{'fraction_non_alphanumeric': 0.0182648401826484, 'fraction_numerical': 0.0015220700152207, 'mean_word_length': 5.514851485148514, '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}}
Robust extraction of urinary stones from CT data using attribute filters

In medical imaging, anatomical and other structures such as urinary stones, are often extracted with the aid of active contour/ surface models. Active surface-based methods have robustness limitations and are computationally expensive. In this paper we present a morphological method based on attribute filters and the newly presented sphericity attribute. The operators involved, extract the targeted objects in their entirety without shape/size distortions and proceed rapidly. Experiments on three real 3D data-sets demonstrate their efficiency and their performance is discussed.