Unimodal Medical Image Registration Based on Genetic Algorithm Optimization

被引：0

作者：

John, J. V. Alexy ^{[1
]}

Kumar, S. N. ^{[1
]}

Fred, A. Lenin ^{[2
]}

Kumar, H. Ajay ^{[1
]}

Abisha, W. ^{[1
]}

机构：

[1] Mar Ephraem Coll Engn & Technol, Sch Elect & Commun Engn, Kanyakumari, India

[2] Mar Ephraem Coll Engn & Technol, Sch Comp Sci & Engn, Kanyakumari, India

来源：

SOFT COMPUTING FOR PROBLEM SOLVING, SOCPROS 2018, VOL 2 | 2020年 / 1057卷

关键词：

Registration; Genetic algorithm; Mutual information; Normalized cross-correlation;

D O I：

10.1007/978-981-15-0184-5_47

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：549 / 562

页数：14

共 17 条

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