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Tytuł artykułu

On the effect of image brightness and contrast nonuniformity on statistical texture parameters

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities – modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.
Rocznik
Strony
163--185
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
autor
  • Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, 90-924 Lodz, Poland
  • Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, 90-924 Lodz, Poland
Bibliografia
  • [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, 3109-3118.21
  • [2] Amadasun M., King R., Textural features corresponding to textural properties, IEEE Trans Syst Man Cybernetics, 19, 1989, 1264-1274.4
  • [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, 1154-1161.10
  • [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, 234-246.14
  • [5] Brodatz P., Textures - A Photographic Album for Artists and Designers, Dover, 1966.24
  • [6] Castelano G., Bonilha L., Li L-M., Cendes F., Texture analysis of medical images, Clin Radiology, 59, 2004, 1061-1069.1
  • [7] Gu J., Ramamoorthi R., Belhumeur P., Nayar S., Removing image artifacts due to dirty camera lenses and thin occluders, SIGGRAPH Asia, 2010.20
  • [8] Hajek M., Dezortova M., Materka A., Lerski R. (eds.), Texture analysis of magnetic resonance imaging, EU COST B21, Prague, Med4Publishing, 2006.8
  • [9] Haralick R. M., Shanmugam K., Dinstein I., Textural features for image classification, IEEE Trans Syst Man Cybern, 3, 1973, 610-621.7
  • [10] Haralick R. M., Statistical and structural approaches to texture, Proc IEEE, 67, 1979, 786-804.17
  • [11] http://docs.scipy.org/doc/scipy-0.14.0/reference/tutorial/optimize.html, accessed on 22 December, 2014.23
  • [12] Kassner A., Thornhill R., Texture analysis: a review of neurologic MR imaging applications, Am J Neuroradiol, 31, 2010, 809-816.2
  • [13] Lespessailles E., et al., Clinical interest of bone texture analysis in osteoporosis: a case control multicenter study, Osteoporosis Int, 19, 2008, 1019-1028.6
  • [14] Levine M. D., Vision in man and machine, New York, Mc-Graw-Hill, 1985.3
  • [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, 1053-1062.16
  • [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, 1611-1625.15
  • [17] Mallat S.G., Multifrequency channel decompositions of images and wavelet models, IEEE Trans. on Acoustics, Speech, and Signal Processing, 37, 1989, 2091–2110.26
  • [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, 197-206.11
  • [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] 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] Rao A. R., Lohse G. L., Towards a texture naming system: Identyfying relevant dimensions of texture, Vision Research, 36, 11, 1996, 1649-1669.5
  • [22] Schürman J., Pattern classification, John Wiley & Sons, 1996. 19
  • [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] 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, 137-140.12
  • [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, 145-168.13
  • [26] Szczypinski P., Strzelecki M., Materka A., Klepaczko A., MaZda - A software package for image texture analysis, Comp Meth Programs Biomed, 94, 2009, 66-76.9
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-731ee1eb-6e6e-4bd1-af76-4f07f0d6e19a
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