Abstract
In this paper we present a novel multiresolution scheme for the detection of spiculated lesions in digital mammograms. First, a multiresolution representation of the original mammogram is obtained using a linear phase nonseparable 2-D wavelet transform. A set of features is then extracted at each resolution in the wavelet pyramid for every pixel. This
approach addresses the difficulty of predetermining the neighborhood size for feature extraction to characterize objects that may appear in different sizes. Detection is performed from
the coarsest resolution to the finest resolution using a binary tree classifier. This top-down
approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions. Experimental results using the MIAS image
database have shown that this algorithm is capable of detecting spiculated lesions of very different sizes at low false positive rates.
