• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Image segmentation using prior information and its application on medical ultrasound image processing.
【24h】

Image segmentation using prior information and its application on medical ultrasound image processing.

机译：使用先验信息的图像分割及其在医学超声图像处理中的应用。

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 Medical Imaging, Ultrasound Imaging is one of the major modalities for Image-Guided surgery and has been playing an increasingly important role in medical diagnosis. This thesis addresses the processing, especially the segmentation, of medical ultrasound imaging. Many traditional segmentation methods fail due to the speckle noise produced by the physical mechanism of ultrasonic devices. This thesis first introduces a preprocessing technique for ultrasound speckle removal using the perception theory. Then the performances of several traditional segmentation methods are studied and a novel method is proposed for fuzzy image segmentation. It is based on the postulate that points with high class uncertainty will have low region homogeneity. Although the proposed method outperforms several traditional segmentation methods in segmenting objects with connective and homogenous regions, it is not adequate for ultrasound image segmentation neither, because of the speckle noise and the weak boundaries between different tissues in ultrasound images. This investigation motivates our further study on the prior knowledge based segmentation approach.; Finally, a novel texture and shape priors based method for ultrasound image segmentation is presented. Texture features are extracted by applying a bank of Gabor filters on test images through a two-sided convolution strategy. Meanwhile, the shape constraints are described by an average zero level set function of the signed distance representations of the training data. Segmentation is implemented by calculating the parameters of the model to minimize a novel texture-based energy function. A series of experimental results on simulated images, natural images and real medical ultrasound images are demonstrated and discussed. These results are compared with other image segmentation methods and manual segmentation to evaluate the effectiveness of this novel knowledge-based segmentation approach.
机译：在医学成像中，超声成像是图像引导手术的主要方式之一，并且在医学诊断中起着越来越重要的作用。本文探讨了医学超声成像的处理方法，尤其是分割方法。许多传统的分割方法由于超声设备的物理机制产生的斑点噪声而失败。本文首先介绍了一种基于感知理论的超声散斑去除预处理技术。然后研究了几种传统分割方法的性能，提出了一种新的模糊图像分割方法。基于这样的假设，具有高类别不确定性的点将具有较低的区域同质性。尽管所提出的方法在分割具有结缔组织和同质区域的对象方面胜过几种传统的分割方法，但是由于斑点噪声和超声图像中不同组织之间的弱边界，因此对于分割超声图像也不足够。这项调查激发了我们对基于先验知识的分割方法的进一步研究。最后，提出了一种新颖的基于纹理和形状先验的超声图像分割方法。通过使用双面卷积策略在测试图像上应用一堆Gabor滤波器来提取纹理特征。同时，形状约束由训练数据的带符号距离表示的平均零水平设置函数来描述。通过计算模型参数以最小化基于纹理的新颖能量函数来实现分割。演示和讨论了一系列关于模拟图像，自然图像和实际医学超声图像的实验结果。将这些结果与其他图像分割方法和手动分割进行比较，以评估这种新颖的基于知识的分割方法的有效性。

著录项

  • 作者

    Xie, Jun.;

  • 作者单位

    The Chinese University of Hong Kong (People's Republic of China).;

  • 授予单位 The Chinese University of Hong Kong (People's Republic of China).;
  • 学科 Computer Science.; Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 204 p.
  • 总页数 204
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;生物医学工程;
  • 关键词

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