Automatic segmentation of liver PET images.
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.
Hsu
Chih-Yu
CY
Department of Information and Communication Engineering, Chaoyang University of Technology, Wufeng, Taichung County, Taiwan, ROC. tccnchsu@gmail.com
Liu
Chun-You
CY
Chen
Chung-Ming
CM
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011877:Radionuclide Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity