Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images
Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain...
Saved in:
Main Authors: | Ketil Oppedal, Trygve Eftestøl, Kjersti Engan, Mona K. Beyer, Dag Aarsland |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2015-01-01
|
Series: | International Journal of Biomedical Imaging |
Online Access: | http://dx.doi.org/10.1155/2015/572567 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Amygdala Nuclei Atrophy in Cognitive Impairment and Dementia: Insights from High-Resolution Magnetic Resonance Imaging
by: Evija Peiseniece, et al.
Published: (2025-01-01) -
A TensorFlow implementation of Local Binary Patterns Transform
by: Devrim Akgün
Published: (2021-06-01) -
A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance
by: Wei-Wen Chen, et al.
Published: (2024-01-01) -
Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid
by: Yujia Jiang, et al.
Published: (2018-01-01) -
Texture Analysis and Classification using Local Binary Patterns and Statistical Features
by: Hasan Maher
Published: (2024-09-01)