...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Academic radiology >Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms.
【24h】

Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms.

机译：计算机辅助诊断方案，用于通过随访放大的乳腺X线照片确定簇状微钙化的组织学分类。

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

RATIONALE AND OBJECTIVES: Our purpose in this study was to investigate the usefulness of follow-up magnification mammograms (i.e., both current and previous magnification mammograms) in a computer-aided diagnosis (CAD) scheme for identifying the histological classification of clustered microcalcifications. MATERIALS AND METHODS: Our database consisted of current and previous magnification mammograms obtained from 93 patients before and after 3-month follow-up: 11 invasive carcinomas, 19 noninvasive carcinomas of the comedo type, 25 noninvasive carcinomas of the noncomedo type, 23 mastopathies, and 15 fibroadenomas. In our CAD scheme, we extracted five objective features of clustered microcalcifications from each of the current and previous magnification mammograms by taking into account image features that experienced radiologists commonly use to identify histological classifications. These features were then merged by a modified Bayes discriminant function for distinguishing among five histological classifications. For the input of the modified Bayes discriminant function, we used five objective features obtained from the previous magnification mammogram (previous features), five objective features obtained from the current magnification mammogram (current features), and the set of the five previous features and the five current features. RESULTS: The classification accuracies with the five current features were higher than those with the five previous features. These classification accuracies were improved substantially by using the set of the five previous features and the five current features. For the set of the five previous features and the five current features, the classification accuracies of our CAD scheme were 81.8% (9 of 11) for invasive carcinoma, 84.2% (16 of 19) for noninvasive carcinoma of the comedo type, 76.0% (19 of 25) for noninvasive carcinoma of the noncomedo type, 73.9% (17 of 23) for mastopathy, and 86.8% (13 of 15) for fibroadenoma. CONCLUSION: Our CAD scheme with use of follow-up magnification mammograms improved classification performance for mammographic clustered microcalcifications.
机译：理由和目的：我们在这项研究中的目的是研究在计算机辅助诊断（CAD）方案中用于确定簇状微钙化的组织学分类的后续放大倍数X线照片（即当前和以前的放大倍数X线照片）的有效性。材料和方法：我们的数据库包含从3个月的随访前后的93例患者获得的当前和以前的放大的乳房X线照片：11种浸润性癌，19个粉刺类型的非浸润性癌，25个非粉刺类型的非浸润性癌，23个乳腺病，和15个纤维腺瘤。在我们的CAD方案中，我们通过考虑经验丰富的放射科医生通常用来识别组织学分类的图像特征，从当前和以前的放大倍数X线照片中提取了簇状微钙化的五个客观特征。然后，通过修改后的贝叶斯判别函数合并这些特征，以区分五个组织学分类。对于修改后的贝叶斯判别函数的输入，我们使用了从先前的放大倍数X线照片（先前的特征）获得的五个客观特征，从当前的放大倍数X线图（当前特征）获得的五个客观特征，以及五个先前的特征和当前的五个功能。结果：具有五个当前功能的分类精度高于具有五个先前功能的分类精度。通过使用五个先前功能和五个当前功能的集合，大大提高了这些分类精度。对于这五个先前特征和五个当前特征的集合，我们的CAD方案的分类准确性对于浸润性癌为81.8％（11个中的9个），粉刺型非浸润性癌为84.2％（19个中的16个），76.0％对于非粉刺型非侵袭性癌，其诊断率为（19分，共25分），对于乳腺病，为73.9％（23分，共17分），对于纤维腺瘤，其比例为86.8％（15分，共13分）。结论：我们的CAD方案采用了后续放大的乳腺X线照片，改善了乳腺X线照相群集微钙化的分类性能。

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