Automatic segmentation of liver PET images

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
Chih-Yu HsuChung-Ming Chen

Abstract

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.

References

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Citations

Mar 26, 2010·European Journal of Nuclear Medicine and Molecular Imaging·Habib Zaidi, Issam El Naqa
Jan 10, 2016·Medical Physics·Huiqian WangDrew A Torigian
May 23, 2015·IEEE Transactions on Bio-medical Engineering·Wei MuJie Tian
May 23, 2013·IEEE Transactions on Medical Imaging·Qi SongXiaodong Wu
Apr 29, 2014·IEEE Transactions on Cybernetics·Eleftheria A MylonaDimitris Maroulis
May 23, 2014·Computers in Biology and Medicine·Brent FosterDaniel J Mollura
Feb 1, 2014·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Pablo MesejoSergio Damas
Sep 17, 2013·Computers in Biology and Medicine·Ziming ZengReyer Zwiggelaar
Sep 12, 2012·Physics in Medicine and Biology·Kitiwat KhamwanCharnchai Pluempitiwiriyawej

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