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.