• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on Medical Imaging 2008: Computer-Aided Diagnosis; 20080219-21; San Diego,CA(US) >Correlative feature analysis of FFDM images
【24h】

Correlative feature analysis of FFDM images

机译：FFDM图像的相关特征分析

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

Identifying the corresponding image pair of a lesion is an essential step for combining information from different views of the lesion to improve the diagnostic ability of both radiologists and CAD systems. Because of the non-rigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this study, we present a computerized framework that differentiates the corresponding images from different views of a lesionfrom non-corresponding ones. A dual-stage segmentation method, which employs an initial radial gradient index (RGI) based segmentation and an active contour model, was initially applied to extract mass lesions from the surrounding tissues. Then various lesion features were automatically extracted from each of the two views of each lesion to quantify the characteristics of margin, shape, size, texture and context of the lesion, as well as its distance to nipple. We employed a two-step method to select an effective subset of features, and combined it with a BANN to obtain a discriminant score, which yielded an estimate of the probability that the two images are of the same physical lesion. ROC analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing between corresponding and non-corresponding pairs. By using a FFDM database with 124 corresponding image pairs and 35 non-corresponding pairs, the distance feature yielded an AUC (area under the ROC curve) of 0.8 with leave-one-out evaluation by lesion, and the feature subset, which includes distance feature, lesion size and lesion contrast, yielded an AUC of 0.86. The improvement by using multiple features was statistically significant as compared to single feature performance. (p < 0.001)
机译：识别病变的相应图像对是组合来自病变不同视图的信息以提高放射科医生和CAD系统的诊断能力的重要步骤。由于乳房的非刚性以及乳房X线照片的2D投影特性，该任务并非易事。在这项研究中，我们提出了一种计算机化的框架，该框架可以将病变的不同视图中的相应图像与不对应的图像区分开。最初使用基于初始径向梯度指数（RGI）的分割和主动轮廓模型的双阶段分割方法从周围组织中提取肿块。然后，从每个病变的两个视图中的每个视图中自动提取各种病变特征，以量化病变的边缘，形状，大小，纹理和背景及其到乳头的距离的特征。我们采用了两步法来选择有效的特征子集，并将其与BANN组合以获得判别分数，从而得出两个图像具有相同物理病变的概率的估计值。在区分相应对和非相应对的任务中，使用了ROC分析来评估单个特征和所选特征子集的性能。通过使用具有124个对应图像对和35个非对应对的FFDM数据库，距离特征得出的AUC（ROC曲线下的面积）为0.8，并通过病灶一劳永逸地进行评估，特征子集包括距离特征，病变大小和病变对比，得出的AUC为0.86。与单个功能的性能相比，使用多个功能的改进具有统计学意义。 （p <0.001）

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