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Published on: January 9, 2015
Aspects of signal-dependent noise characterization
John J Heine1, Madhusmita Behera
1Cancer Prevention and Control, Department of Interdisciplinary Oncology, University of South Florida, and H Lee Moffitt Cancer Center and Research Institute, Tampa 33612-9497, USA.
Summary
This study introduces an automated method for analyzing signal-dependent noise in data acquisition. The technique uses Fourier attributes and wavelet expansion to determine if noise variance depends on the signal, applicable to mammography data.
Area of Science:
- Signal Processing
- Image Analysis
- Medical Imaging
Background:
- Signal-dependent noise is prevalent in data acquisition.
- Existing noise analysis methods often rely on specific noise models.
- Understanding noise is crucial for accurate data interpretation.
Purpose of the Study:
- To develop an automated method for analyzing signal-dependent noise.
- To assess the dependence of noise variance on the signal.
- To provide a model-agnostic approach for noise characterization.
Main Methods:
- Utilizes Fourier attributes of signal and noise.
- Employs wavelet expansion for component separation.
- Approximates functional relations within a signal-noise model framework.
Main Results:
- Demonstrates an automated approach for signal-dependent noise analysis.
- Validates the method using 2D simulations and real-world mammography data.
- The technique is independent of signal and noise probability distributions.
Conclusions:
- The presented automated method effectively analyzes signal-dependent noise.
- Wavelet-based component separation offers a robust analysis technique.
- Applicable to various data-acquisition processes, including medical imaging.
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