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Integrating 3D image descriptors of margin sharpness and texture on a GPU-optimized similar pulmonary nodule retrieval engine

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Abstract

Due to the difficulty to diagnose lung cancer, it is important to integrate content-based image retrieval methods with the pulmonary nodule classification process, since they are capable of retrieving similar cases from large image databases that were previously diagnosed. The goal of this paper is to evaluate an integrated image feature vector, composed of 3D attributes of margin sharpness and texture, on similar pulmonary nodule retrieval, and to optimize the runtime of nodule comparison process with a graphics processing unit (GPU). Retrieval efficiency was evaluated on the ten most similar cases, on different multiprocessor architectures. Results showed that integrated attributes obtained higher efficiency on similar nodule retrieval, with an increase of up to 2.6 percentage points compared to isolated margin sharpness and texture descriptors. Results also showed that GPU increased nodule retrieval performance with a speedup of 23.7\(\times \) on nodule comparison runtime.

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Authors and Affiliations

  1. Lab of Telemedicine and Medical Informatics, University Hospital Prof. Alberto Antunes, Institute of Computing, Federal University of Alagoas, Maceió, Alagoas, Brazil

    José Raniery Ferreira Junior & Marcelo Costa Oliveira

  2. Center of Imaging Sciences and Medical Physics, Internal Medicine Department, Ribeirao Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil

    Paulo Mazzoncini de Azevedo-Marques

Authors
  1. José Raniery Ferreira Junior
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  2. Marcelo Costa Oliveira
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  3. Paulo Mazzoncini de Azevedo-Marques
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Correspondence to José Raniery Ferreira Junior.

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Ferreira Junior, J.R., Oliveira, M.C. & de Azevedo-Marques, P.M. Integrating 3D image descriptors of margin sharpness and texture on a GPU-optimized similar pulmonary nodule retrieval engine. J Supercomput 73, 3451–3467 (2017). https://doi.org/10.1007/s11227-016-1818-4

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  • DOI: https://doi.org/10.1007/s11227-016-1818-4

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