Cosine-enhanced tuna swarm optimized exponential entropy segmentation method for sand grain microscopic images

Abstract

The feature extraction of sand grain size, color and texture is a necessary step to identify clastic components. The sand grain features are complex and various, which brings difficulties to geological identification. Aiming at these images, a cosine-enhanced tuna swarm optimized exponential entropy segmentation method is proposed, which can effectively preserve the texture features of various sand grains. Firstly, for the tuna swarm optimization (TSO) algorithm, three improvement strategies are proposed: cosine spiral movement, cosine parabolic movement and Gauss-Cauchy mutation, which improve the TSO's global and local search. This algorithm is called cosine-enhanced TSO (CETSO). Benchmark function experiments showed that the convergence accuracy and stability of CETSO are greatly improved, and the convergence speed is also slightly increased. Secondly, CETSO optimized the exponential entropy to automatically determine the segmentation thresholds, and the feasibility of the method is verified by taking the information content of the segmented image as the standard. Finally, segmentation experiments were carried out on the Yarlung Zangbo River sand microscopic image dataset, and the results show that the method has high segmentation accuracy and stability for images with high contrast, rich texture, or significant differences in the size of sand debris. Compared with TSO, the CETSO optimized exponential entropy segmented images achieved an improvement of 21% and 93% in the evaluation of the average and standard deviation of the peak signal-to-noise ratio on thirty experiments. And this method has a fast processing speed, and it only takes about 0.85s to divide an image on average, which meets the needs of engineering applications.