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 |
Issue Date: | 2003 | Source: | ICIP 2003 : 2003 International Conference on Image Processing : proceedings : September 14-17, 2003, Barcelona, Spain, v. 1, p. 1065-1068 | 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. | Keywords: | Cluster analysis Segmentation Hepatocellular Carcinoma (HCC) Positron Emission Tomography (PET) Parameter K |
Publisher: | IEEE | 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 | Size | Format | |
---|---|---|---|---|
Chen_Wong_Feng_Automatic_Detection_Approach.pdf | 320.35 kB | Adobe PDF | View/Open |
Page views
53
Last Week
1
1
Last month
Citations as of May 28, 2023
Downloads
46
Citations as of May 28, 2023
SCOPUSTM
Citations
3
Last Week
0
0
Last month
0
0
Citations as of Jun 1, 2023
WEB OF SCIENCETM
Citations
1
Last Week
0
0
Last month
0
0
Citations as of Jun 1, 2023

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