{'fraction_non_alphanumeric': 0.02216538789428815, 'fraction_numerical': 0.0, 'mean_word_length': 5.522222222222222, '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}}
Region growing for image segmentation using an extended PCNN model

Pulse-coupled neural network (PCNN) is a biologically-inspired algorithm suited for image processing. However, determining a set of parameters involved in the alteration of the neural behaviour remains a prevalent research for further application. To apply the model into image segmentation, this study proposes an extended PCNN model by using a strategy of the decision tree, and establishes links between the parameters and image characteristics. Particularly, the adjustable threshold term, interacted with the estimation of the global neural threshold, enables the proposed model to obtain the better results with the use of the fuzzy set theory. Through iterative computation, the proposed model can be considered as a region growing approach for multilevel image segmentation, thus named as an extended PCNN model. Finally, experiments on synthetic and natural images demonstrate the efficiency of the proposed model. Moreover, comparisons with some existing PCNN-based models, and recently graph-based methods, normalised cuts, show that the proposed model can extract regions with more similarity.