Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer-aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker-based watershed segmentation. After segmentation, a two-stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) database and all the mammographic images that contain masses from the mini-mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied.

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开发用于乳房X线图像质量检测的智能CAD系统

乳房X线照相术是在早期阶段更容易治疗的诊断乳腺癌的非常有用的工具。放射线医师在乳房X线照片中寻找的证据有两种：钙化和肿块的存在。在这项研究中，提出了一种智能的计算机辅助诊断系统，用于检测乳房X线照片中的肿块，无论其性质如何。所提出的方法使用具有不同阈值的扩展最大值变换的组合，以便为基于标记的分水岭分割找到合适的内部和外部标记。分割后，使用两阶段分类器从健康的乳腺组织中更好地区分肿块。计算主要基于对比度和纹理特征的特征向量，并采用两种替代方法：为了进一步减少误报区域，实现了贝叶斯分类器和具有高斯核函数的支持向量机（SVM）。使用来自两个在线数据库的数据对系统进行了评估。具体而言，使用了来自新的精选乳腺X射线摄影数字数据库（CBIS-DDSM）数据库的精选乳腺成像子集的73幅乳腺X线照片和包含来自小型乳腺X线图像分析协会（MIAS）数据库的所有乳腺X线照片。在两个数据集中，使用贝叶斯分类器时，整体灵敏度接近80％，而应用SVM时，整体灵敏度则高于85％。使用了来自新的精选乳腺X线摄影数据库（CBIS-DDSM）数据库的新精选乳腺成像子集的73幅乳腺X线照片以及包含来自小型乳腺X线图像分析协会（MIAS）数据库的所有乳腺X线照片。在两个数据集中，使用贝叶斯分类器时，整体灵敏度接近80％，而应用SVM时，整体灵敏度则高于85％。使用了来自新的精选乳腺X线摄影数据库（CBIS-DDSM）数据库的新精选乳腺成像子集的73幅乳腺X线照片以及包含来自小型乳腺X线图像分析协会（MIAS）数据库的所有乳腺X线照片。在两个数据集中，使用贝叶斯分类器时，整体灵敏度接近80％，而应用SVM时，整体灵敏度则高于85％。