{'fraction_non_alphanumeric': 0.010046367851622875, 'fraction_numerical': 0.0007727975270479134, 'mean_word_length': 5.137440758293839, '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}}
Description of brain internal structures by means of spatial relations for MR image segmentation

This paper presents a method for segmenting internal brain structures in MR images. It introduces prior information in an original way through descriptions of the spatial arrangement of structures by means of spatial relations, which are represented in the fuzzy set framework. The method is hierarchical as the segmentation of a given structure is based on the previously segmented ones. The processing of each structure is decomposed into two stages: an initialization stage which makes extensive use of prior knowledge and a refinement stage using a 3D deformable model. The deformable model is guided by an external force representing the combination of a classical data term derived from an edge map and a force corresponding to a given spatial relation. We propose different ways to compute a force from a fuzzy set representing a relation or a combination of relations. Results obtained for the lateral ventricles, the third ventricle, the caudate nuclei and the thalami are promising. The proposed combination of spatial relations and deformable models has proved to be very useful to segment parts of the structures were no visible edges are present, improving the segmentation accuracy.