...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of medical systems >Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for breast cancer diagnosis.
【24h】

Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for breast cancer diagnosis.

机译：基于乳腺X射线图像的乳腺组织分类，采用内核自优化的fisher判别器进行乳腺癌诊断。

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

Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer with digital mammogram. Current methods endure two problems, firstly pectoral muscle influences the classification performance owing to its texture similar to parenchyma, and secondly classification algorithms fail to deal with the nonlinear problem from the digital mammogram. For these problems, we propose a novel framework of breast tissue classification based on kernel self-optimized discriminant analysis combined with the artifacts and pectoral muscle removal with multi-level segmentation based Connected Component Labeling analysis. Experiments on mini-MIAS database are implemented to testify and evaluate the performance of proposed algorithm.
机译：乳腺组织分类是用计算机数字化乳腺X线照片对乳腺癌进行计算机辅助诊断的重要而有效的方法。当前的方法存在两个问题，首先，胸肌由于其质地类似于薄壁组织而影响分类性能，其次，分类算法无法处理数字化乳腺X线照片中的非线性问题。针对这些问题，我们提出了一种新的乳腺组织分类框架，该框架基于核仁自优化判别分析，结合伪影和胸肌去除以及基于多层次分割的连接成分标记分析。在mini-MIAS数据库上进行了实验，以验证和评估该算法的性能。

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