{'fraction_non_alphanumeric': 0.021505376344086023, 'fraction_numerical': 0.0035842293906810036, 'mean_word_length': 5.852760736196319, '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 Efficient Cell Segmentation Algorithm Based on Unsupervised Clustering and Morphology

In traditional Chinese medicine processing domain, the ‘firepower’ and ‘proper roasting’ are vital for medicinal efficacy of Chinese herbs. We take phellodendron microscopic images to determine if the roasting is appropriate according to some features of crystal fiber and stone cell, these features include color, geometrical appearance, and so on. The first step of feature analysis is to segment objects accurately. In the paper, we propose an efficient unsupervised algorithm to segment stone cell and crystal fiber in phellodendron microscopic images automatically without any human's intervene. Firstly, the superpixels is adopted, then the method extracts features such as RGB, Sobel, Harris and so on, clustering superpixels patches according to the characteristic of phellodendron, lastly morphological operations are implemented to produce segmented stone cell and crystal fiber. The experimental evaluation shows that the proposed algorithm can segment objects automatically with about 87% precision and 93% recall.