Local Texture Features for Content-Based Image Retrieval of Interstitial Lung Disease Patterns on HRCT Lung Images

  • Jatindra Kumar DashEmail author
  • Manisha Patro
  • Snehasish Majhi
  • Gandham Girish
  • P. Nancy Anurag
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)


Content-based image retrieval (CBIR) is a technique that may help radiologists in their daily clinical practice by providing reference images against a given subject in hand for diagnosis. Several special purpose medical CBIR systems are built for the diagnosis of interstitial lung diseases (ILDs). Texture is used as a primitive feature to build such systems due to the texture-like appearance of ILD patterns. Therefore, it is necessary to evaluate the efficacy of promising texture feature descriptors proposed recently for building the CBIR system for ILDs. This paper presents an effective and exhaustive evaluation of five such recently proposed texture feature descriptors (viz. local binary pattern (LBP), orthogonal combination of local binary pattern (OC-LBP), center-symmetric local binary pattern (CS-LBP), local neighborhood difference pattern (LNDP), and combination of LNDP and LBP) for the design and development of CBIR system for ILDs. The performance of each method is compared using the most used performance metrics such as precision, recall, and F-score. The LNDP descriptor is found to be the best performer and therefore can be considered as a descriptor for ILD patterns for the design and development of CBIR system.


Content based image retrieval Interstitial lung diseases Local texture pattern 


  1. 1.
    T.E. King Jr., Clinical advances in the diagnosis and therapy of the interstitial lung diseases. Am. J. Respir. Crit. Care Med. 172(3), 268–279 (2005)CrossRefGoogle Scholar
  2. 2.
    Z.A. Aziz, A.U. Wells, D.M. Hansell, G.A. Bain, S.J. Copley, S.R. Desai, S.M. Ellis, F.V. Gleeson, S. Grubnic, A.G. Nicholson et al., HRCT diagnosis of diffuse parenchymal lung disease: Inter-observer variation. Thorax 59(6), 506–511 (2004)CrossRefGoogle Scholar
  3. 3.
    J.K. Leader, T.E. Warfel, C.R. Fuhrman, S.K. Golla, J.L. Weissfeld, R.S. Avila, W.D. Turner, B. Zheng, Pulmonary nodule detection with low-dose ct of the lung: Agreement among radiologists. Am. J. Roentgenol. 185(4), 973–978 (2005)CrossRefGoogle Scholar
  4. 4.
    J.R. Mayo, W.R. Webb, R. Gould, M.G. Stein, I. Bass, G. Gamsu, H.I. Goldberg, High-resolution ct of the lungs: An optimal approach. Radiology 163(2), 507–510 (1987)CrossRefGoogle Scholar
  5. 5.
    C.-R. Shyu, C.E. Brodley, A.C. Kak, A. Kosaka, A.M. Aisen, L.S. Broderick, Assert: A physician-in-the-loop content-based retrieval system for HRCT image databases. Comput. Vis. Image Underst. 75(1–2), 111–132 (1999)CrossRefGoogle Scholar
  6. 6.
    C.-T. Liu, P.-L. Tai, A.Y.-J. Chen, C.-H. Peng, T. Lee, J.-S. Wang et al., A Content-Based CT Lung Image Retrieval System for Assisting Differential Diagnosis Images Collection (Institute of Electrical and Electronics Engineers Inc., 2001)Google Scholar
  7. 7.
    C.-R. Shyu, C. Pavlopoulou, A.C. Kak, C.E. Brodley, L.S. Broderick, Using human perceptual categories for content-based retrieval from a medical image database. Comput. Vis. Image Underst. 88(3), 119–151 (2002)CrossRefGoogle Scholar
  8. 8.
    S.D. MacArthur, C.E. Brodley, A.C. Kak, L.S. Broderick, Interactive content-based image retrieval using relevance feedback. Comput. Vis. Image Underst. 88(2), 55–75 (2002)CrossRefGoogle Scholar
  9. 9.
    J.G. Dy, C.E. Brodley, A. Kak, L.S. Broderick, A.M. Aisen, Unsupervised feature selection applied to content-based retrieval of lung images. IEEE Trans. Pattern Anal. Mach. Intell. 25(3), 373–378 (2003)CrossRefGoogle Scholar
  10. 10.
    T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  11. 11.
    C. Zhu, C.-E. Bichot, L. Chen, Image region description using orthogonal combination of local binary patterns enhanced with color information. Pattern Recogn. 46(7), 1949–1963 (2013)CrossRefGoogle Scholar
  12. 12.
    M. Verma, B. Raman, Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval. J. Vis. Commun. Image Represent. 32, 224–236 (2015)CrossRefGoogle Scholar
  13. 13.
    M. Verma, B. Raman, Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval. Multimedia Tools Appl. 77(10), 11843–11866 (2018)CrossRefGoogle Scholar
  14. 14.
    A. Depeursinge, A. Vargas, A. Platon, A. Geissbuhler, P.-A. Poletti, H. Müller, Building a reference multimedia database for interstitial lung diseases. Comput. Med. Imaging Graph. 36(3), 227–238 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jatindra Kumar Dash
    • 1
    Email author
  • Manisha Patro
    • 2
  • Snehasish Majhi
    • 3
  • Gandham Girish
    • 2
  • P. Nancy Anurag
    • 1
  1. 1.SRM University—AP, AmaravatiGunturIndia
  2. 2.National Institute of Science and TechnologyBerhampurIndia
  3. 3.National Institute of TechnologyRourkelaIndia

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