• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.7 no.30 >Reducing false-positive detections by combining two stage-1 computer-aided mass detection algorithms
【24h】

Reducing false-positive detections by combining two stage-1 computer-aided mass detection algorithms

机译：通过结合两种1级计算机辅助质量检测算法来减少假阳性检测

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 we present a strategy for reducing the number of false-positives in computer-aided mass detection. Our approach is to only mark "consensus" detections from among the suspicious sites identified by different "stage-1" detection algorithms. By "stage-1" we mean that each of the Computer-aided Detection (CADe) algorithms is designed to operate with high sensitivity, allowing for a large number of false positives. In this study, two mass detection methods were used: (1) Heath and Bowyer's algorithm based on the average fraction under the minimum filter (AFUM) and (2) a low-threshold bi-lateral subtraction algorithm. The two methods were applied separately to a set of images from the Digital Database for Screening Mammography (DDSM) to obtain paired sets of mass candidates. The consensus mass candidates for each image were identified by a logical "and" operation of the two CADe algorithms so as to eliminate regions of suspicion that were not independently identified by both techniques. It was shown that by combining the evidence from the AFUM filter method with that obtained from bi-lateral subtraction, the same sensitivity could be reached with fewer false-positives per image relative to using the AFUM filter alone.
机译：在本文中，我们提出了一种减少计算机辅助质量检测中假阳性数的策略。我们的方法是仅标记通过不同“阶段1”检测算法识别的可疑站点中的“共识”检测。所谓“阶段1”，是指每种计算机辅助检测（CADe）算法都被设计为具有高灵敏度，允许出现大量误报。在这项研究中，使用了两种质量检测方法：（1）基于最小滤波器（AFUM）下的平均分数的希思和鲍耶（Heath and Bowyer）算法以及（2）低阈值双向减法算法。将这两种方法分别应用于来自乳腺钼靶筛查数字数据库（DDSM）的一组图像，以获得成对的大量候选对象。通过两种CADe算法的逻辑“和”运算来识别每个图像的共有质量候选物，以消除两个技术都未独立识别的可疑区域。结果表明，通过将AFUM滤波方法的证据与双向减法相结合，相对于单独使用AFUM滤波器，每幅图像的假阳性更少，可以达到相同的灵敏度。

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