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.

Details

Title
3D Tortuosity computation as a shape descriptor and its application to brain structure analysis
Author
Maria-Julieta Mateos; Bribiesca, Ernesto; Guzmán-Arenas, Adolfo; Aguilar, Wendy; Marquez-Flores, Jorge A
Pages
1-12
Section
Research
Publication year
2024
Publication date
2024
Publisher
BioMed Central
e-ISSN
14712342
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3066880510
Copyright
© 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.