Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
Int J Comput Assist Radiol Surg. 2012 Jul;7(4):573-83. doi: 10.1007/s11548-011-0659-0. Epub 2011 Oct 11.
Breast masses exhibit variability in margins, shapes, and dimensions, so their detection is a difficult task in mammographic computer-aided diagnosis. Mass detection is usually a two-step procedure: mass identification and false-positive reduction. A new method to automatically detect mass lesions in mammographic images with tuning according to the breast tissue density was developed and tested.
A modified phase portrait analysis method was introduced, based on the eigenvalue condition number and an eigenvalue intensity map. The method uses an iterative and tissue density-adaptive segmentation procedure with extraction of geometric features. False-positive reduction is accomplished using a fuzzy inference-based classifier. A leave-one-image-out cross-validation procedure was implemented, and stepwise regression analysis was used to automatically extract an optimal set of features. Testing and validation were performed on two different data sets containing at least one malignant mass D1 (388 images) and D2 (674 images), and a third data set N1 (50 images) was used consisting of normal controls. These three data sets were taken from the Digital Database for Screening Mammography.
For sensitivities of 0.9, 0.85, 0.80, and 0.75, the best results on cancer images exhibit an False-Positive per Image (FPpI) equal to 0.6, 0.45, 0.35, and 0.3, respectively, using a Bayes Linear Discriminant Analysis (LDA) classifier and an FPpI of 0.85, 0.7, 0.55, and 0.45 using a fuzzy inference system (FIS) for false-positive reduction. When the algorithm is tested on normal images, an FPpI equal to 0.4, 0.3, 0.25, and 0.2 was observed using LDA and 0.3, 0.25, 0.2, and 0.15 using the FIS.
A preclinical study of an automatic breast mass detection algorithm provided promising results in terms of sensitivity and low false-positive rate. Further development and clinical testing are justified based on the results.
乳腺肿块的边缘、形状和大小存在多样性，因此在乳腺计算机辅助诊断中检测它们是一项艰巨的任务。肿块检测通常是一个两步过程：肿块识别和减少假阳性。本文开发并测试了一种新的方法，根据乳腺组织密度进行调整，以自动检测乳腺图像中的肿块病变。
本文引入了一种改进的相图分析方法，该方法基于特征值条件数和特征值强度图。该方法使用一种迭代的、组织密度自适应的分割过程，提取几何特征。假阳性的减少是通过基于模糊推理的分类器来实现的。采用了一种留一图像交叉验证程序，并进行逐步回归分析以自动提取最优特征集。在包含至少一个恶性肿块 D1（388 幅图像）和 D2（674 幅图像）的两个不同数据集以及由正常对照组成的第三个数据集 N1（50 幅图像）上进行了测试和验证。这三个数据集取自数字筛查乳腺数据库。
对于敏感性为 0.9、0.85、0.80 和 0.75，使用贝叶斯线性判别分析（LDA）分类器，在癌症图像上获得的最佳结果为 FPpI 等于 0.6、0.45、0.35 和 0.3，使用模糊推理系统（FIS）进行假阳性减少的 FPpI 等于 0.85、0.7、0.55 和 0.45。当该算法在正常图像上进行测试时，使用 LDA 观察到 FPpI 等于 0.4、0.3、0.25 和 0.2，使用 FIS 观察到 FPpI 等于 0.3、0.25、0.2 和 0.15。
自动乳腺肿块检测算法的临床前研究在敏感性和低假阳性率方面取得了有希望的结果。基于这些结果，进一步的开发和临床测试是合理的。
