基于混合优化策略的多模态医学图像配准

A. Daly, Hedi Yazid, B. Solaiman, N. Amara
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引用次数: 1

摘要

图像配准是医学应用中的一项重要任务,在临床诊断中具有重要意义。在这项工作中,我们提出了一种基于遗传算法结合梯度下降优化器的多分辨率优化策略。在回顾性图像配准评估(RIR)数据库的真实多模态配准场景中对该方法的性能进行了测试和评估。与现有的配准方法进行了比较,结果表明该方法准确有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Medical Image Registration Based on a Hybrid Optimization Strategy
Image registration is a crucial task in medical applications and is perceived as an optimization problem which has an important interest in clinical diagnosis. In this work, we propose an optimization strategy based on a specific design of genetic algorithm combined with the gradient descent optimizer within multi-resolution scheme. The performance of the proposed method was tested and evaluated on real multimodal registration scenarios from the Retrospective Image Registration Evaluation (RIR) database. Our method results were compared with those of existing registration methods, they are accurate and effective.
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