A support vector machine (SVM) classifier is used to determine whether regions
of interest (ROIs) found on breast radiographic images contain mass or
normal tissue. Before being presented to SVM, ROIs are encoded by means
of a specific image representation. The coefficients resulting from the encoding
are then used as classification features. Pixel and wavelet image representations
have already been discussed in one of our previous works. A
novel orientation–selective, non–parametric and multi–resolution image representation
is developed and evaluated herein, namely a ranklet image representation.
From the digital database for screening mammography (DDSM)
collected by the University of South Florida, a database of ROIs is generated.
A total of 1000 ROIs containing diagnosed masses are extracted from
the DDSM benign and malignant cases, whereas 5000 ROIs containing normal
tissue are extracted from the DDSM normal cases. The area Az under
the receiver operating characteristic curve is adopted for performance evaluation.
By achieving Az values of 0.978 ± 0.003, experiments demonstrate
better classification results with respect to those reached by the previous image
representations. In particular, the improvement on the Az value over that
achieved by the wavelet image representations is statistically relevant with
two–tailed p–value < 0.0001
