Expectation maximization classification and Laplacian based thickness measurement for cerebral cortex thickness estimation

Mark Holden; Rafael Moreno-Vallecillo; Anthony Harris M.D.; Lavier J. Gomes; Than-Mei Diep; Pierrick T. Bourgeat; Sébastien Ourselin

doi:10.1117/12.710510

26 March 2007 Expectation maximization classification and Laplacian based thickness measurement for cerebral cortex thickness estimation

Mark Holden, Rafael Moreno-Vallecillo, Anthony Harris M.D., Lavier J. Gomes, Than-Mei Diep, Pierrick T. Bourgeat, Sébastien Ourselin

Author Affiliations +

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65120M (2007) https://doi.org/10.1117/12.710510
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

We describe a new framework for measuring cortical thickness from MR human brain images. This involves the integration of a method of tissue classification with one to estimate thickness in 3D. We have determined an additional boundary detection step to facilitate this. The classification stage utlizes the Expectation Maximisation (EM) algorithm to classify voxels associated with the tissue types that interface with cortical grey matter (GM, WM and CSF). This uses a Gaussian mixture and the EM algorithm to estimate the position and and width of the Gaussians that model the intensity distributions of the GM, WM and CSF tissue classes. The boundary detection stage uses the GM, WM and CSF classifications and finds connected components, fills holes and then applies a geodesic distance transform to determine the GM/WM interface. Finally the thickness of the cortical grey matter is estimated by solving Laplace's equation and determining the streamlines that connect the inner and outer boundaries. The contribution of this work is the adaptation of the classification and thickness measurement steps, neither requiring manual initialisation, and also the validation strategy. The resultant algorithm is fully automatic and avoids the computational expense associated with preserving the cortical surface topology. We have devised a validation strategy that indicates the cortical segmentation of a gold standard brain atlas has a similarity index of 0.91, thickness estimation has subvoxel accuracy evaluated using a synthetic image and precision of the combined segmentation and thickness measurement of 1.54mm using three clinical images.

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Mark Holden, Rafael Moreno-Vallecillo, Anthony Harris M.D., Lavier J. Gomes, Than-Mei Diep, Pierrick T. Bourgeat, and Sébastien Ourselin "Expectation maximization classification and Laplacian based thickness measurement for cerebral cortex thickness estimation", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65120M (26 March 2007); https://doi.org/10.1117/12.710510

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KEYWORDS

Expectation maximization algorithms

Image segmentation

Magnetic resonance imaging

Brain

Tissues

Cerebral cortex

Image processing algorithms and systems

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