Automating segmentation of dual-echo MR head data
Multiecho MR acquisition yields various information about tissue and csf characteristics. The analysis of two-dimensional scatterplots generated from dual-echo MR data turns out to be a useful tool. It allows the optimization of MR acquisition parameters for later specific recognition or segmentation tasks. 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. A new study will check the reliability of the automated classification in comparison with supervised classification.
The segmentation of head MR data in routine clinical applications gives some important qualitative and quantitative information about brain atrophy, brain volume, volume of csf spaces and morphological changes of local brain areas. 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.
KeywordsMedical image processing 3D segmentation statistical classification clustering magnetic resonance volume data multiecho image data 3D display
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- [Cline et.al., 1990]Cline, H.E., Lorensen, W.E., et.al., 3-D Segmentation of MR Images of the Head using Probability and Connectivity, J Comput Assist Tomogr 1990; 14(6):1037–1045.Google Scholar
- [Coleman and Andrews, 1979]Coleman G.B. and Andrews, H.C., Image Segmentation by Clustering, Proc. of the IEEE, Vol. 67, No. 5, May 1979, pp. 773–85Google Scholar
- [Duda and Hart, 1973]Duda R.O. and Hart, P.E., Pattern Classification and Scene Analysis, by John Wiley & Sons,Inc, 1973Google Scholar
- [Gerig et.al., 1989]Gerig, G., Kuoni, W., Kikinis, R. and Kübler, O., Medical Imaging and Computer Vision: An integrated approach for diagnosis and planning, 11. DAGM-Symposium Mustererkennung, 2.–4. Oct. 1989, Informatik Fachberichte IFB 219, Springer Verlag Berlin, pp. 425–432Google Scholar
- [Gerig and Kikinis, 1990]Gerig,G. and Kikinis, R., Segmentation of 3D Magnetic Resonance Data, in Progress in Image Analysis and Processing, Cantoni, Cordella, Levialdi, Sanniti di Baja edts., World Scientific Singapore, Proceedings of 5th Int. Conf. on Image Analysis and Processing, 1990, pp. 602–609Google Scholar
- [Gerig et.al., 1990]Gerig, G., Kikinis, R., Kübler, O., Significant Improvement of MR Image Data Quality using Anisotropic Diffusion Filtering, Technical Report BIWI-TR-112, Institute for Communication Technology, Image Science Division, ETH-Zurich, Switzerland, March 1990Google Scholar
- [Kikinis et.al., 1990]Kikinis, R., Jolesz, F., Gerig, G., Sandor, T., Cline, H., Lorensen, W., Halle, M., Benton, St., 3D Morphometric and Morphologic Information derived from Clinical Brain MR Images, in: 3D Imaging in Medicine, Höhne K.H., Fuchs H., Pizer St.M. editors, NATO ASI Series, Serie F: Computer and Systems Science Vol. 60, Springer-Verlag, Proceedings of the NATO Advanced Research Workshop, 25–29 June 1990, Travemünde, FRG, pp. 441–454Google Scholar
- [Kübler and Gerig, 1990]Kübler, O. and Gerig, G., Segmentation and Analysis of Multidimensional Data-Sets in Medicine, in: 3D Imaging in Medicine, Höhne K.H., Fuchs H., Pizer St.M. editors, NATO ASI Series, Serie F: Computer and Systems Science Vol. 60, Springer-Verlag, Proceedings of the NATO Advanced Research Workshop, 25–29 June 1990, Travemünde, FRG, pp. 63–81Google Scholar
- [Merickel et.al., 1988]Merickel, M.B. et.al., Multispectral Pattern Recognition of MR Imagery for the Noninvasive Analysis of Atherosclerosis, Proc. of 9th Int. Conf. on Pattern recognition, Rome, Italy, Nov. 1988, pp. 1192–1197Google Scholar
- [Vannier et.al., 1985]Vannier, M.W., Butterfield, R.L., Jordan, D., et.al., Multispectral Analysis of Magnetic Resonance Images, Radiology 154, 1985, pp. 221–224Google Scholar
- [Vannier et.al., 1988 a]Vannier, M.W., Speidel, Ch.M., and Rickman, D.L., Magnetic Resonance Imaging Multispectral Tissue Classification, NIPS Volume 3, August 1988, pp. 148–154Google Scholar
- [Vannier et.al., 1988 b]Vannier, M.W. et.al., Validation of Magnetic Resonance Imaging (MRI) Multispectral Tissue Classification, Proc. of 9th Conf. on Pattern Recognition, Rome, Italy, Nov. 1988, pp. 1182–1186Google Scholar