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Content based medical image retrieval using topic and location model.

P Shamna1, V K Govindan1, K A Abdul Nazeer1

  • 1Department of Computer Science and Engineering, National Institute of Technology Calicut, Calicut, Kerala 673 601, India.

Journal of Biomedical Informatics
|February 10, 2019
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Summary
This summary is machine-generated.

This study introduces an automated medical image retrieval system that uses topic and location probabilities to improve performance. The novel system enhances retrieval efficiency and precision for large medical image datasets.

Keywords:
Content based medical image retrievalLatent Dirichlet allocationSpatial location modelTopic modellingVisual words

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Information Retrieval

Background:

  • Retrieving medical images from diverse datasets is complex.
  • Current systems face challenges in accuracy and efficiency.

Purpose of the Study:

  • To analyze an automated medical image retrieval system.
  • To enhance performance using topic and location probabilities.

Main Methods:

  • Developed an automated medical image retrieval system.
  • Employed Guided Latent Dirichlet Allocation (GuidedLDA) for topic information.
  • Introduced a novel Location Model for spatial information.
  • Proposed a position-weighted Precision (wPrecision) metric.

Main Results:

  • The system outperformed existing methods on IRMA 2009 and Multimodal datasets.
  • Achieved 86.74% Mean Average Precision on the Multimodal dataset.
  • Achieved 97.5% Precision for top ten images on the IRMA 2009 dataset.

Conclusions:

  • Integrating spatial details into Topic Models improves medical image retrieval.
  • Automated systems can assist physicians in retrieving images with high precision.