Hemodynamic segmentation of brain perfusion images with delay and dispersion effects using an expectation-maximization algorithm.
Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles...
Main Authors: | Chia-Feng Lu, Wan-Yuo Guo, Feng-Chi Chang, Shang-Ran Huang, Yen-Chun Chou, Yu-Te Wu |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2013-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3716889?pdf=render |
Similar Items
-
Blind Source Separation of Hemodynamics from Magnetic Resonance Perfusion Brain Images Using Independent Factor Analysis
by: Yen-Chun Chou, et al.
Published: (2010-01-01) -
Tissue Classification from Brain Perfusion MR Images Using Expectation-Maximization Algorithm Initialized by Hierarchical Clustering on Whitened Data
by: Yen-Chun Chou, et al.
Published: (2007) -
Segmentation of normal and stenotic brain matter from CT perfusion images
by: Chia-Ling Chou, et al.
Published: (2014) -
Hemodynamic Parameter Calculation on Brain Perfusion Magnetic Resonance Images
by: Hsiao, Yi-hui, et al.
Published: (2004) -
Hemodynamic effects of perfusion level of peripheral ECMO on cardiovascular system
by: Kaiyun Gu, et al.
Published: (2018-05-01)