Authors: Walker H. Land Jr., Elizabeth A. Verheggen
Addresses: Department of Bioengineering, Binghamton University, Binghamton, NY 13903-6000, USA. ' Department of Systems Science, Binghamton University, Binghamton, NY 13903-6000, USA
Abstract: Parenchymal patterns defining the density of breast tissue are detected by advanced correlation pattern recognition in an integrated Computer-Aided Detection (CAD) and diagnosis system. Fractal signatures of density are modelled according to four clinical categories. A Support Vector Machine (SVM) in the primal formulation solves the multiclass problem using |One-Versus-All| (OVA) and |All-Versus-All| (AVA) decompositions, achieving 85% and 94% accuracy, respectively. Fully automated classification of breast density via a texture model derived from fractal dimension, dispersion, and lacunarity moves current qualitative methods forward to objective quantitative measures, amenable with the overarching vision of substantiating the role of density in epidemiological risk models of breast cancer.
Keywords: parenchyma; breast density; correlation pattern recognition; primal SVM; support vector machines; OVA; one-versus-all; AVA; all-versus-all; lacunarity; CADe; computer-aided detection; computer-aided diagnosis; breast tissue; fractal signatures; texture modelling; epidemiological risk models; breast cancer.
International Journal of Computational Biology and Drug Design, 2009 Vol.2 No.1, pp.21 - 57
Published online: 02 Aug 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article