A novel parallel mammogram sharpening framework using modified Laplacian filter for lumps identification on GPU

Abstract In medical diagnosis, mammographic imaging is mainly concerned with the breast parenchymal patterns (counterbalance of glandular tissue and fatty tissue) by which an expert radiologist can quickly determine the abnormalities in the breast of cancer patients and if the interpretation of mammogram and the quality of mammogram both are well provided. Accordingly, improved mammographic view via an efficient image processing algorithm plays a significant role in diagnosing mammograms. This study introduces a sharpening method based on the modified Laplacian filter on CUDA to improve the visibility and detection of pernicious lesions in a mammogram. To process considerably large mammograms on the CPU, the conventional Laplaciansharpening is more time-consuming due to the processing of all pixels in a serial execution manner. Although this type of image sharpening is well established for improved image quality, its effect on a larger image for use in the GPU environment has not been extensively studied. The proposed model is successfully devised and implemented in an efficient parallel execution manner on a computing platform of GPU. The proposed method applies a new nonlinear filter constraints module in the sharping stage. The Laplacian filter attenuates noise sensitivity and leads to achieving visually improved results in comparison to formal sharping. The proposed method has been extensively compared with other recent baseline methods showing improvement in the computational cost of the image sharping approach. Experimental results establish that the two proposed sharping methods outperform the state-of-the-art methods with respect to execution speed.