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Study of the effect of breast tissue density on detection of masses in mammograms (2013) |
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| Título: | Study of the effect of breast tissue density on detection of masses in mammograms
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| Autores: | Antonio García–Manso, Carlos J. García–Orellana, Horacio M. González–Velasco, Ramón Gallardo–Caballero, and Miguel Macías–Macías |
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Revista: | Computational and Mathematical Methods in Medicine
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Vol./Pag.: | , 1-13 |
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| Ed./Año: | Hindawi Publishing Corporation (Reino Unido), 2013 |
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| DOI: | http://dx.doi.org/10.1155/2013/213794 | | ISSN: | 1748-670X |
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| Abstract: | One of the parameters that are usually stored for mammograms is the BI-RADS density, which gives an idea of the breast tissue composition. In this work, we study the effect of BI-RADS density in our ongoing project for developing an image-based CAD system to detect masses in mammograms. This system consist of two stages. First a blind feature extraction is performed for regions of interest (ROIs), using Independent Component Analysis (ICA). Next, in the second stage, those features form the input vectors to a classifi er, neural network or SVM classifier. To train and test our system, the Digital Database for Screening Mammography (DDSM) was used. The results obtained show that the maximum variation in the performance of our system considering only prototypes obtained from mammograms with a concrete value of density (both for training and test) is about 7%, yielding the best values for density equal to 1, and the worst for density equal to 4, for both classi ers. Finally, with the overall results (i.e., using prototypes from mammograms with all the possible values of densities), we obtained a difference in performance that is only 2% lower than the maximum, also for both classifi ers.
Keywords: Independent Component Analysis (ICA), breast cancer, DDSM, CAD, mass, breast tissue composition. |
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