Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.
翻译:以强度为基础的图像登记方法依靠类似的措施来指导对图像之间高度亲近的几何对应物的搜索; 所用措施的特性对于登记是否稳健和准确至关重要; 在这项研究中,提出了一个基于强度和空间信息相结合的对称、密集度、无内插、近似登记框架; 综合合成测试、恢复已知的噪声变异以及生物医学和医学图像登记中的实际应用,证明了框架的出色性能,对于2D和3D图像而言,该方法都显示出比通用的类似性措施更加稳健和准确,当该方法被插入一个标准的梯度登记框架,作为开放源 Insight Coveration和登记工具包(ITK)的一部分时,该标准梯度登记框架被作为开放源 Insight Coveration和登记工具包(ITK)的一部分使用时,该方法比通用的类似性措施更为可靠和准确。 该方法也从经验上证明具有低的计算成本,使之适用于实际应用。