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Updated: May 3, 2026
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
Published on: December 10, 2014
Comparison of K-means and fuzzy c-means algorithm performance for automated determination of the arterial input
Jiandong Yin1, Hongzan Sun2, Jiawen Yang2
1Sino-dutch Biomedical and Information Engineering School of Northeastern University, Shenyang, Liaoning, China ; Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Abstract:
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.
