A skewness reformed complex diffusion based unsharp masking for the restoration and enhancement of Poisson noise corrupted mammograms. (March 2022) - Kumar, Abhinav; Kumar, Pradeep; Srivastava, Subodh
Highlights: A skewness reformed complex diffusion based unsharp masking is proposed for removal of quantum noise which follows Poisson distribution, enhancement of abnormalities, edge preservation and restoration of information in a single framework. In the proposed method a smoothing filter is swapped with a skewness reformed complex diffusion in unsharp masking. To improve the efficacy of proposed method, the edge threshold constraint of diffusion coefficient has been automated by the help of skewness of image instead of user-based value. It helps to detect the sharp details, boundary and edges of image. In order to judge the proficiency of the proposed work, MIAS, Benign Breast Tumor IEEE dataset and phantom breast mammogram images are used for the qualitative and quantitative analysis. The proposed method is compared with other state of the art with respect to qualitative as well as quantitative analysis. The quantitative analysis is measured in terms of mean squared error, peak signal to noise ratio, correlation parameter, normalized absolute error, universal quality index, structural similarity index, measure of enhancement, measure of enhancement by entropy, second derivative like measurement, average gradient and perceptual sharpness index respectively. Abstract: Mammography is a proven imaging modality for the screening of breast cancer that helps to evaluate the existence of calcification, masses, tissue density, lump shape and edges. These features are generally corrupted by noise, distortion and artifacts during the image acquisition. The presence of these variability reduces the quality of image and limits the efficacy of radiologist's interpretation. To overcome these barriers, a skewness reformed complex diffusion based unsharp masking is proposed, where smoothing filter is changed with a skewness reformed complex diffusion (SRCD) in unsharp masking. To improve the efficacy of SRCD, edge threshold constraint of the diffusion coefficient has been automated instead of manual calculation. It has been automated by the skewness calculation of the image. It helps to detect the fine details, boundary and edges of image. In order to judge the proficiency of the proposed method, Mammography Image Analysis Society, Benign Breast Tumor IEEE dataset and phantom mammogram images are casted-off. The proposed method is compared with other state of the art with respect to qualitative and quantitative analysis. The quantitative analysis is measured in terms of mean squared error, peak signal to noise ratio, correlation parameter, normalized absolute error, universal quality index, structural similarity index, measure of enhancement, measure of enhancement by entropy, second derivative like measurement, average gradient and perceptual sharpness index. The obtained result confines the effectiveness of the proposed method and it provides removal of Poisson noise, enhancement of abnormalities, edge preservation and restoration of information in a single framework.