{'fraction_non_alphanumeric': 0.023284313725490197, 'fraction_numerical': 0.0024509803921568627, 'mean_word_length': 5.69672131147541, '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}}
Parametric estimation of 2-D motion field on ultrasonic images using spatially smoothed regression model and respiration

An extension of a previously presented algorithm for estimating instantaneous 2-D motion fields in the ultrasonography of internal organs is proposed. In the previous algorithm, the motion field was modeled as a regression random process with respect to the respiratory signal, and utilized spatially independent unknown coefficients. However, the value of these coefficients should vary with spatial smoothness, which enables a spatial constraint to be applied. In the proposed extension, the regression coefficients are defined as random variables, i.e. Gaussian-Markov random field (GMRF), with unknown scale factors, allowing a computationally stable estimation algorithm to be constructed.