When developing machine learning models, image quality assessment (IQA) measures are a crucial component for evaluation. However, commonly used 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 IQA measure HaarPSI showed promising behavior for natural and medical images. HaarPSI 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 annotated medical data sets, a photoacoustic and a chest X-Ray data set. We observe that they are more sensitive to the parameter choices than the employed natural images, and on the other hand both medical data sets lead to similar parameter values when optimized. We denote the optimized setting, which improves the performance for the medical images notably, by HaarPSI$_{MED}$. The results suggest that adapting common IQA measures within their frameworks for medical images can provide a valuable, generalizable addition to the employment of more specific task-based measures.

本研究针对现有图像质量评估（IQA）方法在医疗图像领域的适用性不足问题，提出优化HaarPSI的参数设置。通过对两个医疗数据集的实验，发现医疗图像对参数选择更加敏感，并提出了HaarPSI$_{MED}$的优化配置，有效提升了医疗图像的评估性能，表明对常用IQA方法进行针对医疗图像的调整具有重要的实用价值。

医疗图像质量评估中HaarPSI参数选择研究