Twelve automated thresholding methods for segmentation of PET images: a phantom study
Prieto E, Lecumberri P, Pagola M, Gómez M, Bilbao I, Ecay M, Peñuelas I, Martí-Climent JM.
Department of Nuclear Medicine, Clínica Universidad de Navarra, Pío XII 36, 31008 Pamplona, Spain.
Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development.
The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET.
Spherical (18)F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05).
We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.
CITATION Phys Med Biol. 2012 May 31;57(12):3963-3980