Type-2 Fuzzy C-Means Algorithm with Spatial Informations and Membership Constrains for Liver Segmentation

Abstract:

The Fuzzy C-Means algorithm with spatial informations and membership constrains is a very effective and efficient image segmentation method. However£¬it is founded with Type-1 fuzzy sets, which can not handle the uncertainties existing in liver image well.The type-2 fuzzy sets have better performance on handling uncertainties than Type-1 fuzzy set. In this paper, a new robust Type-2 FCM image segmentation algorithm is proposed aiming to improve the segmentation precision and robustness of liver image by introducing the type-2 fuzzy set into FCM with spatial information and membership constrains. We extend the type-1 fuzzy set of membership to interval type-2 fuzzy set using two fuzzifiers and which create a footprint of uncertainty (FOU). The experimental results show that the target area of the liver in CT images can be segmented well by the proposed method.

• Periodical: Advanced Materials Research (Volume 746)
• Pages: 570-574
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.746.570
• Online since: August 2013
• Authors: Qin Li Zhang, Ya Fan Yue, Zhao Zhuang Guo
• Keywords: Fuzzy C-Means, Liver Segmentation, Type-2 Fuzzy Sets

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