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EDR: Enriched Deep Residual Framework with Image Reconstruction for Medical Image Retrieval

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1568))

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Abstract

In recent times, the advancement of Artificial Intelligence (AI) attracted many researchers in medical image analysis. Analyzing the vast medical data through traditional approaches is a bit tedious and time-consuming in designing feature descriptors. Therefore, we presented an EDR: Enriched Deep Residual Framework for robust medical image retrieval in this paper. The proposed EDR framework consists of an image reconstruction module using a residual encoder and sequential decoder. Also, the image matching module is followed by retrieval to retrieve similar images from the database. The encoder module of the EDR framework consists of series of residual connections that encode the features from a given image and are forwarded to the reconstruction decoder module. The extracted encoded features provide the latent representation for the robust reconstruction of the input image. Further, this latent information is used in the image matching and retrieve similar images from the database. The performance of the proposed EDR framework is analyzed on benchmark medical image databases such as VIA/ELCAP-CT, ILD for image retrieval tasks. The proposed EDR framework is compared with the state-of-the-art approaches for average precision and recall over two datasets. The experiments and results show that the proposed framework outperformed existing works in medical image retrieval.

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Authors and Affiliations

  1. JNTUA, Anatapuramu, India

    Rohini Pinapatruni & Shoba Bindu Chigarapalle

Authors
  1. Rohini Pinapatruni
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  2. Shoba Bindu Chigarapalle
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Corresponding author

Correspondence to Rohini Pinapatruni .

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Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Pinapatruni, R., Chigarapalle, S.B. (2022). EDR: Enriched Deep Residual Framework with Image Reconstruction for Medical Image Retrieval. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-11349-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11348-2

  • Online ISBN: 978-3-031-11349-9

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