...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biomedical signal processing and control >A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization
【24h】

A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization

机译：一种基于磷虾群优化的新型改良直方图均衡化医学图像对比度增强

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, a novel krill herd (KH) based optimized contrast and sharp edge enhancement framework is introduced for medical images. Plateau limit and fitness function are proposed in this paper to achieve the best-enhanced image. A new plateau limit is applied to clip the histogram using minimum, maximum, mean, and median of the histogram with a tunable parameter. The residue pixels are reallocated to the relative vacancy available on histogram bins. This method explores KH meta-heuristic algorithm to automatically adjust the tunable parameter based on a novel fitness function. Fitness function contains two different objective functions, which use edge, entropy, gray level co-occurrence matrix (GLCM) contrast, and GLCM energy of image for best visual, contrast enhancement and improved different characteristic information of the anatomical images. This method is compared with a different state of the art methods to check the viability and vigorous of the scheme and salp swarm algorithm (SSA) optimization is also used for the fair comparison of the proposed approach. The results show that the proposed framework is having superior performance compared to all the existing methods, both qualitatively and quantitatively, in terms of contrast, information content, edge details, and structure similarity. (C) 2019 Elsevier Ltd. All rights reserved.
机译：在本文中，介绍了一种新颖的基于磷虾群（KH）的优化对比度和锐利边缘增强框架，用于医学图像。为了达到最佳效果，本文提出了高原极限和适应度函数。应用新的平稳极限，以使用带有可调参数的直方图的最小值，最大值，平均值和中位数来裁剪直方图。残差像素被重新分配为直方图bin上可用的相对空白。该方法探索了一种基于新的适应度函数的KH元启发式算法，以自动调整可调参数。适应度函数包含两个不同的目标函数，它们使用边缘，熵，灰度共生矩阵（GLCM）对比度和图像的GLCM能量来获得最佳视觉效果，对比度增强并改善了解剖图像的不同特征信息。将该方法与现有技术的不同方法进行了比较，以检查该方案的可行性和活力，同时还采用了蜂群算法（SSA）优化来公平比较所提出的方法。结果表明，在对比度，信息内容，边缘细节和结构相似性方面，与所有现有方法相比，该框架在质量和数量上均优于所有现有方法。 （C）2019 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号