Kurt Burçin, Nabiyev Vasif V, Turhan Kemal
Medical Informatics, Karadeniz Technical University, Trabzon, Turkey.
Computer Engineering, Karadeniz Technical University, Trabzon, Turkey.
Stud Health Technol Inform. 2014;205:486-90.
Mammograms are generally contaminated by noise which assures the need for image enhancement to aid interpretation. The enhancement of mammograms is a very important problem for easy extraction of suspicious regions known as regions of interest (ROIs). This paper introduces comparison of various hybrid enhancement algorithms based on mathematical morphology, contrast stretching, wavelet transform, anisotropic diffusion filter and contrast limited adaptive histogram equalization (CLAHE). The performances of algorithms have been compared by using three global image enhancement evaluation measures; Enhancement Measure (EME), Absolute Mean Brightness Error (AMBE) and Peak Signal-to-Noise Ratio (PSNR). For this study, we have used MIAS database. Experimental results show that the combination of mathematical morphology, anisotropic diffusion filter and CLAHE methods, yields significantly superior image quality and provides more visibility for the suspicious regions.
乳房X光照片通常会受到噪声污染，这就使得需要进行图像增强以辅助解读。乳房X光照片的增强对于轻松提取被称为感兴趣区域（ROI）的可疑区域来说是一个非常重要的问题。本文介绍了基于数学形态学、对比度拉伸、小波变换、各向异性扩散滤波器和对比度受限自适应直方图均衡化（CLAHE）的各种混合增强算法的比较。通过使用三种全局图像增强评估指标：增强指标（EME）、绝对平均亮度误差（AMBE）和峰值信噪比（PSNR）来比较算法的性能。在本研究中，我们使用了MIAS数据库。实验结果表明，数学形态学、各向异性扩散滤波器和CLAHE方法的组合产生了显著更优的图像质量，并为可疑区域提供了更高的可见性。
