Medical Images Registration based on Normalized Dissimilarity Index

Abstract

Abstract:Image registration is an essential step in a large number of processing chains for medical images. It is used to align two images taken at different times and from different sensors as well. In this paper, we are interested in the rigid registration and similarity measures. We describe a new registration approach, based on the normalized dissimilarity index that results from the local dissimilarity map (LDP). This LDP is obtained from distance transform applied to gray-scale images, to register, undergoing a binarization. We evaluate the performance of our method compared to the classical registration measurements such as correlation and mutual information, on a medical images database. We show that the mean squared error of our approach is more accurate in comparison to the classical registration methods to which researchers still adhere. The robustness of our proposed index is validated regarding the luminance variation and the presence of "the Pepper and Salt" as much as "the Gaussian" noise.

Modeling a Local Dissimilarity Map With Weibull Distribution–Application to 2-Class and Multi-Class Image Classification

“…In [10], Baudrier proposed a measure to characterize the local differences between binary images. It is based on the Hausdorff distance and has produced good results in several research works [11], [12]. This measure is called the Local Dissimilarity Map (LDM).…”