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Related Experiment Video

Updated: Mar 27, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

Yaron Anavi, Ilya Kogan, Elad Gelbart

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
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    This study explores X-ray image retrieval for chest pathologies. Deep learning features combined with a classification approach yielded the best results for accurate image similarity ranking.

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Accurate retrieval of chest X-ray images with pathologies is crucial for diagnosis.
    • Existing methods for image retrieval often rely on handcrafted features, which may not capture complex pathological patterns.
    • Deep learning offers potential for more robust feature extraction in medical image analysis.

    Purpose of the Study:

    • To investigate and compare various feature extraction and retrieval approaches for chest pathology X-ray images.
    • To evaluate the effectiveness of descriptor-based versus classification-based retrieval strategies.
    • To identify the optimal method for ranking chest X-ray images based on similarity to a query image.

    Main Methods:

    • Utilized a dataset of 443 chest X-ray images.

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  • Examined binary, texture, and deep learning (Convolutional Neural Network - CNN) features.
  • Implemented two retrieval approaches: descriptor-based (image descriptor distance) and classification-based (SVM with probability descriptors).

    • Main Results:

    • Deep learning features demonstrated superior performance compared to traditional binary and texture features.
    • The classification-based retrieval approach outperformed the descriptor-based approach.
    • Combining deep learning features with a classification scheme achieved the best results in ranking similar chest pathology images.

    Conclusions:

    • Deep learning features are highly effective for chest X-ray image retrieval.
    • A classification-based retrieval strategy using deep learning features offers the most promising results for accurate pathology identification.
    • This approach can enhance the efficiency and accuracy of medical image retrieval systems.