A statistical method for automatic labeling of tissues in medical images
Machine Vision and Applications
Non-rigid Registration of Breast MR Images Using Mutual Information
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Atlas-Based Auto-segmentation of Head and Neck CT Images
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Fully-automated fibroglandular tissue segmentation in breast MRI
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
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Organ localization is an important topic in medical imaging in aid of cancer treatment and diagnosis. An example are the pharmacokinetic model calibration methods based on a reference tissue, where a pectoral muscle delineation in breast MRI is needed to detect malignancy signs. Atlas-based segmentation has been proven to be powerful in brain MRI. This is the first attempt to apply an atlas-based approach to segment breast in T1 weighted MR images. The atlas consists of 5 structures (fatty and dense tissues, heart, lungs and pectoral muscle). It has been used in a Bayesian segmentation framework to delineate the mentioned structures. Global and local registration have been compared, where global registration showed the best results in terms of accuracy and speed. Overall, a Dice Similarity Coefficient value of 0.8 has been obtained which shows the validity of our approach to Breast MRI segmentation.