Directionlet transform based sharpening and enhancement of mammographic X-ray images

Abstract Due to difficulty in detecting the low contrast and noisy nature of X-ray mammography images, they have to be enhanced to obtain a clear and good view. Though Sharpening Technique (ST) is used to enhance the contrast, it introduces noise in the enhancement process, and they do not include anisotropic features. This paper proposes a ST, which uses multiscale linear and anisotropic geometrical features obtained from directionlet transform (DT). The newly formulated method that combines multidirectional geometrical information has various tunable parameters and improved noise control by means of multiscale features. The DT that uses skewed and elongated directional basis functions not only captures the point singularities, but also links them into linear structure. The performance of the proposed DT ST is compared with non-linear unsharp masking (NLUSM). While the DT and LoG based sharpened images are given to the input of standard AHE, their performance is improved. Enhancement Measure and structural similarity measure are used to analyze the performance of the proposed method. Though the images are enhanced, the quality of the image is not degraded. As a specific application, the enhanced images are used to detect the microcalcification and spiculated masses in mammograms.