@conference {785,
	title = {Robust Windowed Harmonic Phase Analysis with a Single Acquisition},
	booktitle = {MICCAI },
	year = {2018},
	month = {2018},
	publisher = {MICCAI},
	organization = {MICCAI},
	address = {Granada},
	abstract = {<p>The HARP methodology is a widely extended procedure for cardiac tagged magnetic resonance imaging since it is able to analyse local mechanical behaviour of the heart; extensions and improvements of this method have also been reported since HARP was released. Acquisition of an over-determined set of orientations is one of such alternatives,<br />
which has notably increased HARP robustness at the price of increasing examination time. In this paper, we explore an alternative to this method based on the use of multiple peaks, as opposed to multiple orientations, intended for a single acquisition. Performance loss is explored with respect to multiple orientations in a real setting. In addition, we have assessed, by means of a computational phantom, optimal tag orientations and spacings of the stripe pattern by minimizing the Frobenius norm of the difference between the ground truth and the estimated material deformation gradient tensor. Results indicate that, for a single acquisition, multiple peaks as opposed to multiple orientations, are indeed preferable.</p>
},
	keywords = {Cardiac Tagged Magnetic Resonance Imaging, HARmonic Phase, Multi-Harmonic Analysis, Robust Strain Reconstruction},
	author = {Santiago Sanz-Est{\'e}banez and Lucilio Cordero-Grande and Marcos Martin-Fern{\'a}ndez and Carlos Alberola L{\'o}pez}
}
@proceedings {casaseca2006comparative,
	title = {A comparative study on microcalcification detection methods with posterior probability estimation based on Gaussian mixture models},
	year = {2005},
	pages = {49{\textendash}54},
	publisher = {IEEE},
	abstract = {Automatic detection of microcalcifications in mammograms constitutes a helpful tool in breast cancer diagnosis. Radiologist{\textquoteright}s confidence level on microcalcification detection would be improved if a probability estimate of its presence could be obtained from computer-aided diagnosis. In this paper we explore detection performance of a simple Bayesian classifier based on Gaussian mixture probability density functions (pdf). Posterior probability of microcalcification presence may be estimated from the probabilistic model. Two model selection algorithms have been tested, one based on the minimum message length criterion and the other on discriminative criteria obtained from the classifier performance. In addition, we propose a complementing model selection algorithm in order to improve the initial system performance obtained with these methods. Simulation results show that our model gets a good compromise between classification performance and probability estimation accuracy},
	doi = {https://doi.org/10.1109/IEMBS.2005.1616339},
	url = {https://ieeexplore.ieee.org/abstract/document/1616339},
	author = {Pablo Casaseca-de-la-Higuera and J I Arribas and Emma Mu{\~n}oz-Moreno and Carlos Alberola L{\'o}pez}
}
@conference {arribas2003neural,
	title = {Neural posterior probabilities for microcalcification detection in breast cancer diagnoses},
	booktitle = {Neural Engineering, 2003. Conference Proceedings. First International IEEE EMBS Conference on},
	year = {2003},
	pages = {660{\textendash}663},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We apply the a Posteriori Probability Model Selection (PPMS) algorithm with the help of Generalized Softmax Perceptron (GSP) neural architecture in order to obtain estimates of the posterior class probabilities at its outputs, in the binary problem of microcalcification detection in a hospital digitalized mammogram database. We first detect windowed images with high probability to belong to the class microcalcification is present, then we locally segment the shape of the calcifications, and finally show the segmented microcalcifications to the radiologist. The segmented images together with the posterior probabilities for each window image can be employed as a valuable information to help predicting a breast diagnosis, in order to distinguish between benignant calcium deposit and malignant accumulation, that is, breast carcinoma.},
	doi = {https://doi.org/10.1109/CNE.2003.1196915},
	url = {https://ieeexplore.ieee.org/abstract/document/1196915},
	author = {J I Arribas and Carlos Alberola L{\'o}pez and Mateos-Marcos, A and Jes{\'u}s Cid-Sueiro}
}
@conference {luis2003fully,
	title = {A fully automatic algorithm for contour detection of bones in hand radiographs using active contours},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {3},
	year = {2003},
	pages = {III{\textendash}421},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents an algorithm for automatically detecting bone contours from hand radiographs using active contours. Prior knowledge is first used to locate initial contours for the snakes inside each bone of interest. Next, an adaptive snake algorithm is applied so that parameters are properly adjusted for each bone specifically. We introduce a novel truncation technique to prevent the external forces of the snake from pulling the contour outside the bones boundaries, yielding excellent results.},
	doi = {https://doi.org/10.1109/ICIP.2003.1247271},
	url = {https://ieeexplore.ieee.org/abstract/document/1247271},
	author = {Rodrigo de Luis-Garc{\'\i}a and Marcos Martin-Fernandez and J I Arribas and Carlos Alberola L{\'o}pez}
}
