Automatic Brain Segmentation for 3D Visualization and Analysis of MR Image Sets


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 7, No. 2, pp. 542-551, Feb. 2000
10.3745/KIPSTE.2000.7.2.542,   PDF Download:

Abstract

In this paper, a novel technique is presented for automatic brain region segmentation in single channel MR image data sets for 3D visualization and analysis. The method detects brain contours in 2D and 3D processing of four steps. The first and the second make a head mask and an initial brain mask by automatic thresholding using a curve fitting technique. The stage 3 reconstructs 3D volume of the initial brain mask by cubic interpolation and generates an intermediate brain mask using morphological operation and labeling of connected components. In the final step, the brain mask is refined by automatic thresholding using curve fitting. This algorithm is useful for fully automatic brain region segmentation of T1-weighted, T2-weighted, PD-weighted, SPGR MRI data sets without considering slice direction and covering a whole volume of a brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 in comparison with manual drawing in similarity index.


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Cite this article
[IEEE Style]
T. W. Kim, "Automatic Brain Segmentation for 3D Visualization and Analysis of MR Image Sets," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 2, pp. 542-551, 2000. DOI: 10.3745/KIPSTE.2000.7.2.542.

[ACM Style]
Tae Woo Kim. 2000. Automatic Brain Segmentation for 3D Visualization and Analysis of MR Image Sets. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 2, (2000), 542-551. DOI: 10.3745/KIPSTE.2000.7.2.542.