A cabinet X-ray image system for obtaining X-ray images and colorized or grey scale density X-ray images of a specimen includes a sampling chamber for containing the specimen, a display, an X-ray system including, an X-ray source, a photon counting X-ray detector, and a specimen platform, and a controller configured to selectively energize the X-ray source, control the photon counting X-ray detector to collect a projection X-ray image of the specimen when the X-ray source is energized, determine the density of different areas of the specimen from data collected from the photon counting X-ray detector of the projection X-ray image, create a density X-ray image of the specimen wherein different areas of the specimen are indicated as a density or range of densities based on the determined density of different areas of the specimen, and selectively display the density X-ray image of the specimen on the display.

A cabinet x-ray includes an x-ray tube, an x-ray detector, and a near-infrared optical system mounted onto the cabinet or positioned within the cabinet, for the production of organic and non-organic images. The computing device receives video data from the near-infrared optical system and determines, based on the video data an optical image displaying the captured real-time image or display images adjacently i.e. Picture-In-Picture (PIP). In particular, the present disclosure also relates to a system and method with corresponding apparatus for capturing a near-infrared image simultaneously with the x-ray image allowing a cabinet x-ray unit to attain and optimize images utilizing near-infrared (NIR) fluorescence for sentinel lymph node (SLN) mapping in breast cancer patients to improve the Sentinel Lymph Node identification and procedure.

Techniques for adapting an adaptive specimen image acquisition system using an artificial neural network (ANN) are disclosed. An adaptive specimen image acquisition system is configurable to scan a specimen to produce images of varying qualities. An adaptive specimen image acquisition system first scans a specimen to produce a low-quality image. An ANN identifies objects of interest within the specimen image. A scan mask indicates regions of the image corresponding to the objects of interest. The adaptive specimen image acquisition system scans only the regions of the image corresponding to the objects of interest, as indicated by the scan mask, to produce a high-quality image. The low-quality image and the high-quality image are merged in a final image. The final image shows the objects of interest at a higher quality, and the rest of the specimen at a lower quality.

The disclosure provides an inspection method determining whether there is a defect in an electrode structural body including a cathode electrode layer, an electrolyte layer and an anode electrode layer electrode by an image processor. The inspection method includes a step of scanning the electrode structural body along a scanning direction to obtain a continuous transmission image, a step of digitizing a shade of each pixel of the transmission image, a step of calculating a difference value between a grayscale of a specific pixel and a median value of grayscales of comparison pixels located in front or rear of the specific pixel along the scanning direction, and a step of determining presence or absence of the defect according to the difference value and a predetermined threshold value.

Provided are an object identification device and an object identification method in which objects can be easily identified. The object identification device is provided with a pixel group extraction unit which scans, in units of the image area, an X-ray transferred image obtained from an imaging unit which performs X-ray imaging to an item to be inspected that is supplied, and extracts a plurality of pixel groups including characteristics of a shape of at least a part of the item to be inspected, and a determination unit which determines, with regard to the plurality of pixel groups extracted by the pixel group extraction unit, whether the item to be inspected corresponds to the object by executing all of the series of mappings related to an angle of an n direction by using each of the weight parameters based on the data group read from the memory unit.

Techniques for adapting an adaptive specimen image acquisition system using an artificial neural network (ANN) are disclosed. An adaptive specimen image acquisition system is configurable to scan a specimen to produce images of varying qualities. An adaptive specimen image acquisition system first scans a specimen to produce a low-quality image. An ANN identifies objects of interest within the specimen image. A scan mask indicates regions of the image corresponding to the objects of interest. The adaptive specimen image acquisition system scans only the regions of the image corresponding to the objects of interest, as indicated by the scan mask, to produce a high-quality image. The low-quality image and the high-quality image are merged in a final image. The final image shows the objects of interest at a higher quality, and the rest of the specimen at a lower quality.

The present disclosure relates to the field of a cabinet x-ray incorporating an x-ray tube, an x-ray detector, and a near-infrared optical system mounted onto the cabinet or positioned within the cabinet, for the production of organic and non-organic images. The computing device receives video data from the near-infrared optical system and determines, based on the video data an optical image displaying the captured real-time image or display images adjacently i.e. Picture-In-Picture (PIP). In particular, the present disclosure also relates to a system and method with corresponding apparatus for capturing a near-infrared image simultaneously with the x-ray image allowing a cabinet x-ray unit to attain and optimize images utilizing near-infrared (NIR) fluorescence for sentinel lymph node (SLN) mapping in breast cancer patients to improve the Sentinel Lymph Node identification and procedure.

The present disclosure relates to the field of a cabinet X-ray incorporating an X-ray tube, an X-ray detector, and a real-time camera, either high definition or standard resolution, for the production of organic and non-organic images and a system and method wherein the attained X-ray radiograph may be colorized to designate different densities. In particular, the disclosure relates to a system and method with corresponding apparatus for capturing a real-time image simultaneously with the X-ray image allowing a cabinet X-ray unit to attain and optimize images either in grayscale or colorized with exact orientation of the 2 images and display the resultant images overlaid/blended upon each other and then saved and transmitted in various formats, i.e. .jpeg., .tiff, DICOM, etc.

Constructing a computer image from raw imaging data or encoded imaging data by transforming a first data structure in which the raw imaging data or the encoded imaging data is stored into a second data structure storing reorganized imaging data. The raw imaging data or the encoded imaging data is received, stored in the first data structure. The computer reorganizes the raw imaging data or the encoded imaging data into the reorganized data and stores the reorganized data in the second data structure, which is a multi-dimensional array having subarrays containing local information needed by a convolutional neural network for processing the reorganized data. Other portions of the multi-dimensional array store other portions of the raw imaging data or the encoded imaging data. The computer also processes the reorganized data using the convolutional neural network to construct the image, whereby a constructed image is formed.

The invention relates to a method for operating a particle beam device and a particle beam device for carrying out the method. The particle beam device comprises a first particle beam column for providing a first particle beam and a second particle beam column for providing a second particle beam. The method includes the following steps: supplying the second particle beam with second charged particles onto an object using the second particle beam column, loading a value of a control parameter into a control unit from a data-base or calculating the value of the control parameter in the control unit, setting an objective lens excitation of a first objective lens of the first particle beam column using the value of the control parameter, detecting second interaction particles using a particle detector, wherein the second interaction particles emerge from an interaction of the second particle beam with the object when the second particle beam is incident on the object, wherein the particle detector is disposed in a region between the first objective lens and a first beam generator of the first particle beam column and/or wherein the particle detector is disposed in the first objective lens and/or at one end of the first objective lens.