Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms.

@article{Choi2012MultiresolutionLB,
  title={Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms.},
  author={Jae Young Choi and Yong Man Ro},
  journal={Physics in medicine and biology},
  year={2012},
  volume={57 21},
  pages={
          7029-52
        }
}
  • J. Choi, Yong Man Ro
  • Published 7 November 2012
  • Computer Science
  • Physics in medicine and biology
In this paper, a new and novel approach is designed for extracting local binary pattern (LBP) texture features from the computer-identified mass regions, aiming to reduce false-positive (FP) detection in a computerized mass detection framework. The proposed texture feature, the so-called multiresolution LBP feature, is well able to characterize the regional texture patterns of core and margin regions of a mass, as well as to preserve the spatial structure information of the mass. In addition… 
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Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application.
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Computer-aided detection (CAD) of breast masses in mammography: combined detection and ensemble classification.
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A region-of-interest combination mechanism is developed that integrates detection information gained from unsupervised and supervised detection algorithms to increase detection sensitivity for various types of mammographic masses and reduce the number of false-positive detections.
A generalized multiple classifier system for improving computer-aided classification of breast masses in mammography
TLDR
The authors' multiple classifier system has an effective and efficient mechanism for tuning the level of weakness of base classifiers and shows quite promising results in terms of improving classification performances on the FP reduction application using classification between masses and normal tissues in mammography CAD systems.
An Unsupervised Method for Suspicious Regions Detection in Mammogram Images
TLDR
A new unsupervised method to automatically detect suspicious regions in mammogram images using local keypoints and descriptors such as SURF and proper parameters tuning is proposed.
A comparative analysis of the Ground clutter suppression on Meteorological images
TLDR
The experiments show that the best results are obtained by combining LBP to the variance for Setif while the addition of BSIF and LPQ gives the bestResults when considering the Bordeaux region.
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References

SHOWING 1-10 OF 54 REFERENCES
False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis.
TLDR
The results indicated the effectiveness of the combined global and local features in the classification of masses and normal tissue for false-positive reduction.
Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space.
TLDR
The results demonstrate the feasibility of using linear discriminant analysis in the texture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.
Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms.
TLDR
The combined DWCE and object-based region growing technique increased the initial detection sensitivity, reduced merging between neighboring structures, and reduced the number of FP detections in the authors' automated breast mass detection scheme.
Classification of mass and normal breast tissue on digital mammograms: multiresolution texture analysis.
TLDR
It is found that texture features at large pixel distances are important for the classification task and a linear discriminant classifier using the multiresolution texture features can effectively classify masses from normal tissue on mammograms.
Detection of breast masses in mammograms by density slicing and texture flow-field analysis
TLDR
A method for the detection of masses in mammographic images that employs Gaussian smoothing and subsampling operations as preprocessing steps and methods for analyzing oriented flow-like textural information in mammograms are proposed.
False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns
TLDR
This paper proposes a new approach for false positive reduction in the field of mammographic mass detection based on Local Binary Patterns (LBP) for representing salient micro-patterns and preserving at the same time the spatial structure of the masses.
A textural approach for mass false positive reduction in mammography
Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images
TLDR
The authors' results demonstrate the feasibility of using a convolution neural network for classification of masses and normal tissue on mammograms using a generalized, fast and stable implementation of the CNN.
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
TLDR
A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue.
TLDR
This study indicates that a GA can provide versatility in the design of linear or nonlinear classifiers without a trade-off in the effectiveness of the selected features.
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