In recent decades, medical image analysis and diagnostic techniques have undergone significant advancements and have become a relatively important component of clinical practice. The most popular diagnostic resources are diagnostic images acquired from different modalities such as computed tomography and magnetic resonance imaging. Neonatal neuroimaging is an increasingly developing diagnostic imaging discipline with a particular focus on neonatal brain imaging. The neonatal brain growth and numerous neurological defects can be detected by the newborn brain MRI. MRI images consist primarily of objects of low contrast that are hampered in the image capturing by random noise. Noise produces ambiguous representations which influence disease identification and diagnosis, even mortality, leading to severe loses. Medical image de-noising mainly attempts to reconstruct the original image from its noisy observation as accurately as possible while maintaining the necessary graphical features such as textures and edges. It is also necessary that the medical images that assist healthcare practitioners towards precise disease analysis must be de-noised. This paper provides systematic analysis of de-noising methods for neonatal brain MR images in which each technique has its own conclusions, drawbacks and benefits. This work investigates performance as well as thorough study of different image de-noising approaches for T1 and T2-weighted neonatal Brain MR Images. Utilizing different statistical parameters such as PSNR, SSIM, MSE etc. the image de-noising approaches are compared.

在最近的几十年中，医学图像分析和诊断技术已经取得了重大进展，并已成为临床实践中相对重要的组成部分。最受欢迎的诊断资源是从不同形式（如计算机断层扫描和磁共振成像）获取的诊断图像。新生儿神经影像学是一门发展中的诊断影像学学科，尤其侧重于新生儿脑影像学。新生儿脑部MRI可检测到新生儿脑部生长和许多神经系统缺陷。MRI图像主要由对比度较低的对象组成，这些对象在图像捕获中受到随机噪声的干扰。噪声产生的模棱两可表示会影响疾病的识别和诊断，甚至会影响死亡率，从而导致严重的损失。医学图像降噪主要是尝试在保持必要的图形特征（例如纹理和边缘）的同时，从其嘈杂的观察中尽可能准确地重建原始图像。还必须对有助于医疗保健从业人员进行精确疾病分析的医学图像进行降噪处理。本文提供了针对新生儿脑部MR图像的降噪方法的系统分析，其中每种技术都有其自己的结论，缺点和益处。这项工作调查性能以及T1和T2加权新生儿脑MR图像的不同图像去噪方法的深入研究。利用不同的统计参数，例如PSNR，SSIM，MSE等，对图像降噪方法进行了比较。