Abstract
In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labeling. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
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Keywords
- Magnetic Resonance Imaging Data
- Magnetic Resonance Imaging Signal
- Pepper Noise
- Intensity Inhomogeneity
- Tissue Class
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© 1999 Springer-Verlag Berlin Heidelberg
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Ahmed, M.N., Yamany, S.M., Mohamed, N.A., Farag, A.A. (1999). A Modified Fuzzy C-Means Algorithm for MRI Bias Field Estimation and Adaptive Segmentation. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_8
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DOI: https://doi.org/10.1007/10704282_8
Publisher Name: Springer, Berlin, Heidelberg
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