{'fraction_non_alphanumeric': 0.0125, 'fraction_numerical': 0.0, 'mean_word_length': 5.627586206896551, '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 automatic detection algorithm of MR images for knee pain problem

Anterior knee pain (AKP) is a common pathological condition. The problem causing knee pain is the abnormal patellar tracking mechanism. Kinematic approaches using MR images have been regarded of more accuracy in knee pain detection than stationary approaches. This paper proposes an automatic diagnosis based kinematic patellar tracking for AKP detection. The kinematic patellar tracking uses a hybrid approach for extracting knee organs, where an edge-constrained wavelet enhancement followed by moment preserving segmentation is employed for conquering the soft tissue adhesion for extracting the femur and tibia from axial MR images, and a sliding window based moment preserving for resolving the segmentation difficulty associated with intensity non-uniformity in saggital MR images. The experiment results demonstrate the prominent of the calculated inclination angles in detecting AKP.