A Novel Approach for Mammogram Enhancement using Nonlinear Unsharp Masking and L0 Gradient Minimization

Abstract

Abstract Mammography is an effective modality for detecting and diagnosing breast cancer. Mammogram enhancement is used to improve the local contrast of mammograms, such that lesions appear more visible in the enhanced image. This paper introduces two novel approaches for mammogram enhancement based on a new unsharp masking scheme, called nonlinear UM and L0 Gradient Minimization, NLUML0GMIN for detecting breast cancer at an early stage. Three different techniques are unified in the proposed method; i) a Nonlinear filtering process to enhance the fine details within a 3x3 local neighborhood, ii) an L0 Gradient Minimization step globally preserving the high-contrast edges while suppressing low-contrast details seen as opaque fibrous tissues; and to obtain a detail mammogram after subtracting smoothed mammogram from the original mammogram, iii) Finally, an Unsharp masking technique combining detail mammogram with filtered mammogram passed through Non-linear filter, using PLIP operators which satisfy both Weber’s law and saturation characteristics of the human visual system.. An HVS-based decomposition scheme is used to analyse and visualize the malignant areas in the enhanced mammogram. The distinct arrangement of PLIP operators in the proposed framework has given rise to two methods NLUML0GMINAUTO and NLUML0GMIN, which outperforms NLUM method and other existing techniques for mammogram enhancement. The former is good at enhancing less-dense and medium-dense mammograms whereas the later enhances unseen masses within high-dense mammograms. Experimental results have stated the significance of the proposed method in effectively diagnosing breast cancer in comparison with the previous methods for mammogram enhancement. The mini-MIAS database has categorized mammograms as fatty, fatty-glandular and dense-glandular, which has helped us in focusing on enhancing images uniquely for each category. The proposed NLUML0GMIN scheme is robust and effective in identifying cancerous masses and calcifications within dense X-ray mammograms, aiding doctors in better cancer diagnosis by saving the life of countless patients with cancer.