Comparison of Legendre and united moments in the classification of Alzheimer conditions using T1 weighted MR images. Issue 8 (1st April 2019)

• Record Type: Journal Article
• Authors: Dadsena, R.; Rohini, P.; Anandh, K.R.; Ramakrishnan, S.
• Is Part Of: Electronics letters. Volume 55:Issue 8(2019)
• Page Start: 440
• Page End: 442
• Publication Date: 2019-04-01
• Subjects: statistical analysis -- brain -- support vector machines -- feature extraction -- cognition -- patient diagnosis -- biomedical MRI -- image segmentation -- neurophysiology -- medical image processing -- image classification -- learning (artificial intelligence) -- diseases; united moments -- Alzheimer conditions -- T1 weighted MR images -- structural magnetic resonance imaging -- Alzheimer's disease -- brain structures -- AD conditions -- volume‐based measures -- moment‐based shape descriptors -- mild cognitive impairment -- MCI -- shape variations -- Lattice Boltzmann criterion‐based hybrid level set method -- United moment -- segmented binary images -- significant moments -- shape analysis -- Legendre moment -- control versus AD subjects; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.7762
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Alzheimer's disease (AD) is a progressive neurodegenerative disorder that causes brain regions to undergo structural changes. Shape descriptors are useful in reflecting the morphological alterations of brain structures in AD conditions compared to volume based measures. In this work, moment based shape descriptors are used to classify control, mild cognitive impairment (MCI) and AD subjects. Lattice Boltzmann criterion‐based hybrid level set method (LSM) is used to delineate lateral ventricles. Legendre and United moments are extracted from the segmented binary images and are statistically analysed using Statistical Package for Social Science (SPSS). The performance of significant moments in the shape analysis is validated using machine learning algorithm. Results demonstrate that, level set is able to delineate ventricles and found to have high similarity index with ground truth. The area under curve (AUC) values for Legendre moment is found to be 1.0, 0.75 and 1.0 for Control‐MCI, MCI‐AD and Control‐AD subjects. Rather, the AUC for United moment is found to be 0.98, 0.76 and 0.98 for Control‐MCI, MCI‐AD and Control‐AD subjects respectively. Further, machine learning algorithm could classify Control from AD subjects with high accuracy of 99.25% using Legendre moments and hence the study seems to be clinically significant.