• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Gamma correction FCM algorithm with conditional spatial information for image segmentation

Vol. 12, No.9, September 30, 2018
10.3837/tiis.2018.09.012, Download Paper (Free):

Abstract

Fuzzy C-means (FCM) algorithm is a most usually technique for medical image segmentation. But conventional FCM fails to perform well enough on magnetic resonance imaging (MRI) data with the noise and intensity inhomogeneity (IIH). In the paper, we propose a Gamma correction conditional FCM algorithm with spatial information (GcsFCM) to solve this problem. Firstly, the pre-processing, Gamma correction, is introduced to enhance the details of images. Secondly, the spatial information is introduced to reduce the effect of noise. Then we introduce the effective neighborhood mechanism into the local space information to improve the robustness for the noise and inhomogeneity. And the mechanism describes the degree of participation in generating local membership values and building clusters. Finally, the adjustment mechanism and the spatial information are combined into the weighted membership function. Experimental results on four image volumes with noise and IIH indicate that the proposed GcsFCM algorithm is more effective and robust to noise and IIH than the FCM, sFCM and csFCM algorithms.


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Cite this article

[IEEE Style]
Yang Liu, Haipeng Chen, Xuanjing Shen and Yongping Huang, "Gamma correction FCM algorithm with conditional spatial information for image segmentation," KSII Transactions on Internet and Information Systems, vol. 12, no. 9, pp. 4336-4354, 2018. DOI: 10.3837/tiis.2018.09.012

[ACM Style]
Liu, Y., Chen, H., Shen, X., and Huang, Y. 2018. Gamma correction FCM algorithm with conditional spatial information for image segmentation. KSII Transactions on Internet and Information Systems, 12, 9, (2018), 4336-4354. DOI: 10.3837/tiis.2018.09.012