Twelve automated thresholding methods for segmentation of PET images: a phantom study
Prieto E [SP], Lecumberri P, Pagola M, Gómez M, Bilbao I, Ecay M, Peñuelas I [SP], Martí-Climent JM. [SP]
Department of Nuclear Medicine, Clínica Universidad de Navarra, Pío XII 36, 31008 Pamplona, Spain.
Magazine: Physics in Medicine and Biology
Date: May 31, 2012Nuclear Medicine [SP]
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
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