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BRISC-an open source pulmonary nodule image retrieval framework.

Michael O Lam1, Tim Disney, Daniela S Raicu

  • 1James Madison University, 20002 Wooded View Lane, Elkton, VA 22827, USA. michael.o.lam@gmail.com

Journal of Digital Imaging
|August 19, 2007
PubMed
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This study introduces a framework for retrieving similar pulmonary nodule images from computed tomography scans. Gabor and Markov methods achieved over 88% precision, outperforming Haralick techniques for nodule image analysis.

Area of Science:

  • Medical Imaging
  • Computer Science
  • Radiology

Background:

  • Pulmonary nodules require accurate identification and characterization.
  • Content-based image retrieval (CBIR) offers a potential solution for analyzing large image datasets.
  • Existing methods for nodule image retrieval may lack efficiency and precision.

Purpose of the Study:

  • To develop and evaluate a CBIR framework for computed tomography (CT) images of pulmonary nodules.
  • To compare the effectiveness of different feature extraction methods for nodule image similarity assessment.
  • To provide an open-source tool for research and clinical application in pulmonary nodule analysis.

Main Methods:

  • A CBIR framework was developed using the Lung Image Database Consortium (LIDC) dataset.

Related Experiment Videos

  • Nodule images were extracted based on expert annotations and stored in an XML database.
  • Quantitative descriptors (Haralick, Gabor, Markov) were calculated for texture characterization.
  • Nodule similarity was determined using various measures for query-based retrieval.

    • Main Results:

    • The framework successfully retrieves images of similar pulmonary nodules.
    • Gabor filters and Markov random fields demonstrated superior performance compared to Haralick co-occurrence.
    • Best retrieval precisions exceeded 88% using Gabor and Markov descriptors.
    • The developed software and reference images are open-source and publicly available.

    Conclusions:

    • The developed CBIR framework is effective for pulmonary nodule image retrieval.
    • Gabor and Markov feature extraction methods are more suitable for this task than Haralick methods.
    • The open-source nature of the framework facilitates its adoption and further development in medical imaging research and practice.