TY  - JOUR
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070772272&doi=10.1002%2facm2.12662&partnerID=40&md5=08360578cb56f307ef5cd760922e316e
EP  - 154
ID  - scholars11423
N2  - 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 patientâ??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. © 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.
TI  - Bleeding detection in wireless capsule endoscopy videos â?? Color versus texture features
N1  - cited By 31
JF  - Journal of Applied Clinical Medical Physics
IS  - 8
SN  - 15269914
VL  - 20
Y1  - 2019///
AV  - none
A1  - Pogorelov, K.
A1  - Suman, S.
A1  - Azmadi Hussin, F.
A1  - Saeed Malik, A.
A1  - Ostroukhova, O.
A1  - Riegler, M.
A1  - Halvorsen, P.
A1  - Hooi Ho, S.
A1  - Goh, K.-L.
PB  - John Wiley and Sons Ltd
SP  - 141
KW  - algorithm; automated pattern recognition; capsule endoscopy; color; diagnostic imaging; gastrointestinal hemorrhage; gastrointestinal tract; human; machine learning; pathology; procedures; videorecording; wireless communication
KW  -  Algorithms; Capsule Endoscopy; Color; Gastrointestinal Hemorrhage; Gastrointestinal Tract; Humans; Machine Learning; Pattern Recognition
KW  -  Automated; Video Recording; Wireless Technology
ER  -