Affiliations 

  • 1 Department of Communication Systems, Simula Research Laboratory, Fornebu, Norway
  • 2 Center of Intelligent Signal & Imaging Research Group, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia
  • 3 Research Institute of Multiprocessor Computation Systems, n.a. A.V. Kalyaev, Russia
  • 4 Department of Medicine, University of Malaya Medical Center, Kuala Lumpur, Malaysia
J Appl Clin Med Phys, 2019 Aug;20(8):141-154.
PMID: 31251460 DOI: 10.1002/acm2.12662

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.