Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model

Overview

Journal Front Neurosci

Date 2022 Jun 20

PMID 35720703

Authors

Jingkun Wang

Kun Xiang

Kuo Chen

Rui Liu

Ruifeng Ni

Hao Zhu

Yan Xiong

Affiliations

Soon will be listed here.

Abstract

In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm. The registration performance of the proposed approach on different medical images is verified through extensive computer simulations. Empirical findings confirm that the proposed approach is significantly better than other conventional ones.

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