Adaptive Multi Scale Products Threshold-Based MRI Denoising

A. Kumar, K. Sutariya
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Abstract

Denoising an image has become an extremely important step in medical imaging, and it is performed throughout the entire diagnostic process. In medical imaging, it is imperative that a balance be maintained between the elimination of distracting noise and the maintenance of diagnostically relevant information. Imaging modalities have many objectives, one of the most important of which is to supply the doctor with the most reliable information possible so that they can make an precise diagnosis. The utilization of multiresolution noise filters in a wide range of medical imaging applications is garnering an increasing amount of attention. This study discusses some of the possible uses of new wavelet denoising algorithms for medical magnetic resonance images and reviews some of the techniques that have been used recently. These techniques were used to investigate various areas of the human body. The goal of this project is to demonstrate and evaluate various approaches of noise suppression that are based on both image processing and clinical experience. Rician noise is a phenomenon that is frequently observed in magnetic resonance imaging (MRI). In the field of medical image processing, edge-preserving denoising is becoming an increasingly important technique. In this paper, a wavelet-based multi scale products thresholding system is presented for the purpose of eliminating noise in magnetic resonance pictures. A dyadic wavelet transform that works similarly to an edge detector is used. As a consequence of this, significant features in images will continue to evolve with high magnitude throughout wavelet scales, whereas noise will quickly fade away. The wavelet sub bands that are next to one another are multiplied in order to improve edge structures while simultaneously reducing noise in order to take advantage of wavelet inter scale dependencies. When using the multi scale products, it is possible to differentiate edges from noise in an efficient manner. After that, an adaptive threshold is computed and applied to the products rather than the wavelet coefficients so that relevant features can be identified. Experiments have demonstrated that adaptive multi scale products thresholding is superior to conventional wavelet-thresholding denoising approaches in terms of its ability to reduce noise and retain edges. The fact that the wavelet transform can recreate an image without any noticeable loss of quality is the primary benefit of using this technique.
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基于自适应多尺度产品阈值的MRI去噪
图像去噪已经成为医学成像中极其重要的一步,它贯穿于整个诊断过程。在医学成像中,必须在消除干扰噪声和维护诊断相关信息之间保持平衡。成像模式有许多目的,其中最重要的是为医生提供尽可能可靠的信息,以便他们做出准确的诊断。多分辨率噪声滤波器在广泛的医学成像应用中得到越来越多的关注。本研究讨论了一些新的小波去噪算法在医学磁共振图像中的可能用途,并对最近使用的一些技术进行了综述。这些技术被用来研究人体的各个部位。这个项目的目标是展示和评估基于图像处理和临床经验的各种噪声抑制方法。噪声是磁共振成像(MRI)中常见的一种现象。在医学图像处理领域,边缘保持去噪是一项越来越重要的技术。提出了一种基于小波的多尺度积阈值去除磁共振图像噪声的方法。二进小波变换的工作原理类似于边缘检测器被使用。因此,图像中的重要特征将在整个小波尺度上继续以高幅度发展,而噪声将迅速消失。将相邻的小波子带相乘以改善边缘结构,同时利用小波尺度间依赖性降低噪声。当使用多尺度积时,可以有效地区分边缘和噪声。之后,计算自适应阈值并将其应用于产品而不是小波系数,以便识别相关特征。实验表明,自适应多尺度积阈值去噪方法在降噪和保留边缘方面优于传统的小波阈值去噪方法。事实上,小波变换可以在没有任何明显的质量损失的情况下重建图像,这是使用这种技术的主要好处。
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