A Statistically Model-Based Adaptive Technique to Unsupervised Segmentation of MR Images


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 7, No. 1, pp. 286-295, Jan. 2000
10.3745/KIPSTE.2000.7.1.286,   PDF Download:

Abstract

We present a novel statistically adaptive method using the Minimum Description Length(MDL) principle for unsupervised segmentation of magnetic resonance(MR) images. In the method, Markov random filed(MRF) modeling of tissue region accounts for random noise. Intensity measurements on the local region defined by a window are modeled by a finite Gaussian mixture, which accounts for image inhomogeneities. The segmentation algorithm is based on an iterative conditional modes(ICM) algorithm, approximately finds maximum a posteriori(MAP) estimation, and estimates model parameters on the local region. The size of the window for parameter estimation and segmentation is estimated from the image using the MDL principle. In the experiments, the technique well reflected image characteristic of the local region and showed better results than conventional methods in segmentation of MR images with inhomogeneities, especially.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
T. W. Kim, "A Statistically Model-Based Adaptive Technique to Unsupervised Segmentation of MR Images," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 1, pp. 286-295, 2000. DOI: 10.3745/KIPSTE.2000.7.1.286.

[ACM Style]
Tae Woo Kim. 2000. A Statistically Model-Based Adaptive Technique to Unsupervised Segmentation of MR Images. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 1, (2000), 286-295. DOI: 10.3745/KIPSTE.2000.7.1.286.