@proceedings {988, title = {Comparison of data-driven and physics-informed learning approaches for optimising multi-contrast MRI acquisition protocols}, volume = {3701}, year = {2023}, month = {2023}, abstract = {

Multi-contrast MRI is used to assess the biological properties of tissues, but excessively long times are required to acquire high-quality datasets. To reduce acquisition time, physics-informed Machine Learning approaches were developed to select the optimal subset of measurements, decreasing the number of volumes by approximately 63\%, and predict the MRI signal and quantitative maps. These selection methods were compared to a full data-driven and two manual strategies. Synthetic and real 5D-Diffusion-T1-T2* data from five healthy participants were used. Feature selection via a combination of Machine Learning and physics modelling provides accurate estimation of quantitative parameters and prediction of MRI signal.

}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Descoteaux, Maxime and Aja-Fern{\'a}ndez, Santiago and Hutter, Jana and Jones, Derek K and Tax, Chantal M W} } @conference {975, title = {Comparing signal models for correcting diffusion-weighted MR images for free water partial volume effects}, booktitle = {ISMRM Workshop on Diffusion MRI: From Research to Clinic}, year = {2022}, address = {Amsterdam, The Netherlands}, author = {Guadilla, Irene and Fouto, Ana R. and {\'A}lvaro Planchuelo-G{\'o}mez and Trist{\'a}n-Vega, Antonio and Ruiz-Tagle, Amparo and Esteves, In{\^e}s and Caetano, Gina and Aja-Fern{\'a}ndez, Santiago and Figueiredo, Patr{\'\i}cia and Nunes, Rita G.} } @conference {941, title = {Clinical course of migraine during the COVID-19 Lockdown}, booktitle = {International Headache Congress 2021}, year = {2021}, month = {2021}, publisher = {International Headache Society \& European Headache Federation}, organization = {International Headache Society \& European Headache Federation}, address = {Virtual Congress}, abstract = {

Objective: Previous studies have demonstrated that migraine can worsen due to stress, changes in lifestyle habits or infections. We hypothesize that changes during coronavirus disease 2019 (COVID-19) lockdown might have worsened the clinical course of migraine.
Methods: Retrospective survey study collecting demographic data, clinical variables related to headache (frequency), migraine (subjective worsening, frequency, and intensity), lockdown, and symptoms of post-traumatic stress from migraine patients followed-up at three Headache Units between June-July 2020.
Results: 222 subjects were included. Among them, 201/222 (90.5\%) were women, aged 42.5 +- 12.0 (mean +- SD). Subjective improvement of migraine was reported in 31/222 participants (14.0\%), while worsening in 105/222 (47.3\%) and was associated with changes in migraine triggers such as stress related to going outdoors and intake of specific foods/drinks. Intensity of attacks increased in 67/222 patients (30.2\%), and it was associated with the subjective worsening, female sex, recent insomnia, and use of acute medication during a headache. An increase in monthly days with any headache was observed in 105/222 patients (47.3\%) and was related to symptoms of post-traumatic stress, older age and living with five or more people.

Conclusion: Approximately half the migraine patients reported worsening of their usual pain during the lockdown; worsening was related to changes in triggers and the emotional impact of the lockdown.

}, url = {https://journals.sagepub.com/doi/pdf/10.1177/03331024211034005}, author = {Gonzalez-Martinez, Alicia and {\'A}lvaro Planchuelo-G{\'o}mez and {\'A}ngel L. Guerrero and Garc{\'\i}a-Azor{\'\i}n, David and Santos-Lasaosa, Sonia and P{\'e}rez-Navarro, Mar{\'\i}a P and Odriozola-Gonz{\'a}lez, Paula and Irurtia, Mar{\'\i}a Jes{\'u}s and Quintas, Sonia and Rodrigo de Luis-Garc{\'\i}a and Ana B Gago-Veiga} } @article {925, title = {A Clinically Viable Vendor-Independent and Device-Agnostic Solution for Accelerated Cardiac MRI Reconstruction}, journal = {Computer Methods and Programs in Biomedicine}, volume = {207}, year = {2021}, chapter = {106143}, issn = {0169-2607}, doi = {https://doi.org/10.1016/j.cmpb.2021.106143}, url = {https://www.sciencedirect.com/science/article/pii/S0169260721002182}, author = {Mart{\'\i}n-Gonz{\'a}lez, Elena and Elisa Moya-S{\'a}ez and Mench{\'o}n-Lara, Rosa-Mar{\'\i}a and J Royuela-del-Val and Palencia-de-Lara, C{\'e}sar and M. Rodr{\'\i}guez-Cayetano and Simmross-Wattenberg, Federico and Carlos Alberola-Lopez} } @article {864, title = {A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties}, journal = {Foods}, volume = {9}, year = {2020}, pages = {113}, abstract = {Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert{\textquoteright}s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90{\textdegree}, standard deviation of GLCM matrix at 0{\textdegree}, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 {\textpm} 0.75\% over the test set, after averaging 1000 random iterations.}, doi = {https://doi.org/10.3390/foods9020113}, url = {https://www.mdpi.com/2304-8158/9/2/113}, author = {Pourdarbani, Razieh and Sabzi, Sajad and Kalantari, Davood and Hern{\'a}ndez-Hern{\'a}ndez, Jos{\'e} Luis and J I Arribas} } @article {865, title = {A Computer Vision System for the Automatic Classification of Five Varieties of Tree Leaf Images}, journal = {Computers}, volume = {9}, year = {2020}, pages = {6}, abstract = {A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1{\textendash}Cydonia oblonga (quince), 2{\textendash}Eucalyptus camaldulensis dehn (river red gum), 3{\textendash}Malus pumila (apple), 4{\textendash}Pistacia atlantica (mt. Atlas mastic tree) and 5{\textendash}Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network{\textendash}ant bee colony (ANN{\textendash}ABC), hybrid artificial neural network{\textendash}biogeography based optimization (ANN{\textendash}BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04\%, 89.23\%, and 93.99\%, for hybrid ANN{\textendash}ABC; hybrid ANN{\textendash}BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1{\textendash}Cydonia oblonga (quince) 0.991 (ANN{\textendash}ABC), 95.89\% (ANN{\textendash}ABC), 95.91\% (ANN{\textendash}ABC); 2{\textendash}Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100\% (LDA), 100\% (LDA); 3{\textendash}Malus pumila (apple) 0.996 (LDA), 96.63\% (LDA), 94.99\% (LDA); 4{\textendash}Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71\% (LDA), 82.57\% (LDA); and 5{\textendash}Prunus armeniaca (apricot) 0.994 (LDA), 88.67\% (LDA), 94.65\% (LDA), respectively.}, doi = {https://doi.org/10.3390/computers9010006}, url = {https://www.mdpi.com/2073-431X/9/1/6}, author = {Sabzi, Sajad and Pourdarbani, Razieh and J I Arribas} } @article {816, title = {Computation of exact g-factor maps in 3D GRAPPA reconstructions}, journal = {Magnetic resonance in medicine}, volume = {81}, year = {2019}, pages = {1353{\textendash}1367}, author = {I{\~n}aki Rabanillo-Viloria and Zhu, Ante and Santiago Aja-Fern{\'a}ndez and Alberola-L{\'o}pez, Carlos and Hernando, Diego} } @article {772, title = {Characterisation of Received Signal Strength Perturbations using Allan Variance}, journal = {IEEE Transactions on Aerospace and Electronic Systems}, volume = {54}, year = {2018}, pages = {873-889}, chapter = {873}, author = {Luo, Chunbo and Pablo Casaseca-de-la-Higuera and McClean, Sally and Parr, Gerard and Ren, Peng} } @conference {800, title = {Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps}, booktitle = {Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018}, year = {2018}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Pablo Casaseca-de-la-Higuera and Antonio Trist{\'a}n-Vega and Hoyos-Barcel{\'o}, Carlos and S. Merino-Caviedes and Wang, Qi and Luo, Chunbo and Wang, Xinheng and Wang, Zhi} } @conference {791, title = {On the Construction of Non Linear Adjoint Operators: Application to L1-Penalty Dynamic Image Reconstruction}, booktitle = {Congreso Anual de Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, year = {2018}, month = {11/2018}, address = {Ciudad Real, Espa{\~n}a}, abstract = {

The purpose of this work is to develop a methodology for the adjoint operators application in non linear optimization problems. The use of adjoint operators is very popular for numerical control theory; one of its main applications is devised for image reconstruction. Most of these reconstruction techniques are limited to linear L1-constraints whose adjoints are well-defined. We aim to extend these image reconstruction techniques allowing the terms involved to be non linear. For these purpose, we have generalized the concept of adjoint operator under the basis of Taylor{\textquoteright}s formula, using Gateaux derivatives in order to construct a linearised adjoint operator associated to the non linear operator. The proposed approach has been validated in a Magnetic Resonance Imaging (MRI) reconstruction framework with Cartesian subsampled k-space data using Compressed Sensing based techniques and a groupwise registration algorithm for motion compensation.
The proposed algorithm has shown to be able to effectively deal with the presence of both physiological motion and subsampling artefacts, increasing accuracy and robustness of the reconstruction as compared with its linear counterpart.

}, author = {Santiago Sanz-Est{\'e}banez and Elisa Moya-S{\'a}ez and J Royuela-del-Val and Carlos Alberola-L{\'o}pez} } @conference {663, title = {Cardio-respiratory motion estimation for compressed sensing reconstruction of free-breathing 2D cine MRI}, booktitle = {Modulated/Incomplete Data 2016, SFB Workshop.}, year = {2016}, month = {2016}, publisher = {Mathematical Optimization and Applications in Biomedical Sciences (MOBIS), SFB Research Center}, organization = {Mathematical Optimization and Applications in Biomedical Sciences (MOBIS), SFB Research Center}, address = {Graz, Austria}, abstract = {

Respiratory motion is still an issue in MRI of the heart despite the introduction of Compressed Sensing (CS) techniques, which significantly accelerate acquisition [1]. Recently [2], a double-binning scheme was introduced in which k-space data is split according both to the cardiac and respiratory phases (Fig. 1); at reconstruction, sparsity along both dimensions is exploited. Other methods introduce motion estimation and compensation in CS (MC-CS) either to correct the respiratory motion [3] or to promote sparsity for reconstruction improvement [4]. In this work, we propose a technique to jointly estimate the respiratory and cardiac motions within a double-binning scheme, enabling the MC-CS reconstruction of respiratory resolved free-breathing 2D CINE MRI. Preliminary results on synthetic, highly undersampled (x16) Cartesian setup are shown.

[1] Lustig et al. MRM 2007, [2] Feng et al. MRM 2015, [3] Usman et al. MRM 2013. [4]\ Royuela-del-Val et al. MRM 2015.

}, author = {J Royuela-del-Val and Marcos Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Carlos Alberola-Lopez} } @article {7460246, title = {A computer-aided diagnosis system with EEG based on the P3b wave during an auditory odd-ball task in schizophrenia}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {PP}, number = {99}, year = {2016}, pages = {1-1}, abstract = {Objective: To design a Computer-aided diagnosis (CAD) system using an optimized methodology over the P3b wave in order to objectively and accurately discriminate between healthy controls (HC) and schizophrenic subjects (SZ). Methods: We train, test, analyze, and compare various machine learning classification approaches optimized in terms of the correct classification rate (CCR), the degenerated Youden{\textquoteright}s index (DYI) and the area under the receiver operating curve (AUC). CAD system comprises five stages: electroencephalography (EEG) preprocessing, feature extraction, seven electrode groupings, discriminant feature selection, and binary classification. Results: With two optimal combinations of electrode grouping, filtering, feature selection algorithm, and classification machine, we get either a mean CCR = 93.42\%, specificity = 0.9673, sensitivity = 0.8727, DYI = 0.9188, and AUC = 0.9567 (total-15 Hz-J5-MLP), or a mean CCR = 92.23\%, specificity = 0.9499, sensitivity = 0.8838, DYI = 0.9162, and AUC = 0.9807 (right hemisphere-35 Hz-J5-SVM), which to our knowledge are higher than those available to date. Conclusions: We have verified that a more restrictive low-pass filtering achieves higher CCR as compared to others at higher frequencies in the P3b wave. In addition, results validate previous hypothesis about the importance of the parietal-temporal region, associated with memory processing, allowing us to identify powerful {feature,electrode} pairs in the diagnosis of schizophrenia, achieving higher CCR and AUC in classification of both right and left Hemispheres, and parietal-temporal EEG signals, like, for instance, the {PSE, P4} pair (J5 and mutual information feature selection). Significance: Diagnosis of schizophrenia is made thoroughly by psychiatrists but as any human-based decision that has a subjective component. This CAD system provides the human expert with an objective complimentary measure to help him in diagnosing schizophrenia.}, keywords = {Computer aided diagnosis, Design automation, Electrodes, Electroencephalography, Feature extraction, Indexes, Sensitivity}, issn = {0018-9294}, doi = {10.1109/TBME.2016.2558824}, url = {https://ieeexplore.ieee.org/abstract/document/7460246}, author = {L. Santos-Mayo and Luis Miguel San-Jose-Revuelta and J I Arribas} } @conference {598, title = {Cardiac Strain Assessment for Fibrotic Myocardial Tissue Detection in Left Ventricular Hypertrophic Cardiomyopathy}, booktitle = {XXXIII Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica.}, year = {2015}, month = {2015}, address = {Madrid, Spain.}, abstract = {

This work proposes an image processing methodology to\ distinguish fibrotic from normal tissue by the assessment of the\ local mechanical properties of the myocardium in magnetic\ resonance tagging images. The procedure uses the information\ provided by short axis images of the above mentioned modality\ to estimate the Green-Lagrange strain tensor; a modified\ method based on the Harmonic Phase is employed for motion\ estimation. The method has been applied to the analysis of the\ local deformation patterns in a set of patients affected by\ hypertrophic cardiomyopathy in order to find the agreement\ between hyperenhanced zones in late enhancement images and\ areas in the myocardium with abnormal tensor values (both the\ radial and the circumferential components as well as the\ shearing component have been accounted for). The agreement is\ measured taken as ground truth manual segmentation of late\ enhancement images carried out by two cardiologists. Finally, a\ set of example images illustrate the agreement between both\ techniques.

}, author = {Santiago Sanz-Est{\'e}banez and S. Merino-Caviedes and T. Sevilla-Ruiz and A. Revilla-Orodea and Mart{\'\i}n-Fern{\'a}ndez, M and Carlos Alberola-Lopez} } @conference {443, title = {Classification of delayed enhancement scar islands by means of their local subendocardial transmurality}, booktitle = {Computing in Cardiology Conference (CinC), 2013}, year = {2013}, publisher = {IEEE}, organization = {IEEE}, author = {S. Merino-Caviedes and Lucilio Cordero-Grande and T. Sevilla-Ruiz and P{\'e}rez, Teresa and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {436, title = {Comparativa entre distribuciones alpha-estables para modelar tasas de transferencia obtenidas a partir de registros de SNMP y NetFlow}, booktitle = {Jornadas de Ingenier{\'\i}a Telem{\'a}tica (JITEL)}, year = {2013}, publisher = {Asociaci{\'o}n de Telem{\'a}tica (ATEL)}, organization = {Asociaci{\'o}n de Telem{\'a}tica (ATEL)}, address = {Granada, Spain}, author = {Stoppa, Matteo and L{\'o}pez-de-Vergara-M{\'e}ndez, Jorge E. and Federico Simmross-Wattenberg and Garc{\'\i}a-Dorado, Jos{\'e} Luis} } @conference {435, title = {Caracterizaci{\'o}n de speckle con modelos de cola pesada}, booktitle = {Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, year = {2012}, publisher = {Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, organization = {Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, address = {San Sebasti{\'a}n, Espa{\~n}a}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Federico Simmross-Wattenberg and Marcos Martin-Fernandez and Palencia-de-Lara, C{\'e}sar and Carlos Alberola-Lopez} } @conference {martin2012cardiovascular, title = {Cardiovascular signal reconstruction based on shape modelling and non-stationary temporal modelling}, booktitle = {Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European}, year = {2012}, pages = {1826{\textendash}1830}, publisher = {IEEE}, organization = {IEEE}, author = {Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @inbook {de2012choice, title = {On the choice of a tensor distance for DTI white matter segmentation}, booktitle = {New Developments in the Visualization and Processing of Tensor Fields}, year = {2012}, pages = {283{\textendash}306}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Rodrigo de Luis-Garc{\'\i}a and Carlos Alberola-Lopez and Carl-Fredik Westin} } @article {Trist{\'a}nVega2011586, title = {Comments on: A locally constrained radial basis function for registration and warping of images}, journal = {Pattern Recognition Letters}, volume = {32}, number = {4}, year = {2011}, pages = {586 - 589}, keywords = {Interpolation kernels}, issn = {0167-8655}, doi = {http://dx.doi.org/10.1016/j.patrec.2010.11.012}, url = {http://www.sciencedirect.com/science/article/pii/S0167865510003788}, author = {Antonio Trist{\'a}n-Vega and Ver{\'o}nica Garc{\'\i}a-P{\'e}rez} } @conference {405, title = {Cuantificaci{\'o}n de la insuficiencia mitral funcional mediante el esfuerzo y la velocidad del miocardio}, booktitle = {XXIX Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2011}, address = {Centro de Cirug{\'\i}a de M{\'\i}nima Invasi{\'o}n Jes{\'u}s Us{\'o}n}, author = {Ariel H. Curiale and S{\'a}nchez-Ferrero, G Vegas and Teresa P{\'e}rez-Sanz and Santiago Aja-Fern{\'a}ndez} } @proceedings {515, title = {Characterization of activity epochs in actimetric registries for infantile colic diagnosis: Identification and feature extraction based on wavelets and symbolic dynamics}, volume = {32}, year = {2010}, pages = {2383-2386}, publisher = {IEEE}, author = {Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Gonzalo Vegas-S{\'a}nchez-Ferrero and Lucilio Cordero-Grande and Jesus Maria Andres-de-Llano and Jose Ramon Garmendia-Leiza and Julio Ardura-Fernández} } @inbook {cardenes2009characterization, title = {Characterization of anatomic fiber bundles for diffusion tensor image analysis}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {903{\textendash}910}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Rub{\'e}n C{\'a}rdenes-Almeida and Daniel Argibay-Qui{\~n}ones and Emma Mu{\~n}oz-Moreno and Marcos Martin-Fernandez} } @article {munoz2009characterization, title = {Characterization of the similarity between diffusion tensors for image registration}, journal = {Computers in biology and medicine}, volume = {39}, number = {3}, year = {2009}, pages = {251{\textendash}265}, publisher = {Pergamon}, author = {Emma Mu{\~n}oz-Moreno and Marcos Martin-Fernandez} } @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} } @article {lopez2004cardiovascular, title = {Cardiovascular risk factors in the circadian rhythm of acute myocardial infarction}, journal = {Revista Espa{\~n}ola de Cardiolog{\'\i}a (English Edition)}, volume = {57}, number = {9}, year = {2004}, pages = {850{\textendash}858}, publisher = {Elsevier}, author = {L{\'o}pez Messa, Juan B and JR Garmendia-Leiza and Aguilar Garc{\'\i}a, Mar{\'\i}a D and Jes{\'u}s Mar{\'\i}a And De Llano and Alberola L{\'o}pez, Carlos and Fern{\'a}ndez, Julio Ardura} } @article {alberola2004comments, title = {Comments on: A methodology for evaluation of boundary detection algorithms on medical images}, journal = {Medical Imaging, IEEE Transactions on}, volume = {23}, number = {5}, year = {2004}, pages = {658{\textendash}660}, publisher = {IEEE}, author = {Carlos Alberola-Lopez and Marcos Martin-Fernandez and Juan Ruiz-Alzola} } @article {aja2004computational, title = {A computational TW3 classifier for skeletal maturity assessment. A Computing with Words approach}, journal = {Journal of Biomedical Informatics}, volume = {37}, number = {2}, year = {2004}, pages = {99{\textendash}107}, publisher = {Academic Press}, author = {Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a and Miguel Angel Martin-Fernandez and Carlos Alberola-Lopez} } @conference {martin2000coding, title = {Coding technique with progressive reconstruction based on VQ and entropy coding applied to medical images}, booktitle = {International Symposium on Optical Science and Technology}, year = {2000}, pages = {171{\textendash}181}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Marcos Martin-Fernandez and Carlos Alberola-Lopez and Guerrero-Rodriguez, David and Juan Ruiz-Alzola} } @article {409, title = {Cost functions to estimate a posteriori probabilities in multiclass problems}, journal = {IEEE Transactions on Neural Networks}, volume = {10}, year = {1999}, pages = {645-656}, abstract = {

The problem of designing cost functions to estimate a posteriori probabilities in multiclass problems is addressed in this paper. We establish necessary and sufficient conditions that these costs must satisfy in one-class one-output networks whose outputs are consistent with probability laws. We focus our attention on a particular subset of the corresponding cost functions; those which verify two usually interesting properties: symmetry and separability (well-known cost functions, such as the quadratic cost or the cross entropy are particular cases in this subset). Finally, we present a universal stochastic gradient learning rule for single-layer networks, in the sense of minimizing a general version of these cost functions for a wide family of nonlinear activation functions.

}, keywords = {Cost functions, Estimation, Functions, Learning algorithms, Multiclass problems, Neural networks, Pattern recognition, Probability, Problem solving, Random processes, Stochastic gradient learning rule}, issn = {10459227}, doi = {10.1109/72.761724}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0032643080\&partnerID=40\&md5=d528195bd6ec84531e59ddd2ececcd46}, author = {Jes{\'u}s Cid-Sueiro and J I Arribas and S Urban-Munoz and A R Figueiras-Vidal} } @conference {450, title = {A Comparison of CFAR Strategies for Blob Detection in Textured Images}, booktitle = {European Signal Processing Conference (EUSIPCO), 1996}, year = {1996}, publisher = {Elsevier}, organization = {Elsevier}, author = {Carlos Alberola-Lopez and Casar-Corredera, Jos{\'e} Ramon and Juan Ruiz-Alzola} }