Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1891
Title: A new automatic detection approach for hepatocellular carcinoma using ¹¹C-acetate positron emission tomography
Authors: Chen, S
Wong, LK
Feng, DD
Keywords: Cluster analysis
Segmentation
Hepatocellular Carcinoma (HCC)
Positron Emission Tomography (PET)
Parameter K
Issue Date: 2003
Publisher: IEEE
Source: ICIP 2003 : 2003 International Conference on Image Processing : proceedings : September 14-17, 2003, Barcelona, Spain, v. 1, p. 1065-1068 How to cite?
Abstract: Functional imaging techniques such as Positron Emission Tomography (PET) has the potential for early diagnosis of malignant tumors. However, 40-50% of Hepatocellular Carcinoma (HCC), a common malignancy worldwide, can hardly be detected by the widely used [sup 18]F-2-fluoro-2-deoxy-D-glucose (FDG) PET. ¹¹C-acetate PET has recently been found effective for detecting HCC. To perform quantitative analysis to obtain the diagnosis information, regions of interest (ROls) are needed to be extracted. Manual placement of ROIs is subject to operator's skill and time-consuming. Furthermore, the small sizes of some ROIs make the task even more difficult. In this paper, we propose an approach to segment the dynamic ¹¹C-acetate PET liver images automatically. The curves extracted from some segmented ROIs are then fitted to the presented ¹¹C-acetate liver model. Finally, the parameter K, which has been validated as an indicator for detecting HCC, can be calculated.
URI: http://hdl.handle.net/10397/1891
ISBN: 0-7803-7750-8
DOI: 10.1109/ICIP.2003.1247150
Rights: © 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
Chen_Wong_Feng_Automatic_Detection_Approach.pdf320.35 kBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Citations as of Jun 4, 2016

Page view(s)

239
Last Week
1
Last month
Checked on Aug 21, 2016

Download(s)

167
Checked on Aug 21, 2016

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.