Affiliations: Department of Medical Imaging and Radiological
Sciences, I-Shou University, Taiwan | Department of Biomedical Imaging and Radiological
Sciences, National Yang-Ming University, Taiwan | Institute of Statistics, National Chiao Tung
University, Taiwan
Note: [] Correspondence author: Henry Horng-Shing Lu, Institute of
Statistics, National Chaio Tung University, 1001 Ta Hsueh Road, Hsinchu 30010,
Taiwan. Tel.: +886 3 5731870, Fax: +886 3 5728745, E-mail:
[email protected]
Abstract: Segmentation of positron emission tomography (PET) is typically
achieved using the K-Means method or other approaches. In preclinical and
clinical applications, the K-Means method needs a prior estimation of
parameters such as the number of clusters and appropriate initialized values.
This work segments microPET images using a hybrid method combining the Gaussian
mixture model (GMM) with kernel density estimation. Segmentation is crucial to
registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation
locations with functional diagnosis and to estimate standardized uptake values
(SUVs) of region of interests (ROIs) in PET images. Therefore, simulation
studies are conducted to apply spherical targets to evaluate segmentation
accuracy based on Tanimoto's definition of similarity. The proposed method
generates a higher degree of similarity than the K-Means method. The PET images
of a rat brain are used to compare the segmented shape and area of the cerebral
cortex by the K-Means method and the proposed method by volume rendering. The
proposed method provides clearer and more detailed activity structures of an
FDG accumulation location in the cerebral cortex than those by the K-Means method.
