학술논문
Unimodal Medical Image Registration Based on Genetic Algorithm Optimization
Document Type
Chapter
Author
Das, Kedar Nath, Editor; Bansal, Jagdish Chand, Editor; Deep, Kusum, Editor; Nagar, Atulya K., Editor; Pathipooranam, Ponnambalam, Editor; Naidu, Rani Chinnappa, Editor; Alexy John, J. V.; Kumar, S. N.; Lenin Fred, A.; Ajay Kumar, H.; Abisha, W.; Kacprzyk, Janusz, Series Editor; Pal, Nikhil R., Advisory Editor; Bello Perez, Rafael, Advisory Editor; Corchado, Emilio S., Advisory Editor; Hagras, Hani, Advisory Editor; Kóczy, László T., Advisory Editor; Kreinovich, Vladik, Advisory Editor; Lin, Chin-Teng, Advisory Editor; Lu, Jie, Advisory Editor; Melin, Patricia, Advisory Editor; Nedjah, Nadia, Advisory Editor; Nguyen, Ngoc Thanh, Advisory Editor; Wang, Jun, Advisory Editor
Source
Soft Computing for Problem Solving : SocProS 2018, Volume 2. 01/01/2020. 1057:549-562
Subject
Language
English
ISSN
2194-5357
2194-5365
2194-5365
Abstract
This research work proposes unimodal image registration based on genetic algorithm. The intensity-based image registration is employed here, and normalized cross-correlation is used as the similarity index, and for choosing the optimal values of image registration parameters, genetic algorithm was employed. The performance of the image registration was validated by the performance metrics and tested on MR brain images of BrainWeb database. The performance metrics peak-to-signal noise ratio (PSNR), mean squared error (MSE), normalized cross-correlation (NCC), and mutual information (MI) reveals the superiority of the image registration algorithm.