Arce-Santana Edgar, Campos-Delgado Daniel U, Mejia-Rodriguez Aldo, Reducindo Isnardo
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:5223-6. doi: 10.1109/EMBC.2015.7319569.
In this paper, we present a methodology for multimodal/ multispectral image registration of medical images. This approach is formulated by using the Expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.
在本文中，我们提出了一种用于医学图像的多模态/多光谱图像配准的方法。这种方法是通过使用期望最大化（EM）方法来制定的，以便我们估计使多模态/多光谱图像对齐的几何变换参数。在此框架中，隐藏随机变量与所研究图像之间的强度关系相关联，这使得能够比较不同模态图像之间的多光谱强度值。该方法主要由一个迭代的两步过程组成，其中在每一步中，都会计算联合条件多光谱强度分布和几何变换的新估计值。所提出的算法用不同类型的医学图像进行了测试，所得结果表明所提出的方法可用于有效地对齐多模态/多光谱医学图像。
