FUZZY WEIGHTED MEDIAN FILTER WITH UNSHARP MASKING FOR ENHANCEMENT OF DBT IMAGES IN BREAST CANCER DETECTION

Abstract

Breast cancer survival rates can be increased by providing early treatment to patients; thereby, microcalcification detection is critical because microcalcifications are an early sign of breast cancer. The visibility of microcalcifications can be improved by using Digital Breast Tomosynthesis (DBT) images, which have been shown to improve the overlapping issue in mammograms. However, since DBT screening techniques generate blurry artefacts and noise, this study proposes a DBT image enhancement procedure. As a result, this study indicated an enhancement method based on Non-Linear Unsharp Masking filters (NLUM). A filter, such as the Median Filter in conventional NLUM, is required to complete the non-linear element in the algorithm. Other researchers have previously proposed and demonstrated the Fuzzy Weighted Median Filter (FWMF) to improve medical images; thus, these filters can be adapted to the NLUM and replaced with the conventional filter. Following that, the enhancement process's performance will be evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). When compared to the Median Filter, the results show that the FWMF is the best filter to use in NLUM and successfully enhances DBT images with MSE and PSNR averages of 0.0171 and 67.0574, respectively.