Genetic algorithms for Voxel-based medical image registration

A. Valsecchi, S. Damas, J. Santamaría, L. Marrakchi-Kacem
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引用次数: 21

Abstract

Image registration (IR) - the task of aligning different images having a common content - is a fundamental problem in computer vision. In particular, IR is one of the key steps in medical imaging, with applications ranging from computer assisted diagnosis to computer aided therapy and surgery. As IR can be formulated as an optimization problem, a large family of metaheuristics methods can be used to improve the results obtained by classic gradient-based, continuous optimization techniques. In this work, we extend our previous intensity-based image registration (IR) technique based on a real-coded genetic algorithm with a more appropriate design. The performance evaluation of an heterogeneous group of state-of-the-art IR techniques is also extended to two experimental studies on both synthetic and real-word medical IR problems. The results prove the accuracy and applicability of our new method.
基于体素的医学图像配准遗传算法
图像配准(IR)是计算机视觉中的一个基本问题,即对具有相同内容的不同图像进行对齐。特别是,红外成像是医学成像的关键步骤之一,其应用范围从计算机辅助诊断到计算机辅助治疗和手术。由于IR可以表述为一个优化问题,因此可以使用大量的元启发式方法来改进经典的基于梯度的连续优化技术所获得的结果。在这项工作中,我们扩展了先前基于实数编码遗传算法的基于强度的图像配准(IR)技术,并采用了更合适的设计。一组异质的最先进的红外技术的性能评估也扩展到两个实验研究合成和现实世界的医疗红外问题。结果证明了新方法的准确性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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