Segmentation of dynamic PET images using cluster analysis

Author

Wong, Koon-Pong ; Feng, Dagan ; Meikle, Steven R. ; Fulham, Michael J.

Author_Institution

R. Prince Alfred Hosp., Dept. of PET & Nucl. Medicine, Camperdown, NSW, Australia

Volume

49

Issue

1

fYear

2002

fDate

2/1/2002 12:00:00 AM

Firstpage

200

Lastpage

207

Abstract

Quantitative positron emission tomography (PET) studies provide in vivo measurements of dynamic physiological and biochemical processes in humans. A limitation of PET is an inability to provide precise anatomic localization due to relatively poor spatial resolution when compared to magnetic resonance (MR) imaging. Manual placement of region-of-interest (ROI) is commonly used in clinical and research settings in analysis of PET datasets. However, this approach is operator dependent and time-consuming. A semi- or fully-automated ROI delineation (or segmentation) method offers advantages by reducing operator error/subjectivity and thereby improving reproducibility. In this work, we describe an approach to automatically segment dynamic PET images using cluster analysis and we validate our approach with a simulated phantom study and assess its performance with real dynamic PET data. Our preliminary results suggest that cluster analysis can automatically segment tissues in dynamic PET studies and has the potential to replace manual ROI delineation for some applications

Keywords

image segmentation; positron emission tomography; PET; cluster analysis; delineation; magnetic resonance imaging; phantom; positron emission tomography; region-of-interest; segmentation; spatial resolution; tissue; Data analysis; Humans; Image analysis; Image segmentation; In vivo; Magnetic analysis; Magnetic resonance; Magnetic resonance imaging; Positron emission tomography; Spatial resolution;

fLanguage

English

Journal_Title

Nuclear Science, IEEE Transactions on

Publisher

ieee

ISSN

0018-9499

Type

jour

DOI

10.1109/TNS.2002.998752

Filename

998752