Advances in Texture Analysis for Emphysema Classification

  • Rodrigo Nava
  • J. Victor Marcos
  • Boris Escalante-Ramírez
  • Gabriel Cristóbal
  • Laurent U. Perrinet
  • Raúl San José Estépar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

In recent years, with the advent of High-resolution Computed Tomography (HRCT), there has been an increased interest for diagnosing Chronic Obstructive Pulmonary Disease (COPD), which is commonly presented as emphysema. Since low-attenuation areas in HRCT images describe different emphysema patterns, the discrimination problem should focus on the characterization of both local intensities and global spatial variations. We propose a novel texture-based classification framework using complex Gabor filters and local binary patterns. We also analyzed a set of global and local texture descriptors to characterize emphysema morphology. The results have shown the effectiveness of our proposal and that the combination of descriptors provides robust features that lead to an improvement in the classification rate.

Keywords

Co-occurrence matrices Emphysema Gabor filters LBP Sparsity Tchebichef Texture analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rodrigo Nava
    • 1
  • J. Victor Marcos
    • 2
  • Boris Escalante-Ramírez
    • 1
  • Gabriel Cristóbal
    • 2
  • Laurent U. Perrinet
    • 3
  • Raúl San José Estépar
    • 4
  1. 1.Posgrado en Ciencia e Ingeniería de la ComputaciónUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  2. 2.Instituto de Óptica, Spanish National Research Council (CSIC)MadridSpain
  3. 3.INCM, UMR6193, CNRSAix-Marseille UniversityMarseille Cedex 20France
  4. 4.Harvard Medical SchoolBrigham and Women’s HospitalBostonUnited States

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