@article {419, title = {A fast B-spline pseudo-inversion algorithm for consistent image registration}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {4673 LNCS}, year = {2007}, pages = {768-775}, abstract = {

Recently, the concept of consistent image registration has been introduced to refer to a set of algorithms that estimate both the direct and inverse deformation together, that is, they exchange the roles of the target and the scene images alternatively; it has been demonstrated that this technique improves the registration accuracy, and that the biological significance of the obtained deformations is also improved. When dealing with free form deformations, the inversion of the transformations obtained becomes computationally intensive. In this paper, we suggest the parametrization of such deformations by means of a cubic B-spline, and its approximated inversion using a highly efficient algorithm. The results show that the consistency constraint notably improves the registration accuracy, especially in cases of a heavy initial misregistration, with very little computational overload. {\^A}{\textcopyright} Springer-Verlag Berlin Heidelberg 2007.

}, keywords = {Approximation algorithms, Computational overload, Consistent registration, Constraint theory, Image registration, Inverse problems, Inverse transformation, Parameterization}, isbn = {9783540742715}, issn = {03029743}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-38149022572\&partnerID=40\&md5=627751cd7654872cbd9ee74a249752eb}, author = {Antonio Trist{\'a}n-Vega and J I Arribas} } @conference {413, title = {Fusing Output Information in Neural Networks: Ensemble Performs Better}, booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings}, year = {2003}, address = {Cancun}, abstract = {

A neural network ensemble is a learning paradigm where a finite number of component neural networks are trained for the same task. Previous research suggests that an ensemble as a whole is often more accurate than any of the single component networks. This paper focuses on the advantages of fusing different nature network architectures, and to determine the appropriate information fusion algorithm in component neural networks by several approaches within hard decision classifiers, when solving a binary pattern recognition problem. We numerically simulated and compared the different fusion approaches in terms of the mean-square error rate in testing data set, over synthetically generated binary Gaussian noisy data, and stated the advantages of fusing the hard outputs of different component networks to make a final hard decision classification. The results of the experiments indicate that neural network ensembles can indeed improve the overall accuracy for classification problems; in all fusion architectures tested, the ensemble correct classification rates are better than those achieved by the individual component networks. Finally we are nowadays comparing the above mentioned hard decision classifiers with new soft decision classifier architectures that make use of the additional continuous type intermediate network soft outputs, fulfilling probability fundamental laws (positive, and add to unity), which can be understood as the a posteriori probabilities of a given pattern to belong to a certain class.

}, keywords = {Algorithms, Backpropagation, Classification (of information), Computer simulation, Decision making, Estimation, Gaussian noise (electronic), Information fusions, Mathematical models, Medical imaging, Model selection, Multilayer neural networks, Neural network ensembles, Pattern recognition, Probability, Probability estimation, Problem solving, Regularization, Statistical methods, Statistical pattern recognition, Vectors}, doi = {https://doi.org/10.1109/IEMBS.2003.1280254}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-1542301061\&partnerID=40\&md5=32dbadb3b6ac3c6ae1ea33d89b52c75f}, author = {Y Wu and J I Arribas} } @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}, author = {Rodrigo de Luis-Garc{\'\i}a and Marcos Martin-Fernandez and J I Arribas and Carlos Alberola-Lopez} } @conference {414, title = {A fully automatic algorithm for contour detection of bones in hand radiographs using active contours}, booktitle = {IEEE International Conference on Image Processing}, year = {2003}, address = {Barcelona}, abstract = {

This paper1 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 excelent results.

}, keywords = {Active contours, Algorithms, Bone, Cocentric circumferences, Contour measurement, Medical imaging, Object recognition, Radiography}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0344271749\&partnerID=40\&md5=5fcf06edb482cc1527b2e8d3a940065b}, author = {Rodrigo de Luis-Garc{\'\i}a and Marcos Martin-Fernandez and J I Arribas and Carlos Alberola-Lopez} } @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} }