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21 March 2016 Segmentation techniques evaluation based on a single compact breast mass classification scheme
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Abstract
In this work some segmentation techniques are evaluated by using a simple centroid-based classification system regarding breast mass delineation in digital mammography images. The aim is to determine the best one for future CADx developments. Six techniques were tested: Otsu, SOM, EICAMM, Fuzzy C-Means, K-Means and Level-Set. All of them were applied to segment 317 mammography images from DDSM database. A single compact set of attributes was extracted and two centroids were defined, one for malignant and another for benign cases. The final classification was based on proximity with a given centroid and the best results were presented by the Level-Set technique with a 68.1% of Accuracy, which indicates this method as the most promising for breast masses segmentation aiming a more precise interpretation in schemes CADx.
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Bruno R. N. Matheus, Karem D. Marcomini, and Homero Schiabel "Segmentation techniques evaluation based on a single compact breast mass classification scheme", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978426 (21 March 2016); https://doi.org/10.1117/12.2217026
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