Noise-Enhanced Detection of Micro-Calcifications in Digital Mammograms
Noise-Enhanced Detection of Micro-Calcifications in Digital Mammograms
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DOI：
10.1109/jstsp.2008.2011162
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发表时间：
2009-02-01
影响因子：
7.5
通讯作者：
Varshney, Pramod K.
中科院分区：
文献类型：
--
作者：
Peng, Renbin;Chen, Hao;Varshney, Pramod K.
乳房X光检查中微钙化的出现是乳腺癌的重要早期征兆。自动微钙化检测技术在癌症诊断和治疗中发挥着重要作用。然而，这仍然是一项具有挑战性的任务。本文提出了使用随机共振（SR）噪声检测微钙化的新算法。在这些算法中，将适当剂量的噪声添加到异常乳房X光照片中，以便在不改变检测器参数的情况下改进次优病变检测器的性能。首先，提出了一种基于 SR 噪声的检测方法来改进一些由于高斯假设而导致模型失配的次优检测器。此外，提出了一种基于 SR 噪声的检测增强框架来处理更一般的模型不匹配情况。我们的算法和框架在一组 75 张有代表性的异常乳房 X 光照片上进行了测试。与我们工作中开发的几种分类和检测方法以及文献中提供的方法相比，它们具有卓越的性能。
The appearance of micro-calcifications in mammograms is a crucial early sign of breast cancer. Automatic micro-calcification detection techniques play an important role in cancer diagnosis and treatment. This, however, still remains a challenging task. This paper presents novel algorithms for the detection of micro-calcifications using stochastic resonance (SR) noise. In these algorithms, a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. First, a SR noise-based detection approach is presented to improve some suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a SR noise-based detection enhancement framework is presented to deal with more general model mismatch cases. Our algorithms and the framework are tested on a set of 75 representative abnormal mammograms. They yield superior performance when compared with several classification and detection approaches developed in our work as well as those available in the literature.