Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 213901, 13 pages
http://dx.doi.org/10.1155/2013/213901
Research Article

Customized First and Second Order Statistics Based Operators to Support Advanced Texture Analysis of MRI Images

1Department of Life, Health and Environmental Sciences, University of L’Aquila, Via Vetoio Coppito 2, 67100 L’Aquila, Italy
2Department of Computer Science, Sapienza University of Rome, Via Salaria 113, 00198 Rome, Italy

Received 26 February 2013; Revised 1 May 2013; Accepted 8 May 2013

Academic Editor: Younghae Do

Copyright © 2013 Danilo Avola et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Tuceryan and A. K. Jain, “Texture analysis,” in Handbook of Pattern Recognition & Computer Vision, pp. 235–276, World Scientific Publishing, River Edge, NJ, USA, 1993. View at Google Scholar
  2. N. Sebe and M. S. Lew, “Texture features for content-based retrieval,” in Principles of Visual Information Retrieval, pp. 51–85, Springer, London, UK, 2000. View at Google Scholar
  3. P. Maillard, “Comparing texture analysis methods through classification,” Photogrammetric Engineering and Remote Sensing, vol. 69, no. 4, pp. 357–367, 2003. View at Google Scholar · View at Scopus
  4. J. Chen, T. N. Pappas, A. Mojsilović, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1524–1536, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. A. K. Jain and F. Farrokhnia, “Unsupervised texture segmentation using Gabor filters,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 14–19, November 1990. View at Scopus
  6. J. C. Russ, The Image Processing Handbook, CRC Press, Taylor & Francis Group, 2007.
  7. P. Soille, Morphological Image Analysis: Principles and Applications, Springer, New York, NY, USA, 2010.
  8. V. Randle and O. Engler, Introduction to Texture Analysis: Macrotexture, Microtexture and Orientation Mapping, CRC Press, Taylor & Francis Group, Amsterdam, The Netherlands, 2000.
  9. M. E. Mavroforakis, H. V. Georgiou, N. Dimitropoulos, D. Cavouras, and S. Theodoridis, “Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers,” Artificial Intelligence in Medicine, vol. 37, no. 2, pp. 145–162, 2006. View at Google Scholar
  10. T. Hofmann, J. Puzicha, and J. M. Buhmann, “Unsupervised texture segmentation in a deterministic annealing framework,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 803–818, 1998. View at Publisher · View at Google Scholar · View at Scopus
  11. G. K. von Schulthess, Morphology and Function in MRI, Springer, London, UK, 2012.
  12. P. V. Prased, Magnetic Resonance Imaging: Methods and Biologic Applications, Humana Press Inc., Totowa, NJ, USA, 2006.
  13. D. Weishaupt, V. D. Koechli, and B. Marincek, How Does MRI Work? An Introduction to the Physics and Function of Magnetic Resonance Imaging, Springer, New York, NY, USA, 2006.
  14. G. Placidi, MRI: Essentials for Innovative Technologies, CRC Press, Taylor & Francis Group, 2012.
  15. R. M. Haralick, “Statistical and structural approaches to texture,” Proceedings of the IEEE, vol. 67, no. 5, pp. 786–804, 1979. View at Publisher · View at Google Scholar · View at Scopus
  16. S. W. Zucker, “Toward a model of texture,” Computer Graphics and Image Processing, vol. 5, no. 2, pp. 190–202, 1976. View at Google Scholar · View at Scopus
  17. J. Zhang and T. Tan, “Brief review of invariant texture analysis methods,” Pattern Recognition, vol. 35, no. 3, pp. 735–747, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. A. R. Rao, A Taxonomy for Texture Description and Identification, Springer, Berlin, Germany, 1990.
  19. N. V. Lobo, T. Kasparis, F. Roli, J. T. Kwok, M. Georgiopoulos, and M. Loog, Structural, Syntactic and Statistical Pattern Recognition, Springer, Berlin, Germany, 2008.
  20. M. A. Brown and R. C. Semelka, MRI: Basic Principles and Applications, Wiley-Blackwell, 2010.
  21. W. I. Mangrum, K. L. Christianson, S. M. Duncan, P. B. Hoang, A. W. Song, and E. M. Merkle, Duke Review of MRI Principles, Elsevier Mosby, 2012.
  22. H. Mobahi, S. R. Rao, A. Y. Yang, Y. Allen, S. S. Sastry, and Y. Ma, “Segmentation of natural images by texture and boundary compression,” International Journal of Computer Vision, vol. 95, no. 1, pp. 86–98, 2011. View at Google Scholar
  23. T. V. Papathomas, R. S. Kashi, and A. Gorea, “A human vision based computational model for chromatic texture segregation,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 27, no. 3, pp. 428–440, 1997. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Zheng, Q. Qin, G. Liu, and Y. Hu, “Image segmentation based on multiresolution Markov random field with fuzzy constraint in wavelet domain,” IET Image Processing, vol. 6, no. 3, pp. 213–221, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Malik, S. Belongie, T. Leung, and J. Shi, “Contour and texture analysis for image segmentation,” International Journal of Computer Vision, vol. 43, no. 1, pp. 7–27, 2001. View at Publisher · View at Google Scholar · View at Scopus
  26. Q. Xu, J. Yang, and S. Ding, “Color texture analysis using the wavelet-based hidden Markov model,” Pattern Recognition Letters, vol. 26, no. 11, pp. 1710–1719, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Madanian, A. Vafaei, and A. H. Monadjemi, “Segmentation and classification of texture images inspired by natural vision system and HMAX algorithm,” International Journal of Research and Reviews in Computer Science, vol. 3, no. 1, pp. 1467–1472, 2012. View at Google Scholar
  28. G. F. McLean, “Vector quantization for texture classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 637–649, 1993. View at Publisher · View at Google Scholar · View at Scopus
  29. D. Avola, L. Cinque, and G. Placidi, “Medical image analysis through a texture based computer aided diagnosis framework,” International Journal of Biometrics and Bioinformatics, vol. 6, no. 5, pp. 144–152, 2012. View at Google Scholar
  30. D. Avola, L. Cinque, and M. Di Girolamo, “A novel T-CAD framework to support medical image analysis and reconstruction,” in Proceeding of the 16th International Conference on Image Analysis and Processing (ICIAP ’11), vol. 6979, pp. 414–423, Springer, 2011.
  31. D. Avola and L. Cinque, “Encephalic NMR image analysis by textural interpretation,” in Proceedings of the 23rd Annual ACM Symposium on Applied Computing (SAC '08), pp. 1338–1342, ACM, March 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. L. C. V. Harrison, M. Raunio, K. K. Holli et al., “MRI texture analysis in multiple sclerosis: toward a clinical analysis protocol,” Academic Radiology, vol. 17, no. 6, pp. 696–707, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Zhang, “MRI texture analysis in multiple sclerosis,” International Journal of Biomedical Imaging, vol. 2012, Article ID 762804, 7 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Herlidou-Même, J. M. Constans, B. Carsin et al., “MRI texture analysis on texture test objects, normal brain and intracranial tumors,” Magnetic Resonance Imaging, vol. 21, no. 9, pp. 989–993, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. M. E. Mayerhoefer, W. Schima, S. Trattnig, K. Pinker, V. Berger-Kulemann, and A. Ba-Ssalamah, “Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a feasibility study in cysts and hemangiomas,” Journal of Magnetic Resonance Imaging, vol. 32, no. 2, pp. 352–359, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. G. Bahl, I. Cruite, T. Wolfson et al., “Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images,” Journal of Magnetic Resonance Imaging, vol. 36, no. 5, pp. 1154–1161, 2012. View at Google Scholar
  37. MaZda software, 2013, http://www.eletel.p.lodz.pl/programy/mazda/.
  38. S. Rathore, M. A. Iftikhar, M. Hussain, and A. Jalil, “Texture analysis for liver segmentation and classification: a survey,” in Proceedings of the 9th International Conference on Frontiers of Information Technology (FIT '11), pp. 121–126, December 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Histace, B. Matuszewski, and Y. Zhang, “Segmentation of myocardial boundaries in tagged cardiac MRI using active contours: a gradient-based approach integrating texture analysis,” International Journal of Biomedical Imaging, vol. 2009, Article ID 983794, 8 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Huang, X. Huang, D. Metaxas, and L. Axel, “Dynamic texture based heart localization and segmentation in 4-D cardiac images,” in Proceedings of the 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '07), pp. 852–855, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. X. Yang and K. Murase, “Tagged cardiac MR image segmentation by contrast enhancement and texture analysis,” in Proceedings of the 9th International Conference on Electronic Measurement and Instruments (ICEMI '09), pp. 4210–4214, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  42. L. C. V. Harrison, R. Nikander, M. Sikiö et al., “MRI texture analysis of femoral neck: detection of exercise load-associated differences in trabecular bone,” Journal of Magnetic Resonance Imaging, vol. 34, no. 6, pp. 1359–1366, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. C. Chevrefils, F. Cheriet, C. Aubin, and G. Grimard, “Texture analysis for automatic segmentation of intervertebral disks of scoliotic spines from MR images,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 4, pp. 608–620, 2009. View at Publisher · View at Google Scholar · View at Scopus
  44. L. M. Lorigo, O. Faugeras, W. E. L. Grimson, R. Keriven, and R. Kikinis, “Segmentation of bone in clinical knee MRI using texture-based geodesic active contours,” in Proceeding of the International Conference on Medical Image Computing and Computer-Assisted Interventation (MICCAI ’98), vol. 1496, pp. 1195–1204, Springer, 1998.
  45. M. C. de Oliveira and R. I. Kitney, “Texture analysis for discrimination of tissues in MRI data,” in Proceedings of the 18th Annual Conference on Computers in Cardiology, pp. 481–484, September 1991. View at Scopus
  46. V. A. Kovalev, F. Kruggel, H. Gertz, and D. Y. Von Cramon, “Three-dimensional texture analysis of MRI brain datasets,” IEEE Transactions on Medical Imaging, vol. 20, no. 5, pp. 424–433, 2001. View at Publisher · View at Google Scholar · View at Scopus
  47. K. Wu, C. Garnier, J. Coatrieux, and H. Shu, “A preliminary study of moment-based texture analysis for medical images,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society, vol. 2010, pp. 5581–5584, 2010. View at Google Scholar · View at Scopus
  48. S. Nagaraj, G. N. Rao, and K. Koteswararao, “The role of pattern recognition in computer-aided diagnosis and computer-aided detection in medical imaging: a clinical validation,” International Journal of Computer Applications, vol. 8, no. 5, pp. 18–22, 2010. View at Google Scholar
  49. K. Somkantha, N. Theera-Umpon, and S. Auephanwiriyakul, “Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 567–573, 2011. View at Publisher · View at Google Scholar · View at Scopus
  50. N. Alamgir, K. Myeongsu, K. Yung-Keun, K. Cheol-Hong, and K. Jong-Myon, “A hybrid technique for medical image segmentation,” Journal of Biomedicine and Biotechnology, vol. 2012, Article ID 830252, 7 pages, 2012. View at Publisher · View at Google Scholar
  51. N. Sharma, A. Ray, S. Sharma, K. Shukla, S. Pradhan, and L. Aggarwal, “Segmentation and classification of medical images using texture-primitive features: application of BAM-type artificial neural network,” Journal of Medical Physics, vol. 33, no. 3, pp. 119–126, 2008. View at Publisher · View at Google Scholar · View at Scopus
  52. J. S. De Bonet, “Multiresolution sampling procedure for analysis and synthesis of texture images,” in Proceedings of the Conference on Computer Graphics (SIGGRAPH '97), pp. 361–368, ACM, August 1997. View at Scopus
  53. D. J. Heeger and J. R. Bergen, “Pyramid-based texture analysis/synthesis,” in Proceedings of the 22nd Annual ACM Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '95), pp. 229–238, ACM, August 1995. View at Scopus
  54. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973. View at Google Scholar · View at Scopus
  55. B. Sebastian, A. Unnikrishnan, and K. Balakrishnan, “Grey level co-occurrence matrices: generalization and some new features,” International Journal of Computer Science, Engineering and Information Technology, vol. 2, no. 2, pp. 151–157, 2012. View at Google Scholar
  56. A. R. Backes, A. S. Martinez, and O. M. Bruno, “Texture analysis using graphs generated by deterministic partially self-avoiding walks,” Pattern Recognition, vol. 44, no. 8, pp. 1684–1689, 2011. View at Publisher · View at Google Scholar · View at Scopus
  57. R. Suguna and P. Anandhakumar, “A novel feature extraction technique for texture discrimination using orthogonal polynomial operators,” European Journal of Scientific Research, vol. 51, no. 4, pp. 550–563, 2011. View at Google Scholar · View at Scopus
  58. H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460–473, 1978. View at Publisher · View at Google Scholar · View at Scopus
  59. E. Alpaydini, Introduction to Machine Learning, MIT Press, 2nd edition, 2010.