An ICTM-RMSAV Framework for Bias-Field Aware Image Segmentation under Poisson and Multiplicative Noise

TL;DR

This paper introduces a novel variational image segmentation model within the ICTM framework that effectively handles Poisson and multiplicative noise by integrating adaptive denoising and bias field correction, leading to improved segmentation accuracy.

Contribution

The paper presents a new ICTM-RMSAV framework that combines adaptive denoising, bias field estimation, and efficient optimization for robust image segmentation under complex noise conditions.

Findings

Achieves superior accuracy on synthetic and real images.

Robust against intensity inhomogeneity and diverse noise types.

Outperforms existing segmentation methods in experiments.

Abstract

Image segmentation is a core task in image processing, yet many methods degrade when images are heavily corrupted by noise and exhibit intensity inhomogeneity. Within the iterative-convolution thresholding method (ICTM) framework, we propose a variational segmentation model that integrates denoising terms. Specifically, the denoising component consists of an I-divergence term and an adaptive total-variation (TV) regularizer, making the model well suited to images contaminated by Gamma--distributed multiplicative noise and Poisson noise. A spatially adaptive weight derived from a gray-level indicator guides diffusion differently across regions of varying intensity. To further address intensity inhomogeneity, we estimate a smoothly varying bias field, which improves segmentation accuracy. Regions are represented by characteristic functions, with contour length encoded accordingly. For efficient optimization, we couple ICTM with a relaxed modified scalar auxiliary variable (RMSAV) scheme. Extensive experiments on synthetic and real-world images with intensity inhomogeneity and diverse noise types show that the proposed model achieves superior accuracy and robustness compared with competing approaches.