Filtering noise on mammographic phantom images using local contrast modification functions

This paper deals with filtering signal-dependent noise on digitized mammographic phantom images using a direct contrast modification method. First, a local contrast is computed for each pixel depending on the statistical properties of its neighbourhood. An optimal modification contrast function is then applied. This function is found by solving an optimisation problem using the mean squared error as a criterion. At last the enhanced pixel value is calculated using an inverse local contrast method. Simulated images containing objects similar to those observed in the phantom are built with different contrast and Signal to Noise Ratio (SNR) levels. Noise reduction results obtained are then compared to those of classical noise filtering methods. This comparison shows that the developed method gives better results. Evaluation was also done on real phantom images with the help of radiologists. Good results obtained lead us to consider the developed method as a good preprocessing step for quality control in mammographic facilities using image processing techniques.