Neighbourhood search feature selection method for content-based mammogram retrieval
Medical and Biological Engineering and Computing, ISSN: 1741-0444, Vol: 55, Issue: 3, Page: 493-505
2017
- 10Citations
- 17Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Content-based image retrieval plays an increasing role in the clinical process for supporting diagnosis. This paper proposes a neighbourhood search method to select the near-optimal feature subsets for the retrieval of mammograms from the Mammographic Image Analysis Society (MIAS) database. The features based on grey level cooccurrence matrix, Daubechies-4 wavelet, Gabor, Cohen–Daubechies–Feauveau 9/7 wavelet and Zernike moments are extracted from mammograms available in the MIAS database to form the combined or fused feature set for testing various feature selection methods. The performance of feature selection methods is evaluated using precision, storage requirement and retrieval time measures. Using the proposed method, a significant improvement is achieved in mean precision rate and feature dimension. The results show that the proposed method outperforms the state-of-the-art feature selection methods.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85013895082&origin=inward; http://dx.doi.org/10.1007/s11517-016-1513-x; http://www.ncbi.nlm.nih.gov/pubmed/27262458; http://link.springer.com/10.1007/s11517-016-1513-x; https://dx.doi.org/10.1007/s11517-016-1513-x; https://link.springer.com/article/10.1007/s11517-016-1513-x
Springer Science and Business Media LLC
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