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Title: The impact of reconstruction algorithms on semi-automatic small lesion segmentation for PET : a phantom study
Authors: Ballangan, C
Chan, C
Wang, X
Feng, D
Keywords: Diseases
Expectation-maximisation algorithm
Fuzzy reasoning
Image reconstruction
Image segmentation
Medical image processing
Positron emission tomography
Smoothing methods
Issue Date: 2011
Publisher: IEEE
Source: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, August 30 2011-September 3 2011, Boston, MA, p. 8483-8436 How to cite?
Abstract: A robust lesion segmentation method is critical for quantification of lesion activity in positron emission tomography (PET), especially for the cases where lesion boundary is not discernible in the corresponding computed tomography (CT). However, lesion delineation in PET is a challenging task, especially for small lesions, due to the low intrinsic resolution, image noise and partial volume effect. The combinations of different reconstruction methods and post-reconstruction smoothing on PET images also affect the segmentation result significantly which has always been overlooked. Therefore, the aim of this study was to investigate the impact of different reconstruction methods on semi-automated small lesion segmentation for PET images. Four conventional segmentation methods were evaluated including region growing technique based on maximum intensity (RGmax) and mean intensity (RGmean) thresholds, Fuzzy c-mean (FCM) and watershed (WS) technique. All these methods were evaluated on a physical phantom scan which was reconstructed with Ordered Subset Expectation Maximization (OSEM) with Gaussian post-smoothing and Maximum a Posteriori (MAP) with quadratic prior respectively. The results demonstrate that: 1) the performance of all the segmentation methods subject to the smoothness constraint applied on the reconstructed images; 2) FCM method applied on MAP reconstructed images yielded overall superior performance than other evaluated combinations.
ISBN: 978-1-4244-4121-1
978-1-4244-4122-8 (E-ISBN)
ISSN: 1557-170X
DOI: 10.1109/IEMBS.2011.6092093
Appears in Collections:Conference Paper

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