{'fraction_non_alphanumeric': 0.01996007984031936, 'fraction_numerical': 0.0, 'mean_word_length': 5.470967741935484, '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}}
Multimodal Medical Image Fusion Based on Hybrid Bilateral Filter and Contrast Adjustment Model

The purpose of multimodal medical image fusion (MMIF) is to integrate images of different modes with different details into a result image with rich information, which is convenient for doctors to accurately diagnose and treat the diseased tissues of patients. Encouraged by this aim, this paper proposes a novel method based on a hybrid bilateral filter (HBF) and contrast adjustment model. First, HBF is applied to decomposing the input images to obtain the structure layer and energy layer, which have the property of detail preservation. Then, two fusion rules based on structure tensor operator (STO) and contrast adjustment model are designed, which greatly improve the image performance from the perspective of balancing between information retention and enhancing contrast. Experiments demonstrate that the proposed method has superior performance compared with the state-of-the-art fusion methods.