Multiresolution detection of stellate lesions in mammograms

Abstract

Presents a new multiresolution scheme for the detection of stellate 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 are then extracted at each resolution for every pixel. This addresses the difficulty of predetermining the neighborhood size for feature extraction to characterize objects that may appear with different sizes. Detection is performed from the coarsest resolution to the finest resolution using binary tree classifiers. This top-down approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions. Experimental results on the MIAS image database have shown that this algorithm is capable of detecting stellate lesions of very different sizes.