Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, 13000, Tlemcen, Algeria.
J Digit Imaging. 2022 Dec;35(6):1544-1559. doi: 10.1007/s10278-022-00677-w. Epub 2022 Jul 19.
Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs. The proposed method is divided into four main steps: a mammogram pre-processing step based on breast selection, breast density reduction using haze removal algorithm and contrast enhancement using multifractal measures. The second step consists of extracting the normal and abnormal ROIs and calculating the multifractal spectrum of each ROI. The next step represents the extraction of the multifractal features from the multifractal spectrum and the GLCM characteristics of each ROI. The last step is the classification of ROIs where three classifiers are tested (KNN, DT, and SVM). The system is evaluated on images from the INbreast database (308 images) with a total of 2688 extracted ROIs (1344 normal, 1344 with MC) from different BI-RADS classes. In this study, the SVM classifier gave the best classification results with a sensitivity, specificity, and precision of 98.66%, 97.77%, and 98.20% respectively. These results are very satisfactory and remarkable compared to the literature.
微钙化（MCs）是癌前细胞的主要特征。开发辅助系统来检测它们已成为该领域研究人员的一项挑战。在本文中，我们提出了一种基于多重分形方法的 MCs 检测系统，该系统将乳腺 ROI 分为正常（健康）或包含 MCs 的异常 ROI。所提出的方法分为四个主要步骤：基于乳腺选择的乳腺预处理步骤、使用去雾算法降低乳腺密度和使用多重分形度量进行对比度增强。第二步包括提取正常和异常 ROI，并计算每个 ROI 的多重分形谱。第三步表示从多重分形谱和每个 ROI 的 GLCM 特征中提取多重分形特征。最后一步是对 ROI 进行分类，测试了三种分类器（KNN、DT 和 SVM）。该系统在 INbreast 数据库（308 张图像）上进行了评估，共从不同 BI-RADS 类别中提取了 2688 个 ROI（1344 个正常，1344 个有 MC）。在这项研究中，SVM 分类器的分类效果最好，其灵敏度、特异性和精度分别为 98.66%、97.77%和 98.20%。与文献相比，这些结果非常令人满意和显著。
