Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Department of Electrical, Biomedical and Mechatronic Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Comput Methods Programs Biomed. 2015 Nov;122(2):89-107. doi: 10.1016/j.cmpb.2015.06.009. Epub 2015 Jul 4.
Breast cancer is one of the most perilous diseases among women. Breast screening is a method of detecting breast cancer at a very early stage which can reduce the mortality rate. Mammography is a standard method for the early diagnosis of breast cancer. In this paper, a new algorithm is proposed for breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution (SR). The presented algorithm includes three main parts including pre-processing, feature extraction and classification. In the pre-processing stage, after determining the region of interest (ROI) by an automatic technique, the quality of image is improved using NSCT and SR algorithm. In the feature extraction part, several features of the image components are extracted and skewness of each feature is calculated. Finally, AdaBoost algorithm is used to classify and determine the probability of benign and malign disease. The obtained results on Mammographic Image Analysis Society (MIAS) database indicate the significant performance and superiority of the proposed method in comparison with the state of the art approaches. According to the obtained results, the proposed technique achieves 91.43% and 6.42% as a mean accuracy and FPR, respectively.
乳腺癌是女性最危险的疾病之一。乳房筛查是一种在早期发现乳腺癌的方法，可以降低死亡率。乳腺 X 线摄影是乳腺癌早期诊断的标准方法。本文提出了一种基于非下采样轮廓变换（NSCT）和超分辨率（SR）的数字乳腺 X 线摄影中乳腺癌检测和分类的新算法。该算法包括预处理、特征提取和分类三个主要部分。在预处理阶段，通过自动技术确定感兴趣区域（ROI）后，使用 NSCT 和 SR 算法来提高图像质量。在特征提取部分，提取图像成分的多个特征，并计算每个特征的偏度。最后，使用 AdaBoost 算法对良性和恶性疾病进行分类并确定概率。在 Mammographic Image Analysis Society（MIAS）数据库上获得的结果表明，与现有方法相比，该方法具有显著的性能优势。根据获得的结果，该技术的平均准确率和 FPR 分别为 91.43%和 6.42%。
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