N Yousefi Moteghaed, M Tabatabaeefar, A Mostaar
PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
MD, Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Biomed Phys Eng. 2020 Feb 1;10(1):83-92. doi: 10.31661/jbpe.v0i0.1016. eCollection 2020 Feb.
Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise.
This study has focused on the sequence filters which are selected by a hybrid genetic algorithm and particle swarm optimization.
In this analytical study, we have applied the composite of different types of noise such as salt and pepper noise, speckle noise and Gaussian noise to images to make them noisy. The Median, Max and Min filters, Gaussian filter, Average filter, Unsharp filter, Wiener filter, Log filter and Sigma filter, are the nine filters that were used in this study for the denoising of medical images as digital imaging and communications in medicine (DICOM) format.
The model has been implemented on medical noisy images and the performances have been determined by the statistical analyses such as peak signal to noise ratio (PSNR), Root Mean Square error (RMSE) and Structural similarity (SSIM) index. The PSNR values were obtained between 59 to 63 and 63 to 65 for MRI and CT images. Also, the RMSE values were obtained between 36 to 47 and 12 to 20 for MRI and CT images.
The proposed denoising algorithm showed the significantly increment of visual quality of the images and the statistical assessment.
如今，图像去噪在医学分析应用和预处理步骤中起着非常重要的作用。许多滤波器是针对图像处理设计的，假设特定的噪声分布，因此通过不同医学成像模态获取的图像必须去除噪声。
本研究聚焦于通过混合遗传算法和粒子群优化选择的序列滤波器。
在本分析研究中，我们将椒盐噪声、斑点噪声和高斯噪声等不同类型的噪声复合应用于图像，使其产生噪声。中值滤波器、最大值滤波器、最小值滤波器、高斯滤波器、均值滤波器、非锐化滤波器、维纳滤波器、对数滤波器和西格玛滤波器，是本研究中用于对医学图像（医学数字成像和通信（DICOM）格式）进行去噪的九种滤波器。
该模型已在医学噪声图像上实现，性能通过峰值信噪比（PSNR）、均方根误差（RMSE）和结构相似性（SSIM）指数等统计分析来确定。MRI和CT图像的PSNR值分别在59至63以及63至65之间。此外，MRI和CT图像的RMSE值分别在36至47以及12至20之间。
所提出的去噪算法显示出图像视觉质量和统计评估的显著提高。
