Open Access
Issue
EPJ Nonlinear Biomed Phys
Volume 3, Number 1, December 2015
Article Number 3
Number of page(s) 14
DOI https://doi.org/10.1140/epjnbp/s40366-015-0017-1
Published online 19 March 2015
  1. For review, Hajek M, Dezortova M, Materka A, Lerski RA. Texture Analysis for Magnetic Resonance Imaging. Prague: Med4Publishing; 2006. [Google Scholar]
  2. 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 muscle. Magn Reson Imaging. 1999;17(9):1393–7. [Google Scholar]
  3. Mahmoud-Ghoneim D, Cherel Y, Lesoeur J, Lemaire L, Rocher C, de Certaines JD, et al. Texture analysis of MR Images of rat muscles during atrophy and regeneration. Magn Reson Imaging. 2006;24(2):167–71. [Google Scholar]
  4. Mamhoud-Ghoneim D, Bonny JM, Renou JP, 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]
  5. Nguyen F, Eliat PA, Pinot M, Franconi F, Lemaire L, de Certaines JD, et al. Correlations between Magnetic Resonance Imaging and histopathology in mdx (X-linked Muscular Dystrophy) murine model of Duchenne Muscular Dystrophy, 24th congress of the European Society of Veterinary Pathology, Edinburgh. 2006. [Google Scholar]
  6. Rosenholtz R. Texture perception. In: Wagemans J, editor. Oxford Handbook of Perceptual Organization (in press). Oxford, U.K: Oxford University Press; 2014. [Google Scholar]
  7. Landy MS. Texture Perception. In: Adelman G, editor. Encyclopedia of Neuroscience. Amsterdam: Elsevier; 1996. [Google Scholar]
  8. Giora E, Casco C. Region- and edge-based configurational effects in texture segmentation. Vis Res. 2007;47(7):879–86. [Google Scholar]
  9. Machilsen B, Wagemans J. Integration of contour and surface information in shape detection. Vis Res. 2011;51:179–86. [Google Scholar]
  10. Julesz B. Visual Pattern Discrimination. IRE Transactions on Information Theory. 1962;8(2):84–92. [Google Scholar]
  11. Julesz B. Texture and Visual Perception. Scientific American, 212, 38–48. Julesz, B. (1975). Experiments in the visual perception of texture. Sci Am. 1965;232(4):34–43. [Google Scholar]
  12. Julesz B, Gilbert EN, Victor JD. Visual discrimination of textures with identical third- order statistics. Biol Cybernet. 1978;31:137–40. [Google Scholar]
  13. Klein SA, Tyler CW. Phase discrimination of compound gratings: generalized autocorrelation analysis. J Opt Soc Am A. 1986;3:868–79. [Google Scholar]
  14. Malik J, Perona J. Preattentive texture discrimination with early vision mechanisms. J Opt Soc Am A. 1990;7:923–32. [Google Scholar]
  15. Tyler CW. Theory of texture discrimination based on higher-order perturbations in individual texture samples. Vis Res. 2004;44:2179–86. [Google Scholar]
  16. Heeger DJ, Bergen JR. Pyramid-based texture analysis/synthesis. In: Proceedings of the 22nd annual conference on Computer graphics and interactive techniques (SIGGRAPH’95). Los Angeles (USA): IEEE Comput. Soc. Press; 1995. p. 229–38. [Google Scholar]
  17. Portilla J, Simoncelli EP. A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients. Int J Comput Vis. 2000;40(1):49–71. [Google Scholar]
  18. Rosenholtz R. Significantly different textures: A computational model of pre-attentive texture segmentation. In D. Vernon (Ed.), Proc. European Conf. on Computer Vision (ECCV’00). LNCS. 2000;1843:197–211. [Google Scholar]
  19. Nothdurft HC. Texture segmentation and pop-out from orientation contrast. Vis Res. 1991;31(6):1073–8. [Google Scholar]
  20. Sikio M, Harrison LCV, Nikander R, Ryymin P, Dastidar P, Eskola HI, et al. Influence of exercise loading on MRI texture of thigh soft tissue. Clin Physiol Funct Imaging. 2014;34:370–6. [Google Scholar]
  21. 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]
  22. Thibaud JL, Monnet A, Bertoldi D, Barthélémy I, Blot S, Carlier PG. Characterization of dystrophic muscle in Golden Retriever Muscular Dystrophy dogs by nuclear magnetic resonance imaging. Neuromuscul Disord. 2007;17(7):575–84. [Google Scholar]
  23. Wang J, Fan Z, Vandenborne K, Walter G, Shilhoh-Malmawsky Y, An H, et al. A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy. Int J CARS. 2013;8:763–74. [Google Scholar]
  24. Bottomley PA, Foster TH. Argensinger R.E?, Pfeiffer L.M., A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1–100 MHz: Dependence on tissue type, NMR frequency, temperature, species, excision and age. Med Phys. 1984;11(4):425–48. [Google Scholar]
  25. Henriksen O, de Certaines JD, Spisni A, Cortsen M, Muller RN, Ring PB. In-vivo field dependence of proton relaxation times in human brain, liver and skeletal muscle: a multicenter study: ii. Magn Reson Imaging. 1993;11:851–6. [Google Scholar]
  26. de Certaines JD, Henriksen O, Spisni A, Cortsen M, Ring PB. In-vivo measurement of proton relaxation times in human brain, liver and skeletal muscle: a multicenter study: i. Magn Reson Imaging. 1993;11:841–50. [Google Scholar]
  27. Bernard AM, De Certaines JD, Delaval P, Louvet M, Coetmeur D. Histological explanation of proton T1 and T2 variations in human lung tumors. In: Allen PS, Boisvert DPJ, Lentle BC, editors. Magnetic resonance in cancer. Toronto (Canada): Pergamon press; 1986. p. 49–50. [Google Scholar]
  28. Le Rumeur E, de Certaines J, Toulouse P, Rochcongar P. Water phases in rat striated muscles as determined by T2 proton NMR relaxation times. Magn Reson Imaging. 1987;5(4):267–72. [Google Scholar]
  29. Araujo ECA, Fromes Y, Carlier PG. New insights on human skeletal muscle tissue compartments revealed by in-vivo T2 NMR relaxometry. Biophys J. 2014;106:2267–74. [Google Scholar]
  30. Lerski RA, de Certaines JD, Duda D, Klonowski W, Yang G, Coatrieux JL, et al. Application of texture analysis to muscle MRI: 2- Technical recommendations. EPJ Nonlinear Biomedical Physics 2015, 3:2. [Google Scholar]
  31. Galloway MM. Texture analysis using grey level run lengths. Computer Graphics and Image Processing. 1975;4:172–9. [Google Scholar]
  32. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification, IEEE Transactions on Systems. Man Cybernetics. 1973;3:610–21. [Google Scholar]
  33. Weszka JS, Dyer CR, Rosenfeld A. A Comparative Study of Texture Measures for Terrain Classification, IEEE Trans. Systems, Man, Cybernetics. 1976;6:269–85. [Google Scholar]
  34. Lerski RA, 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]
  35. Shu H, Luo L, Coatrieux J-L. Moment-based approaches in imaging. Part 1, basic features. IEEE EngMedBiol Magazine. 2007;5:70–4. [Google Scholar]
  36. Shu H, Luo L, Coatrieux J-L. Derivation of moments invariants. In: Papakostas GA, editor. Moments and moments invariants. Xanthi (Greece): Science Gate Publishing; 2014. [Google Scholar]
  37. Chen B, Shu H, Zhang H, Coatrieux G, Luo L, Coatrieux J-L. Combined invariants to similarity transformation and to blur using orthogonal Zernike moments. IEEE Trans Image Process. 2011;20(2):345–60. [Google Scholar]
  38. Nketiah G, Savio S, Dastidar P, Nikander R, Eskola H, Sieväwen H. Detection of exercise load-associated differences in hip muscles by texture analysis. Scand J Med Sports. 2014;20:10.1111. [Google Scholar]
  39. Szczypinski P, Strzelecki M, Materka A, Klepaczko A. MaZda-A software package for image texture analysis. Comput Methods Prog Biomed. 2009;94(1):66–76. [Google Scholar]
  40. Kornegay JN, Bogan JR, Bogan DJ, Childers MK, Li J, Nghiem P, et al. Canine models of Duchenne muscular dystrophy and their use in therapeutic strategies. Mamm Genome. 2012;23(1–2):85–108. [Google Scholar]
  41. Kornegay JN, Tuler SM, Miller DM, Levesque DC. Muscular dystyrophy in a litter of golden retriever dog. Muscle Nerve. 1988;11:1056–64. [Google Scholar]
  42. Nguyen F. Muscle Lesions Associated with Dystrophin Deficiency in Neonatal Golden Retriever Puppies. J Comp Pathol. 2002;126(2–3):100–8. [Google Scholar]
  43. Valentine BA, Cooper BJ, Cummings JF, de Lahunta A. Canine X-linked muscular dystrophy: morphologic lesions. J Neurol Sci. 1990;7(1):1–23. [Google Scholar]
  44. Cooper BJ, Winand NJ, Stedman H, Valentine BA, Hoffman EP, Kunkel LM, et al. The homologue of the Duchenne locus is defective in X-linked muscular dystrophy of dogs. Nature. 1988;334(6178):154–6. [Google Scholar]
  45. Cozzi F, Cerletti M, Luvoni GC, Lombardo R, Brambilla PG, Faverzani S, et al. Development of muscle pathology in canine X-linked muscular dystrophy. II. Quantitative characterization of histopathological progression during postnatal skeletal muscle development. Acta Neuropathol. 2001;101(5):469–78. [Google Scholar]
  46. Nguyen F, Guigand L, Goubault-Leroux I, Wyers M, Cherel Y. Microvessel density in muscles of dogs with golden retriever muscular dystrophy. Neuromuscul Disord. 2005;15(2):154–63. [Google Scholar]
  47. Fan Z, Wang J, Ahn M. Characteristics of magnetic resonance imaging biomarkers in a natural history study of golden retriever muscular dystrophy. Neuromuscul Disord. 2014;24:178–91. [Google Scholar]
  48. Snezhko EV, Carlier P, Kovalev VA, Azzabou N, Dmitruk AA, Shukelovich AV. Application of Texture Analysis Techniques on NMR Images for Quantitative Assessment of Muscle Disorders. Informatics. 2014;3:5–13. [Google Scholar]
  49. Duda D. Medical image classification based on texture analysis. PhD Thesis, University of Rennes 1, Rennes, France, 2009. [Google Scholar]
  50. Sáez A, Acha B, Montero-Sánchez A, Rivas E, Escudero LM, Serrano C. A., Neuromuscular disease classification system. J Biomed Opt. 2013;18(6):66017. [Google Scholar]
  51. Sáez A, Rivas E, Montero-Sánchez A, Paradas C, Acha B, Pascual A, et al. Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis. BMC Medicine. 2013;11:77. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.