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http://dx.doi.org/10.7471/ikeee.2011.15.3.191

MRI Data Segmentation Using Fuzzy C-Mean Algorithm with Intuition  

Kim, Tae-Hyun (Dept. of Electronics Engineering, Myong Ji University)
Park, Dong-Chul (Dept. of Electronics Engineering, Myong Ji University)
Jeong, Tai-Kyeong (Dept. of Electronics Engineering, Myong Ji University)
Lee, Yun-Sik (System IC R&D Division, KETI)
Min, Soo-Young (System IC R&D Division, KETI)
Publication Information
Journal of IKEEE / v.15, no.3, 2011 , pp. 191-197 More about this Journal
Abstract
An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.
Keywords
Data Clustering; Clustering algorithm; MRI Segmentation;
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