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Open Access Research article

Quantification of heterogeneity observed in medical images

Frank J Brooks1* and Perry W Grigsby1234

Author Affiliations

1 Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Saint Louis MO 63110, USA

2 Division of Nuclear Medicine, Mallinckrodt Institute of Radiology, Medical Center, Saint Louis MO, USA

3 Department of Obstetrics and Gynecology, Washington University Medical Center, Saint Louis MO, USA

4 Alvin J. Siteman Cancer Center, Washington University Medical Center, Saint Louis MO, USA

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BMC Medical Imaging 2013, 13:7  doi:10.1186/1471-2342-13-7

Published: 2 March 2013



There has been much recent interest in the quantification of visually evident heterogeneity within functional grayscale medical images, such as those obtained via magnetic resonance or positron emission tomography. In the case of images of cancerous tumors, variations in grayscale intensity imply variations in crucial tumor biology. Despite these considerable clinical implications, there is as yet no standardized method for measuring the heterogeneity observed via these imaging modalities.


In this work, we motivate and derive a statistical measure of image heterogeneity. This statistic measures the distance-dependent average deviation from the smoothest intensity gradation feasible. We show how this statistic may be used to automatically rank images of in vivo human tumors in order of increasing heterogeneity. We test this method against the current practice of ranking images via expert visual inspection.


We find that this statistic provides a means of heterogeneity quantification beyond that given by other statistics traditionally used for the same purpose. We demonstrate the effect of tumor shape upon our ranking method and find the method applicable to a wide variety of clinically relevant tumor images. We find that the automated heterogeneity rankings agree very closely with those performed visually by experts.


These results indicate that our automated method may be used reliably to rank, in order of increasing heterogeneity, tumor images whether or not object shape is considered to contribute to that heterogeneity. Automated heterogeneity ranking yields objective results which are more consistent than visual rankings. Reducing variability in image interpretation will enable more researchers to better study potential clinical implications of observed tumor heterogeneity.