Quantum noise is a signal-dependent, Poisson-distributed noise and the dominant noise source in digital mammography. Quantum noise removal or equalization has been shown to be an important step in the automatic detection of microcalcifications. However, it is often limited by the difficulty of robustly estimating the noise parameters on the images. In this study, a nonparametric image intensity transformation method that equalizes quantum noise in digital mammograms is described. A simple Look-Up-Table for Quantum Noise Equalization (LUT-QNE) is determined based on the assumption that noise properties do not vary significantly across the images. This method was evaluated on a dataset of 252 raw digital mammograms by comparing noise statistics before and after applying LUT-QNE. Performance was also tested as a preprocessing step in two microcalcification detection schemes. Results show that the proposed method statistically significantly improves microcalcification detection performance.

LUT-QNE: Look-up-table quantum noise equalization in digital mammograms

BRIA, Alessandro;MARROCCO, Claudio;MOLINARA, Mario;TORTORELLA, Francesco
2016

Abstract

Quantum noise is a signal-dependent, Poisson-distributed noise and the dominant noise source in digital mammography. Quantum noise removal or equalization has been shown to be an important step in the automatic detection of microcalcifications. However, it is often limited by the difficulty of robustly estimating the noise parameters on the images. In this study, a nonparametric image intensity transformation method that equalizes quantum noise in digital mammograms is described. A simple Look-Up-Table for Quantum Noise Equalization (LUT-QNE) is determined based on the assumption that noise properties do not vary significantly across the images. This method was evaluated on a dataset of 252 raw digital mammograms by comparing noise statistics before and after applying LUT-QNE. Performance was also tested as a preprocessing step in two microcalcification detection schemes. Results show that the proposed method statistically significantly improves microcalcification detection performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/58008
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