Techniques for Processing and Analyzing Grayscale Images Using Morphological Methods

This article utilizes rice.tif as a sample image to demonstrate key techniques for processing and analyzing grayscale images through morphological methods. The implementation begins with background homogenization to eliminate uneven illumination, typically achieved using morphological opening operations with a structuring element larger than the objects of interest. This preprocessing step ensures more accurate image data for subsequent analysis. Following background correction, we apply thresholding techniques (such as Otsu's method) to convert the processed grayscale image into a binary image, facilitating easier object manipulation and analysis. The final stage involves component labeling through connected component analysis, which identifies individual objects and extracts their properties. This process enables statistical feature calculation including object count, area measurements, centroid coordinates, and other morphological characteristics. Through these implemented techniques - background correction using morphological operations, adaptive thresholding, and connected component analysis - we can effectively process grayscale images and extract valuable quantitative information for comprehensive image analysis and conclusions.