• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Fuzzy Systems >EFIS—Evolving Fuzzy Image Segmentation
【24h】

EFIS—Evolving Fuzzy Image Segmentation

机译：EFIS-不断发展的模糊图像分割

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

Despite the large number of techniques proposed in recent years, accurate segmentation of digital images remains a challenging task for automated computer algorithms. Approaches based on machine learning hold particular promise in this regard, because in many applications, e.g., medical image analysis, frequent user intervention can be assumed to correct the results, thereby generating valuable feedback for algorithmic learning. In order to learn segmentation of new (unseen) images, such user feedback (correction of current or past results) seems indispensable. In this paper, we propose the formation and evolution of fuzzy rules for user-oriented environments in which feedback is captured by design. The evolving fuzzy image segmentation (EFIS) can be used to adjust the parameters of existing segmentation methods, switch between their results, or fuse their results. Specifically, we propose a single-parametric EFIS (SEFIS), apply its rule evolution to breast ultrasound images, and evaluate the results using three segmentation methods, namely, global thresholding, region growing, and statistical region merging. The results show increased accuracy across all tests and for all methods. For instance, the accuracy of statistical region merging can be improved from 59% ± 30% to 71% ± 22%. We also propose a multiparametric EFIS (MEFIS) for switching between or fusing the results of multiple segmentation methods. Preliminary results indicate that MEFIS can further increase overall segmentation accuracy. Three thresholding methods with accuracies of 62% ± 11%, 64% ± 16%, and 61% ± 9% were combined to reach an overall accuracy of 66% ± 15%. Finally, we compare our SEFIS scheme with five other thresholding methods to evaluate its overall performance.
机译：尽管近年来提出了大量技术，但是对于自动计算机算法而言，数字图像的精确分割仍然是一项艰巨的任务。在这方面，基于机器学习的方法具有特殊的前景，因为在许多应用中，例如医学图像分析，可以假设频繁的用户干预以纠正结果，从而为算法学习生成有价值的反馈。为了学习新的（看不见的）图像的分割，这种用户反馈（当前或过去结果的校正）似乎是必不可少的。在本文中，我们提出了面向用户的环境中模糊规则的形成和演化，在这种环境中，设计可以捕获反馈。不断发展的模糊图像分割（EFIS）可用于调整现有分割方法的参数，在其结果之间切换或融合其结果。具体而言，我们提出了一种单参数EFIS（SEFIS），将其规则演化应用于乳房超声图像，并使用三种分割方法（即全局阈值化，区域增长和统计区域合并）评估结果。结果表明，所有测试和所有方法的准确性均得到提高。例如，统计区域合并的准确性可以从59％±30％提高到71％±22％。我们还提出了一种多参数EFIS（MEFIS），用于在多种细分方法的结果之间切换或融合。初步结果表明，MEFIS可以进一步提高整体分割的准确性。结合了三种阈值方法，其准确度分别为62％±11％，64％±16％和61％±9％，从而达到66％±15％的整体精度。最后，我们将SEFIS方案与其他五种阈值方法进行比较，以评估其总体性能。

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