Two-Stage Deep Denoising With Self-Guided Noise Attention for Multimodal Medical Images

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-03-21 DOI:10.1109/TRPMS.2024.3380090
S. M. A. Sharif;Rizwan Ali Naqvi;Woong-Kee Loh
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Abstract

Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have notable drawbacks as they often generate visual artifacts when applied to heterogeneous medical images. This study addresses the limitation of the contemporary denoising methods with an artificial intelligence (AI)-driven two-stage learning strategy. The proposed method learns to estimate the residual noise from the noisy images. Later, it incorporates a novel noise attention mechanism to correlate estimated residual noise with noisy inputs to perform denoising in a course-to-refine manner. This study also proposes to leverage a multimodal learning strategy to generalize the denoising among medical image modalities and multiple noise patterns for widespread applications. The practicability of the proposed method has been evaluated with dense experiments. The experimental results demonstrated that the proposed method achieved state-of-the-art performance by significantly outperforming the existing medical image denoising methods in quantitative and qualitative comparisons. Overall, it illustrates a performance gain of 7.64 in peak signal-to-noise ratio (PSNR), 0.1021 in structural similarity index (SSIM), 0.80 in DeltaE $(\Delta E)$ , 0.1855 in visual information fidelity pixelwise (VIFP), and 18.54 in mean squared error (MSE) metrics.
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针对多模态医学图像的两级深度去噪与自引导噪声关注
医学图像去噪被认为是最具挑战性的视觉任务之一。尽管具有现实世界的意义,但现有的去噪方法存在明显的缺陷,因为它们在应用于异构医学图像时往往会产生视觉伪影。本研究采用人工智能(AI)驱动的两阶段学习策略,解决了当代去噪方法的局限性。所提出的方法通过学习来估计噪声图像中的残余噪声。随后,它采用了一种新颖的噪声关注机制,将估计的残余噪声与噪声输入相关联,以 "从过程到细化 "的方式执行去噪。本研究还建议利用多模态学习策略,在医学图像模式和多种噪声模式之间进行通用去噪,以实现广泛应用。通过密集的实验评估了所提方法的实用性。实验结果表明,所提出的方法在定量和定性比较方面明显优于现有的医学图像去噪方法,达到了最先进的性能。总体而言,该方法在峰值信噪比(PSNR)、结构相似性指数(SSIM)、DeltaE $(\Delta E)$ 0.80、视觉信息像素保真度(VIFP)和均方误差(MSE)指标上的性能增益分别为 7.64、0.1021、0.80、0.1855 和 18.54。
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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