Title: Comparative evaluation of geometrical, Zernike moments, and volumetric features of the corpus callosum for discrimination of ASD using machine learning algorithms

Authors: Aditi Bhattacharya; Gokul Manoj; Vaibhavi Gupta; Abdul Aleem Shaik Gadda; Dhanvi Vedantham; A. Amalin Prince; Priya Rani; Anandh Kilpattu Ramaniharan; Jac Fredo Agastinose Ronickom

Addresses: Department of Biomedical Engineering, National Institute of Technology, Raipur, India ' School of Bio-Medical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh-221005, India ' School of Bio-Medical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh-221005, India ' School of Bio-Medical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh-221005, India ' School of Bio-Medical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh-221005, India ' EEE Department, BITS Pilani Goa Campus, Sancoale, India ' Applied Artificial Intelligence Institute, Deakin University, Victoria, Australia ' Neuroimaging Analysis Lab, Epilepsy Division, Department of Neurology, The University of Alabama at Birmingham, 35233, USA ' School of Bio-Medical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh-221005, India

Abstract: In this study, we compared the performance of geometrical, Zernike moments, and volumetric features of the corpus callosum (CC) to diagnose autism spectrum disorder (ASD). Initially, the CC was segmented from the midsagittal view of 2D structural magnetic resonance images using the distance regularised level set evolution (DRLSE). The segmented images were validated with the ground truth using similarity measures. The geometrical and Zernike moments were extracted from the 2D segmented region, and the volumetric features were extracted from 3D images of CC. The features extracted were then used to train classifiers. The segmented images were highly matched with the ground truth with mean similarity measure values of Sokal and Sneath-II = 0.9928 and Pearson and Heron-II = 0.9924. We achieved the highest site-specific classification accuracy of 72.69% using the random forest (RF) classifier. The pipeline followed in this study can be used for mass screening of ASD-like neurodevelopmental disorders.

Keywords: autism spectrum disorder; corpus callosum; level set method; volumetric features; Zernike moments; random forest.

DOI: 10.1504/IJBET.2023.134588

International Journal of Biomedical Engineering and Technology, 2023 Vol.43 No.3, pp.275 - 296

Received: 31 Aug 2022
Accepted: 03 Dec 2022
Published online: 30 Oct 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article