Twelve automated thresholding methods for segmentation of PET images: a phantom study

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 re...

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Bibliographic Details
Published in:Physics in medicine & biology 2012-06, Vol.57 (12), p.3963-3980
Main Authors: Prieto, Elena, Lecumberri, Pablo, Pagola, Miguel, Gómez, Marisol, Bilbao, Izaskun, Ecay, Margarita, Peñuelas, Iván, Martí-Climent, Josep M
Format: Article
Language:English
Subjects:
Automation
Image Processing, Computer-Assisted - instrumentation
Phantoms, Imaging
Positron-Emission Tomography - instrumentation
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Summary: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 18F-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.
ISSN:0031-9155
1361-6560
DOI:10.1088/0031-9155/57/12/3963