Detection of Variation Instances on Colonoscopy Videos using Structural Similarity Index

Abstract

The aim of this study is to reduce the number of images extracted from the videos recorded by the specialists during the colonoscopy process for further examination, thereby enabling the specialist to deal with fewer images. Since the images obtained from the videos are very similar, the main assumption of this study is that the whole video can be represented by fewer images. The approach used in this study is the structural similarity index. Totally, images were obtained from 4 different videos coming from healthy, ulcerative colitis, Crohn's, and polyp patients. The noisy images in these videos were eliminated manually. When the structural similarity index between two consecutive clear images was less than 0.83, the second image was selected and shown to the specialist for his/her examination. By this way, the frames carrying significantly new information from the videos were defined as the variation instances. The tests on healthy or diseased colon videos showed that only 5-10% of the clear images provide significantly new information.