3D Tortuosity computation as a shape descriptor and its application to brain structure analysis

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 (\(\tau _{3D}\)) as a shape descriptor was investigated by characterizing brain structures. The results of the \(\tau _{3D}\) 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.