Brain White Matter Hyperintensity Lesion Characterization in T2 Fluid-Attenuated Inversion Recovery Magnetic Resonance Images: Shape, Texture, and Potential Growth

Prior methods in characterizing age-related white matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have mainly been limited to understanding the sizes of, and occasionally the locations of WMH lesions. Systematic morphological char...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in neuroscience 2019-04, Vol.13
Main Authors: Gwo, Chih-Ying, Zhu, David C., Zhang, Rong
Format: Article
Language:English
Subjects:
Age
Alzheimer's disease
Biomarkers
Brain research
brain T2 FLAIR hyperintensity
Classification
Dementia
Fuzzy logic
Genetic transformation
Image retrieval
Lesions
Magnetic resonance imaging
Medical imaging
Methods
Morphology
Neuroscience
Noise
Older people
potential growth
shape
Signal processing
Statistical analysis
Statistics
Substantia alba
texture
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Prior methods in characterizing age-related white matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have mainly been limited to understanding the sizes of, and occasionally the locations of WMH lesions. Systematic morphological characterization has been missing. In this work, we proposed innovative methods to fill this knowledge gap. We developed an innovative and proof-of-concept method to characterize and quantify the shape (based on Zernike transformation) and texture (based on fuzzy logic) of WMH lesions. We have also developed a multi-dimension feature vector approach to cluster WMH lesions into distinctive groups based on their shape and then texture features. We then developed an approach to calculate the potential growth index (PGI) of WMH lesions based on the image intensity distributions at the edge of the WMH lesions using a region-growing algorithm. High-quality T2 FLAIR images containing clearly identifiable WMH lesions with various sizes from six cognitively normal older adults were used in our method development Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH group clusters in terms of either the shape (P = 1.06×10-2) or the texture (P < 1×10-20) features. In conclusion, we propose a systematic framework on which the shape and texture features of WMH lesions can be quantified and may be used to predict lesion growth in older adults.
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2019.00353