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Segmentation of PET Images based on Affinity Propagation Clustering

version (3.6 MB) by Brent Foster
A user friendly GUI for the segmentation and quantification of small animal and human PET images


Updated 11 Dec 2013

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Presented here is a GUI for segmenting and quantifying PET images with multi-focal and diffuse uptakes as commonly seen in pulmonary infections. The segmentation algorithm was presented at 2013 IEEE International Symposium on Biomedical Imaging (ISBI) and was recently accepted to IEEE Transactions on Biomedical Engineering (in press) and validated using a rabbit model infected with Tuberculosis (TB).

The GUI imports a PET image (either Analyze format or Matlab format) and allows the user to draw region of interests (ROIs) in 2D or 3D to roughly separate the object of interest from the background PET image. Then, once the ROI or multiple ROIs have been selected, the areas are segmented using a PET image segmentation method based on Affinity Propagation clustering to cluster the image intensities into meaningful groups.

For quantification, the software calculates the Standardized Uptake Value (SUV) of the binary or ROI which is the standard quantification metric widely used in the clinical and research environment. The parameters needed for accurate SUV quantification are inputted by the user and clearly shown in the GUI. The SUVmax, SUVmean, and Volume (mm^3) of the pathologies are calculated and can be exported into an excel sheet. In this excel sheet, the quantification metrics are also split by the groups found by the segmentation method. We believe that there is meaningful information in the secondary groups, not just the highest uptake group which is the current standard.

Renderings with the functional information overlaid can also be made using the GUI for visualization purposes.

A detailed instruction pdf is provided in the zip folder.

The software is based on the following two publications:

Brent Foster, Ulas Bagci, Ziyue Xu, Bappaditya Dey, Brian Luna, William Bishai, Sanjay Jain, Daniel J. Mollura. “Segmentation of PET Images for Computer-Aided Functional Quantification of Tuberculosis in Small Animal Models.” IEEE Transactions on Biomedical Engineering. (In Press).

Brent Foster, Ulas Bagci, Brian Luna, Bappaditya Dey, William Bishai, Sanjay Jain, Ziyue Xu, Daniel J Mollura. “Robust segmentation and accurate target definition for positron emission tomography images using Affinity Propagation.” 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI). pp. 1461-1464.

Cite As

Brent Foster (2022). Segmentation of PET Images based on Affinity Propagation Clustering (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2010a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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