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Updated: Jul 25, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

一种高效的图像细分方法,基于预期最大化和Salp群群算法.

Ehsan Ehsaeyan¹

¹Electrical Engineering Department, Sirjan University of Technology, Sirjan, Iran.

Multimedia tools and applications

|June 26, 2023

概括

本研究引入了一种新的图像值方法,将预期最大化 (EM) 与Salp Swarm算法 (SSA) 结合起来. 改进的方法比传统的EM方法提高了医疗图像细分的准确性.

科学领域:

医学图像分析医学图像分析
计算智能是一种计算智能.
计算机视觉计算机视觉

背景情况:

多级图像值对于图像细分至关重要.
期望最大化 (EM) 是高效的,但受到局部最佳和无法保证类号的困扰.
高斯混合模型 (GMM) 用于在EM中进行组图估计.

研究的目的:

开发一种新的值方法,克服EM的局限性.
通过混合EM-SSA方法来增强医疗图像细分.
为了确保在分割过程中保持所需的集群数量.

主要方法:

一种混合方法,将预期最大化 (EM) 和Salp Swarm算法 (SSA) 结合起来,用于多层次的图像值.
使用SSA引导EM走向更好的解决方案并逃避局部最佳.
实施一个机制来维持指定数量的集群.

主要成果:

与传统的EM相比,提议的EM-SSA方法在峰值信号噪声比率 (PSNR) 中平均有5.27%的改善,在特征模拟 (FSIM) 中有2.01%的改善.
在CT扫描图像中,该方法在PSNR中排名第一,在FSIM中排名第二,与其他四种细分技术相比.
定性和定量结果显示,与现有最先进的方法相比,细分性能优越.

关键词:

人工智能的人工智能是人工智能.期望最大化期望最大化图像细分图像细分图像细分多级值设置多级值设置萨尔普群群算法萨尔普群群算法

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结论:

组合的EM-SSA算法为多层次图像值提供了强大的和有效的解决方案.
这种新的方法显著提高了医疗图像细分的准确性和可靠性.
该方法为医学成像中的复杂图像细分任务提供了一个有希望的替代方案.