Affine-invariant feature learning for accurate ulcer detection in wireless capsule endoscopy images

Abstract

Ulcers are lesions that develop in the lining of the gastrointestinal (GI) tract, particularly in the stomach and small intestine, and may lead to severe complications such as Crohn’s disease and ulcerative colitis if not detected at an early stage. Conventional endoscopic procedures are often uncomfortable for patients and may provide limited visualization of the entire small intestine. Wireless capsule endoscopy (WCE) has emerged as a non-invasive alternative for comprehensive GI tract examination; however, automated ulcer detection from WCE images remains challenging due to image noise, complex tissue structures, and computational requirements. To address these issues, this paper proposes a Camargo’s Indexive Kuwahara filtering-based affine-invariant sliced regression (CIKF-AISR) framework for accurate and efficient ulcer detection. The proposed framework consists of image acquisition, preprocessing, segmentation, and feature extraction stages. Adaptive CIKF is employed to suppress noise while preserving edge information. Subsequently, Von Neumann locality segmentation combined with the Canberra distance measure is utilized to identify regions of interest (ROIs). Finally, affine-invariant saliency sliced regression extracts discriminative shape, color, and texture features for ulcer detection. Experimental evaluation on the Hyper-Kvasir dataset demonstrates that the proposed method achieves higher ulcer detection accuracy, improved precision, enhanced peak signal-to-noise ratio (PSNR), and lower detection time compared with existing deep CNN and VAE-GAN approaches. These results confirm the effectiveness of the proposed framework for computer-aided GI diagnosis.