Brief Introduction to the Principle and Functions of Microscope Image Analyzers
The system of the image analyzer consists of an optical imaging system consisting of a metallographic microscope and a microscopic camera stage, which is used to form images of metallographic specimens or photographs. Metallographic microscope can directly perform quantitative metallographic analysis on metallographic samples; Microscopic camera station is used for analyzing metallographic photos, negatives, and objects.
In order to store, process, and analyze images using a computer, the first step is to digitize the images. A frame of image is composed of a distribution that does not match the grayscale, and is displayed as j=j (x, y) using mathematical symbols. x and y are the coordinates of the pixels on the image, and j is the grayscale value displayed through leakage. So, a frame of image can be displayed using an m × n-order moment leakage, where each element in the moment corresponds to a pixel in the image, and the value of aij is the grayscale value of the pixel belonging to the i-th row and j-th column in the leakage display image. CCD camera (charge coupled device camera) is an image digitization device. The microscopic features on the metallographic specimen are imaged on a CCD through an optical system and undergo photoelectric conversion and scanning by the CCD. Then, they are extracted as image flag codes, expanded by an expander, and quantified into grayscale levels for storage, and finally digital images are obtained. The accounting machine sets a grayscale threshold T based on the grayscale values of the features to be measured in the digital image. For any pixel in the digital image, if its grayscale is greater than or equal to T, white (grayscale value 255) is used to replace its original grayscale; If it is less than T, black (gray value 0) can be used to replace the original gray level, which can convert the gray level image into a binary image that only requires black and white gray levels, and then perform the required processing on the image, making it easy for the accounting function to perform image analysis obligations such as particle counting, area, and perimeter measurement on the binary image. If pseudo colorful processing is used, 256 gray levels can be converted into corresponding colors, making details with similar gray levels and their surrounding conditions or other details easy to recognize, and then improving the image, which is more conducive to the processing of multi feature images by the computer.
