{'fraction_non_alphanumeric': 0.0344149459193707, 'fraction_numerical': 0.011799410029498525, 'mean_word_length': 4.784090909090909, '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}}
Using HOG-LBP features and MMP learning to recognize imaging signs of lung lesions

This paper proposes an approach to recognize Common Imaging Signs of Lesions (CISLs) in lung CT images. We combine the bag-of-visual-words based on the Histograms of Oriented Gradients (HOG) and the Local Binary Pattern (LBP) to represent regions of interest (ROIs) in lung CT images. Then the Max-Min posterior Pseudo-probabilities (MMP) learning method is applied to recognize the category of the imaging sign contained in each ROI. We conducted the 5-fold cross validation experiments on a set of 696 ROIs captured from real lung CT images. The proposed approach achieved the average sensitivity of 91.8%, the average specificity of 98.5% and the average accuracy of 98%. Furthermore, the HOG-LBP features surpassed individual HOG or LBP as well as the hybrid of LBP and intensity histograms, and the MMP behaved better than the Support Vector Machines (SVMs). These experimental results confirm the effectiveness of our approach.