{'fraction_non_alphanumeric': 0.02138157894736842, 'fraction_numerical': 0.0, 'mean_word_length': 5.09, '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}}
Medical Image Registration by Simulated Annealing and genetic algorithms

Registration techniques in medical image processing are used to match anatomic structures from two or more images (CT, MRI, PET....) taken at different times to track for example the evolution of a disease. The core of the registration process is the maximization of a cost function expressing the similarity between these images. To resolve this problem, we have tested two global optimization techniques that are genetic algorithms and simulated annealing. In this paper we show some results obtained in medical images registration.