Ensemble image registration by a spatially constrained clustering approach. (30th September 2016)

• Record Type: Journal Article
• Authors: Zhu, Hao; Shi, Qiqun; Li, Yongfu; Wu, Qiuxuan
• Is Part Of: International journal of advanced robotic systems. Volume 13:Number 5(2016)
• Publication Date: 2016-09-30
• Subjects: Spatially constrained Gaussian mixture model -- Markov random field -- ensemble registration -- expectation maximization; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881416663367
• Languages: English
• ISSNs: 1729-8806

Abstract

In this article, a novel spatially constrained clustering approach is proposed for ensemble image registration. We use a spatially constrained Gaussian mixture model, which is based on a joint Gaussian mixture model and Markov random field, to model the joint intensity scatter plot of the unregistered images. The spatially constrained Gaussian mixture model has the capability of performing the correlation among neighboring observations. A cost function of reducing the dispersion in the joint intensity scatter plot is proposed using the spatially constrained Gaussian mixture model to simultaneously register a group of images. We derive an expectation maximization algorithm for the proposed model. Computer simulations demonstrate the effectiveness of the proposed method.