TY - GEN
T1 - Comparison of four breast tissue segmentation algorithms for multi-modal MRI to X-ray mammography registration
AU - Garcìa, E.
AU - Oliver, A.
AU - Diez, Y.
AU - Diaz, O.
AU - Gubern-Mèrida, A.
AU - Lladò, X.
AU - Martì, J.
N1 - Funding Information:
This research has been partially supported from the Ministry of Economy and Competitiveness of Spain, under project references TIN2012-37171-C02-01 and DPI2015-68442-R, and the FPI grant BES-2013-065314.
Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Breast MRI to X-ray mammography registration using patient-specific biomechanical models is one challenging task in medical imaging. To solve this problem, the accurate knowledge about internal and external factors of the breast, such as internal tissues distribution, is needed for modelling a suitable physical behavior. In this work, we compare four different tissue segmentation algorithms, two intensity-based segmentation algorithms (Fuzzy C-means and Gaussian mixture model) and two improvements that incorporate spatial information (Kernelized Fuzzy C-means and Markov Random Fields, respectively), and analyze their effect to the multi-modal registration. The overall framework consists on using a density estimation software (Volpara™) to extract the glandular tissue from full-field digital mammograms, meanwhile, a biomechanical model is used to mimic the mammographic acquisition from the MRI, computing the glandular tissue traversed by the X-ray beam. Results with 40 patients show a high agreement between the amount of glandular tissue computed for each method.
AB - Breast MRI to X-ray mammography registration using patient-specific biomechanical models is one challenging task in medical imaging. To solve this problem, the accurate knowledge about internal and external factors of the breast, such as internal tissues distribution, is needed for modelling a suitable physical behavior. In this work, we compare four different tissue segmentation algorithms, two intensity-based segmentation algorithms (Fuzzy C-means and Gaussian mixture model) and two improvements that incorporate spatial information (Kernelized Fuzzy C-means and Markov Random Fields, respectively), and analyze their effect to the multi-modal registration. The overall framework consists on using a density estimation software (Volpara™) to extract the glandular tissue from full-field digital mammograms, meanwhile, a biomechanical model is used to mimic the mammographic acquisition from the MRI, computing the glandular tissue traversed by the X-ray beam. Results with 40 patients show a high agreement between the amount of glandular tissue computed for each method.
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U2 - 10.1007/978-3-319-41546-8_62
DO - 10.1007/978-3-319-41546-8_62
M3 - Conference contribution
AN - SCOPUS:84977503433
SN - 9783319415451
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 493
EP - 500
BT - Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings
A2 - Lang, Kristina
A2 - Tingberg, Anders
A2 - Timberg, Pontus
PB - Springer Verlag
T2 - 13th International Workshop on Breast Imaging, IWDM 2016
Y2 - 19 June 2016 through 22 June 2016
ER -