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Journal of Digital Imaging logoLink to Journal of Digital Imaging
. 2001 Jun;14(Suppl 1):238–240. doi: 10.1007/BF03190353

Adaptable fuzzy C-Means for improved classification as a preprocessing procedure of brain parcellation

Ui-Cheul Yoon 1, June-Sic Kim 1, Jae-Seok Kim 1, In-Young Kim 1, Sun I Kim 1,
PMCID: PMC3452731  PMID: 11442112

Abstract

Parcellation, one of several brain analysis methods, is a procedure popular for subdividing the regions identified by segmentation into smaller topographically defined units. The fuzzy clustering algorithm is mainly used to preprocess parcellation into several segmentation methods, because it is very appropriate for the characteristics of magnetic resonance imaging (MRI), such as partial volume effect and intensity inhomogeneity. However, some gray matter, such as basal ganglia and thalamus, may be misclassified into the white matter class using the conventional fuzzy C-Means (FCM) algorithm. Parcellation has been nearly achieved through manual drawing, but it is a tedious and time-consuming process. We propose improved classification using successive fuzzy clustering and implementing the parcellation module with the modified graphic user interface (GUI) for the convenience of users.

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Footnotes

Supported by the Ministry of Health and Welfare (hmp-98g-1-002-b), Korea.

References

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