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Medical Image Retrieval with Compact Binary Codes Generated in Frequency Domain Using Highly Reactive Convolutional

Jamil Ahmad1, Khan Muhammad1, Sung Wook Baik2

  • 1Digital Contents Research Institute, Sejong University, Seoul, Republic of Korea.

Journal of Medical Systems
|December 21, 2017
PubMed
Summary
This summary is machine-generated.

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This study introduces an efficient method for medical image retrieval using compressed convolutional features and Fast Fourier Transform (FFT) hashing. The approach significantly improves retrieval effectiveness and efficiency on large medical image datasets.

Area of Science:

  • Medical imaging
  • Computer vision
  • Machine learning

Background:

  • Medical image retrieval systems are crucial for diagnosis but struggle with large datasets.
  • Deep convolutional neural networks (CNNs) offer state-of-the-art performance in image retrieval.
  • Locality-sensitive hashing (LSH) is popular for efficient large-scale data retrieval.

Purpose of the Study:

  • To develop a highly efficient method for compressing convolutional features for medical image retrieval.
  • To leverage Fast Fourier Transform (FFT) for creating compact binary codes from image features.
  • To improve the speed and accuracy of retrieving semantically similar medical cases.

Main Methods:

  • Selective compression of convolutional features using Fast Fourier Transform (FFT).
Keywords:
Convolutional neural networkFeature selectionFourier transformHash codesImage retrieval

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  • Identification of reactive feature maps via optimal subset selection based on neuronal responses.
  • Layer-wise global mean activations transformed into binary hash codes via Fourier spectrum binarization.

    • Main Results:

    • The proposed method significantly outperforms existing feature extraction and hashing schemes.
    • Achieved superior effectiveness and efficiency in medical image retrieval tasks.
    • Demonstrated high discriminative power of the generated hash codes.

    Conclusions:

    • The FFT-based feature compression and hashing method offers a highly efficient solution for medical image retrieval.
    • The approach enables fast and accurate retrieval of relevant medical cases from large repositories.
    • This technique can assist medical experts in timely decision-making and diagnosis.