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Digital Microfluidics for Automated Proteomic Processing
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Automated and effective content-based image retrieval for digital mammography.

Vibhav Prakash Singh1, Subodh Srivastava2, Rajeev Srivastava1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.

Journal of X-Ray Science and Technology
|February 27, 2018
PubMed
Summary

This study introduces an automated content-based image retrieval (CBIR) system for mammograms, improving breast cancer diagnosis by removing noise and retrieving similar images. The system achieved an average precision of 72% for normal and 61.30% for abnormal mammograms.

Keywords:
Computer aided diagnosiscontent-based image retrievalfeature extractionfeature selectionsegmentation

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Area of Science:

  • Medical Imaging
  • Computer Science
  • Biomedical Engineering

Background:

  • Mammograms are crucial for breast cancer diagnosis, but image noise and non-breast regions can hinder accurate analysis.
  • Content-based image retrieval (CBIR) systems can enhance diagnostic reliability by finding similar annotated mammograms.

Purpose of the Study:

  • To develop and evaluate an efficient, automated CBIR system for mammograms.
  • To address pre-processing challenges like artifact suppression and pectoral muscle removal for improved retrieval accuracy.

Main Methods:

  • Applied pre-processing: adaptive median filtering, artifact suppression, and pectoral muscle removal.
  • Image segmentation using co-occurrence thresholds and seeded region growing.
  • Feature extraction (shape, histogram, Gabor, wavelet, GLCM) followed by minimum redundancy maximum relevance (mRMR) feature selection.
  • Image retrieval via Euclidean distance similarity measure.

Main Results:

  • The automated CBIR system demonstrated effectiveness on the MIAS database.
  • Achieved average precision of 72% for normal and 61.30% for abnormal mammogram classes.

Conclusions:

  • The proposed automated CBIR system effectively enhances mammogram analysis for breast cancer diagnosis.
  • Automated pre-processing and optimized feature selection are key to improving retrieval accuracy in medical image databases.