With the development of mineral resources, minerals are becoming increasingly difficult to process. In order to utilize these resources more effectively, in-depth research into process mineralogy has become increasingly important in the field of mineralogy, and particle size measurement under the microscope is one of the critical aspects of process mineralogy. At present, the use of scanning electron microscopes and other equipment for measurement is very expensive, and manual measurement has problems such as poor accuracy and low efficiency. In addition, there is a lack of reference materials for the segmentation algorithm of mineral light images. This article proposes a Gaussian pyramid based on bilateral filtering combined with directional maximum intercept to measure mineral particle size under the microscope. In the experiments, different segmentation algorithms were studied, including Gaussian pyramid segmentation based on bilateral filtering, segmentation based on Fuzzy C-Means, and the rapidly developing deep learning segmentation algorithms in recent years. By comparing the segmentation effects of these three algorithms on various mineral thin-section images, the Gaussian pyramid segmentation algorithm based on bilateral filtering was selected as the optimal one. This was then combined with the directional maximum intercept method to measure the particle size of ilmenite and pyrite images. The experimental results show that the segmentation method based on the bilateral filtering Gaussian pyramid technique has higher segmentation accuracy than the other two algorithms, and can accurately measure the particle size of minerals under the microscope. Compared with manual measurement, this method can effectively and accurately measure the microscopic particle size of target minerals, greatly reducing the workload of measurement personnel and reducing the time spent on measurement.
