.png){kind=icon}
返回
An efficient image segmentation method based on expectation maximization and Salp swarm algorithm
.png){kind=icon}
OA
AI
DOI：10.1007/s11042-023-15149-8.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.
Keyword:
Image segmentation
Multilevel thresholding
Expectation maximization
Salp swarm algorithm
Artificial intelligence
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
暂无机构信息
引用论文
A new EM algorithm for flexibly tied GMMs with large number of components.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models基于深度迁移学习的结直肠癌组织病理学分割模型: 深度预训练模型的比较研究
PREAI
分享收藏Efficient active contour model for medical image segmentation and correction based on edge and region information基于边缘和区域信息的高效活动轮廓医学图像分割与校正
PREAI
分享收藏The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detectionINFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏Vascular pattern and diagnostic accuracy of contrast-enhanced ultrasound (CEUS) in spleen alterations脾脏病变的增强超声（CEUS）血管模式与诊断准确性
PREAI
分享收藏
查看更多内容