Parametric estimate of intensity inhomogeneities
Martin Andreas Styner, Christian Brechbühler, Gábor J. Székely, Guido Gerig · 2000
This paper presents a new approach to the cor- rection of intensity inhomogeneities in Magnetic Resonance Imaging (MRI) that signicantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias eld impedes visual inspec- tion and segmentation. The new correction method called PABIC (PArametric BIas eld Correction) is based on a simplied model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inho- mogeneity eld. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is cor- rupted by noise and a low-frequency inhomogeneity eld. The estimation of the parametric bias eld is formulated as a non-linear energy minimization problem using an Evo- lution Strategy. The resulting bias eld is independent of the image region congurations and thus overcomes limita- tions of methods based on homomorphic ltering. Further, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statis- tics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with his- togram adjustment, making it well-suited for normalizing the intensity histogram 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 2D and 3D, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.