El-Said Shaimaa A, Azar Ahmad Taher
Electronics and Communications Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt.
Faculty of Engineering, Misr University for Science & Technology (MUST), 6th of October City, Egypt.
J Med Imaging Radiat Sci. 2012 Dec;43(4):200-213. doi: 10.1016/j.jmir.2012.06.001. Epub 2012 Sep 28.
Most medical images have a poorer signal to noise ratio than scenes taken with a digital camera, which often leads to incorrect diagnosis. Speckles suppression from ultrasound images is one of the most important concerns in computer-aided diagnosis. This article proposes two novel, robust and efficient ultrasound images denoising techniques. The first technique is the enhanced ultrasound images denoising (EUID) technique, which estimates automatically the speckle noise amount in the ultrasound images by estimating important input parameters of the filter and then denoising the image using the sigma filter. The second technique is the ultrasound image denoising using neural network (UIDNN) that is based on the second-order difference of pixels with adaptive threshold value in order to identify random valued speckles from images to achieve high efficient image restoration. The performances of the proposed techniques are analyzed and compared with those of other image denoising techniques. The experimental results show that the proposed techniques are valuable tools for speckles suppression, being accurate, less tedious, and preventing typical human errors associated with manual tasks in addition to preserving the edges from the image. The EUID algorithm has nearly the same peak signal to noise ratio (PSNR) as Frost and speckle-reducing anisotropic diffusion 1, whereas it achieves higher gains, on average-0.4 dB higher PSNR-than the Lee, Kuan, and anisotropic diffusion filters. The UIDNN technique outperforms all the other techniques since it can determine the noisy pixels and perform filtering for these pixels only. Generally, when relatively high levels of noise are added, the proposed algorithms show better performances than the other conventional filters.
大多数医学图像的信噪比低于数码相机拍摄的场景，这常常导致误诊。超声图像的斑点抑制是计算机辅助诊断中最重要的问题之一。本文提出了两种新颖、稳健且高效的超声图像去噪技术。第一种技术是增强型超声图像去噪（EUID）技术，它通过估计滤波器的重要输入参数自动估计超声图像中的斑点噪声量，然后使用西格玛滤波器对图像进行去噪。第二种技术是基于具有自适应阈值的像素二阶差分的神经网络超声图像去噪（UIDNN），以便从图像中识别随机值斑点以实现高效的图像恢复。分析了所提出技术的性能，并与其他图像去噪技术进行了比较。实验结果表明，所提出的技术是用于斑点抑制的有价值工具，准确、不太繁琐，除了保留图像边缘外，还能防止与手动任务相关的典型人为错误。EUID算法的峰值信噪比（PSNR）与弗罗斯特算法和去斑各向异性扩散算法1几乎相同，而其平均PSNR比李算法、关算法和各向异性扩散滤波器高0.4 dB。UIDNN技术优于所有其他技术，因为它可以确定噪声像素并仅对这些像素进行滤波。一般来说，当添加相对高水平的噪声时，所提出的算法比其他传统滤波器表现出更好的性能。
