Abstract
Bias field signal is a low-frequency and very smooth signal that corrupts MRI images specially those produced by old MRI (Magnetic Resonance Imaging) machines. Image processing algorithms such as segmentation, texture analysis or classification that use the graylevel values of image pixels will not produce satisfactory results. A pre-processing step is needed to correct for the bias field signal before submitting corrupted MRI images to such algorithms or the algorithms should be modified. In this report we discuss two approaches to deal with bias field corruption. The first approach can be used as a preprocessing step where the corrupted MRI image is restored by dividing it by an estimated bias field signal using a surface fitting approach. The second approach shows how to modify the fuzzy c-means algorithm so that it can be used to segment an MRI image corrupted by a bias field signal.
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© 2005 Springer-Verlag Berlin Heidelberg
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Juntu, J., Sijbers, J., Van Dyck, D., Gielen, J. (2005). Bias Field Correction for MRI Images. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_64
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DOI: https://doi.org/10.1007/3-540-32390-2_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
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