Parametric estimate of intensity inhomogeneities applied to MRI

Abstract

This paper presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. 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 field. 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 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 can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram 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 two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

Anatomical Correlates of Directional Hypokinesia in Patients with Hemispatial Neglect

“…Lesion boundaries were determined with the aid of an unsupervised fuzzy class means-based segmentation procedure. We automatically corrected for image intensity inhomogeneity using a variant of Styner (Styner et al, 2000) and the assumption that the objects imaged in the Siemens (Munich, Germany) circularly polarized head coil exhibit a parabolic threedimensional gain field (10 free parameters). Voxels were classified into one of four tissue types: air, CSF, gray matter, and white matter.…”

The fast automatic algorithm for correction of MR bias field

“…Similar basis model described in Ref. 17 uses Legendre polynomials; however, dsf was more stable with a simpler polynomial basis.…”

“…It is common for such methods to construct a target function and iteratively minimize it to find the basis coefficients (15)(16)(17). Fuzzy clustering methods (3,18,19) are hybrids of surface fitting and EM algorithms.…”

MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering: A Unified Energy Minimization Framework

“…In parametric methods (e.g. [10,12]), which model the bias field as a linear combination of polynomial basis functions, the computed bias field is always smooth. However, such parametric methods are not able to capture bias fields that cannot be well approximated by polynomials, such as the bias field in the 7T MR images shown in Section 3.2.…”

“…In contrast, retrospective methods rely only on the information in the acquired images, and thus they can remove intensity inhomogeneities regardless of their sources. Retrospective methods include those based on filtering [1,2,3,4], surface fitting [5,6,7,8], histogram [9,10], and segmentation [11,12,13,14,15,16,17].…”