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Learning image representation from image reconstruction for a content-based medical image retrieval

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Abstract

In this paper, we propose a novel approach of feature learning through image reconstruction for content-based medical image retrieval. We propose an image reconstruction network to encode the input image into a set of features followed by the reconstruction of the input image from the encoded features. The robust reconstruction of the input image from encoded features shows that the encoded features can be used as an abstract version of an input image. Thus, we make use of these encoded features for medical image retrieval task. The performance of the proposed method has been analyzed with the help of three benchmark medical image databases. Average retrieval rate and average precision rate are used to evaluate the performance of proposed and existing state-of-the-art methods for medical image retrieval task. Experimental analysis shows that the proposed approach for image retrieval outperforms the other existing methods.

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Authors and Affiliations

  1. JNTUA, Anatapuramu, India

    Rohini Pinapatruni & C. Shoba Bindu

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  1. Rohini Pinapatruni
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  2. C. Shoba Bindu
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Correspondence to Rohini Pinapatruni.

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Pinapatruni, R., Shoba Bindu, C. Learning image representation from image reconstruction for a content-based medical image retrieval. SIViP 14, 1319–1326 (2020). https://doi.org/10.1007/s11760-020-01670-y

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  • DOI: https://doi.org/10.1007/s11760-020-01670-y

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