Neighbourhood search feature selection method for content-based mammogram retrieval

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 feat...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2017-03, Vol.55 (3), p.493-505
Main Authors: Chandy, D. Abraham, Christinal, A. Hepzibah, Theodore, Alwyn John, Selvan, S. Easter
Format: Article
Language:English
Subjects:
Algorithms
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Computer Applications
Databases as Topic
Datasets
Diabetic retinopathy
Diagnostic tests
Entropy
Feature extraction
Feature selection
Female
Human Physiology
Humans
Image management
Image retrieval
Imaging
Information Storage and Retrieval
Mammography
Mammography - methods
Medical imaging
Methods
Neighborhoods
Original Article
Principal components analysis
Radiology
Retrieval
State of the art
Studies
Test procedures
Time Factors
Time measurement
Wavelet
Wavelet transforms
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Summary: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.
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-016-1513-x