Abstract
Complex images from different processes are often acquired with a low signal to noise ratio, as it is the case with Magnetic Resonance Imaging. Noise filtering is used to recover the associated phase images, mitigating negative effects such as loss of contrast and the introduction of phase residues, which constitute a major drawback for phase unwrapping processes. In this work, a group of algorithms combining nonlinear filters and wavelet de-noising were developed and applied to MRI images, in order to recover the phase information. The results obtained with the two algorithms that exhibited the best performance when applied to both phantom and real images, are shown. Application of these algorithms resulted in improvements both in terms of SNR and of the decrement in the number of phase residues.
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Cruz-Enríquez, H., Lorenzo-Ginori, J.V. (2009). Combined Wavelet and Nonlinear Filtering for MRI Phase Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_9
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DOI: https://doi.org/10.1007/978-3-642-02611-9_9
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