JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (6): 64-79.doi: 10.6040/j.issn.1671-9352.0.2025.392

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Image segmentation based on bias field correction

RUAN Ping, ZHA Yuanhao*   

  1. School of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, Hubei, China
  • Published:2026-06-04

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Abstract: To address the performance degradation caused by intensity inhomogeneity in low-contrast image segmentation, this paper proposes a joint model for image segmentation and bias field correction, which achieves simultaneous estimationof the multiplicative bias field, additive bias field, and the true image. On this basis, an alternating minimization method(ADM)is designed to solve the variational functional minimization problem involving multiple unknown functions. Under given conditions, we prove the convergence of the proposed ADM. Experimental results demonstrate that the proposed image segmentation method has significant advantages in handling low-contrast, blurred boundary, and intensity inhomogeneous images.

Key words: image segmentation, low-contrast, bias field correction, convex optimization, alternating minimization

CLC Number: 

  • TP391
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