Medical image registration and model construction using genetic algorithms

C. Chow, H. Tsui, Tong Lee, Tze Kin Lau
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引用次数: 19

Abstract

The free-form surface registration problem is important in medical image processing and reconstruction. An accurate, robust and fast solution is, therefore, of great significance. Most existing approaches, like iterative closest point (ICP) or mutual information, can only work under many assumptions or limitations. In this paper we propose an new approach using an improved genetic algorithm for finding the transformation, a translation and a rotation, between two free-form surfaces, that are partially overlapped. Application of the algorithm to 3D object model construction using multiple image integration is investigated. The experimental results showed that the improved genetic algorithm for surface registration and model constriction is robust and is faster than all existing methods in the literatures.
基于遗传算法的医学图像配准与模型构建
自由曲面配准问题是医学图像处理和重建中的一个重要问题。因此,一个准确、稳健、快速的解决方案具有重要意义。大多数现有的方法,如迭代最近点(ICP)或互信息,只能在许多假设或限制下工作。在本文中,我们提出了一种新的方法,使用改进的遗传算法来寻找两个部分重叠的自由曲面之间的变换,平移和旋转。研究了该算法在多图像集成三维目标模型构建中的应用。实验结果表明,改进的遗传算法在表面配准和模型收缩方面具有鲁棒性,并且比现有的所有方法都快。
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