Unsupervised vector image segmentation by a tree structure-ICM algorithm

In recent years, many image segmentation approaches have been based on Markov random fields (MRFs). The main assumption of the MRF approaches is that the class parameters are known or can be obtained from training data. In this paper the authors propose a novel method that relaxes this assumption an...

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Bibliographic Details
Published inIEEE transactions on medical imaging Vol. 15; no. 6; pp. 871 - 880
Main Authors Jong-Kae Fwu, Djuric, P.M.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 1996
Institute of Electrical and Electronics Engineers
Subjects
Biological and medical sciences
Biomedical imaging
Clustering algorithms
Computer simulation
Computerized, statistical medical data processing and models in biomedicine
Image segmentation
Markov random fields
Medical computing and teaching
Medical sciences
Medical tests
Multidimensional systems
Parameter estimation
Training data
Tree data structures
Cluster analysis
Bayes estimation
Human
Parameter estimation
Tree structured method
Segmentation
Computer simulation
Image processing
Vector method
Nuclear magnetic resonance imaging
Biomedical data processing
Algorithm performance
Medical imagery
Comparative study
Brain (vertebrata)
Online AccessGet full text
ISSN0278-0062
1558-254X
DOI10.1109/42.544504

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Summary:In recent years, many image segmentation approaches have been based on Markov random fields (MRFs). The main assumption of the MRF approaches is that the class parameters are known or can be obtained from training data. In this paper the authors propose a novel method that relaxes this assumption and allows for simultaneous parameter estimation and vector image segmentation. The method is based on a tree structure (TS) algorithm which is combined with Besag's iterated conditional modes (ICM) procedure. The TS algorithm provides a mechanism for choosing initial cluster centers needed for initialization of the ICM. The authors' method has been tested on various one-dimensional (1-D) and multidimensional medical images and shows excellent performance. In this paper the authors also address the problem of cluster validation. They propose a new maximum a posteriori (MAP) criterion for determination of the number of classes and compare its performance to other approaches by computer simulations.
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ISSN:0278-0062
1558-254X
DOI:10.1109/42.544504