Abstract
This paper considers the problem of classification of Magnetic Resonance Images using 2D and 3D texture measures. Joint statistics such as co-occurrence matrices are common for analysing texture in 2D since they are simple and effective to implement. However, the computational complexity can be prohibitive especially in 3D. In this work, we develop a texture classification strategy by a sub-band filtering technique that can be extended to 3D. We further propose a feature selection technique based on the Bhattacharyya distance measure that reduces the number of features required for the classification by selecting a set of discriminant features conditioned on a set training texture samples. We describe and illustrate the methodology by quantitatively analysing a series of images: 2D synthetic phantom, 2D natural textures, and MRI of human knees.
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References
Bhalerao, A., Rajpoot, N.: Selecting Discriminant Subbands for Texture Classification. Submitted to BMVC’U3 (2003)
COST European Cooperation in the field of Scientific and Technical Research. COST B11 Quantitation of MRI Texture (2002), http://www.uib.no/costb11/
Cross, G.R., Jain, A.K.: Markov Random Field Texture Models. IEEE Trans. on PAMI PAMI-5(1), 25–39 (1983)
James, D., et al.: Texture Detection of Simulated Microcalcification Suceptibility Effects in MRI of the Breasts. J. Mag. Res. Imaging 13, 876–881 (2002)
Lerski, R.A., et al.: MR Image Texture Analysis – An Approach to Tissue Characterization. Mag. Res. Imaging 11(6), 873–887 (1993)
Fukanaga, K.: Introd. to Statistical Pattern Recognition. Academic Press, London (1972)
Haralick, R.M.: Statistical and Structural Approaches to Texture. Proceedings of the IEEE 67(5), 786–804 (1979)
Kapur, T.: Model based three dimensional Medical Image Segmentation. PhD thesis, AI Lab, Massachusetts Institute of Technology (May 1999)
Keller, J.M., Chen, S.: Texture Description and Segmentation through Fractal Geometry. Computer Vision, Graphics und Image Processing 45, 150–166 (1989)
Eden, M., Unser, M.: Multiresolution Feature Extraction and Selection for Texture Segmentation. IEEE Trans. on PAMI 11(7), 717–728 (1989)
Mathias, J.M., Tofts, P.S., Losseff, N.A.: Texture Analysis of Spinal Cord Pathology in Multiple Sclerosis. Mag. Res. in Medicine 42, 929–935 (1999)
Namer, I.J., Yu, O., Mauss, Y., Chambron, J.: Existence of contralateral abnormalities revealed by texture analysis in unilateral intractable hippocampal epilepsy. Magnetic Resonance Imaging 19, 1305–1310 (2001)
Randen, T., Håkon Husøy, J.: Filtering for Texture Classification: A Comparative Study. IEEE Trans. on PAMI 21(4), 291–310 (1999)
Reyes-Aldasoro, C.C., Bhalerao, A.: Sub-band filtering for mr texture segmentation. In: Proceedings of Medical Image Understanding und Analysis, Portsmouth, UK, July 2002, pp. 185–188 (2002)
Saeed, N., Piri, B.K.: Cerebellum Segmentation Employing Texture Properties and Knowledge based Image Processing: Applied to Normal Adult Controls and Patients. Magnetic Resonance Imaging 20, 425–429 (2002)
Schad, L.R., Bluml, S., Zuna, I.: MR Tissue Characterization of Intracrianal Tumors by means of Texture Analysis. Mag. Res. Imaging 11, 889–896 (1993)
Unser, M.: Texture Classification and Segmentation Using Wavelet Frames. IEEE Trans. on Image Processing 4(11), 1549–1560 (1995)
Wilson, R., Spann, M.: Finite Prolate Spheroidal Sequences and Their Applications. IEEE Trans. on PAMI 10(2), 193–203 (1988)
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Reyes-Aldasoro, C.C., Bhalerao, A. (2003). Volumetric Texture Description and Discriminant Feature Selection for MRI. In: Taylor, C., Noble, J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45087-0_24
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DOI: https://doi.org/10.1007/978-3-540-45087-0_24
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