A fuzzy logic model based Markov random field for medical image segmentation

被引：21
作者：
Nguyen T.M. [1 ]
Wu Q.M.J. [1 ]
机构：
[1] Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, N9B-3P4
来源：
Evolving Systems | 2013年 / 4卷 / 3期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Fuzzy logic; Markov random field; Medical image segmentation; Spatial constraints;
D O I：
10.1007/s12530-012-9066-1
中图分类号：
学科分类号：
摘要：
Fuzzy logic incorporates human knowledge into the system via facts and rules and hence widely used in image segmentation. Another successful approach in image segmentation is the use of Markov random field (MRF) to incorporate local spatial information between neighboring pixels of an image. This paper studies the benefits of these two approaches and combines fuzzy logic with MRF to develop a new adaptive fuzzy inference system. The premise part of each fuzzy if-then rule in our approach adopts MRF to utilize the spatial constraint in an image, while the consequent part specifies the pixel distance map. Unlike other fuzzy logic models that require training data that is not always available to train the system before segmenting an image, we propose an unsupervised learning algorithm for automatic image segmentation. To impose the spatial information on the fuzzy if-then rule base, a new clique potential MRF function is proposed in this paper. Our approach is used to segment many medical images from simulated brain images to real brain ones with excellent results. © 2012 Springer-Verlag Berlin Heidelberg.
引用
收藏
页码：171 / 181
页数：10
相关论文
共 52 条
  • [1] Ahmed M.N., Yamany S.M., Mohamed N., Farag A.A., Moriarty T., A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans Med Imaging, 21, 3, pp. 193-199, (2002)
  • [2] Azeem M.F., Hanmandlu M., Ahmad N., Generalization of adaptive neuro-fuzzy inference systems, IEEE Trans Neural Netw, 11, 6, pp. 1332-1346, (2000)
  • [3] Besag J., On the statistical analysis of dirty pictures, J R Stat Soc Ser B, 48, 3, pp. 259-302, (1986)
  • [4] Besag J., Spatial interaction and the statistical analysis of lattice systems (with discussion), J R Stat Soc Ser B, 36, 2, pp. 192-326, (1974)
  • [5] Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, (1981)
  • [6] Blekas K., Likas A., Galatsanos N.P., Lagaris I.E., A spatially constrained mixture model for image segmentation, IEEE Trans Neural Netw, 16, 2, pp. 494-498, (2005)
  • [7] Boskovitz V., Guterman H., An adaptive neuro fuzzy system for automatic image segmentation and edge detection, IEEE Trans Fuzzy Syst, 10, 2, pp. 247-262, (2002)
  • [8] BrainWeb, (2012)
  • [9] Cai W., Chen S., Zhang D., Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognit, 40, 3, pp. 825-838, (2007)
  • [10] Celeux G., Forbes F., Peyrard N., EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognit, 36, 1, pp. 131-144, (2003)
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