Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting.
This paper proposes an unsupervised tumour segmentation scheme for PET data. The method computes the volume of interests (VOIs) with subpixel precision by considering the limited resolution and partial volume effect. Firstly, it uses local and global intensity active surface modelling to segment VOIs, then an alpha matting method is used to eliminate false negative classification and refine the segmentation results. We have validated our method on real PET images of head-and-neck cancer patients as well as images of a custom designed PET phantom. Experiments show that our method can generate more accurate segmentation results compared with alternative approaches.
Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China. zzz09@aber.ac.uk
Ziming
Z
Zeng
Tony
T
Shepherd
Reyer
R
Zwiggelaar
D001185:Artificial Intelligence; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity