A robust non-local total-variation based image registration method under illumination changes in medical applications

Abstract

Abstract Independence of neighboring pixels and image stationarity are major concepts in conventional similarity metrics, used in image registration tasks. The accuracy of image registration decreases due to the presence of spatially varying intensity distortion in images. In this study, we hypothesized that changes in image illumination have limited total variation (TV). Accordingly, we introduced a similarity metric by reducing the weighted TV in the residual image. The primal dual method was then chosen to solve the proposed registration problem. The efficiency of the proposed method was compared to conventional methods, including the residual complexity (RC) method, the robust Huber similarity measure (RHSM), and the local linear reconstruction method (LLRM) which have been very successful in this field. The efficacy of the proposed method was confirmed by experimental findings on real-world and synthetic images.