Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33353
Title: Lung tumor segmentation in PET images using graph cuts
Authors: Ballangan, C
Wang, X
Fulham, M
Eberl, S
Feng, DD
Keywords: Graph cuts
Lung tumor segmentation
Non-small cell lung cancer (NSCLC)
Positron emission tomography (PET)
Tumor delineation
Issue Date: 2013
Publisher: Elsevier Ireland Ltd
Source: Computer methods and programs in biomedicine, 2013, v. 109, no. 3, p. 260-268 How to cite?
Journal: Computer Methods and Programs in Biomedicine 
Abstract: The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill.
URI: http://hdl.handle.net/10397/33353
ISSN: 0169-2607
DOI: 10.1016/j.cmpb.2012.10.009
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