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Proceedings Paper

The evaluation of a highly automated mixture model based technique for PET tumor volume segmentation
Author(s): Michalis Aristophanous; Charles A. Pelizzari
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Paper Abstract

PET-based tumor volume segmentation techniques are under investigation in recent years due to the increased utilization of FDG-PET imaging in radiation therapy. We have taken the approach of using a Gaussian mixture model (GMM) to model the image intensity distribution of a selected 3D region that completely covers the tumor, called the "analysis region". The modeling is performed with a predetermined number of Gaussian classes and results in a classification of every voxel into one of these classes. The classes are then grouped together to obtain the tumor volume. The only user interaction required is the selection of the "analysis region" and then the algorithm proceeds automatically to initialize the parameters of the different classes and finds the maximum likelihood estimate with expectation maximization. We used 13 clinical and 19 phantom cases to evaluate the precision and accuracy of the segmentation. Reproducibility was within 10% of the average tumor volume estimate and accuracy was ±35% of the true tumor volume and better when compared to two other proposed techniques. The GMM segmentation is extremely user friendly with good precision and accuracy. It has shown great potential to be used in the clinical environment.

Paper Details

Date Published: 11 March 2008
PDF: 12 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69141M (11 March 2008); doi: 10.1117/12.772184
Show Author Affiliations
Michalis Aristophanous, The Univ. of Chicago (United States)
Charles A. Pelizzari, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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