Abstract

Image segmentation in complex scenes remains challenging due to intensity inhomogeneity, intricate textures, and noise interference. Traditional active contour models (ACMs) offer topological adaptability while suffering from over-segmentation and boundary leakage under such conditions. In this paper, we propose MFJD-Seg, a novel ACM that integrates morphological fitting with an energy formulation derived from Jeffreys divergence for robust and efficient image segmentation. Morphological erosion and dilation are applied to construct foreground and background fitting images, which capture fine-grained structural features while suppressing background interference. Subsequently, a symmetric discrepancy consistent with Jeffreys divergence is leveraged to quantify the statistical difference between the original image and the fitting representations, enabling the compact construction of an unbiased energy function. An arctangent energy constraint and mean filtering are further incorporated to stabilize contour evolution and suppress redundant artifacts. Extensive experiments on BSDS, ADE20K, and COCO datasets show that MFJD-Seg achieves the best mIoU and mDSC in comparisons with five representative ACMs and five mainstream deep learning segmentation models, improving ACM baselines by up to 4.8% in both metrics while maintaining the highest FPS among ACMs and competitive speed against deep learning counterparts. These results verify the superior segmentation capabilities of MFJD-Seg in challenging imaging scenarios.

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