Abstract
In this chapter, we will describe the theoretical background of noise reduction in medical imaging as well as give some examples of noise reduction methods. To do so, we start with a fundamental description of digital image generation in medical imaging, since we will only focus on digital images and noise reduction by means of digital image processing. In the next part, we will discuss the corresponding processing in general before we describe the approaches typically used mainly based on linear filtering and some new approaches based on nonlinear approaches.
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Notes
- 1.
It is not always the case in real systems.
- 2.
Norbert Wiener had stated and solved this problem under special conditions for stationary stochastic time series (see [3]).
- 3.
The equality \( f=g \) holds in the weak sense if \( \left\langle {f,h} \right\rangle =\left\langle {g,h} \right\rangle \) for any \( h\in H \).
- 4.
That is, errors that are described with asymmetric density distribution.
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Tischenko, O., Hoeschen, C. (2013). Noise Reduction. In: Giussani, A., Hoeschen, C. (eds) Imaging in Nuclear Medicine. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31415-5_8
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DOI: https://doi.org/10.1007/978-3-642-31415-5_8
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