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Abstract：
In this study, we propose a novel method for quantifying tortuosity in 3D voxelized objects. As a shape characteristic, tortuosity has been widely recognized as a valuable feature in image analysis, particularly in the field of medical imaging. Our proposed method extends the two-dimensional approach of the Slope Chain Code (SCC) which creates a one-dimensional representation of curves. The utility of 3D tortuosity ( τ 3 D ) as a shape descriptor was investigated by characterizing brain structures. The results of the τ 3 D computation on the central sulcus and the main lobes revealed significant differences between Alzheimer\'s disease (AD) patients and control subjects, suggesting its potential as a biomarker for AD. We found a p < 0.05 for the left central sulcus and the four brain lobes.
摘要：
在这项研究中,我们提出了一种量化三维体素化物体弯曲度的新方法。作为一种形状特征，弯曲度已被广泛认为是图像分析中的一个有价值的特征，尤其是在医学成像领域。我们提出的方法扩展了斜坡链码（SCC）的二维方法，该方法创建了曲线的一维表示。通过表征大脑结构研究了3D弯曲度（τ3D）作为形状描述符的实用性。对中央沟和主叶的τ3D计算结果表明，阿尔茨海默病（AD）患者与对照组之间存在显着差异，表明其作为AD生物标志物的潜力。我们发现左中央沟和四个脑叶的p<0.05。
