优化哈爾小波PSI参数，提升医学图像质量评估效果
Parameter choices in HaarPSI for IQA with medical images
- 针对医学图像优化哈爾小波PSI的两个参数
- 在两类医学图像上显著提升评估性能（p<0.05）
- 适合医学图像质量评估研究者使用
在机器学习模型开发中，图像质量评估（IQA）是评价输出图像的关键。然而，常用全参考IQA方法主要为自然图像设计，难以适配医学图像。此前研究显示，基于哈尔小波的HaarPSI具有良好的泛化能力，其框架包含两个可调参数。本文针对光声成像和胸部X光数据集，基于专家评分优化这两个参数，发现其最优值与自然图像不同且对变化更敏感。将此新设置称为HaarPSI$_{MED}$，显著提升医学图像评估性能（p<0.05）。此外，独立的CT数据集验证了其泛化能力，并通过可视化示例展示改进效果。结果表明，在框架内适配通用IQA度量可为医学图像评估提供有价值的补充。
原文摘要 · Abstract (English)
When developing machine learning models, image quality assessment (IQA) measures are a crucial component for the evaluation of obtained output images. However, commonly used full-reference IQA (FR-IQA) measures have been primarily developed and optimized for natural images. In many specialized settings, such as medical images, this poses an often overlooked problem regarding suitability. In previous studies, the FR-IQA measure HaarPSI showed promising behavior regarding generalizability. The measure is based on Haar wavelet representations and the framework allows optimization of two parameters. So far, these parameters have been aligned for natural images. Here, we optimize these parameters for two medical image data sets, a photoacoustic and a chest X-ray data set, with IQA expert ratings. We observe that they lead to similar parameter values, different to the natural image data, and are more sensitive to parameter changes. We denote the novel optimized setting as HaarPSI$_{MED}$, which improves the performance of the employed medical images significantly (p<0.05). Additionally, we include an independent CT test data set that illustrates the generalizability of HaarPSI$_{MED}$, as well as visual examples that qualitatively demonstrate the improvement. The results suggest that adapting common IQA measures within their frameworks for medical images can provide a valuable, generalizable addition to employment of more specific task-based measures.
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