Mammogram denoising to improve the calcification detection performance of convolutional nets

Claudio Marrocco; Alessandro Bria; Valerio Di Sano; Lucas R. Borges; Benedetta Savelli; Mario Molinara; Jan-Jurre Mordang; Nico Karssemeijer; Francesco Tortorella

doi:10.1117/12.2318069

6 July 2018 Mammogram denoising to improve the calcification detection performance of convolutional nets

Claudio Marrocco, Alessandro Bria, Valerio Di Sano, Lucas R. Borges, Benedetta Savelli, Mario Molinara, Jan-Jurre Mordang, Nico Karssemeijer, Francesco Tortorella

Author Affiliations +

Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 107180W (2018) https://doi.org/10.1117/12.2318069
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

Abstract

Recently, Convolutional Neural Networks (CNNs) have been successfully used to detect microcalcifications in mammograms. An important step in CNN-based detection is image preprocessing that, in raw mammograms, is usually employed to equalize or remove the intensity-dependent quantum noise. In this work, we show how removing the noise can significantly improve the microcalcification detection performance of a CNN. To this end, we describe the quantum noise with a uniform square-root model. Under this assumption, the generalized Anscombe transformation is applied to the raw mammograms by estimating the noise characteristics from the image at hand. In the Anscombe domain, noise is filtered through an adaptive Wiener filter. The denoised images are recovered with an appropriate inverse transformation and are then used to train the CNN-based detector. Experiments were performed on 1,066 mammograms acquired with GE Senographe systems. MC detection performance of a CNN on noise-free mammograms was statistically significantly higher than on unprocessed mammograms. Results were also superior in comparison with a nonparametric noise-equalizing transformation previously proposed for digital mammograms.

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Claudio Marrocco, Alessandro Bria, Valerio Di Sano, Lucas R. Borges, Benedetta Savelli, Mario Molinara, Jan-Jurre Mordang, Nico Karssemeijer, and Francesco Tortorella "Mammogram denoising to improve the calcification detection performance of convolutional nets", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107180W (6 July 2018); https://doi.org/10.1117/12.2318069

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