{'fraction_non_alphanumeric': 0.02643171806167401, 'fraction_numerical': 0.001762114537444934, 'mean_word_length': 6.144654088050315, '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}}
Performance assessment of mammography image segmentation algorithms

In this paper, we present a comprehensive validation analysis to evaluate the performance of three existing mammogram segmentation algorithms against manual segmentation results produced by two expert radiologists. These studies are especially important for the development of computer-aided cancer detection (CAD) systems, which will significantly help improve early detection of breast cancer. Three typical segmentation methods were implemented and applied to 50 malignant mammography images chosen from the University of South Florida's Digital Database for Screening Mammography (DDSM): (a) region growing combined with maximum likelihood modeling (Kinnard model), (b) an active deformable contour model (snake model), and (c) a standard potential field model (standard model). A comprehensive statistical validation protocol was applied to evaluate the computer and expert outlined segmentation results; both sets of results were examined from the inter- and intra-observer points of view. Experimental results are presented and discussed in this communication