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Robust Feature for Transcranial Sonography Image Classification Using Rotation-Invariant Gabor Filter

  • Arkan Al-Zubaidi
  • Lei Chen
  • Johann Hagenah
  • Alfred Mertins
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Transcranial sonography is a new tool for the diagnosis of Parkinson’s disease according to a distinct hyperechogenic pattern in the substantia nigra region. In order to reduce the influence of the image properties from different settings of ultrasound machine, we propose a robust feature extraction method using rotation-invariant Gabor filter bank. Except the general Gabor features, such as mean and standard deviation, we suggest to use the entropy of the filtered images for the TCS images classification. The performance of the Gabor features is evaluated by a feature selection method with the objective function of support vector machine classifier. The results show that the rotationinvariant Gabor filter is better than the conventional one, and the entropy is invariant to the intensity and the contrast changes.

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Literatur

  1. Becker G, Seufert J, Bogdahn U, et al. Degeneration of substantia nigra in chronic Parkinson’s diseasvisualized by transcranial color-coded real-time sonograph. Neurology. 1995;45:182–4.Google Scholar
  2. Behnke S, Berg D, Becker G. Does ultrasound disclose a vulnerability factor for Parkinson’s disease? J Neural. 2003;250 Suppl 1:I24–I2.Google Scholar
  3. Hagenah JM, Hedrich K, Becker B, et al. Distinguishing early-onset PD from doparesponsive dystonia with transcranial sonography. Neurology. 2006;66:1951–52.Google Scholar
  4. Kier C, Seidel G, Bregemann N, et al. Transcranial sonography as early indicator for genetic Parkinson’s disease. Proc IFMBE. 2009; p. 456–9.Google Scholar
  5. Chen L, Seidel G, Mertins A. Multiple feature extraction for early Parkinson risk assessment based on transcranial sonography image. Proc Int Conf Image Proc. 2010.Google Scholar
  6. Chen L, Hagenah J, Mertins A. Texture analysis using gabor filter based on transcranial sonography image. Proc BVM. 2011; p. 249–53.Google Scholar
  7. Han J, Ma KK. Rotation-invariant and scale-invariant gabor features for texture image retrieval. Image Vis Comput. 2007;25:1474–81.Google Scholar
  8. D Zhang MI A Wong, Lu G. Content-based image retrival using gabor texture features. IEEE Trans Trans Pattern Anal Mach Intell. 2000; p. 13–5.Google Scholar
  9. Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell. 1996;18:837–42.Google Scholar
  10. Cover TM, Thomas JA. Elements of information theory. USA: John Wiley; 2006.Google Scholar
  11. Zuiderveld K. Contrast limited adaptive histogram equalization. In: Heckbert PS, editor. Graphics gems IV. San Diego, CA, USA: Academic Press Professional, Inc.;1994. p. 474–85.Google Scholar
  12. Lazebnik S, Schmid C, Ponce J. A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell. 2005;27:1265–78.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arkan Al-Zubaidi
    • 1
  • Lei Chen
    • 1
    • 3
  • Johann Hagenah
    • 2
  • Alfred Mertins
    • 1
  1. 1.Institute for Signal ProcessingUniversity of LuebeckLübeckDeutschland
  2. 2.Department of NeurologyUniversity Hospital Schleswig-HolsteinKielDeutschland
  3. 3.Graduate SchoolUniversity of LuebeckLübeckDeutschland

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