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Proceedings Paper

Automatic detection of arachnoid contours in MR images
Author(s): Babak A. Ardekani; Michael Braun; Brian F. Hutton; I. Kanno
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Paper Abstract

An algorithm is presented for the automatic detection of arachnoid contours in MR images of the human head. The primary motivation behind the present work has been to serve as a pre- processing step in automatic segmentation of brain tissue and CSF. A second objective was to use the algorithm in a fully automatic PET-MR registration algorithm. The method is primarily designed for, and requires, dual-echo (T1- and T2-weighted) MR images with transaxial orientations. The algorithm consists of three main stages. First, the head contour is detected using a series of low-level image processing techniques. In the second stage, the pixels inside the head contour are clustered into a number of connected components using the K-means algorithm. Finally, the extra-arachnoid connected components are eliminated based on a number of heuristics. Test results are presented for 10 MR image sets. As a quantitative measure of accuracy, manual segmentations were performed by radiologists on a number of slices and compared with the results obtained automatically. Visual inspection and quantitative validation of the results indicate that the algorithm accurately detects the arachnoid contours in MR images. This is an important step in fully automatic segmentation and registration of MR images.

Paper Details

Date Published: 9 September 1994
PDF: 9 pages
Proc. SPIE 2359, Visualization in Biomedical Computing 1994, (9 September 1994); doi: 10.1117/12.185201
Show Author Affiliations
Babak A. Ardekani, Univ. of Technology Sydney (Australia) and Research Institute for Brain and Blood Vessels (Japan)
Michael Braun, Univ. of Technology Sydney (Australia)
Brian F. Hutton, Royal Prince Alfred Hospital (Australia)
I. Kanno, Research Inst. for Brain and Blood Vessels (Japan)

Published in SPIE Proceedings Vol. 2359:
Visualization in Biomedical Computing 1994
Richard A. Robb, Editor(s)

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