An adaptive redundant image elimination for Wireless Capsule Endoscopy review based on temporal correlation and color-texture feature similarity

This paper proposes an approach to eliminate redundant images adaptively for Wireless Capsule Endoscopy (WCE) video summarization by considering temporal correlation and feature similarity between adjacent WCE frames. The color and texture features, generated by HSV color histogram model and Gray Level Co-occurrence Matrix, have been taken into account. It is noted that frames from different WCE videos may have different dynamic information ranges. Hence a data-driven threshold termed as W-parametric mean value threshold (W-MVT) is developed to improve robustness of the proposed method. By comparing the color-texture feature similarity of adjacent WCE frames with W-MVT sequentially, the temporal correlated images with certain similarity are grouped into the same clip. Eventually, to consider gradient varying characteristic in one clip, the adaptive K-means clustering algorithm is adopted to keep key frames while remove redundant frames further. Experimental results show that two evaluation indicators-F-measure and compression ratio achieve 81.94% and 80.31%, which validates the effectiveness of the proposed WCE redundant image elimination (WCE-RIE) method.