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Automatic microcalcification and cluster detection for digital and digitised mammograms

机译:自动微钙化和聚类检测,用于数字化和数字化的乳房X光照片

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摘要

In this paper we present a knowledge-based approach for the automatic detection of microcalcifications and clusters in mammographic images. Our proposal is based on using local features extracted from a bank of filters to obtain a local description of the microcalcifications morphology. The developed approach performs an initial training step in order to automatically learn and select the most salient fea tures, which are subsequently used in a boosted classifier to perform the detection of individual micro calcifications. Subsequently, the microcalcification detection method is extended in order to detect clusters. The validity of our approach is extensively demonstrated using two digitised databases and one full-field digital database. The experimental evaluation is performed in terms of ROC analysis for the microcalcification detection and FROC analysis for the cluster detection, resulting in better than 80% sensitivity at 1 false positive cluster per image.
机译:在本文中,我们提出了一种基于知识的方法,可以自动检测乳房X线照片中的微钙化和簇。我们的建议基于使用从一组过滤器中提取的局部特征来获得微钙化形态的局部描述。所开发的方法执行初始训练步骤,以便自动学习和选择最显着的功能,随后将其用于增强分类器中以执行单个微钙化的检测。随后,扩展了微钙化检测方法以检测团簇。使用两个数字化数据库和一个全场数字数据库已充分证明了我们方法的有效性。实验评估是根据ROC分析(用于微钙化检测)和FROC分析(用于聚类检测)进行的,因此每个图像1个假阳性聚类的灵敏度高于80%。

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