The purpose of this work is to develop a computer‐aided diagnosis (CAD) system to assist radiologists in the classification of mammogram images. The CAD system is composed of three main steps. The first step is image preprocessing and segmentation with the seeded region growing algorithm applied on a localized triangular region to remove only the muscle. In the second step of the CAD system, we proposed a novel features extraction method, which consists of three stages. In the first, the discrete cosine transform (DCT) is applied on all obtained regions of interest and then only the upper left corner (ULC) of DCT coefficients is retained. Second, we have applied the energy probability to the ULCs that is used as a criterion for selecting discriminant information. At the last stage, a new Most Discriminative power coefficient algorithm has been proposed to select the most significant features. In the final step of the CAD, the support vector machines, Naive Bayes, and artificial neural network (ANN) classifiers are used to make an effective classification. The evaluation of the proposed algorithm on the mini‐Mammographic Image Analysis Society database shows its efficiency over other recently proposed CAD systems in the literature, whereas an accuracy of 100% can be achieved using ANN with a small number of features.

中文翻译：

---

使用频域中的能量概率和最具辨别力的功率系数对乳房 X 光图像进行分类

这项工作的目的是开发一种计算机辅助诊断 (CAD) 系统，以帮助放射科医生对乳房 X 光照片图像进行分类。CAD系统由三个主要步骤组成。第一步是图像预处理和分割，将种子区域生长算法应用于局部三角形区域以仅去除肌肉。在CAD系统的第二步中，我们提出了一种新的特征提取方法，它由三个阶段组成。首先，离散余弦变换 (DCT) 应用于所有获得的感兴趣区域，然后仅保留 DCT 系数的左上角 (ULC)。其次，我们将能量概率应用于 ULC，用作选择判别信息的标准。在最后阶段，提出了一种新的最具判别力系数算法来选择最重要的特征。在 CAD 的最后一步，使用支持向量机、朴素贝叶斯和人工神经网络 (ANN) 分类器进行有效分类。在微型乳腺摄影图像分析协会数据库上对所提出算法的评估表明其效率高于文献中最近提出的其他 CAD 系统，而使用具有少量特征的 ANN 可以实现 100% 的准确度。