Abstract
This paper considers the problem of tissue classification in 3D MRI. More specifically, a new set of texture features, based on phase information, is used to perform the segmentation of the bones of the knee. The phase information provides a very good discrimination between the bone and the surrounding tissues, but is usually not used due to phase unwrapping problems. We present a method to extract textural information from the phase that does not require phase unwrapping. The textural information extracted from the magnitude and the phase can be combined to perform tissue classification, and used to initialise an active shape model, leading to a more precise segmentation.
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Haacke, E., Brown, R., Thompson, M., Venkatesan, R.: Magnetic Resonance Imaging: Principles and Sequence Design. John Wiley & Sons, New York (1999)
Drapé, J., Pessis, E., Sarazin, L., Minoui, A., Godefroy, D., Chevrot, A.: MR imaging of articular cartilage. J. Radiology 79, 391–402 (1998)
Ghiglia, D., Pritt, M.: Two-dimensional Phase Unwrapping: Theory Algorithms And Software. John Wiley & Sons, New York (1998)
Chavez, S., Xiang, Q., An, L.: Understanding phase maps in MRI: a new cutline phase unwrapping method. IEEE Trans. Medical Imaging 21, 966–977 (2002)
Reyes-Aldasoro, C.C., Bhalerao, A.: Volumetric texture description and discriminant feature selection for MRI. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 282–293. Springer, Heidelberg (2003)
Bourgeat, P., Meriaudeau, F., Gorria, P., Tobin, K., Truchetet, F.: Features extraction on complex images. In: ICIP 2004. IEEE, Singapore (2004)
Bovik, A., Clark, M., Geisler, W.: Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Analysis and Machine Intelligence 12, 55–73 (1990)
Grigorescu, S., Petkov, N., Kruizinga, P.: Comparison of texture features based on Gabor filters. IEEE Trans. on Image Processing 11, 1160–1167 (2002)
Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Undertanding 61, 38–59 (1995)
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Bourgeat, P., Fripp, J., Janke, A., Galloway, G., Crozier, S., Ourselin, S. (2005). The Use of Unwrapped Phase in MR Image Segmentation : A Preliminary Study. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_100
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DOI: https://doi.org/10.1007/11566489_100
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