A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding
Abstract It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size...
Enregistré dans:
Auteurs principaux: | Inyoung Bae, Jong-Hee Chae, Yeji Han |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/b8a75ca265314cd0b83431c1e29b7bd9 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Fuzzy Multilevel Image Thresholding Based on Improved Coyote Optimization Algorithm
par: Linguo Li, et autres
Publié: (2021) -
Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction
par: Almazroa A, et autres
Publié: (2017) -
An Adaptive Threshold for the Canny Algorithm With Deep Reinforcement Learning
par: Keong-Hun Choi, et autres
Publié: (2021) -
Value of diffusion-weighted magnetic resonance imaging in the diagnosis of pyomyoma
par: Helena Peris, MD, et autres
Publié: (2022) -
Restrictive versus liberal transfusion thresholds in very low birth weight infants: A systematic review with meta-analysis.
par: Peng Wang, et autres
Publié: (2021)