Comparison of K-Means and Fuzzy c-Means Algorithm Performance for Automated Determination of the Arterial Input Function

The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used...

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Bibliographic Details
Published in:PloS one Vol. 9; no. 2; p. e85884
Main Authors: Yin, Jiandong, Sun, Hongzan, Yang, Jiawen, Guo, Qiyong
Format: Journal Article
Language:English
Published: United States Public Library of Science 04.02.2014
Public Library of Science (PLoS)
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ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is better for AIF detection. Hence, we compared the performance of these two clustering methods using both simulated and clinical data. The results demonstrate that K-means analysis can yield more accurate and robust AIF results, although it takes longer to execute than the FCM method. We consider that this longer execution time is trivial relative to the total time required for image manipulation in a PACS setting, and is acceptable if an ideal AIF is obtained. Therefore, the K-means method is preferable to FCM in AIF detection.
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Conceived and designed the experiments: QYG. Performed the experiments: JDY. Analyzed the data: JWY. Contributed reagents/materials/analysis tools: HZS. Wrote the paper: JDY.
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0085884