Avcı Hanife, Karakaya Jale
Department of Biostatistics, Hacettepe University School of Medicine, Sihhiye, Ankara 06230, Turkey.
Diagnostics (Basel). 2023 Jan 18;13(3):348. doi: 10.3390/diagnostics13030348.
Mammography is the most preferred method for breast cancer screening. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. The main contribution of this study is to reveal the optimal combination of various pre-processing algorithms to enable better interpretation and classification of mammography images because pre-processing algorithms significantly affect the accuracy of segmentation and classification methods. In this study, the effect of combinations of different preprocessing methods in differentiating benign and malignant breast lesions was investigated. All image processing algorithms used for lesion detection were used in the mini-MIAS database. In the first step, label information and pectoral muscle resulting from the acquisition of mammography images were removed. In the second step, median filter (MF), contrast limited adaptive histogram equalization (CLAHE), and unsharp masking (USM) algorithms with different combinations of the resolution and visibility of images are increased. In the third step, suspicious regions are extracted from the mammograms using the k-means clustering technique. Then, features were extracted from the obtained ROIs. Finally, feature datasets were classified as normal/abnormal, and benign/malign (two class classification) using Machine Learning algorithms. Test performance measures of the classification methods were examined. In both classifications made in the study, lower classification performance values were obtained when the CLAHE algorithm was used alone as a pre-processing method compared to other pre-processing combinations. When the median filter and unsharp masking algorithms are added to the CLAHE algorithm, the performance of the classification methods has increased. In terms of classification success, Support Vector Machines, Random Forest, and Neural Networks showed the best performance. It was found by comparing the performances of the classification methods that different preprocessing algorithms were effective in detecting the presence of breast lesions and distinguishing benign and malignant.
乳腺钼靶摄影是乳腺癌筛查最常用的方法。在本研究中，使用计算机辅助诊断（CAD）系统来提高乳腺钼靶图像的质量并检测可疑区域。本研究的主要贡献在于揭示各种预处理算法的最佳组合，以实现对乳腺钼靶图像更好的解读和分类，因为预处理算法会显著影响分割和分类方法的准确性。在本研究中，研究了不同预处理方法组合在鉴别乳腺良性和恶性病变方面的效果。用于病变检测的所有图像处理算法均应用于mini-MIAS数据库。第一步，去除乳腺钼靶图像采集过程中产生的标记信息和胸肌。第二步，通过不同分辨率和图像可见性组合的中值滤波（MF）、对比度受限自适应直方图均衡化（CLAHE）和锐化掩膜（USM）算法来提高图像质量。第三步，使用k均值聚类技术从乳腺钼靶片中提取可疑区域。然后，从获得的感兴趣区域（ROI）中提取特征。最后，使用机器学习算法将特征数据集分类为正常/异常以及良性/恶性（二类分类）。对分类方法的测试性能指标进行了检验。在本研究进行的两种分类中，与其他预处理组合相比，单独使用CLAHE算法作为预处理方法时，分类性能值较低。当将中值滤波和锐化掩膜算法添加到CLAHE算法中时，分类方法的性能有所提高。在分类成功率方面，支持向量机、随机森林和神经网络表现出最佳性能。通过比较分类方法的性能发现，不同的预处理算法在检测乳腺病变的存在以及区分良性和恶性方面是有效的。
