, 43:20 | Cite as

Biomedical image retrieval using microscopic configuration with local structural information

  • G DEEP
  • L KAUR


This paper focusses on the use of microscopic configuration (MiC) for discriminative information to retrieve lung cancer images. Existing local binary pattern (LBP) detects the local structures, such as lighting spots and edges in images, whereas the local configuration pattern (LCP) explores multi-channel discriminative information of both the MiC and local structures of images. Both methods are used to extract the rotation- and scale-invariant features from all lung cancer images. The performance of these methods is tested by conducting experiments on benchmark biomedical database of Lung Image Database Consortium and Image Database Resource Initiative-computer tomography. The database includes CT images with region of interest. The results show that LCP yields significant improvement in terms of average retrieval precision and average retrieval rate as compared with LBP and other state-of-the-art texture descriptors.


Image retrieval local binary pattern local configuration pattern medical imaging texture 



We wish to express our appreciation to the Lung Image Database Consortium, who contributed to the creation of the LIDC collection as an independent testing dataset. We would like to thank anonymous reviewers for insightful comments and valuable suggestions to improve the quality of this manuscript.


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Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Chandigarh Engineering College (CEC), LandranPunjab Technical UniversityMohaliIndia
  2. 2.Department of Computer EngineeringPunjabi UniversityPatialaIndia
  3. 3.Department of Computer Science and Engineering, University Institute of Engineering and TechnologyPanjab UniversityChandigarhIndia

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