A Multiscale Contrast Enhancement for Mammogram Using Dynamic Unsharp Masking in Laplacian Pyramid

Mammography is the first option for screening breast cancer. However, some lesions may be missed due to superimposition of breast parenchymal patterns/tissues. Current enhancement methods can highlight some specific tissues, but the edges are weakened or extra noise is added. For improving the solution, this paper proposed a multiscale contrast enhancement for mammogram using dynamic unsharp masking (UM) in Laplacian pyramid. Laplacian pyramid is utilized to preserve the fine structure at each scale. Dynamic UM is presented to adaptively enhance details and suppress noise simultaneously. The proposed method mainly consists of five steps: 1) down-sample images; 2) calculate the weight of dynamic UM; 3) utilize dynamic UM enhancement for each scale image; 4) subtract the enhancement results for each scale image to acquire Laplacian pyramid images; and 5) restore the final enhanced image. To evaluate the proposed method qualitatively and quantitatively, one phantom and three clinical mammography cases were evaluated. Results showed the proposed method can provide much clearer details of the mammary gland. Quantitatively, information entropy and peak signal-to-noise ratio are increased by 0.96 and 3.89 at most compared with the state-of-the-art method. The proposed method has demonstrated that different types of regions are enhanced with the help of a regional adaptive evolution, which has great potential for adaptively enhancing the details and relatively suppressing the noise.