Quantitative Characterization Of Particle Morphology By Image Processing Method

Among many particle measurement methods,image processing can measure particle size and quantitatively describe particle shape,so it is widely used.According to the different scales,the quantitative characterization parameters of particle morphology are divided into three categories: macroscopic,mesoscopic and microscopic.At present,some commonly used algorithms for calculating particle quantitative characterization parameters are difficult to meet the increasing application requirements in terms of accuracy and calculation speed,and the existing quantitative characterization parameters are not enough to accurately describe the characteristics of some particle contours.Therefore,based on the image method,this paper studies the optimization of parameter algorithm and the expansion of parameter representation range.The main research contents are as follows:1 Analyzeing the algorithm principles and application scope of existing characterization parameters based on the existing macroscopic,mesoscopic and microscopic quantitative characterization parameters.The advantages and disadvantages of the optimization methods of some parameter algorithms are analyzed.2 Roundness is a parameter to describe the shape of particle contour on the mesoscopic level.Aiming at the difficulties of existing roundness algorithms in finding angular circles,a new roundness optimization algorithm is proposed,which is divided into four steps: Firstly,the particle contour is transformed into a polar coordinate expansion curve,and then the curve is segmented using the minimum points,and the segmented curve is fitted using Fourier fitting algorithm.Secondly,the convex parts in the fitted contours are selected,and the convex parts containing the corner circles are selected according to the prominence of the convex parts.Finally,the key points used to fit the corner circles in the convex part are selected,and the radius of the corner circles is obtained to calculate the roundness.The K-S standard particle chart and other scholars’ research results are used to verify the algorithm.The experimental results show that the accuracy of the number of corner circles calculated by the optimization selection method and the accuracy of roundness classification are 100%.3 In order to expand the scope of application of quantitative characterization parameters and fill the gap of existing characterization parameters in accurately describing the roundness of particle contour,this paper proposes to use parameter bluntness to describe the roundness of particles based on the way of image construction.Because the calculation of bluntness needs to introduce mellow figure and observation scale as auxiliary,this paper gives the construction process of roundness figure and the definition of observation scale.Then,the accuracy of bluntness and the selection criteria of observation scale are analyzed using the Powers roundness chart.The results show that using the bluntness and observation scale as the evaluation criteria of particle roundness is not only consistent with the original quantitative characterization results,but also can refine and identify the roundness of different particles.