Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Polyp Segmentation Using Wavelet-Based Cross-Band Integration for Enhanced Boundary Representation

Haesung Oh, Jaesung Lee

TL;DR

This work analyzed polyp-background contrast in the wavelet domain, revealing that grayscale representations consistently preserve higher boundary contrast than RGB images across all frequency bands, suggesting that boundary cues are more distinctly represented in the grayscale domain than in the color domain.

Abstract

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.

Polyp Segmentation Using Wavelet-Based Cross-Band Integration for Enhanced Boundary Representation

TL;DR

This work analyzed polyp-background contrast in the wavelet domain, revealing that grayscale representations consistently preserve higher boundary contrast than RGB images across all frequency bands, suggesting that boundary cues are more distinctly represented in the grayscale domain than in the color domain.

Abstract

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.
Paper Structure (6 sections, 2 figures, 1 table)

This paper contains 6 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Experimental Results
  5. Discussion
  6. Conclusion

Figures (2)

  • Figure 1: Structural contrast comparison between RGB and grayscale images in the wavelet domain, showing consistently higher contrast for grayscale across all detail sub-bands.
  • Figure 2: Proposed wavelet-based cross-band integration framework that fuses frequency-consistent information from RGB and grayscale features for enhanced boundary representation.