Mammogram's denoising in spatial and frequency domain

Breast cancer is one of the most incurable diseases, which leads to the death of women globally every year. For early detection of a tumor in the breast, a basic technique called `Mammography' is used, which is an x-ray analysis of breast. This work emphasizes on the proper selection of denoising techniques for the mammographic images. To achieve the objective of this work, exhaustive experiments are carried out using spatial domain filtering techniques as well as frequency domain filtering techniques on mammograms of the Mammographic Image Analysis Society (MIAS) data. The effectiveness of the techniques is evaluated in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Mean Structure Similarity Index (MSSIM), Maximum Difference (MD), Normalized Absolute Error (NAE), and Structural Content (SC). It is observed that Wavelet denoising and Median filter show better results than Adaptive Histogram Equalization (AHE), Butterworth and Frost filters.