{'fraction_non_alphanumeric': 0.012732615083251714, 'fraction_numerical': 0.0039177277179236044, 'mean_word_length': 5.3478260869565215, '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}}
Automatic registration of 3D images of the brain based on fuzzy objects

Multimodal fuzzy voxel labeling is presented as the basis for a new image registration criterion. The corresponding registration system's architecture performs an iterative calculation of the labeling and the registration process simultaneously, while most other registration systems perform segmentation and iterative estimation of registration parameters sequentially. It will be argued that its application leads to more automated and more accurate registration solutions than does e.g. the use of typical surface based registration systems. In order to support the arguments raised we have performed a case study using both 2D MR software phantom image, and 2D and 3D MR/CT image data. In this case study we looked at the behaviour of maximum likelihood voxel labeling as the simplest instantiation of a fuzzy voxel labeling algorithm. However, the architecture is open to integration of more general multimodal fuzzy voxel labeling algorithms.