Texture description and segmentation through fractal geometry
Computer Vision, Graphics, and Image Processing
The Strength of Weak Learnability
Machine Learning
Classification of breast tissue by texture analysis
Image and Vision Computing - Special issue: BMVC 1991
Markov random fields for texture classification
Pattern Recognition Letters
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Significance of classification scores subsequent to feature selection
Pattern Recognition Letters
Breast density segmentation using texture
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Texture based mammogram classification and segmentation
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Texture classification using spectral histograms
IEEE Transactions on Image Processing
Mammographic segmentation and risk classification using a novel binary model based bayes classifier
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Intensity independent texture analysis in screening mammograms
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Constructing and applying higher order textons: Estimating breast cancer risk
Pattern Recognition
Texture and region dependent breast cancer risk assessment from screening mammograms
Pattern Recognition Letters
| Hi-index | 0.10 |
Image intensity and texture in screening mammograms are thought to be associated with the risk of breast cancer. Studies on developing automatic breast cancer risk assessment schemes tend to employ texture measures which are correlated to local background intensity. Accordingly, the contribution of texture alone to risk assessment is not known. Here background intensity independent texture measures are used to assess cancer risk. Moreover risk assessment based on background intensity independent texture outperforms intensity dependent texture suggesting that local image background intensity may confound risk assessment. Performance seems to depend on the view of the breast and so suggests that optimizing schemes for different views may improve risk assessment.