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

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 easily 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 the medical diagnosis of mammograms. This study introduces a sharpening method based on the modified Laplacian filter (MLF) on compute unified device architecture (CUDA) to improve the visibility and detection of pernicious lesions in a mammogram. To process considerably large mammograms on CPU, the conventional Laplacian sharpening is more time-consuming due to the processing of all pixels with 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 framework is successfully devised and implemented in an efficient parallel execution manner on a computing platform of graphic processing units (GPU). To examine the impact of mammograms and filter size on performance along with the comparative processing time between serial execute on CPU and parallel computing on GPU (except data transfer time). To accelerate the performance of the proposed model, we adopt both global and shared memory in GPU to realize further improvements of the execution speed. The proposed framework applies a new nonlinear filter constraints module in the sharping stage while the Laplacian filter attenuate noise sensitivity and leads to achieving visually improved results in comparison with formal sharping. The proposed framework has been extensively compared with other recent baseline methods showing to 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.