Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18586
Title: Automated delineation of lung tumors in PET images based on monotonicity and a tumor-customized criterion
Authors: Ballangan, C
Wang, X
Fulham, M
Eberl, S
Yin, Y
Feng, D
Keywords: Lung tumor segmentation
nonsmall cell lung cancer (NSCLC)
positron emission tomography (PET)
tumor delineation
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information technology in biomedicine, 2011, v. 15, no. 5, 5873153, p. 691-702 How to cite?
Journal: IEEE transactions on information technology in biomedicine 
Abstract: Reliable automated or semiautomated lung tumor delineation methods in positron emission tomography should provide accurate tumor boundary definition and separation of the lung tumor from surrounding tissue or "hot spots" that have similar intensities to the lung tumor. We propose a tumor-customized downhill (TCD) method to achieve these objectives. Our approach includes: 1) automatic formulation of a tumor-customized criterion to improve tumor boundary definition, 2) a monotonic property of the standardized uptake value (SUV) of tumors to separate the tumor from adjacent regions of increased metabolism ("hot spot"), and 3) accounts for tumor heterogeneity. Three simulated lesions and 30 PET-CT studies, grouped into "simple" and "complex" groups, were used for evaluation. Our main findings are that TCD, when compared to the threshold based on 40% and 50% maximum SUV, adaptive threshold, Fuzzy c-means, and watershed techniques achieved the highest Dices similarity coefficient average for simulation data (0.73) and "complex" group (0.71); the least volumetric error in the "simple" (1.76mL) and the "complex group" (14.59mL); and TCD solves the problem of leakage into adjacent tissues when many other techniques fail.
URI: http://hdl.handle.net/10397/18586
ISSN: 1089-7771
DOI: 10.1109/TITB.2011.2159307
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