Single-Channel Sparse Non-Negative Blind Source Separation Method For Automatic 3-D Delineation Of Lung Tumor In Pet Images

Keywords

Lung tumor delineation; non-negative matrix factorization (NMF); positron emission tomography (PET); single-channel blind source separation; sparseness

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 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.

Publication Date

11-1-2017

Publication Title

IEEE Journal of Biomedical and Health Informatics

Volume

21

Issue

6

Number of Pages

1656-1666

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/JBHI.2016.2624798

Socpus ID

85035784242 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85035784242

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