J Korean Soc Magn Reson Med.  1998 Jun;2(1):58-66.

Classification of a volumetric MRI using gibbs distributions and a line model

• Chung JC
• Kwon SI

Abstract

PURPOSE
This paper introduces a new three dimensional magnetic Resonance Image classification which is based on Mar kov Random Field-Gibbs Random Field with a line model. MATERIAL AND METHODS: The performance of the Gibbs Classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at the local neighborhood level. This usually involves the construction of a line model for the image. In this paper we construct a line model for multisignature images based on the differential of the image which can provide an a priori estimate of the unobservable line field, which may lie in regions with significantly different statistics. the line model estimated from the original image data can in turn be used to alter the values of the interaction parameters of the Gibbs Classifier.
RESULTS
MRF-Gibbs classifier for volumetric MR images is developed under the condition that the domain of the image classification is E3 space rather thatn the conventional E2 space. Compared to context free classification, MRF-Gibbs classifier performed better in homogeneous and along boundaries since contextual information is used during the classification.
CONCLUSION
We construct a line model for multisignature, multidimensional image and derive the interaction parameter for determining the energy function of MRF-Gibbs classifier.

Keyword

Markov Random Field; Gibbs Random Field; Line Model Gibbs Classifier; Volumetric MRI

MeSH Terms

Classification*
Magnetic Resonance Imaging*
Residence Characteristics