmentation. Binarization processing is executed on the likelihood map image to generate a binary image. A skin ridge region is extracted based on the binary image to calculate the area of the skin ridge region.

A method and a device are useful for detecting a binding of autoantibodies from a patient sample to double-stranded deoxyribonucleic acid (DNA) using Crithidia luciliae cells by fluorescence microscopy and by digital image processing.

A substrate inspection apparatus includes: an image sensor which obtains first image data of a first substrate and second image data of a second substrate; and a processor which obtains synthetic image data by using the first and second image data, where the processor obtains first first spot information and first non-spot information of a first first spot area and a first non-spot area on the first substrate, based on the first image data, obtain first second spot information and second non-spot information of a first second spot area and a second non-spot area on locations on the second substrate corresponding to locations of the first first spot area and the first non-spot area on the first substrate, based on the second image data, and obtain the synthetic image data by using the first first spot information, the first second spot information and the first and second non-spot information.

Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target electronic image, the machine learning system having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the target electronic image identifying an area of interest based on the at least one characteristic of the target specimen and/or the at least one characteristic of the target electronic image.

Methods and systems for learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.

A cancer cell detection device includes a computer with a database and a display and a microscope coupled to the computer. The microscope has a base upon which a biopsy sample can be placed. The device further includes a camera coupled to the microscope and computer. The camera is configured to capture images of the biopsy sample. The device also has a filter configured to attach to the microscope and a connection feature for connecting the computer to the camera and the filter. The computer further includes a processor that processes the images captured by the camera and classifies the images according to known variables stored in the database.

An imaging system includes a microscope to generate magnified images of regions of interest of a tissue sample, a camera to capture and store the magnified images, and a controller. The controller is configured to, for each magnification level in a sequence of increasing magnification levels, image one or more regions of interest of the tissue sample at the current magnification level. For each region of interest, data is generated defining one or more refined regions of interest based on the magnified image of the region of interest of the tissue sample at the current magnification level. Each refined region of interest corresponds to a proper subset of the tissue sample, and the refined regions of interest of the tissue sample provide the regions of interest to be imaged at a next magnification level from the sequence of increasing magnification levels.

A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.

The method for automated cell positioning can include: sampling a video of a scene having a gamete, tracking the gamete, and positioning the gamete within a target region. The method can optionally include: determining attribute values for the gamete, selecting the gamete, reorienting the gamete, and/or manipulating the gamete, and/or any suitable steps.

A high depth of field image generating apparatus according to the present disclosure includes a region segmentation unit which segments a region for a stereo image to generate region data, a depth estimating unit which estimates depths for the stereo image to generate depth data, and a high depth of field image generating unit which generates a high depth of field image from the stereo image, the region data, and the depth data.