Have a personal or library account? Click to login
On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters Cover

On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

Open Access
|Sep 2015

References

  1. [1] Altunbas M. C. et al., A post-reconstruction method to correct cupping artifacts in cone beam breast computed tomography, Med Phys, 34, 7, 2007, 3109-3118.2110.1118/1.2748106199565317822018
  2. [2] Amadasun M., King R., Textural features corresponding to textural properties, IEEE Trans Syst Man Cybernetics, 19, 1989, 1264-1274.410.1109/21.44046
  3. [3] Bahl G. et al., Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance image, J Magn Res Imaging, 36, 2012, 1154-1161.1010.1002/jmri.23759480347722851409
  4. [4] Belaroussi B., Milles J., Carme S., Zhu J-M., Benoit-Cattin H., Intensity nonuniformity correction in MRI: Existing methods and their validation, Med Image Anal, 10, 2006, 234-246.1410.1016/j.media.2005.09.00416307900
  5. [5] Brodatz P., Textures - A Photographic Album for Artists and Designers, Dover, 1966.24
  6. [6] Castelano G., Bonilha L., Li L-M., Cendes F., Texture analysis of medical images, Clin Radiology, 59, 2004, 1061-1069.110.1016/j.crad.2004.07.00815556588
  7. [7] Gu J., Ramamoorthi R., Belhumeur P., Nayar S., Removing image artifacts due to dirty camera lenses and thin occluders, SIGGRAPH Asia, 2010.2010.1145/1661412.1618490
  8. [8] Hajek M., Dezortova M., Materka A., Lerski R. (eds.), Texture analysis of magnetic resonance imaging, EU COST B21, Prague, Med4Publishing, 2006.8
  9. [9] Haralick R. M., Shanmugam K., Dinstein I., Textural features for image classification, IEEE Trans Syst Man Cybern, 3, 1973, 610-621.710.1109/TSMC.1973.4309314
  10. [10] Haralick R. M., Statistical and structural approaches to texture, Proc IEEE, 67, 1979, 786-804.1710.1109/PROC.1979.11328
  11. [11] http://docs.scipy.org/doc/scipy-0.14.0/reference/tutorial/optimize.html, accessed on 22 December, 2014.23
  12. [12] Kassner A., Thornhill R., Texture analysis: a review of neurologic MR imaging applications, Am J Neuroradiol, 31, 2010, 809-816.210.3174/ajnr.A2061796417420395383
  13. [13] Lespessailles E., et al., Clinical interest of bone texture analysis in osteoporosis: a case control multicenter study, Osteoporosis Int, 19, 2008, 1019-1028.610.1007/s00198-007-0532-818196441
  14. [14] Levine M. D., Vision in man and machine, New York, Mc-Graw-Hill, 1985.3
  15. [15] Li X-Z., Williams S., Bottema M.J., Background intensity independent texture features for assessing breast cancer risk in screening mammograms, Pattern Rec Letters, 34, 2013, 1053-1062.1610.1016/j.patrec.2013.01.031
  16. [16] Madabhushi A., Feldman M. D., Metaxas D. N., Tomaszewski J., Chute D., Automated detection of prostatic adenocarcinoma from high-resolution ex-vivo MRI, IEEE Trans Med Imaging, 24, 2005, 1611-1625.1510.1109/TMI.2005.859208
  17. [17] Mallat S.G., Multifrequency channel decompositions of images and wavelet models, IEEE Trans. on Acoustics, Speech, and Signal Processing, 37, 1989, 2091–2110.2610.1109/29.45554
  18. [18] Materka A., Strzelecki M., Lerski R., Schad L., Feature evaluation of texture test objects for magnetic resonance imaging, in: M. K. Pietikainen (editor), Texture Analysis in Machine Vision, Series in Machine Perception & Artificial Intelligence, Singapore, World Scientific, 40, 2000, 197-206.1110.1142/9789812792495_0015
  19. [19] Materka A., Strzelecki M., On the Importance of MRI Nonuniformity Correction for Texture Analysis, Proc. of IEEE SPA 2013, 26-28 September 2013, Poznan, Poland, 118-123.22
  20. [20] Materka A., Strzelecki M., Texture analysis methods—a review, Brussels, EU COST B11 Report, 1998. Available at: http://eletel.eu/programy/cost/pdf_1.pdf. Last accessed on 22 December, 2014.18
  21. [21] Rao A. R., Lohse G. L., Towards a texture naming system: Identyfying relevant dimensions of texture, Vision Research, 36, 11, 1996, 1649-1669.510.1016/0042-6989(95)00202-2
  22. [22] Schürman J., Pattern classification, John Wiley & Sons, 1996. 19
  23. [23] Strzelecki M., Materka A., On sensitivity of texture parameters to smooth variations of local image intensity and contrast, Proc. of IEEE SPA 2014, 22-24 September 2014, Poznan, Poland, 48-53.25
  24. [24] Strzelecki M., Szczypiński P., Materka A., Klepaczko A., A software tool for automatic classification and segmentation of 2D/3D medical images, Nucl Instrum Meth A, 702, 2013, 137-140.1210.1016/j.nima.2012.09.006
  25. [25] Styner M., Van Leemput K., Retrospective evaluation and correction of intensity inhomogeneities, in: L. Landini, V. Positano, M. F. Santarelli (eds.), Advanced Image Processing in Magnetic Resonance Imaging, Boca Raton, Taylor & Francis CRC Press, 2005, 145-168.1310.1201/9781420028669.ch5
  26. [26] Szczypinski P., Strzelecki M., Materka A., Klepaczko A., MaZda - A software package for image texture analysis, Comp Meth Programs Biomed, 94, 2009, 66-76.910.1016/j.cmpb.2008.08.00518922598
DOI: https://doi.org/10.1515/fcds-2015-0011 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Language: English
Page range: 163 - 185
Published on: Sep 30, 2015
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2015 Andrzej Materka, Michał Strzelecki, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.