Automatic segmentation of liver PET images

被引：44
|
作者：
Hsu, Chih-Yu [1 ]
Liu, Chun-You [2 ,3 ]
Chen, Chung-Ming [2 ,3 ]
机构：
[1] Chaoyang Univ Technol, Dept Informat & Commun Engn, Wufeng 41349, Taichung County, Taiwan
[2] Natl Taiwan Univ, Inst Biomed Engn, Coll Med, Taipei, Taiwan
[3] Natl Taiwan Univ, Coll Engn, Taipei, Taiwan
来源：
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS | 2008年 / 32卷 / 07期
关键词：
PET images; Image segmentation; Active contour model; Genetic algorithm;
D O I：
10.1016/j.compmedimag.2008.07.001
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Automation of liver positron emission tomography (PET) image segmentation is proposed in this paper. A new active contour model (ACM), called Poisson Gradient Vector Flow (PGVF), with genetic algorithm (GA) constructs a scheme to automatically find the contour of liver in the PET images. PET is widely used for the clinical purpose, but image quality of PET makes the image segmentation be a tough work. Three image data sets are tested for evaluating the new segmentation approach of liver PET images. One image data set is adapted from the study of one person with a normal liver. The other two image data sets are adapted from the studies of two patients with abnormal livers. The results show that the regions of interest (ROI) of liver are automatically segmented from the images of three data sets. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：601 / 610
页数：10
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