Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian segmentation and motion estimation in video sequences using a Markov-Potts model
Math'04 Proceedings of the 5th WSEAS International Conference on Applied Mathematics
Texture-Based Simultaneous Registration and Segmentation of Breast DCE-MRI
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Texture Based Segmentation of Breast DCE-MRI
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
A review of breast tissue classification in mammograms
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Pattern Recognition Letters
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
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Several studies have showed that increased mammographic density is an important risk factor for breast cancer. Dense tissue often appears as textured regions in mammograms, so density and texture estimation are inextricably linked. It has been demonstrated that texture classes can be learned, and that subsequently textures can be classified using the joint distribution of intensity values over extremely compact neighbourhoods. Motivated by the success of texture classification, we propose an fully automated scheme for mammogram texture classification and segmentation. The classification method first has a training step to model the joint distribution for each breast density class. Subsequently, a statistical comparison is used to determine the class label for new images. Inspired by the classification, we combine the so-called image patch method with a HMRF(Hidden Markov Random Field) to achieve mammogram segmentation.