Study on K- mean and Mathematical Morphology Segmentation Algorithm of Coal and Rock Micro Images

We use SEM scanning to observe the micro morphology of coal and rock in Ⅴ₁₀-20230 coal face of Pingdingshan No.10 coal mine. For the SEM images of coal samples without obvious gray area, we explore the feasibility and effectiveness of the first open and then close operation segmentation algorithm in K-means and mathematical morphology, and adopt relevant segmentation algorithm for contrast test. The results show that the matching rate of the algorithm in this paper is 75.48%, and the classification error rate is 16.96%. Compared with the other 4 algorithms, this algorithm enjoys obvious advantages for its higher matching rate and lower classification error rate, therefore, it can segment the pores from the scanned images more accurately. Through this algorithm, when the TPF is 34.79% and the TNF is 97.18%, the correct segmentation ratio is the highest. When the FNF value is 65.21%, and the FPF value is 2.82%, the error segmentation ratio is the lowest. When the sum of the TPF value and the FNF value is 1, and the sum of the TNF value and the FPF value is also 1, the evaluation index is correct.