This study was to analyze the ultrasound imaging characteristics of infectious pneumonia of newborn in different conditions and the differences in neurobehavioral development. An adaptive image denoising (AID) algorithm was constructed based on multiscale wavelet features. It was compared with the transform domain denoising (TDD) algorithm and spatial domain denoising (SDD) algorithm and applied to ultrasound images of newborns with infectious pneumonia. It was found that the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity index (FSIM) of the constructed algorithm were higher than those of the TDD and SDD algorithms (). The ultrasound scores of newborns in noncritical group (group A, 1.54 ± 0.62 scores) were all lower than those of the critical group (group B, 3.96 ± 0.41 scores) and extremely critical group (group C, 4.25 ± 0.35 scores) (). The behavioral ability, passive muscle tension, active muscle tension, and original reflection of the newborns in group A were better than other groups (). It indicated that the constructed algorithm showed better denoising effect on ultrasound images, which could effectively evaluate the severity of newborns’ infectious pneumonia.

中文翻译：

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自适应图像去噪算法下超声图像评估新生儿感染性肺炎严重程度

本研究旨在分析新生儿感染性肺炎在不同情况下的超声影像特点及神经行为发育的差异。基于多尺度小波特征构建了自适应图像去噪(AID)算法。将其与变换域去噪（TDD）算法和空间域去噪（SDD）算法进行对比，应用于新生儿感染性肺炎超声图像。发现所构建算法的峰值信噪比（PSNR）、结构相似度（SSIM）和特征相似度指数（FSIM）均高于TDD和SDD算法（）。非危重组（A组，1.54±0.62分）新生儿超声评分均低于危重组（B组，3.96±0.41分）和极危重组（C组，4.25±0.35分）（）。A组新生儿的行为能力、被动肌张力、主动肌张力和原始反射均优于其他组（）。表明所构建的算法对超声图像显示出较好的去噪效果，可以有效评估新生儿感染性肺炎的严重程度。