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Multimodal image registration for potential diagnosis and monitoring of morphoea using a hybrid NGC method

机译:使用混合NGC方法进行多模态图像配准,以潜在地诊断和监测形态

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In medicine, thermal imaging diagnostic tool is increasingly employed thanks to its low cost, non-harmful and non-invasive nature. However, a thermogram can be quite ambiguous due to its low spatial resolution. This ambiguity can be reduced by incorporating information or data from different imaging sensors. This paper is an extension of a modified Normalized Gradient Correlation (NGC) employed in the initial phase of this study for multimodal image registration to assist in diagnosis and monitoring of linear morphoea. The proposed method is an improved, hybrid version of the modified NGC that incorporates an iterative based normalized cross-correlation coefficient (NCC) method for retrieval of translational differences based on the spatial domain in the initial method. The hybrid NGC method is found to reduce misregistration due to inaccurate retrieval of translational differences suffered by the initial NGC method in this multimodal image registration by up to 77.4% for over-detection error.
机译:在医学上,由于其低成本,无害且无创的性质,热成像诊断工具越来越多地被采用。但是,由于空间分辨率低,温度记录图可能会非常模棱两可。通过合并来自不同成像传感器的信息或数据,可以减少这种歧义。本文是在本研究的初始阶段使用的改进的归一化梯度相关性(NGC)的扩展,用于多模态图像配准,以帮助诊断和监测线性形态。所提出的方法是改进的NGC的改进混合版本,它结合了基于迭代的归一化互相关系数(NCC)方法,用于在初始方法中基于空间域检索平移差异。发现混合NGC方法可将由于在该多峰图像配准中初始NGC方法所获得的翻译差异检索不准确而导致的配准错误减少多达77.4%(用于过度检测错误)。

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