Purpose

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Design/methodology/approach

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Findings

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

Research limitations/implications

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

Practical implications

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

Originality/value

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

中文翻译：

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平稳小波域下贝叶斯阈值去噪对脑损伤和肿瘤 MRI 噪声方差估计的评估

目的

关于熟练的贝叶斯阈值去噪的一个主要成功因素是噪声方差估计。本文重点评估两种不同特征性脑损伤/肿瘤磁共振成像 (MRI) 的三个贝叶斯阈值模型中的不同噪声方差估计。

设计/方法/方法

在这里，与最先进的非局部均值 (NLM) 相比，主要评估了在平稳小波变换 (SWT) 域下基于不同噪声方差估计的三个贝叶斯阈值去噪模型。这三个模型中的每一个，即 D1、GB 和 DR 模型，分别取决于第一分辨率级别的最详细的小波子带、完全全局的细节子带和每个方向/分辨率的细节子带。通过阈值去噪和分割识别结果连续评估显式和隐式去噪性能。

发现

隐式性能评估分出第一至第二的最佳准确度，分别为 0.9181 和 0.9048 Dice 相似系数（Dice），依次由 GB 和 DR 产生；当脑损伤 MRI 的噪声水平增加 0.2 至 0.9 时，DR 的 45.66% 骰子下降表明可靠性，而 D1 GB 和 NLM 的 53.38、61.03 和 35.48%。对于0.2噪声水平下的脑肿瘤MRI，它表示0.9592 Dice的最佳精度，由DR得出；然而，表示 DR 的 8.09% Dice 下降，相对于 D1、GB 和 NLM 的 6.72%、8.85 和 39.36%。显然指出了 NLM 的最低显式和隐式去噪性能。

研究限制/影响

去噪性能的未来改进可能是指创建一个半监督去噪联合模型。这种模型利用 DR 和 D1 阈值模型产生的去噪 MRI 作为未损坏的图像版本以及噪声 MRI，代表在自动编码器训练阶段的损坏版本，以重建原始的干净图像。

实际影响

本文应该引起计算和信息科学技术领域的读者的兴趣，包括数据科学和应用、计算健康信息学，特别是作为医学图像处理的决策支持工具。

原创性/价值

在大多数情况下，DR 和 D1 在模拟的低细节小尺寸感兴趣区域 (ROI) 脑损伤和逼真的高细节大尺寸脑损伤的准确性和可靠性方面提供了第一到第二的最佳隐式性能ROI 脑肿瘤 MRI。