MRI bias field estimation and tissue segmentation using multiplicative intrinsic component optimization and its extensions. (English) Zbl 07914368
Chen, Ke (ed.) et al., Handbook of mathematical models and algorithms in computer vision and imaging. Mathematical imaging and vision. Springer Reference. Cham: Springer. 1203-1234 (2023).
Summary: In medical image analysis, energy minimization-based optimization approaches are invaluable. This chapter presents a joint optimization method called multiplicative intrinsic component optimization (MICO) for magnetic resonance (MR) images in bias field estimation and segmentation. Due to the intensity inhomogeneity in MR images, there are overlaps between the ranges of the intensities of different tissues, which often causes misclassification of tissues. To overcome this problem, our proposed method MICO can estimate bias field without avoiding intensity inhomogeneity and can benefit to achieve superior tissue segmentation results. We extended MICO formulation by connecting total variation (TV) as a convex regularization. In addition, for the TV-based MICO model, we implemented the alternating direction method of multipliers (ADMM), which can solve the model efficiently and guarantee its convergence. Quantitative evaluations and comparisons with other popular software have shown that MICO and TVMICO outperform them in terms of robustness and accuracy.
For the entire collection see [Zbl 1527.94003].
For the entire collection see [Zbl 1527.94003].
Keywords:
MRI; brain segmentation; intensity inhomogeneity; bias field estimation; bias field correction; energy minimization; multiplicative intrinsic component optimization; 4D segmentation; total variation; ADMMSoftware:
N4itkReferences:
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