Abstract
We propose a novel method for the segmentation of Multiple Sclerosis (MS) lesions in MRI. The method is based on a three-step approach: first a conventional k-NN classifier is applied to pre-classify gray matter (GM), white matter (WM), cerebro-spinal fluid (CSF) and MS lesions from a set of prototypes selected by an expert. Second, the classification of problematic patterns is resolved computing a fast distance transformation (DT) algorithm from the set of prototypes in the Euclidean space defined by the MRI dataset. Finally, a connected component filtering algorithm is used to remove lesion voxels not connected to the real lesions. This method uses distance information together with intensity information to improve the accuracy of lesion segmentation and, thus, it is specially useful when MS lesions have similar intensity values than other tissues. It is also well suited for interactive segmentations due to its efficiency. Results are shown on real MRI data as wall as on a standard database of synthetic images.
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References
Warfield, S., Kaus, M., Jolesz, F., Kikinis, R.: Adaptive, template moderated, spatially varying statistical classification. Medical Image Analisys 4, 43–55 (2000)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory IT-13(1), 21–27 (1967)
Clarke, L., Velthuizen, R., Phuphanich, S., Schellenberg, J., Arrington, J., Silbiger, M.: MRI: Stability of three supervised segmentation techniques. Magnetic Resonance Imaging 11, 95–106 (1993)
Friedman, J., Baskett, F., Shustek, L.: An algorithm for finding nearest neighbors. IEEE Trans. on Computers C-24(10), 1000–1006 (1975)
Jian, Q., Zhang, W.: An improved method for finding nearest neighbors. Pattern Recognition Letters 14, 531–535 (1993)
Fukunaga, K., Narendra, P.: A branch and bound algorithm for computing knearest neighbors. IEEE Transactions On Computers C-24, 750–753 (1975)
Warfield, S.: Fast k-nn classification for multichannel image data. Pattern Recognition Letters 17(7), 713–721 (1996)
Cuisenaire, O., Macq, B.: Fast k-nn classification with an optimal k-distance transformation algorithm. In: Proc. 10th European Signal Processing Conf., pp. 1365–1368 (2000)
Kaus, M.: Contributions to the Automated Segmentation of Brain Tumors in MRI. PhD thesis, Berlin, Germany (2000)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley &, Chichester (1973)
Okabe, A., Boots, B., Sugihara, K.: Spatial Tesselations: Concepts and Applications of Voronoi Diagrams. Wiley, Chichester (1992)
Aurenhammer, F., Klein, R.: Voronoi diagrams. In: Sack, J., Urrutia, G. (eds.) Handbook of Computational Geometry, pp. 201–290. Elsevier Science Publishing, Amsterdam (2000)
Verwer, B., Verbeek, P., Dekker, S.: An efficient uniform cost algorithm applied to distance transforms. IEEE Transactions on Pattern Analysis an Machine Intelligence 11(4), 425–429 (1989)
Borgefors, G.: Distance transformations in arbitrary dimensions. Computer Vision, Graphics and Image Processing 27, 321–345 (1984)
Danielsson, P.: Euclidean distance mapping. Comput. Graph. Image Process. 14, 227–248 (1980)
Ragnemalm, I.: The Euclidean distance transform in arbitrary dimensions. Pattern Recognition Letters 14, 883–888 (1993)
Cocosco, C., Kollokian, V., Kwan, R.S., Evans, A.: Brainweb: online interface to a 3D MRI simulated brain database. Neuroimage 5(425), Copenhagen (1997)
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Cárdenes, R., Warfield, S.K., Macías, E.M., Santana, J.A., Ruiz-Alzola, J. (2003). An Efficient Algorithm for Multiple Sclerosis Lesion Segmentation from Brain MRI. In: Moreno-Díaz, R., Pichler, F. (eds) Computer Aided Systems Theory - EUROCAST 2003. EUROCAST 2003. Lecture Notes in Computer Science, vol 2809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45210-2_49
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DOI: https://doi.org/10.1007/978-3-540-45210-2_49
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