Discipline of Radiology, Janeway Child Health Centre, Memorial University of Newfoundland, Newfoundland A1B 3V6, Canada.
BMC Med Imaging. 2013 Dec 13;13:43. doi: 10.1186/1471-2342-13-43.
Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported.
In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized.The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society's database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues.
The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert.
Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%.
在乳房筛查计划中，诊断性能可能受到疾病先验概率的影响。由于乳腺癌的发病率在普通人群中约为百分之零点五，因此筛查检查正常的可能性很大。这一因素可能导致假阴性。筛查计划通常表现出约 83%的敏感性和 91%的特异性。这项研究旨在确定是否可以开发一种系统，根据其包含疾病的可能性，将筛查图像预先分类为正常和可疑的图像。小波被研究为一种解析图像数据的方法，可能会去除混杂信息。报告了一种基于从小波变换乳房 X 光片中提取的特征开发分类系统的方法。
在多步过程中，使用二维离散小波变换处理图像，以在不同的大小尺度上创建一组图像。接下来，从每个图像计算统计特征，并从这些特征中选择一个子集作为一组天真贝叶斯分类器的输入。分类器网络的构建是为了计算父乳腺 X 光片中存在异常的概率。未识别异常，也未对其进行区域化。该算法在两个公开可用的数据库上进行了测试：数字筛查乳腺数据库（DDSM）和乳腺图像分析协会数据库（MIAS）。这些数据库包含放射科医生验证的图像和常见的异常特征，包括：刺状突起、肿块、几何变形和纤维组织。
测试的分类器网络设计实现了足够高的敏感性和特异性，有望在临床环境中得到应用。这一系列首次测试确定了具有 100%敏感性和高达 79%特异性的网络，用于异常。这种性能明显超过了文献中未辅助人类专家报告的平均敏感性。
基于小波特征的分类器对多种病理表现非常敏感，因此几乎消除了第二类错误。对图像进行预分类后，将排序数据库中的先验概率从 37%提高到 74%。
