Segmentation of Dual-Echo MR Head Data
Multiecho acquisition in Magnetic Resonance Imaging (MRI) allows better discrimination of different tissue types and anatomical functional units because specific characteristics are enhanced in multiple spectral channels.
Multivariate statistical classification techniques are applied to dual-echo MR data to segment volume head data into anatomical objects and tissue categories (brain white and gray matter, ventricular system, outer csf space, bone structure, tumor).
To overcome the sensitivity of voxel-based classification to noise we applied a preprocessing technique based on anisotropic diffusion. This preprocessing increases the separability of clusters. We illustrate the robustness of supervised classification with the segmentation of a series of MR head data in a research study.
For a given set of MR parameters we show that the configuration of clusters in feature space is comparable between studies. This allows us to develop an automated clustering technique that considers a priori knowledge about cluster attributes and their configuration in feature space. The automated classification technique omits subjective criteria in the training stage of supervised classification and yields reproducible segmentation results.
Combined 3D views of multiple anatomical objects are shown. They clarify the perception of 3D relationship and highlight the locations and types of structural abnormalities.
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