DocumentCode :
2726522
Title :
A fast kernel-based clustering algorithm with application in MRI image segmentation
Author :
Liao, Liang ; Wang, Dong-yun ; Wang, Feng-ge ; Yuan, Lei
Author_Institution :
Sch. of Electron. & Inf. Eng., Zhongyuan Univ. of Technol., Zhengzhou, China
Volume :
4
fYear :
2009
fDate :
20-22 Nov. 2009
Firstpage :
405
Lastpage :
410
Abstract :
In kernel-based algorithms, Mercer kernel techniques have been used for improving the separability of input patterns. Although designed to tackle the problem of curse of dimensionality, non-accelerated kernel-based clustering algorithms fail to provide enough time efficiency for practical applications, such as medical image segmentation. For improving the time efficiency of kernel-based clustering, different speed-up schemes can be adopted. Different from the approximate pre-image technique used in KFCM-II, this work proposes a novel approach based on the technique of reduced set for speeding up kernel clustering process. This algorithm, called KFCM-III, balancing the power of KFCM-I and the efficiency of KFCM-II, has potentials to outperform its rivals and has been applied in segmenting MRI images. The experiments on synthetic images and MRI brain phantom have shown the effectiveness of the proposed algorithm, which can not only reduce computational complexity of kernel clustering but also retain better segmentation results when using well-tuned parameters of reduced set.
Keywords :
biomedical MRI; image segmentation; pattern clustering; KFCM-III algorithm; MRI brain phantom; MRI image segmentation; Mercer kernel technique; kernel-based clustering algorithm; magnetic resonance imaging; Algorithm design and analysis; Biomedical imaging; Clustering algorithms; Computational complexity; Image analysis; Image segmentation; Imaging phantoms; Kernel; Magnetic resonance imaging; Parameter estimation; Image segmentation; Kernel-based clustering; Magnetic resonance imaging; the Gaussian kernel parameter estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
Type :
conf
DOI :
10.1109/ICICISYS.2009.5357645
Filename :
5357645
Link To Document :
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