Computer-aided diagnosis of masses with full-field digital mammography
Computer-aided diagnosis of masses with full-field digital mammography
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DOI：
10.1016/s1076-6332(03)80290-8
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发表时间：
2002-01-01
影响因子：
4.8
通讯作者：
Thomas, JA
中科院分区：
文献类型：
--
作者：
Li, LH;Clark, RA;Thomas, JA
基本原理和目标。作者开发并评估了一种利用全场数字乳腺 X 线摄影 (FFDM) 进行质量检测的计算机辅助诊断 (CAD) 方法。材料和方法。 FFDM 的新 CAD 方法采用自适应、非线性多尺度处理和混合分类方法。主要策略是（a）在将乳房X线照相图像输入到分析模块之前对其进行“标准化”，（b）调整可疑区域的分割以适应肿块和乳房X线照片的不同特征，以及（c）在区分肿块和正常组织区域时使用组合的“硬”和“软”决策。使用了诊断 FFDM 乳房 X 光照片的两个数据集。训练数据集包括 36 个正常和 24 个异常乳房 X 光照片（34 个肿块），测试数据集包括 24 个正常和 10 个异常乳房 X 光照片。 （10 块）。该诊断数据库中的肿瘤比作者之前使用的筛查数据库中的肿瘤更加微妙且难以检测。结果。在有限的数据库和部分优化的情况下，训练时获得了 91% 的灵敏度，每张图像的误报率为 3.21。在 CAD 系统的这个经过训练的操作点，测试中检测到了 10 个细微质量中的 6 个。结论。在屏幕胶片乳房 X 线摄影中开发的 CAD 算法可以针对 FFDM 进行修改。在将 CAD 有效集成到 FFDM 性能中之前，需要更多的数据分析以及系统优化和评估。
Rationale and Objectives. The authors developed and evaluated a method of computer-aided diagnosis (CAD) for mass detection with full-field digital mammography (FFDM).Materials and Methods. The new CAD method for FFDM employs adaptive, nonlinear multiscale processing and hybrid classification methods. The major strategies are (a) to "standardize" the mammographic image before it is input to the analysis modules, (b) to adapt the segmentation of suspicious regions adapt to accommodate different characteristics of masses and mammograms, and (c) to use combined "hard" and "soft" decision making in discriminating between mass and normal tissue regions. Two data sets of diagnostic FFDM mammograms were used. The training data set includes 36 normal and 24 abnormal mammograms (34 masses), and the testing data set includes 24 normal and 10 abnormal mammograms. (10 masses). The tumors in this diagnostic database were more subtle and difficult to detect than those in screening databases the authors have used before.Results. With the limited database and a partial optimization, a sensitivity of 91% was obtained in training, with a false-positive rate of 3.21 per image. At this trained operating point of the CAD system, six of 10 subtle masses were detected in testing.Conclusion. The CAD algorithms developed in screen-film mammography can be modified for FFDM. More data analysis and system optimization and evaluation will be needed before CAD can be integrated efficiently into the performance of FFDM.