Study On Processing Method Of SEM Soil Image Based On Matlab

Soil is porous and weakly cemented sediment. Its microstructure great affects the physical and mechanical properties. When evaluating the engineering properties of soil, we have to pay attention to microstructure. To a great extend, the microscopic geometrical properties of the soil influenced its strength and deformation characteristic. Therefore, qualitative and quantitative studies on soil microstructures are essential to its macroscopic mechanical properties. Meanwhile, the image processing method for SEM images of soil is the foundation of soil microstructure analysis.For the special characteristics of soil SEM images, this paper presents all flow paths of image processing method by using Matlab. It includes image preprocessing, image segmentation and optimizing, image quantitative statistics as well. After lots of testing on the image accurately cut, non-uniform background elimination, contrast adjustment, image noise reduction, image segmentation, optimize imaging, quantitative analysis, etc, the following conclusions are drawn out:1) Compared to other image processing software, Matlab opens full liberalisation of interactive design, and possess more advantages in soil SEM image processing. The treatment process can be visualized with data, and showed liberalisation of interactive operation.2) Focusing on the common problems for soil microstructure images such as image brightness uneven, large image noise, poor image contrast, several methods are discussed to solve those problems. And the corresponding image processing function and parameter setting range are suggested.3) During the image segmentation stage, different binary segmentation methods are proposed for different types of soil microstructure, and a real-time image segmentation tool is designed.4) Because the binary image segmentation sometimes has impact on subsequent precise quantitative statistics, morphological operations are used to optimize the imaging results under the premise of improving statistical accuracy.5) In the image quantitative analysis phase, the quantitative measurement of geometrical parameters in target particle region are achieved based on the code design; By using of inverting, the quantitative measurement of pore geometrical parameters in background are achieved too. The operational results can be automatically extract and storage.