{'fraction_non_alphanumeric': 0.017148014440433214, 'fraction_numerical': 0.0036101083032490976, 'mean_word_length': 5.523529411764706, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Limitations of the SSIM quality metric in the context of diagnostic imaging

Lossy image compression is increasingly used in medical applications, but great care must be taken to ensure that no diagnostically relevant features are altered. Guidelines based on compression ratios are often use to mitigate this issue, but are criticized due to the considerable compressibility variations between images. Objective image quality assessment metrics should be used instead, but the most common, mean squared error, is known to be poorly correlated with our perception of quality. Structural similarity (SSIM) is probably currently the most popular alternative, but it is also increasingly criticized. Using computed tomography simulations, this paper shows some of the limitations of SSIM when used with medical images: uniform pooling, distortion underestimation near hard edges, instabilities in regions of low variance and insensitivity in regions high intensities. Furthermore, this paper demonstrates the effect of these limitations when SSIM is used to bound compression in a block coder such as JPEG 2000.