Title
Single-Channel Sparse Non-Negative Blind Source Separation Method for Automatic 3-D Delineation of Lung Tumor in PET Images.
Abstract
In this paper, we propose a novel method for single-channel blind separation of nonoverlapped sources and, to the best of our knowledge, apply it for the first time to automatic segmentation of lung tumors in positron emission tomography (PET) images. Our approach first converts a 3-D PET image into a pseudo-multichannel image. Afterward, regularization free sparseness constrained non-negative mat...
Year
DOI
Venue
2017
10.1109/JBHI.2016.2624798
IEEE Journal of Biomedical and Health Informatics
Keywords
Field
DocType
Positron emission tomography,Tumors,Image segmentation,Lungs,Computed tomography,Three-dimensional displays,Cancer
Standardized uptake value,Cut,Computer vision,Affinity propagation,Pattern recognition,Computer science,Segmentation,Matrix decomposition,Artificial intelligence,Non-negative matrix factorization,Positron emission tomography,Blind signal separation
Journal
Volume
Issue
ISSN
21
6
2168-2194
Citations 
PageRank 
References 
0
0.34
27
Authors
9
Name
Order
Citations
PageRank
Ivica Kopriva114616.60
Wei Ju220.71
Bin Zhang36040.23
Fei Shi4245.42
Dehui Xiang5172.40
Kai Yu68213.41
Ximing Wang761.13
Ulaş Bağcı8779.70
XinJian Chen950253.39