his is a Matlab implementation of different tools for processing digital mammography images developed by Universidad Industrial de Santander. OpenBreast was publicly released in  and has been clinically evaluated for the task of breast cancer risk assessment in . The following tasks have been implemented:
* Feature extraction for parenchymal analysis 
* Image standardization for (RAW and PROCESSED) digital mammography images
* Breast segmentation and chest wall detection 
* Detection of regions on interest within the breast [4,5]
* Breast density segmentation 
To get started first run setup.m to configure Openbreast. Then run the following demos:
* demo01 Breast segmentation
* demo02 ST mapping
* demo03 ROI detection
* demo04 Feature extraction on FFDM images
* demo05 Breast density segmentation
For further details, please refer to: https://sites.google.com/view/cvia/openbreast
 S. Pertuz et al., Open Framework for Mammography-based Breast Cancer Risk Assessment, IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019.
 S. Pertuz et al., Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample,
European Journal of Radiology: 121, 2019.
 B. Keller et al., Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation, Med. Phys, 2012.
 S. Pertuz, C. Julia, D. Puig, A novel mammography image representation framework with application to image registration, Proc. International Conference on Pattern Recognition, 2014.
 G. Torres, S. Pertuz, Automatic Detection of the Retroareolar Region in Mammograms, Proc. Latin American Congress on Biomedical Engineering, 2016
 G. F. Torres et al., "Morphological Area Gradient: System-independent Dense Tissue Segmentation in Mammography Images," Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019.
Said Pertuz (2020). OpenBreast (https://www.github.com/spertuz/openbreast), GitHub. Retrieved .
S. Pertuz, G. F. Torres, R. Tamimi, J. Kamarainen, Open Framework for Mammography-based Breast Cancer Risk Assessment, IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019
Hi asmaa_BD, this tool is has not been tested in digitized images -like those in the miniMIAS dataset. However, could you send the image and detailed instructions on how to reproduce your error?
hello sir, ive tried you method of features extraction on the mini_MIAS databse and in normal image i get this error
In an assignment A(:) = B, the number of elements in A and B must be the same.
Error in features_GLHA (line 27)
f(n) = min(x);
Error in xfeatures (line 86)
f1 = features_GLHA(im, flist1, mask);
Error in mytestonthismethod (line 68)
x = xfeatures(im1, features, J);
can you help please?
- Included breast density segmentation