Single-Channel Sparse Non-Negative Blind Source Separation Method for Automatic 3-D Delineation of Lung Tumor in PET Images

Ivica Kopriva, Wei Ju, Bin Zhang, Fei Shi, Dehui Xiang, Kai Yu, Ximing Wang, Ulas Bagci, Xinjian Chen
2017 IEEE journal of biomedical and health informatics  
In this paper, we propose a novel method for single-channel blind separation of non-overlapped 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 3D PET image into a pseudo multichannel image. Afterwards, regularization free sparseness constrained nonnegative matrix factorization is used to separate tumor from other tissues. By using complexity based criterion,
more » ... e 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 non-small cell lung cancer datasets with respect to ground truth provided by two radiologists. Dice similarity coefficient averaged with respect to two ground truths 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 . Index Terms-Single-channel blind source separation, nonnegative matrix factorization, sparseness, lung tumor delineation, positron emission tomography (PET).
doi:10.1109/jbhi.2016.2624798 pmid:27834658 fatcat:c6kbhcz3abcjnhzdwqcaxl5u4u