Digital mammography: Hybrid four-channel wavelet transform for microcalcification segmentation

The authors evaluated an algorithm for the automatic segmentation of microcalcification clusters (MCCs) at digital mammography. Two- and four-channel wavelet transforms were evaluated to determine whether sensitivity in the detection of MCCs can be improved and if the selective reconstruction of the higher-order M 2 subimages allows better preservation of the segmented MCCs, which is required for their classification. The hybrid method involved the use of a nonlinear filter for image noise suppression coupled with wavelet transforms for image decomposition and an adaptive method for selective subimage reconstruction as a basis for segmentation of MCCs. The two- and four-channel wavelet transforms were implemented with different filter bank structures (ie, polyphase quadrature mirror filters [QMFs], tree structure, and lattice structure) to determine if their computational efficiency can be improved while retaining properties such as near-perfect reconstruction. The hybrid wavelet transforms were applied to a common image database of biopsy-proved MCCs (100 images, 105-μm resolution, 12 bits deep; 52 cases with at least one MCC of varying subtlety [46 malignant and six benign cases] and eight normal cases). The two- and four-channel wavelet transforms yielded sensitivities of 93% and 94% and false-positive (FP) detection rates of 1.58 and 1.35 MCCs per image, respectively. The lattice structure provided greater than fivefold improvement in computational speed compared to the polyphase QMF structure, particularly for the higher order of channels (M = 4). The four-channel wavelet transform provided better sensitivity and FP detection rates and greater image detail preservation for the segmented MCCs.