A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data

• Published in: IEEE transactions on medical imaging Vol. 21; no. 3; pp. 193 - 199
• Main Authors: Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological and medical sciences; Brain - anatomy & histology; Brain Neoplasms - diagnosis; Classification algorithms; Cluster Analysis; Coils; Computerized, statistical medical data processing and models in biomedicine; Data acquisition; Efficiency; Fuzzy; Fuzzy Logic; Fuzzy set theory; Fuzzy sets; General aspects. Methods; Humans; Image Enhancement - methods; Image segmentation; Imaging phantoms; Inhomogeneities; Investigative techniques, diagnostic techniques (general aspects); Labeling; Magnetic resonance imaging; Magnetic Resonance Imaging - instrumentation; Magnetic Resonance Imaging - methods; Marking; Medical sciences; Miscellaneous. Technology; Nonuniform electric fields; Phantoms, Imaging; Polynomials; Radio frequency; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Segmentation; Sensitivity and Specificity; Spatial variables measurement; Stochastic Processes; Studies; Human; Biomedical data processing; Evaluation; Heterogeneity; Fuzzy algorithm; Segmentation; Bias; Signal processing; Mammary gland; Error correction; Signal analysis; Nuclear magnetic resonance imaging
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.996338

We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to 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 toward piecewise-homogeneous labelings. 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.