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 ner 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.


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This is the author accepted manuscript version of S. Liu, C.F. Babbs, E.J. Delp, Multiresolution detection of spiculated lesions in digital mammograms, submitted IEEE Transactions on Image Processing, June, 1998. The version of record is available at https://doi.org/10.1109/83.923284.


digital mammogram, spiculated lesion, multiresolution, feature analysis, binary classification tree

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