Author: Siyue HAN, ZhizhaoYU, Jiahui YU, Simon James Fong, Yongze GUO, HongweiZHANG, Huicong DONG, Saicong LU, Xian WANG, Feng TIAN
Journal: Journal of Digital Information Management
Year: 2026
Gastric cancer is among the leading causes of cancer related mortality worldwide, and its early detection
remains challenging due to subtle endoscopic manifestations. Artificial intelligence (AI) assisted analysis of
endoscopic images offers a promising pathway for improving diagnostic accuracy in real time. This paper
presents a real time feature extraction framework for fine mucosal structures (FMS) in gastroscopy images
using a structure enhanced morphological watershed segmentation algorithm. The method performs image
preprocessing through directional filtering and morphological operations to highlight FMS patterns, followed
by marker controlled watershed segmentation to deline ate structural boundaries. Geometric features
including area, circularity, convexity, and Feret diameter are quantified and evaluated using multiple
machine learning classifiers. Experimental results on endoscopic datasets demonstrate that filtering
segmentation outliers significantly improves classification performance, with neural networks achieving
an AUC of 0.813. The results indicate that max Feret diameter is the most discriminative feature for
distinguishing early gastric cancer from non cancerous tissues. This study contributes to real time intelligent
healthcare systems by providing an interpretable and computationally efficient feature extraction pipeline
that can support AI-driven clinical decision making in endoscopic screening.
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