TY - JOUR
T1 - Single-Channel Sparse Non-Negative Blind Source Separation Method for Automatic 3-D Delineation of Lung Tumor in PET Images
AU - Kopriva, Ivica
AU - Ju, Wei
AU - Zhang, Bin
AU - Shi, Fei
AU - Xiang, Dehui
AU - Yu, Kai
AU - Wang, Ximing
AU - Bagci, Ulas
AU - Chen, Xinjian
N1 - Funding Information:
Manuscript received June 6, 2016; revised September 22, 2016 and October 31, 2016; accepted November 1, 2016. Date of publication November 3, 2016; date of current version November 3, 2017. This work was supported in part by the bilateral Chinese–Croatian Grant “Dealina-tion of lung tumor though nonlinear decomposition of PET/CT image;” in part by the Croatian Science Foundation Grant “Structured Decompositions of Empirical Data for Computationally-Assisted Diagnoses of Disease;” in part by the National Basic Research Program of China (973 Program) under Grant 2014CB748600; and in part by the National Natural Science Foundation of China under Grant 81371629. (Corresponding author: Xinjian Chen.) I. Kopriva is with the Division of Electronics, Ruđer Bosˇković Institute, Zagreb 10000, Croatia (e-mail: ikopriva@irb.hr).
Publisher Copyright:
© 2013 IEEE.
PY - 2017/11
Y1 - 2017/11
N2 - 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 matrix factorization is used to separate tumor from other tissues. By using complexity based criterion, we select tumor component as the one with minimal complexity. We have compared the proposed method with threshold based on 40% and 50% maximum standardized uptake value (SUV), graph cuts (GC), random walks (RW), and affinity propagation (AP) algorithms on 18 nonsmall cell lung cancer datasets with respect to ground truth (GT) provided by two radiologists. Dice similarity coefficient averaged with respect to two GTs is: 0.78 ± 0.12 by the proposed algorithm, 0.78 ± 0.1 by GC, 0.77 ± 0.13 by AP, 0.77 ± 0.07 by RW, and 0.75 ± 0.13 by 50% maximum SUV threshold. Since the proposed method achieved performance comparable with interactive methods, considering the unique challenges of lung tumor segmentation from PET images, our findings support possibility of using our fully automated method in routine clinics. The source codes will be available at www.mipav.net/English/research/research.html.
AB - 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 matrix factorization is used to separate tumor from other tissues. By using complexity based criterion, we select tumor component as the one with minimal complexity. We have compared the proposed method with threshold based on 40% and 50% maximum standardized uptake value (SUV), graph cuts (GC), random walks (RW), and affinity propagation (AP) algorithms on 18 nonsmall cell lung cancer datasets with respect to ground truth (GT) provided by two radiologists. Dice similarity coefficient averaged with respect to two GTs is: 0.78 ± 0.12 by the proposed algorithm, 0.78 ± 0.1 by GC, 0.77 ± 0.13 by AP, 0.77 ± 0.07 by RW, and 0.75 ± 0.13 by 50% maximum SUV threshold. Since the proposed method achieved performance comparable with interactive methods, considering the unique challenges of lung tumor segmentation from PET images, our findings support possibility of using our fully automated method in routine clinics. The source codes will be available at www.mipav.net/English/research/research.html.
KW - Lung tumor delineation
KW - non-negative matrix factorization (NMF)
KW - positron emission tomography (PET)
KW - single-channel blind source separation
KW - sparseness
UR - http://www.scopus.com/inward/record.url?scp=85035784242&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035784242&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2016.2624798
DO - 10.1109/JBHI.2016.2624798
M3 - Article
C2 - 27834658
AN - SCOPUS:85035784242
VL - 21
SP - 1656
EP - 1666
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 6
M1 - 7733134
ER -