Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Automated and effective content-based image retrieval for digital mammography

Automated and effective content-based image retrieval for digital mammography Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. In this study, an efficient and automated CBIR system of mammograms was developed and tested. First, the pre-processing steps including automatic labelling-artifact suppression, automatic pectoral muscle removal, and image enhancement using the adaptive median filter were applied. Next, pre-processed images were segmented using the co-occurrence thresholds based seeded region growing algorithm. Furthermore, a set of image features including shape, histogram based statistical, Gabor, wavelet, and Gray Level Co-occurrence Matrix (GLCM) features, was computed from the segmented region. In order to select the optimal features, a minimum redundancy maximum relevance (mRMR) feature selection method was then applied. Finally, similar images were retrieved using Euclidean distance similarity measure. The comparative experiments conducted with reference to benchmark mammographic images analysis society (MIAS) database confirmed the effectiveness of the proposed work concerning average precision of 72% and 61.30% for normal & abnormal classes of mammograms, respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of X-Ray Science and Technology IOS Press

Automated and effective content-based image retrieval for digital mammography

Download PDF
21 pages

Loading next page...
 
/lp/ios-press/automated-and-effective-content-based-image-retrieval-for-digital-8hNgBVWQCm

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
IOS Press
Copyright
Copyright © 2018 © 2018 – IOS Press and the authors. All rights reserved
ISSN
0895-3996
eISSN
1095-9114
DOI
10.3233/XST-17306
pmid
29480232
Publisher site
See Article on Publisher Site

Abstract

Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. In this study, an efficient and automated CBIR system of mammograms was developed and tested. First, the pre-processing steps including automatic labelling-artifact suppression, automatic pectoral muscle removal, and image enhancement using the adaptive median filter were applied. Next, pre-processed images were segmented using the co-occurrence thresholds based seeded region growing algorithm. Furthermore, a set of image features including shape, histogram based statistical, Gabor, wavelet, and Gray Level Co-occurrence Matrix (GLCM) features, was computed from the segmented region. In order to select the optimal features, a minimum redundancy maximum relevance (mRMR) feature selection method was then applied. Finally, similar images were retrieved using Euclidean distance similarity measure. The comparative experiments conducted with reference to benchmark mammographic images analysis society (MIAS) database confirmed the effectiveness of the proposed work concerning average precision of 72% and 61.30% for normal & abnormal classes of mammograms, respectively.

Journal

Journal of X-Ray Science and Technology – IOS Press

Published: Jan 1, 2018

References

  • Breast cancer statistics and markers
    Donepudi
  • Themammographic image analysis society digital mammogram database. InExerpta Medica
    Suckling
  • Image processing algorithms for digital mammography: A pictorial essay 1
    Pisano
  • Screening mammography with computer-aided detection: Prospective study of 12,860 patients in a community breast center 1
    Freer
  • Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval
    Chandy
  • Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests
    Singh
  • Design, analysis and classifier evaluation for a CAD tool for breast cancer detection from digital mammograms
    Srivastava