ArticleJournal of applied clinical medical physics2019
Bleeding detection in wireless capsule endoscopy videos - Color versus texture features.
Article in Journal of applied clinical medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- An End-to-End Deep Learning System for Gastrointestinal Bleeding Detection and Quantification in Wireless Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2026Article
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models.World journal of gastroenterology · 2024Article
- Cascade-EC Network: Recognition of Gastrointestinal Multiple Lesions Based on EfficientNet and CA_stm_Retinanet.Journal of imaging informatics in medicine · 2024Article
- Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy.World journal of gastroenterology · 2023Article
- Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network.GE Portuguese journal of gastroenterology · 2022Article
- Video Capsule Endoscopy in Gastroenterology.Gastroenterology research · 2022Review
- Proposing Novel Data Analytics Method for Anatomical Landmark Identification from Endoscopic Video Frames.Journal of healthcare engineering · 2022Article
- Automated Bowel Polyp Detection Based on Actively Controlled Capsule Endoscopy: Feasibility Study.Diagnostics (Basel, Switzerland) · 2021Article
- Kvasir-Capsule, a video capsule endoscopy dataset.Scientific data · 2021Article
- Automated bleeding detection in wireless capsule endoscopy images based on color feature extraction from Gaussian mixture model superpixels.Medical & biological engineering & computing · 2021Article
- Artificial intelligence and capsule endoscopy: unravelling the future.Annals of gastroenterology · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wireless capsule endoscopy (WCE) is an effective technology that can be used to make a gastrointestinal (GI) tract diagnosis of various lesions and abnormalities. Due to a long time required to pass through the GI tract, the resulting WCE data stream contains a large number of frames which leads to a tedious job for clinical experts to perform a visual check of each and every frame of a complete patient's video footage. In this paper, an automated technique for bleeding detection based on color and texture features is proposed. The approach combines the color information which is an essential feature for initial detection of frame with bleeding. Additionally, it uses the texture which plays an important role to extract more information from the lesion captured in the frames and allows the system to distinguish finely between borderline cases. The detection algorithm utilizes machine-learning-based classification methods, and it can efficiently distinguish between bleeding and nonbleeding frames and perform pixel-level segmentation of bleeding areas in WCE frames. The performed experimental studies demonstrate the performance of the proposed bleeding detection method in terms of detection accuracy, where we are at least as good as the state-of-the-art approaches. In this research, we have conducted a broad comparison of a number of different state-of-the-art features and classification methods that allows building an efficient and flexible WCE video processing system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.