@article {10.3844/jcssp.2017.380.392,
article_type = {journal},
title = {Medical Images Registration based on Normalized Dissimilarity Index},
author = {KHARBACH, Amina and EL OMARI, Mouad and MARDANI, Amar and BELLACH, Benaissa and RAHMOUN, Mohammed},
volume = {13},
number = {9},
year = {2017},
month = {Sep},
pages = {380-392},
doi = {10.3844/jcssp.2017.380.392},
url = {https://thescipub.com/abstract/jcssp.2017.380.392},
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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}