Preprocessing fast filters and mass segmentation for mammography images

Digital mammography is a valuable technique for breast cancer detection, because it is safe, noninvasive and can reduce unnecessary biopsies. However, it is difficult to distinguish masses from normal or dense regions because of their morphological characteristics and ambiguous margins. Thus, improvement of image quality, highlighting the tissues details and performing mass segmentation are important tasks for early breast cancer diagnosis. This work presents a mini-Mammographic Image Analysis Society (MIAS) database preprocessing, system which combines classic and efficient techniques of Median, Wiener and Gaussian filters to remove salt and pepper, speckle and gaussian noise in mammography images. The experimental results indicates that the Gaussian filter outperforms other filtering techniques, as shown by evaluated by Peak Signal to Noise Ratio and Mean Square Error metrics.