Comparison of noise removal techniques used in digital mammography

Abstract

Four distinct methods have recently been proposed for noise removal in mammography images: a tree-structured iterative selective median filter, a selective median filter, an improved median filter, and a weighted majority minimum range filter. These four methods, along with two general noise removal techniques, and the three standard techniques of mean, median, and k-nearest neighbors, are compared. A simulated mammography phantom to which varying levels of binary and Gaussian noise have been added is used. Two methods of measuring noise, a normalized root mean squared error and a signal-to-noise ratio, were used to compare and rank the nine different filters. There are no clear winners in all categories. Further research is needed to discover whether, by combining the best features of the best filters, one can produce a significantly improved method of noise removal. >