Photoacoustic data annotations supplementing the paper: "A study on the adequacy of common IQA measures for medical images"

We have decided to make the data set available to the research community free of charge under the Creative Commons Attribution 4.0 International license. If you use these images in your research, we kindly ask that you reference this website and our papers listed below. [1] A. Breger, C. Karner, I. Selby, J. Gröhl, S. Dittmer, E. Lilley, J. Babar, J. Beckford, T. J Sadler, S. Shahipasand, A. Thavakumar, M. Roberts, C.-B. Schönlieb: A study on the adequacy of common IQA measures for medical images. Springer Lecture Notes in Electrical Engineering, Proceedings of MICAD 2024. DOI: 10.1007/978-981-96-3863-5_41 [2] Gröhl, J., Else, T.R., Hacker, L., Bunce, E.V., Sweeney, P.W., Bohndiek, S.E.: Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms. IEEE Trans Med Imaging PP (Nov 2023). https:// doi.org/10.1109/TMI.2023.3331198 Before matching the images with the provided expert ratings, please scale the images in the following way import numpy as np def normalize(arr: np.ndarray)-> np.ndarray: arr = arr.astype(float) min_val, max_val = np.min(arr), np.max(arr) return (255 * (arr - min_val) / (max_val - min_val)).astype(np.uint8)