构建1134张光声图像质量评级数据集，助力医学影像评估
PhotIQA: A photoacoustic image data set with image quality ratings
- 基于专家评分构建全参考图像质量评估数据集
- 包含1134张图像，覆盖5种质量属性，支持多场景测试
- 公开可用，适用于光声成像及更广医学图像评估
图像质量评估（IQA）在新型图像算法（包括传统与基于机器学习的方法）的评估阶段至关重要。由于缺乏已标注质量的医学图像，目前广泛使用的全参考IQA方法主要针对自然图像开发和验证。将这些方法应用于医学图像时出现的缺陷与不一致性并不意外，因为其依赖的特性与自然图像不同。在光声成像（PAI）中，图像重建质量的基准评估方法尤为缺失。PAI是一种多物理场成像模态，需解决声学与光学两个逆问题，使IQA应用面临独特挑战。为此，我们构建了PhotIQA数据集，包含1134张光声图像。这些图像由五位专家在全参考设置下，对五个质量属性进行评分，详细标注可支持超出PAI的应用。该数据集连同图像与评分已公开发布于Zenodo。
原文摘要 · Abstract (English)
Image quality assessment (IQA) is crucial in the evaluation stage of novel algorithms operating on images, including traditional and machine learning based methods. Due to the lack of available quality-rated medical images, most commonly used full-reference IQA measures have been developed and tested for natural images. Reported pitfalls and inconsistencies arising when applying such measures for medical images are not surprising, as they rely on different properties than natural images. In photoacoustic imaging (PAI), especially, standard benchmarking approaches for assessing the quality of image reconstructions are lacking. PAI is a multi-physics imaging modality, in which two inverse problems have to be solved, which makes the application of IQA measures uniquely challenging due to both, acoustic and optical, artifacts. To support the development and testing of IQA measures we assembled PhotIQA, a data set consisting of 1134 photoacoustic images. The images were rated by five experts across five quality properties in a full-reference setting, where the detailed rating enables usage beyond PAI. The data set with the images and corresponding ratings is publicly available on Zenodo.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。