Capsule Endoscopy Image Enhancement for Small Intestinal Villi Clarity

Shaojie Zhang, Yinghui Wang (), Peixuan Liu (), Yukai Wang, Liangyi Huang, Mingfeng Wang and Ibragim Atadjanov
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Shaojie Zhang: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Yinghui Wang: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Peixuan Liu: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Yukai Wang: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Liangyi Huang: School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
Mingfeng Wang: Department of Mechanical and Aerospace Engineering, Brunel University London, London UB8 3PH, UK
Ibragim Atadjanov: Department of Computer Engineering, Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi, Tashkent 100084, Uzbekistan

Mathematics, 2024, vol. 12, issue 21, 1-17

Abstract: Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the low- and high-frequency components of WCE images. Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. This approach improves edge detail while suppressing noise and avoiding edge overshoot, providing clearer images for diagnosis. Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images.

Keywords: gain factor; images; small intestinal villi; wireless capsule endoscopy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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