A modified FCM algorithm for adaptive segmentation, bias field elimination, and partial volume estimation in MRI data
Summary form only given. We present the application of a novel algorithm for adaptive fuzzy segmentation of MRI data, estimation of intensity inhomogeneities in MRI volumes, and partial volume estimation 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 labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. The new algorithm also solves the partial volume (PV) effect. Due to the partial volume effect, boundary transitions from one tissue to another can occupy more than one voxel. In most researches this has been treated as an artifact that affect the classification of PV voxels into the right cluster. However, this effect can not be neglected when accurate volumetric measurements are brought to focus. The Modified FCM (MFCM) algorithm is shown to have the ability to overcome the noise sensitivity problem in segmentation without changing the voxel values thus keeping the whole information at the edges for accurate PV estimation.