{'fraction_non_alphanumeric': 0.018368846436443792, 'fraction_numerical': 0.0, 'mean_word_length': 5.676470588235294, '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}}
An Integrated Approach for Locating Neuroanatomical Structure from MRI

The wide availability of high resolution magnetic resonance images (MRI) of the brain has facilitated tremendous progress in neuroscience. Accurate automated segmentation and quantification of neuroanatomical structure from such images is crucial for the advancement of the understanding of brain morphology, both in normal variation and in disease. Gradient-based deformable surface finding is a powerful technique for locating structure in three-dimensional images. However, it often suffers from poorly defined edges and noise. This paper proposes a gradient-based deformable surface finding approach that integrates region information. This makes the resulting procedure more robust to noise and improper initialization. In addition, prior shape information may be incorporated. The algorithm uses Gauss's Divergence theorem to find the surface of a homogeneous region-classified area in the image and integrates this with a gray-level gradient-based surface finder. Experimental results on synthetic and MR brain images show that a significant improvement is achieved as a consequence of the use of this extra information. Further, these improvements are achieved with little increase in computational overhead, an advantage derived from the application of Gauss's Divergence theorem.