Polyp Segmentation Using Wavelet-Based Cross-Band Integration for Enhanced Boundary Representation
This work addresses the challenge of reliable boundary localization in polyp segmentation, which is crucial for early cancer detection, but it appears incremental as it builds on existing methods by enhancing boundary representation.
The paper tackled the problem of accurate polyp segmentation for early colorectal cancer detection by proposing a model that integrates grayscale and RGB representations through wavelet-based cross-band interaction, achieving superior boundary precision and robustness on four benchmark datasets.
Accurate polyp segmentation is essential for early colorectal cancer detection, yet achieving reliable boundary localization remains challenging due to low mucosal contrast, uneven illumination, and color similarity between polyps and surrounding tissue. Conventional methods relying solely on RGB information often struggle to delineate precise boundaries due to weak contrast and ambiguous structures between polyps and surrounding mucosa. To establish a quantitative foundation for this limitation, we analyzed polyp-background contrast in the wavelet domain, revealing that grayscale representations consistently preserve higher boundary contrast than RGB images across all frequency bands. This finding suggests that boundary cues are more distinctly represented in the grayscale domain than in the color domain. Motivated by this finding, we propose a segmentation model that integrates grayscale and RGB representations through complementary frequency-consistent interaction, enhancing boundary precision while preserving structural coherence. Extensive experiments on four benchmark datasets demonstrate that the proposed approach achieves superior boundary precision and robustness compared to conventional models.