A Highlight Removal Method for Capsule Endoscopy Images

TL;DR

This work tackles specular highlight removal in Wireless Capsule Endoscopy (WCE) images, where highlights erase diagnostic information. It presents a Criminisi-based method enhanced with an R/B channel ratio as confidence, a weighted combination with a feature term, adaptive sample-block sizing guided by local variance, and distance-aware best-matching block selection to improve texture continuity. The approach yields lower standard deviation and coefficient of variation in the repaired regions and produces color- and texture-similar results to surrounding tissue, outperforming DeepGin and the classic Criminisi method. The method offers practical implications for more reliable lesion assessment in WCE and shows promise for real-time or near-real-time applications with future runtime optimizations.

Abstract

The images captured by Wireless Capsule Endoscopy (WCE) always exhibit specular reflections, and removing highlights while preserving the color and texture in the region remains a challenge. To address this issue, this paper proposes a highlight removal method for capsule endoscopy images. Firstly, the confidence and feature terms of the highlight region's edges are computed, where confidence is obtained by the ratio of known pixels in the RGB space's R channel to the B channel within a window centered on the highlight region's edge pixel, and feature terms are acquired by multiplying the gradient vector of the highlight region's edge pixel with the iso-intensity line. Subsequently, the confidence and feature terms are assigned different weights and summed to obtain the priority of all highlight region's edge pixels, and the pixel with the highest priority is identified. Then, the variance of the highlight region's edge pixels is used to adjust the size of the sample block window, and the best-matching block is searched in the known region based on the RGB color similarity and distance between the sample block and the window centered on the pixel with the highest priority. Finally, the pixels in the best-matching block are copied to the highest priority highlight removal region to achieve the goal of removing the highlight region. Experimental results demonstrate that the proposed method effectively removes highlights from WCE images, with a lower coefficient of variation in the highlight removal region compared to the Crinimisi algorithm and DeepGin method. Additionally, the color and texture in the highlight removal region are similar to those in the surrounding areas, and the texture is continuous.

Figures (9)

• Figure 1: Methodological Framework.
• Figure 2: Symbolic Diagram of Criminisi Algorithm.
• Figure 3: Example of the CVC-ClinicSpec Database.
• Figure 4: Supplementary Database Example.
• Figure 5: Results of Highlight Removal in WCE Images with Different Values of Parameter $\beta$.