Nonlinear sharpening of MR images using a locally adaptive sharpness gain and a noise reduction parameter

Abstract

Well-defined boundaries are necessary to allow precise delineation of morphological structures from magnetic resonance images. Existing state-of-the-art sharpening techniques like unsharp masking (UM) produce discontinuity artefacts and have multiple operational parameters. Tuning multiple parameters together is cumbersome. A computationally efficient and noise robust nonlinear sharpening scheme which is free from discontinuity and saturation artefacts, with very less number of arbitrary parameters, is proposed in this paper. As an inverse mathematical problem, the sharpened image is computed from the amplified first derivative. To constrain the noise amplification, the local value of the amplification factor is considered as a nonlinear function of local average of absolute directional gradients. The proposed sharpening scheme is compared with two of its best possible alternatives, contrast limited adaptive histogram equalization (CLAHE) and UM in terms of sharpness of the output image, thinness of salient edges, feature preservation, saturation and edge quality degradation due to noise, using perceptual sharpness index (PSI), second-order derivative-based measure of enhancement (SDME), edge model-based blur metric (EMBM), structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR), saturation evaluation index (SEI) and sharpness of ridges (SOR). The proposed nonlinear sharpening scheme exhibited higher PSI, SDME, PSNR and SSIM and lower EMBM, SOR and computational time, compared to CLAHE and UM. The proposed scheme is found to be superior to the existing state-of-the-art techniques like CLAHE and UM, in terms of sharpness as well as thinness of salient edges in the sharpened image, robustness to noise, discontinuity artefacts, saturation and computational time. Because of minimum number of operational parameters, it is user friendly too.