SFENet: A Spatial-Frequency Dual-Branch Image Quality Enhancement Network for Breast Ultrasound Tumor Segmentation

Breast ultrasound (BUS) images are commonly degraded by speckle noise, intensity inhomogeneity, acoustic shadowing, and low contrast, while breast tumors often exhibit irregular morphology, heterogeneous echogenicity, and blurred boundaries. These factors make accurate lesion segmentation difficult. We propose SFENet, an end-to-end spatial-frequency dual-branch image quality enhancement network for BUS tumor segmentation. Built on a U-shaped segmentation framework, SFENet jointly models lesion morphology, global spectral responses, and local image-quality degradation through a wavelet-frequency deformable spatial (WFDS) block. Specifically, the lesion-adaptive deformable scanning (LADS) module captures irregular lesion morphology and long-range spatial dependencies; the unified spectral filtering attention (USFA) module performs learnable Fourier-domain spectral recalibration; and the wavelet image enhancement module (WIEM) conducts local brightness correction and edge-aware contrast enhancement through wavelet sub-band decomposition. Because the enhancement pathway is optimized under segmentation supervision, it tends to strengthen lesion-relevant structural cues rather than generic visual quality alone. Experiments on the public BUSI data set show that SFENet achieves a Dice coefficient of 81.3% and a Jaccard index of 73.5%, outperforming the representative CNN-, Transformer-, and Mamba-based segmentation models included in this study. Qualitative results and Grad-CAM visualizations further suggest that SFENet produces more stable lesion contours in low-contrast, heterogeneous, and boundary-ambiguous cases. These results indicate that joint spatial-frequency modeling with task-driven wavelet-domain enhancement is an effective strategy for automatic BUS tumor segmentation.