A modified fuzzy c-means algorithm for segmentation of MRI

In this paper, we present a novel algorithm for fuzzy segmentation of the osteosarcoma magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneties. The algorithm is formulated by modifying the objective function in the fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities. Our experiments demonstrate the effectiveness of the method.

[1]  Mehmed Ozkan,et al.  Image segmentation in MRI using true T/sub 1/ and true PD values , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.

[3]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[4]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[5]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[6]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .