{'fraction_non_alphanumeric': 0.02118270079435128, 'fraction_numerical': 0.0, 'mean_word_length': 5.129729729729729, '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 survey and evaluation of edge detection operators application to medical images

One of the objectives of image analysis is to extract its dominating information. Thus we use segmentation to associate a stamp to each pixel according to the carried information (gray level or color) and its specific distribution in the image. Thereby, the segmentation of the image is defined as being the low level step of processing that extracts and describes present significant objects in a scene, the most often in the form of regions or edges. In the literature, different methods have been elaborated in order to detect image edges. They are gathered in two families: on the one hand methods privileging an approach by border (derivative, surfaces, and morphological methods) named the edge approach; on the other hand those privileging an approach by regions (Markovian and structural methods). In this work, we are interested in the different methods using the edge approach for the image segmentation. Many image segmentation techniques are available. We describe derivative methods, optimal filtering, and segmentation for color images.