Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1999-05

AUTHORS

Paul D. Acton, Lyn S. Pilowsky, Hank F. Kung, Peter J. Ell

ABSTRACT

The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D2/D3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [123I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r2>0.99, P<0.0001). Similarly, FCM gave segmentation of the [123I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r2=0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner. More... »

PAGES

581-590

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s002590050425

DOI

http://dx.doi.org/10.1007/s002590050425

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010135971

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/10369943


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