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MRI Bias Field Estimation and Tissue Segmentation Using Multiplicative Intrinsic Component Optimization and Its Extensions

In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

Author

Listed:
  • Samad Wali

    (University of Electronic Science and Technology of China, School of Information and Communication Engineering
    Namal Univeristy, Department of Mathematics)

  • Chunming Li

    (University of Electronic Science and Technology of China, School of Information and Communication Engineering)

  • Lingyan Zhang

    (University of Electronic Science and Technology of China, School of Information and Communication Engineering)

Abstract

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.

Suggested Citation

  • Samad Wali & Chunming Li & Lingyan Zhang, 2023. "MRI Bias Field Estimation and Tissue Segmentation Using Multiplicative Intrinsic Component Optimization and Its Extensions," Springer Books, in: Ke Chen & Carola-Bibiane Schönlieb & Xue-Cheng Tai & Laurent Younes (ed.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, chapter 34, pages 1203-1234, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_110
    DOI: 10.1007/978-3-030-98661-2_110
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