Methodology Research on Thyroid Volume Estimation based on
Radionuclide Imaging
XU Leia, LIU Ren-congb,MENG Qing-leb, YANG Ruib,JIANG Hong-binga
a.Department of Medical Equipment;
b.Department of Nuclear Medicine, the
Affiliated Nanjing Hospital of Nanjing
Medical University (the First Hospital
of Nanjing ), Nanjing Jiangsu 210006,
China
Abstract:Objective To develop an imaging segmentation algorithm based on adaptive thresholding to
be used for thyroid volume estimation in radionuclide imaging. Methods First, image preprocessing
was performed on images collected by thyroid radionuclide imaging, which contained smoothness,
enhancement, and grey level transformation. Second, adaptive thresholding algorithm and morphological
operations were adapted to extract the rough thyroid area. Finally, the maximum height and the area of
each lobe can be achieved by setting reference points so that the thyroid volume could be calculated.
Results The thyroid volume obtained through radionuclide imaging was used as the reference standard.
Assessment indexes such as deviation, precision, relative differences, and degree of association were
selected to compare different methods used to estimate thyroid volumes. The results of the comparison
indicate that the approach of thyroid volume estimation proposed in this research was not only highly
correlated with the results obtained through ultrasonography (R2=0.99), the result also has the best
deviation (0.9), the lowest precision (±2.14 mL), and relative differences (3.2%±4.36%). Conclusion
The approach to thyroid volume estimation proposed in this research is precise, simple and convenient,
and can avoid dependence on ultrasound, which can help physicians determine the individualized dosage
regimen for each patient in the treatment of thyroid disease.
徐磊a,刘任从b,孟庆乐b,杨瑞b,蒋红兵a. 基于核素显像的甲状腺体积测定方法研究[J]. 中国医疗设备, 2016, 31(3): 39-41.
XU Leia, LIU Ren-congb,MENG Qing-leb, YANG Ruib,JIANG Hong-binga. Methodology Research on Thyroid Volume Estimation based on
Radionuclide Imaging. China Medical Devices, 2016, 31(3): 39-41.