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首页> 外文期刊>Journal of Imaging >Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images
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Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images

机译：空间自相关（LISA）的本地指标：在基于盲噪声的磁共振图像感知质量指标中的应用

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摘要

Noise-based quality evaluation of MRI images is highly desired in noise-dominant environments. Current noise-based MRI quality evaluation methods have drawbacks which limit their effective performance. Traditional full-reference methods such as SNR and most of the model-based techniques cannot provide perceptual quality metrics required for accurate diagnosis, treatment and monitoring of diseases. Although techniques based on the Moran coefficients are perceptual quality metrics, they are full-reference methods and will be ineffective in applications where the reference image is not available. Furthermore, the predicted quality scores are difficult to interpret because their quality indices are not standardized. In this paper, we propose a new no-reference perceptual quality evaluation method for grayscale images such as MRI images. Our approach is formulated to mimic how humans perceive an image. It transforms noise level into a standardized perceptual quality score. Global Moran statistics is combined with local indicators of spatial autocorrelation in the form of local Moran statistics. Quality score is predicted from perceptually weighted combination of clustered and random pixels. Performance evaluation, comparative performance evaluation and validation by human observers, shows that the proposed method will be a useful tool in the evaluation of retrospectively acquired MRI images and the evaluation of noise reduction algorithms.
机译：在噪声为主的环境中，非常需要基于噪声的MRI图像质量评估。当前基于噪声的MRI质量评估方法具有限制其有效性能的缺点。传统的全参考方法（例如SNR）和大多数基于模型的技术无法提供准确诊断，治疗和监测疾病所需的感知质量指标。尽管基于Moran系数的技术是感知质量指标，但它们是全参考方法，在没有参考图像的应用中将无效。此外，由于质量指标未标准化，因此难以解释预测的质量得分。在本文中，我们提出了一种新的无参考感知质量评估方法，用于灰度图像（如MRI图像）。我们的方法旨在模仿人类如何感知图像。它将噪声水平转换为标准化的感知质量得分。全球Moran统计与本地Moran统计形式的空间自相关的局部指标结合在一起。质量分数是根据聚类像素和随机像素的感知加权组合预测的。性能评估，比较性能评估和人类观察者的验证表明，该方法将成为回顾性采集MRI图像评估和降噪算法评估的有用工具。

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