Automatic detection of arachnoid contours in MR images

Babak A. Ardekani; Michael Braun; Brian F. Hutton; I. Kanno

doi:10.1117/12.185201

9 September 1994 Automatic detection of arachnoid contours in MR images

Babak A. Ardekani, Michael Braun, Brian F. Hutton, I. Kanno

Proceedings Volume 2359, Visualization in Biomedical Computing 1994; (1994) https://doi.org/10.1117/12.185201
Event: Visualization in Biomedical Computing 1994, 1994, Rochester, MN, United States

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.

Citation Download Citation

Babak A. Ardekani, Michael Braun, Brian F. Hutton, and I. Kanno "Automatic detection of arachnoid contours in MR images", Proc. SPIE 2359, Visualization in Biomedical Computing 1994, (9 September 1994); https://doi.org/10.1117/12.185201

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