Automatic segmentation of aggregate images with MET optimized by chaos SSA

WANG Mengfei; WANG Weixing; LI Limin

doi:10.37188/OPE.20233113.1973

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Automatic segmentation of aggregate images with MET optimized by chaos SSA

Information Sciences | 更新时间：2023-07-17

- Automatic segmentation of aggregate images with MET optimized by chaos SSA
- Optics and Precision Engineering Vol. 31, Issue 13, Pages: 1973-1987(2023)
- 作者机构：
  
  1.长安大学信息学院，陕西西安 710064
  2.温州大学电气与电子工程学院，浙江温州 325035
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233113.1973
  CLC： TP394.1;TH691.9
- Received：30 August 2022，
  
  Revised：11 October 2022，
  
  Published：10 July 2023
- 稿件说明：
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王梦菲,王卫星,李理敏.混沌SSA优化多重熵阈值的骨料图像自动分割[J].光学精密工程,2023,31(13):1973-1987.

WANG Mengfei,WANG Weixing,LI Limin.Automatic segmentation of aggregate images with MET optimized by chaos SSA[J].Optics and Precision Engineering,2023,31(13):1973-1987.
王梦菲,王卫星,李理敏.混沌SSA优化多重熵阈值的骨料图像自动分割[J].光学精密工程,2023,31(13):1973-1987. DOI： 10.37188/OPE.20233113.1973.

WANG Mengfei,WANG Weixing,LI Limin.Automatic segmentation of aggregate images with MET optimized by chaos SSA[J].Optics and Precision Engineering,2023,31(13):1973-1987. DOI： 10.37188/OPE.20233113.1973.

摘要

多重熵阈值（MET）随阈值个数

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的增加其运算时间成倍增长，而相关优化策略的精度与稳定性低，使得分割的骨料图像缺失大量表面粗糙度与边缘等特征信息。为了克服这一问题，提出了一种基于混沌麻雀搜索算法（SSA）优化MET的骨料自动分割模型。为增强SSA的全局优化能力和鲁棒性，在种群位置初始化时加入Logistic混沌映射均匀麻雀分布，并提出扩张参数扩大全局搜索，控距精英变异及时跳出局部停滞，将该算法称为LSSA，可以在不降低收敛速度的情况下提升求解质量。LSSA用于MET参数的自动选取，以Renyi熵、对称交叉熵和Kapur熵作为目标函数，快速确定最佳阈值集。对不同特征的骨料图像进行了分割实验，通过对比6类组合算法与FCM算法，证明了LSSA-MET的有效性，随着

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的增加仍然保持着较快的运行速度，即使

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8.21266651

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时，平均分割一张图片也只需1.532 s。其中LSSA-Renyi熵表现最佳，峰值信噪比、结构相似性和特征相似度分别提高了29.92%，10.67%和5.16%，有效地保留了骨料表面纹理和边缘特征，同时达到了精度与速度的最佳平衡。

Abstract

Multiple entropy thresholding （MET） increases exponentially with an increase in the number of thresholds

. Related optimization strategies exhibit low accuracy and stability with the segmented aggregate images lacking considerable feature information such as surface roughness and edges. To overcome these problems， an automatic image segmentation model based on a chaotic sparrow search algorithm （SSA） was developed to optimize MET. SSA is a newer intelligent optimization algorithm. To enhance the global optimization capability and robustness of SSA， a logistic map is added to the uniform sparrow distribution at the time of population position initialization， an expansion parameter is applied to expand the global search， and temporal local stagnation is avoided by range-control elite mutation jumps. This algorithm is called logistic SSA （LSSA） and can improve the solution quality without reducing convergence speed. LSSA is used for the automatic selection of MET parameters， with the Renyi entropy， symmetric-cross entropy， and Kapur entropy as objective functions to quickly determine the correct thresholds. In this study， image segmentation and algorithm comparison experiments are conducted on aggregate images with different characteristics. The effectiveness of LSSA-MET was demonstrated by comparing six types of combined algorithms with the fuzzy C-means （FCM） algorithm. The proposed algorithm maintains a relatively high speed with an increase in

， taking 1.532 s to split an image on average even when

=6. Among the variousm entropies， LSSA-Renyi entropy performed the best， achieving 29.92%， 10.67%， and 5.16% accuracy improvements in peak signal-to-noise ratio （PSNR）， structural similarity （SSIM）， and feature similarity （FSIM）， respectively， thereby effectively retaining the aggregate surface texture and edge characteristics while achieving the optimum balance between precision and speed.

关键词

Keywords

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