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Intensity Gradient Self-organizing Map for Cerebral Cortex Reconstruction

机译:强度梯度自组织映射的大脑皮层重建。

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This paper presents an application of a self-organizing map (SOM) model based on the image intensity gradient for the reconstruction of cerebral cortex from MR images. The cerebral cortex reconstruction is important for many brain science or medicine related researches. However, it is difficult to extract deep cortical folds. In our method, we apply the SOM model based on the image intensity gradient to deform the easily extracted white matter surface and extract the cortical surface. The intensity gradient vectors are calculated according to the intensities of image data. Thus the proper cortical surface can be extracted from the image information itself but not artificial features. The simulations on T1-weighted MR images show that the proposed method is robust to reconstruct the cerebral cortex.
机译:本文提出了一种基于图像强度梯度的自组织图(SOM)模型在MR图像重建大脑皮层中的应用。大脑皮层重建对于许多脑科学或医学相关研究而言都是重要的。但是,很难提取深层皮层褶皱。在我们的方法中,我们基于图像强度梯度应用SOM模型,以使易于提取的白质表面变形并提取皮质表面。根据图像数据的强度计算强度梯度矢量。因此,可以从图像信息本身而不是人工特征中提取适当的皮质表面。 T1加权MR图像的仿真表明,该方法对重建大脑皮层具有鲁棒性。

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