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Proceedings Paper

Segmentation techniques evaluation based on a single compact breast mass classification scheme
Author(s): Bruno R. N. Matheus; Karem D. Marcomini; Homero Schiabel
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Paper 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.

Paper Details

Date Published: 21 March 2016
PDF: 8 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978426 (21 March 2016); doi: 10.1117/12.2217026
Show Author Affiliations
Bruno R. N. Matheus, Univ. de São Paulo (Brazil)
Karem D. Marcomini, Univ. de São Paulo (Brazil)
Homero Schiabel, Univ. de São Paulo (Brazil)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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