Open Access
Issue |
EPJ Nonlinear Biomed Phys
Volume 3, Number 1, December 2015
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Article Number | 2 | |
Number of page(s) | 20 | |
DOI | https://doi.org/10.1140/epjnbp/s40366-015-0018-0 | |
Published online | 19 March 2015 |
- Haralick R. Statistical and structural approaches to texture. IEEE Proc. 1979;67:786–804. [Google Scholar]
- Hajek M, Dezortova M, Materka A, Lerski R. Texture Analysis for Magnetic resonance Imaging. Prague: Med 4 publishing; 2006. [Google Scholar]
- Biondetti PR, Ehman RL. Soft-tissue sarcomas: use of textural patterns in skeletal muscle as a diagnostic feature in postoperative MR imaging. Radiology. 1992;183(3):845–8. [Google Scholar]
- Skoch A, Jirak D, Vyhnanovska P, Dezortova M, Fendrych P, Rolencova E, et al. Classification of calf muscle MR images by texture analysis. MAGMA. 2004;16:259–67. [Google Scholar]
- Wang J, Fan Z, Vandenborne K, Walter G, Shiloh-Malawsky Y,et al. Statistical texture analysis based MRI quantification of Duchenne muscular dystrophy in a canine model. Proc. SPIE 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging, 86720 F (March 29, 2013); [Google Scholar]
- Nketiah G, Sievanen H, Eskola H. Correlation between hip muscles MRI texture parameters and femoral neck boneareal bone mineral density (aBMD) in different athletes groups. Phys Med. 2014;30(Supplement 1):e38–9. [Google Scholar]
- Herlidou S, Rolland Y, Bansard JY, Le Rumeur E, de Certaines JD. Comparison of automated and visual texture analysis in MRI: characterization of normal and diseased skeletal muscle. Magn Reson Imaging. 1999;17(9):1393–7. [Google Scholar]
- Mahmoud-Ghoneim D, Cherel Y, Lesoeur J, Lemaire L, Rocher C, de Certaines JD, et al. Texture analysis of magnetic resonance images of rat muscles during atrophy and regeneration. Magn Reson Imaging. 2006;24(2):167–71. [Google Scholar]
- Mamhoud-Ghoneim D, Bonny JM, Renou J-P, de Certaines JD. Ex-vivo Magnetic Resonance Imaging Texture Analysis can identify genotypic origin in bovine meat. J Sci Food Agric. 2005;85:629–32. [Google Scholar]
- Nguyen F, Eliat PA, Pinot M, Franconi F, Lemaire L, de Certaines JD, et al. Correlations between Magnetic Resonance Imaging histopathology in mdx (X-linked Muscular Dystrophy) murine model of Duchenne Muscular Dystrophy. Edinburgh: 24th congress of the European Society of Veterinary Pathology; 2006. [Google Scholar]
- Lerski RA, de Wilde J, Boyce D, Ridgway J. Quality control in magnetic resonance imaging. IPEM Report no 80. 1998. ISBN: 0904181901. [Google Scholar]
- European Communities Research Project (COMAC BME II 2.3). Protocols and test objects for the assessment of MRI equipment. Magn Reson Imaging. 1988;6:195–9. [Google Scholar]
- Lerski RA, McRobbie DW, Straughan K, Walker PM, de Certaines JD, Bernard AM. Multi-center trial with protocols and prototype test objects for the assessment of MRI equipment. Magn Reson Imaging. 1988;6:201–14. [Google Scholar]
- Lerski RA, de Certaines JD. Performance assessment and quality control in MRI by Eurospin test objects and protocols. Magn Reson Imaging. 1993;11:817–33. [Google Scholar]
- Jackson EF, Bronskill MJ, Drost DJ, Och J, Pooley RA, Sobel WT, et al. AAPM Report 100: Acceptance Testing and Quality Assurance Procedures for Magnetic Resonance Imaging Facilities. College Park, MD: American Association of Physicists in Medicine; 2010. ISBN 978-1-936366-02-6. [Google Scholar]
- Bydder GM, Pennock JM, Steiner RE, Khenia S, Payne JA, Young IR. The short TI inversion recovery sequence—An approach to MR imaging of the abdomen. Magn Reson Med. 1985;3:251–4. [Google Scholar]
- Keller PJ, Hunter WW, Schmalbrock P. Multisection fat–water imaging with chemical shift selective presaturation. Radiology. 1987;164:539–41. [Google Scholar]
- Kobayashi M, Nakamura A, Hasegawa D, Fujita M, Orima H, Takeda S. Evaluation of dystrophic dog pathology by fat-suppressed T2-weighted imaging. Muscle Nerve. 2009;40:815–26. [Google Scholar]
- Glover GH, Schneider E. Three-point Dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med. 1991;18:371–83. [Google Scholar]
- Reeder SB, Pineda AR, Wen Z, Shimakawa A, Yu H, Brittain JH, et al. Iterative decomposition of water and fat with echo asymmetry and least- squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med. 2005;54:636–44. [Google Scholar]
- Janiczek RL, Gambarota G, Sinclair CDJ, Yousry TA, Thornton JS, Golay X, et al. Simultaneous T2 and Lipid Quantitation Using IDEAL-CPMG. Magn Reson Med. 2011;66:1293–302. [Google Scholar]
- Carlier PG. Global T2 versus water T2 in NMR imaging of fatty infiltrated muscles: different methodology, different information and different implications. Neuromuscul Disord. 2014;24:390–2. [Google Scholar]
- Thibaud JL, Monnet A, Bertoldi D, Barthelemy I, Blot S, Carlier PG. Characterization of dystrophic muscle in golden retriever muscular dystrophy dogs by nuclear magnetic resonance imaging. Neuromuscul Disord. 2007;17:575–84. [Google Scholar]
- Lewa CJ, de Certaines JD. Viscoelastic property detection by elastic displacement NMR measurements. J Magn Reson Imaging. 1995;5:242–4. [Google Scholar]
- Muthupillai R, Lomas DJ, Rossman PJ, Greenleaf JF, Manduca A, Ehman RL. Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science. 1995;269(5232):1854. [Google Scholar]
- Qin EC, Juge L, Lambert S, Sinkus R, Bilston L. MR-Elastography and diffusion tensor imaging to measure the in-vivo anisotropic elasticity of skeletal muscles of Mdx and healthy mice. Proc Int Soc Mag Reson Med. 2012;20:3269. [Google Scholar]
- McMillan A.B., Shi D., Pratt S.J.P., Lovering R.M., Diffusion Tensor MRI to Assess Damage in Healthy and Dystrophic Skeletal Muscle after Lengthening Contractions, Journal of Biomedicine and Biotechnology, vol 2011, Article ID 970726, 10 pages, [Google Scholar]
- Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. 2004;22(1):81–91. [Google Scholar]
- Mahmoud-Ghoneim D, Alkaabi MK, de Certaines JD, Goettsche FM. The impact of image dynamic range on texture classification of brain white matter. BMC Med Imaging. 2008;8:18. [Google Scholar]
- Wang J, Fan Z, Vandenborne K, Walter G, Shiloh-Malawsky Y, An H, et al. A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy. Int J Comput Assist Radiol Surg. 2013;8(5):763–74. [Google Scholar]
- Fan Z, Wang J, Ahn M, Shiloh-Malawsky Y, Chahin N, Elmore S, et al. Characteristics of magnetic resonance imaging biomarkers in a natural history study of golden retriever muscular dystrophy. Neuromuscul Disord. 2014;24(2):178–91. [Google Scholar]
- Gonzalez RC, Woods RE. Image Compression. In: Digital Image Processing. 2nd ed. Reading, MA: Addison-Wesley; 2002. [Google Scholar]
- Lerski R, Straughan K, Shad L, Boyce D, Bluml S, Zuna I. MR Image Texture Analysis - An Approach to Tissue Characterization. Magn Reson Imaging. 1993;11:873–87. [Google Scholar]
- Weszka JS, Dyer CR, Rosenfeld A. A Comparative Study of Texture Measures for Terrain Classification. IEEE Trans Syst Man Cybern. 1976;6:269–85. [Google Scholar]
- Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3:610–21. [Google Scholar]
- Bankman I. N. (Ed.). Handbook of Medical Imaging, Processing and Analysis, Academic Press, 2000. [Google Scholar]
- Galloway MM. Texture analysis using grey level run lengths. Comput Graphics Image Process. 1975;4:172–9. [Google Scholar]
- Chu A, Sehgal CM, Greenleaf JF. Use of grey value distribution of run lengths for texture analysis. Pattern Recogn Lett. 1990;11:415–20. [Google Scholar]
- Albregtsen F, Nielsen B, Danielsen HE. Adaptive grey level run length features from class distance matrices. Proc 15th Int Conf Pattern Recognition. 2000;3:738–41. [Google Scholar]
- Laws KI. Textured image segmentation. Los Angeles, California, USA: Unpublished doctoral dissertation. University of Southern California; 1980. [Google Scholar]
- Edgar G. A. Measure, Topology and Fractal Geometry, Springer-Verlag, 1990. [Google Scholar]
- Falconer K. Fractal Geometry, Mathematical Foundations and Applications, John Wiley & Sons, 1990. [Google Scholar]
- Peitgen H. O., Jürgens H., Saupe D. Fractals for the Classroom. Part 1: Introduction to Fractals and Chaos, Springer-Verlag, 1992. [Google Scholar]
- Mandelbrot B. The Fractal Geometry of Nature, W. H. Freeman and Co., 1982 [Google Scholar]
- Chen EL, Chung PC, Chen CL, Tsai HM, Chang CI. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng. 1998;45(6):783–94. [Google Scholar]
- Klonowski W, Pierzchalski M, Stepien P, Stepien R, Sedivy R, Ahammer H. Application of Higuchi’s fractal dimension in analysis of images of Anal Intraepithelial Neoplasia. Chaos, Solitons Fractals (Elsevier). 2013;48:54–60. [Google Scholar]
- Shu H, Luo L, Coatrieux JL. Moment-Based Approaches in Image, Part 1. Basic Features. IEEE Eng Med Biol Mag. 2007;26(5):70–4. [Google Scholar]
- Shu HZ, Luo LM, Coatrieux JL. Moment-based approaches in image, Part2: invariance. IEEE Eng Med Biol Mag. 2008;27(1):81–3. [Google Scholar]
- Shu HZ, Luo LM, Coatrieux JL. Moment-based approaches in image, Part 3: computational considerations. IEEE Eng Med Biol Mag. 2008;27(3):89–91. [Google Scholar]
- Vincent Spruyt, About the Curse of Dimensionality, June 6, 2014, http://www.datasciencecentral.com/profiles/blogs/about-the-curse-of-dimensionality. [Google Scholar]