Validating cardiac echo diagnosis through video similarity

Abstract

Video data is increasingly being used in medical diagnosis. Due to the quality of the video and the complexities of underlying motion captured, it is difficult for an in-experienced physician/radiologist to describe motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this paper, we present a method of capturing video similarity and its use for diagnosis verification during decision support. Specifically, we describe the motion information in videos using average velocity curves. Second-order motion statistics are extracted from average velocity curves and serve as features for computing video similarity. Given a new video sample already labeled with a diagnosis, a neighborhood of similar videos is assembled from the training set and their diagnosis labels are used to verify the diagnosis. Copyright © 2005 ACM.