Parametric estimate of intensity inhomogeneities applied to MRI

This paper presents a new approach to the correction of intensity inhomogen eities in magnetic resonance imaging (MRI) that significantly improves inte nsity-based tissue segmentation. The distortion of the image brightness val ues by a low-frequency bias field impedes visual inspection and segmentatio n. The new correction method called parametric bias field correction (PABIC ) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity fi eld. We assume that the image is composed of pixels assigned to a small num ber of categories with a priori known statistics. Further we assume that th e image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC c an correct bias distortions much larger than the image contrast. Input para meters are the intensity statistics of the classes and the degree of the po lynomial function. The polynomial approach combines bias correction with hi stogram adjustment, making it well suited for normalizing the intensity his togram of datasets from serial studies. We present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processe d and demonstrate the versatility and robustness of this new bias correctio n scheme.