Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation

Chunming Li, John C. Gore, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

250 Scopus citations


This paper proposes a new energy minimization method called multiplicative intrinsic component optimization (MICO) for joint bias field estimation and segmentation of magnetic resonance (MR) images. The proposed method takes full advantage of the decomposition of MR images into two multiplicative components, namely, the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity, and their respective spatial properties. Bias field estimation and tissue segmentation are simultaneously achieved by an energy minimization process aimed to optimize the estimates of the two multiplicative components of an MR image. The bias field is iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. More importantly, the energy in our formulation is convex in each of its variables, which leads to the robustness of the proposed energy minimization algorithm. The MICO formulation can be naturally extended to 3D/4D tissue segmentation with spatial/sptatiotemporal regularization. Quantitative evaluations and comparisons with some popular softwares have demonstrated superior performance of MICO in terms of robustness and accuracy.

Original languageEnglish (US)
Pages (from-to)913-923
Number of pages11
JournalMagnetic Resonance Imaging
Issue number7
StatePublished - Sep 2014


  • 4D segmentation
  • Bias field correction
  • Bias field estimation
  • Brain segmentation
  • Intensity inhomogeneity
  • MRI

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation'. Together they form a unique fingerprint.

Cite this