Detecting informative frames from wireless capsule endoscopic video using color and texture features.
Despite emerging technology, wireless capsule endoscopy needs high amount of diagnosis-time due to the presence of many useless frames, created by turbid fluids, foods, and faecal materials. These materials and fluids present a wide range of colors and/or bubble-like texture patterns. We, therefore, propose a cascade method for informative frame detection, which uses local color histogram to isolate highly contaminated non-bubbled (HCN) frames, and Gauss Laguerre Transform (GLT) based multiresolution norm-1 energy feature to isolate significantly bubbled (SB) frames. Supervised support vector machine is used to classify HCN frames (Stage-1), while automatic bubble segmentation followed by threshold operation (Stage-2) is adopted to detect informative frames by isolating SB frames. An experiment with 20,558 frames from the three videos shows 97.48% average detection accuracy by the proposed method, when compared with methods adopting Gabor based-(75.52%) and discrete wavelet based features (63.15%) with the same color feature.
Bashar
M K
MK
Graduate School of Engineering, Nagoya University, Japan. kbashar@suenaga.m.is.nagoya-u.ac.jp
Mori
K
K
Suenaga
Y
Y
Kitasaka
T
T
Mekada
Y
Y
D000465:Algorithms; D001185:Artificial Intelligence; D053704:Capsule Endoscopy / Q000379:methods*; D003116:Color; D003124:Colorimetry / Q000379:methods*; D016145:Endoscopy, Digestive System / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013686:Telemetry / Q000379:methods*