{'fraction_non_alphanumeric': 0.019744483159117306, 'fraction_numerical': 0.0, 'mean_word_length': 5.580152671755725, '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}}
A kernel-based method for real-time markerless tumor tracking in fluoroscopic image sequence

This paper presents a kernel-based method for tracking tumor motion in fluoroscopic image sequence for real-time image-guided radiation therapy (IGRT). The proposed method employs a histogram-based feature space, which is robust against image deformation, to represent a tumor target in a given fluoroscopic image. The target tracking is then formulated by maximizing a Bhattacharyya coefficient that measures the similarity between the target model and target candidates in the subsequent image. The numerical solution for maximizing the Bhattacharyya coefficient is performed by a mean-shift algorithm. Experimental results demonstrate that the proposed method is superior to conventional template matching-based methods in terms of accuracy and computational cost.