Application of Hybrid c-Means Clustering Models in Inhomogeneity Compensation and MR Brain Image Segmentation

被引:0
作者
Szilagyi, Laszlo [1 ]
Szilagyi, Sandor M. [1 ]
Benyo, Balazs [2 ]
Benyo, Zoltan [2 ]
机构
[1] Sapientia Hungarian Sci Univ Targu Mures, Fac Tech & Human Sci, Targu Mures, Romania
[2] Budapest Univ Technol & Econ, Dept Control Engn & Informat Technol, Budapest, Hungary
来源
SACI: 2009 5TH INTERNATIONAL SYMPOSIUM ON APPLIED COMPUTATIONAL INTELLIGENCE AND INFORMATICS | 2009年
关键词
MEANS ALGORITHM; FUZZY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a hybrid c-means clustering approach to replace the FCM algorithm found in several existing solutions. The novel clustering model is assisted by a pre-filtering technique for Gaussian and impulse noise elimination, and a smoothening filter that helps the c-means algorithm at the estimation of inhomogeneity as a slowly varying additive or multiplicative noise. The slow variance of the estimated INU is assured by a smoothening filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show that the proposed method provides more accurate and more efficient segmentation than the FCM based approach. The produced segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.
引用
收藏
页码:95 / +
页数:2
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