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Combination of different texture features for mammographic breast density classification
- Source :
- BIBE, IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012, 12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012
- Publication Year :
- 2012
- Publisher :
- IEEE, 2012.
-
Abstract
- Mammographic breast density refers to the prevalence of fibroglandular tissue as it appears on a mammogram. Breast density is not only an important risk for developing breast cancer but can also mask abnormalities. Breast density information can be used for planning individualized screening and treatment. In this work, statistical distributions of different texture descriptors and their combination are investigated with Support Vector Machines (SVMs) for objective breast density classification: Scale Invariant Feature Transforms (SIFT), Local Binary Patterns (LBP) and texton histograms. SIFT is an approach for detecting and extracting local feature descriptors that are reasonably invariant to changes in illumination, image noise, rotation, scaling and small changes in viewpoint. The SIFT descriptor is a coarse descriptor of the edges found in the keypoints. LBPs provide a robust and computationally simple way for describing pure local binary patterns in a texture. They provide information regarding the prevalence of different edge patterns and uniformity. Textons are defined under the operational definition of clustered filter responses and provide a statistical and structural unifying approach for texture characterization. The breast density classification accuracy of the SVM classifiers modeled on the histograms of the three different sets of texture features separately and their combination is evaluated on the Medical Image Analysis Society (MIAS) mammographic database and the results are presented. The combination of the statistical distributions of all the different texture features allows for the highest classification accuracy, reaching over 93%. © 2012 IEEE. 732 737 Sponsors: IEEE IEEE Computer Society University of Cyprus Biological and AI Foundation (BAIF) Frederick University Conference code: 95206 Cited By :9
- Subjects :
- Filter response
Classification accuracy
Statistical distribution
Bioinformatics
Local binary patterns
Fibroglandular tissue
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-invariant feature transform
Descriptors
Local Binary Patterns
Scale Invariant Feature Transforms
Edge detection
Image texture
Medical image analysis
Histogram
Breast Cancer
Sift descriptor
Texture characterizations
Computer vision
breast density
Texture features
Edge patterns
Mammographic
Mathematics
Image noise
Image segmentation
Support vector machines
Classification (of information)
Texture descriptors
business.industry
Texton
Textures
Pattern recognition
Keypoints
SVM classifiers
Local feature
Support vector machine
textons
Operational definition
ComputingMethodologies_PATTERNRECOGNITION
Graphic methods
Feature (computer vision)
Artificial intelligence
business
texture
Mammography
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)
- Accession number :
- edsair.doi.dedup.....bbf92df460ddc36194daed39b6dd8942