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Energy Minimization in Medical Image Analysis: Methodologies & Applications / Feng Zhao; Xianghua Xie

International Journal for Numerical Methods in Biomedical Engineering, Volume: 32, Issue: 2

Swansea University Author: Xianghua, Xie

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DOI (Published version): 10.1002/cnm.2733

Abstract

Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two c...

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Published in: International Journal for Numerical Methods in Biomedical Engineering
Published: 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa22240
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Abstract: Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree- reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, e.g., image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well.
Keywords: Energy minimisation, medial image analysis, computer vision
College: College of Science
Issue: 2