Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method-Reference-Cited by-同舟云学术

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Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method

Published:2017-01 Issue: Volume:219 Page:107-129
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Feng Chaolu,Zhao Dazhe,Huang Min

Funder

Postdoctoral Science Foundation of China

Fundamental Research Funds for the Central Universities of China

National Science Foundation for Distinguished Young Scholars of China

National Natural Science Foundation of China

Postdoctoral Scientific Research Funds of Northeastern University

Fundamental Research Funds for State Key Laboratory of Synthetical Automation for Process Industries

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference43 articles.

1. Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images;Zhou;Neurocomputing,2015

2. A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement;Wang;Pattern Recognit.,2015

3. Structural brain abnormalities are related to retinal nerve fiber layer thinning and disease duration in neuromyelitis optica spectrum disorders;von Glehn;Mult. Scler. J.,2014

4. DTI-based segmentation and quantification of human brain lateral ventricular CSF volumetry and mean diffusivity: validation, age, gender effects and biophysical implications;Hasan;Magn. Reson. Imaging,2014

5. Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets;Feng;Med. Phys.,2016

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