



@book {2217,
	title = {New results on minimum error entropy decision trees},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7042 LNCS},
	year = {2011},
	note = {Conference code: 87418Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-25085-9_42Language of Original Document: EnglishCorrespondence Address: De S{\'a}, J.P.M.; INEB-Instituto de Engenharia Biom{\'e}dica, FEUP, Universidade Do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal; email: jmsa@fe.up.ptReferences: Rokach, L., Maimon, O., Decision Trees (2005) Data Mining and Knowledge Discovery Handbook, , Maimon, O., Rokach, L. (eds.) Springer, Heidelberg;Marques De S{\'a}, J.P., Sebasti{\~a}o, R., Gama, J., Tree Classifiers Based on Minimum Error Entropy Decisions (2011) Can. J. Artif. Intell., Patt. Rec. and Mach. Learning, , in press; 
Silva, L., Felgueiras, C.S., Alexandre, L., Marques De S{\'a}, J., Error Entropy in Classification Problems: A Univariate Data Analysis (2006) Neural Computation, 18, pp. 2036-2061; 
Asuncion, A., Newman, D.J., (2010) UCI Machine Learning Repository, , http://www.ics.uci.edu/~mlearn/MLRepository.html; 
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Dem{\v s}ar, J., Statistical Comparisons of Classifiers over Multiple Data Sets (2006) J. of Machine Learning Research, 7, pp. 1-30; 
Garc{\'\i}a, S., Fern{\'a}ndez, A., Luengo, J., Herrera, F., Advanced Nonparametric Tests for Multiple Comparisons in the Design of Experiments in Computational Intelligence and Data Mining: Experimental Analysis of Power (2010) Information Sciences, 180, pp. 2044-2064; 
Hochberg, Y., Tamhane, A.C., (1987) Multiple Comparison Procedures, , John Wiley \& Sons, Inc; 
Salzberg, S.L., On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach (1997) Data Mining and Knowledge Discovery, 1, pp. 317-328; 
Jensen, D., Oates, T., Cohen, P.R., Building Simple Models: A Case Study with Decision Trees (1997) LECTURE NOTES IN COMPUTER SCIENCE, (1280), pp. 211-222. , Advances in Intelligent Data Analysis Reasoning about Data; 
Li, R.-H., Belford, G.G., Instability of Decision Tree Classification Algorithms (2002) Proc. 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 570-575
Sponsors: Universidad de La Frontera (UFRO); The International Association for Pattern Recognition (IAPR); Asociacon Chilena de Reconocimiento de Patrones (AChiRP); Asociacion Cubana de Reconocimiento de Patrones (ACPR); Mex. Assoc. Comput. Vis., Neural Comput. Rob. (MACVNR)},
	month = {2011///},
	pages = {355 - 362},
	address = {Pucon},
	abstract = {We present new results on the performance of Minimum Error Entropy (MEE) decision trees, which use a novel node split criterion. The results were obtained in a comparive study with popular alternative algorithms, on 42 real world datasets. Carefull validation and statistical methods were used. The evidence gathered from this body of results show that the error performance of MEE trees compares well with alternative algorithms. An important aspect to emphasize is that MEE trees generalize better on average without sacrifing error performance. {\textcopyright} 2011 Springer-Verlag.},
	keywords = {Algorithms, Alternative algorithms, Computer vision, Decision Theory, decision trees, entropy, entropy-of-error, Error entropy, Error performance, Forestry, New results, node split criteria, Real-world datasets, Vision},
	isbn = {03029743 (ISSN); 9783642250842 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-81855161442\&partnerID=40\&md5=0d71ee4459a1a6a01536cfec0ff05664},
	author = {De S{\'a}, J.P.M. and Sebasti{\~a}o, R. and Gama, J. and Fontes, T.}
}
@article {2242,
	title = {Segmentation of ultrasound images of the carotid using RANSAC and cubic splines},
	journal = {Computer Methods and Programs in BiomedicineComput. Methods Programs Biomed.},
	volume = {101},
	year = {2011},
	note = {Cited By (since 1996): 10Export Date: 21 November 2012Source: ScopusCODEN: CMPBEdoi: 10.1016/j.cmpb.2010.04.015PubMed ID: 20554343Language of Original Document: EnglishCorrespondence Address: Rocha, R.; INEB, Divis{\~a}o de Sinal e Imagem, Campus FEUP, 4200-465 Porto, Portugal; email: rhr@isep.ipp.ptReferences: Baldassarre, D., Amato, M., Bondioli, A., Sirtori, C.R., Tremoli, E., Carotid artery intima-media thickness measured by ultrasonography in normal clinical practice correlates well with atherosclerosis risk factors (2000) Stroke, 31, pp. 2426-2430;O{\textquoteright}Leary, D., Polak, J., Kronmal, R., Manolio, T., Burke, G.W.S., Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults (1999) N. Engl. J. Med., 340, pp. 14-22; 
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Chum, O., Matas, J., Kittler, J., (2003), 81, pp. 236-243. , Locally optimized RANSAC, in: Lecture Notes in Computer Science - Pattern Recognition, Springer Berlin/Heidelberg, Springer LNCSBland, J.M., Altman, D.G., Statistical methods for assessing agreement between two methods of clinical measurement (1986) Lancet, 1 (8476), pp. 307-310
},
	month = {2011///},
	pages = {94 - 106},
	abstract = {A new algorithm is proposed for the semi-automatic segmentation of the near-end and the far-end adventitia boundary of the common carotid artery in ultrasound images. It uses the random sample consensus method to estimate the most significant cubic splines fitting the edge map of a longitudinal section. The consensus of the geometric model (a spline) is evaluated through a new gain function, which integrates the responses to different discriminating features of the carotid boundary: the proximity of the geometric model to any edge or to valley shaped edges; the consistency between the orientation of the normal to the geometric model and the intensity gradient; and the distance to a rough estimate of the lumen boundary.A set of 50 longitudinal B-mode images of the common carotid and their manual segmentations performed by two medical experts were used to assess the performance of the method. The image set was taken from 25 different subjects, most of them having plaques of different classes (class II to class IV), sizes and shapes.The quantitative evaluation showed promising results, having detection errors similar to the ones observed in manual segmentations for 95\% of the far-end boundaries and 73\% of the near-end boundaries. {\textcopyright} 2011 Elsevier Ireland Ltd.},
	keywords = {Algorithms, article, B scan, B-mode images, Carotid, carotid artery, Carotid Artery Diseases, Carotid Artery, Common, Class II, clinical article, Common carotid artery, Cubic spline, Detection error, echography, Edge map, Female, Gain function, Geometric models, Geometry, human, Humans, Image Interpretation, Computer-Assisted, image processing, Image segmentation, Image sets, Intensity gradients, Longitudinal section, male, Manual segmentation, mathematical model, Medical experts, Medical imaging, Non-linear smoothing, Pattern Recognition, Automated, quantitative analysis, Quantitative evaluation, random sample, Random sample consensus, RANSAC, Semi-automatic segmentation, Splines, Ultrasonic applications, Ultrasonics, Ultrasonography, Ultrasound image, Ultrasound images},
	isbn = {01692607 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650679026\&partnerID=40\&md5=8bc951c3b232b4b7dd95eea0fc8671ef},
	author = {Rocha, R. and Campilho, A. and Silva, J. and Azevedo, E. and Santos, R.}
}
@article {2283,
	title = {Comparison of a computer system evaluation of intrapartum cardiotocographic events and a consensus of clinicians.},
	journal = {Journal of perinatal medicineJ Perinat Med},
	volume = {38},
	year = {2010},
	note = {Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1515/JPM.2010.030PubMed ID: 20121542Language of Original Document: EnglishCorrespondence Address: Costa, M.A.email: cosantonia@gmail.com},
	month = {2010///},
	pages = {191 - 195},
	abstract = {AIMS: To compare between computer analysis of intrapartum cardiotocography (CTG) features by the Omniview-SisPorto 3.5 and a consensus of clinicians. METHODS: Agreement study using 50 consecutively acquired tracings (206 h of signals) with >60 min duration, <10\% signal loss and recorded in labor at term by internal fetal heart rate (FHR) monitoring. Tracings were divided into 10-min segments and independently analyzed by three experienced clinicians, in order to estimate the FHR baseline and identify periodic events. A consensus was reached using a three round Delphi procedure. Results were compared with the analysis provided by the Omniview-SisPorto 3.5 system. RESULTS: For baseline estimation, agreement between the computer and the consensus was high [intraclass correlation coefficient (ICC)=0.85; 95\% confidence interval (CI) 0.46-0.93], with a mean difference of 3.7 bpm (limits of agreement -4.4-11.9 bpm), and 99\% of differences under 15 bpm. A concordant identification was observed in 71\% of accelerations (95\% CI: 69\%-73\%), 68\% of decelerations (95\% CI: 66\%-70\%), and 87\% of uterine contractions (95\% CI: 85\%-89\%). CONCLUSIONS: A high agreement was observed between the Omniview-SisPorto 3.5 and a consensus of clinicians in evaluation of intrapartum CTG baseline, accelerations, decelerations and uterine contractions.},
	keywords = {algorithm, Algorithms, article, Cardiotocography, comparative study, computer assisted diagnosis, Diagnosis, Computer-Assisted, Female, Fetal Heart, Fetal monitoring, fetus heart, fetus monitoring, human, Humans, instrumentation, methodology, physician, Physicians, physiology, Pregnancy},
	isbn = {16193997 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953038696\&partnerID=40\&md5=bca187de4ffea0b2f2c4d2dcfb2140d9},
	author = {Costa, M.A. and Ayres-de-Campos, D. and Machado, A.P. and Santos, C.C. and Bernardes, J.}
}
@article {2253,
	title = {The MEE principle in data classification: A perceptron-based analysis},
	journal = {Neural ComputationNeural Comp.},
	volume = {22},
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},
	month = {2010///},
	pages = {2698 - 2728},
	abstract = {This letter focuses on the issue of whether risk functionals derived from information-theoretic principles, such as Shannon or R{\'e}nyi{\textquoteright}s entropies, are able to cope with the data classification problem in both the sense of attaining the risk functional minimum and implying the minimum probability of error allowed by the family of functions implemented by the classifier, here denoted by min Pe. The analysis of this so-called minimization of error entropy (MEE) principle is carried out in a single perceptron with continuous activation functions, yielding continuous error distributions. In spite of the fact that the analysis is restricted to single perceptrons, it reveals a large spectrum of behaviors that MEE can be expected to exhibit in both theory and practice. In what concerns the theoretical MEE, our study clarifies the role of the parameters controlling the perceptron activation function (of the squashing type) in often reaching the minimum probability of error. Our study also clarifies the role of the kernel density estimator of the error density in achieving the minimum probability of error in practice. {\textcopyright} 2010 Massachusetts Institute of Technology.},
	keywords = {algorithm, Algorithms, artificial intelligence, artificial neural network, automated pattern recognition, Computer simulation, entropy, letter, Mathematical Concepts, mathematical phenomena, Models, Statistical, Neural Networks (Computer), Pattern Recognition, Automated, standard, statistical model},
	isbn = {08997667 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78149334887\&partnerID=40\&md5=97c7445cfa4693d406d979afc4b42319},
	author = {Silva, L.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
@conference {2292,
	title = {A new method for the detection of singular points in fingerprint images},
	booktitle = {2009 Workshop on Applications of Computer Vision, WACV 2009Workshop Appl. Comput. Vis., WACV},
	series = {2009 Workshop on Applications of Computer Vision, WACV 2009},
	year = {2009},
	note = {Conference code: 79821Export Date: 21 November 2012Source: ScopusArt. No.: 5403106doi: 10.1109/WACV.2009.5403106Language of Original Document: EnglishCorrespondence Address: Magalh{\~a}es, F.; INESC, Porto, Portugal; email: filipe.magalhaes@inescporto.ptReferences: Drets, G.A., Liljenstr{\"o}m, H.G., (1999) Intelligent Biometric Techniques in Fingerprint and Face Recognition, , CRC Press, New York;Hong, L., Wan, Y., Fingerprint image enhancement: Algorithm and performance evaluation (1998) IEEE Transactions On, Pattern Analysis and Machine Intelligence, 20 (8), pp. 777-789; 
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Zhou, J., Chen, F., Gu, J., A novel algorithm for detecting singular points from fingerprint images (2009) IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (7), pp. 1239-1250
},
	month = {2009///},
	address = {Snowbird, UT},
	abstract = {Automatic biometric identification based on fingerprints is still one of the most reliable identification method in criminal and forensic applications. A critical step in fingerprint analysis without human intervention is to automatically and reliably extract singular points from the input fingerprint images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. Poincar{\'e} Index-based methods are one of the most common for singular points detection. However, these methods usually result in many spurious detections. Therefore, we propose an enhanced version of the method presented by Zhou et al. [13] that introduced a feature called DORIC to improve the detection. Our principal contribution lies in the adoption of a smoothed orientation field and in the formulation of a new algorithm to analyze the DORIC feature. Experimental results show that the proposed algorithm is accurate and robust, giving better results than the best reported results so far, with improvements in the range of 5\% to 7\%. {\textcopyright} 2009 IEEE.},
	keywords = {Algorithms, Biometric identifications, Biometrics, Computer applications, Computer vision, Critical steps, Fingerprint analysis, Fingerprint images, Forensic applications, Human intervention, Identification method, Orientation fields, Poincare, Ridge patterns, Singular points, Structural optimization, Technical presentations, Topological structure},
	isbn = {9781424454976 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77951158127\&partnerID=40\&md5=718a6649dbc13fcb895f23ffff1363d4},
	author = {Magalh{\~a}es, F. and Oliveira, H.P. and Campilho, A.C.}
}
@article {2348,
	title = {Chromatographic pattern classification},
	journal = {IEEE Transactions on Biomedical EngineeringIEEE Trans. Biomed. Eng.},
	volume = {55},
	year = {2008},
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},
	month = {2008///},
	pages = {1687 - 1696},
	abstract = {In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting a mixture of Gaussians. The resulting profiles are represented by a set of appropriate features trying to characterize the state of nature, here spread out over four classes, one for normal subjects and the other three corresponding to lysosomal diseases, which are disorders responsible for severe nerve degeneration. For classification purposes, a novel solution based on a hierarchical structure is proposed. The main conclusion of this paper is that an automatically generated decision tree presents better results than more conventional solutions, being able to deal with the natural imbalance of the data that, as consequence of the rarity of lysosomal disorders, has very few representative cases in the disease classes when compared with the normal population. {\textcopyright} 2008 IEEE.},
	keywords = {Acoustic variables control, Algorithms, article, Chromatographic analysis, Chromatographic pattern features, Chromatographic profiles, Chromatography, Chromatography, Thin Layer, Classification, Diagnosis, Computer-Assisted, Gaussian noise (electronic), Hierarchical classifier, Humans, Hydrolases, imaging, Lysosomal Storage Diseases, lysosome storage disease, mathematical analysis, nerve degeneration, noise, normal distribution, Pattern recognition, Pattern Recognition, Automated, Reproducibility of Results, sensitivity and specificity, Urinalysis},
	isbn = {00189294 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-44349183146\&partnerID=40\&md5=df677f44d528c936310d8c547624a550},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2335,
	title = {Data classification with multilayer perceptrons using a generalized error function},
	journal = {Neural NetworksNeural Netw.},
	volume = {21},
	year = {2008},
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},
	month = {2008///},
	pages = {1302 - 1310},
	abstract = {The learning process of a multilayer perceptron requires the optimization of an error function E (y, t) comparing the predicted output, y, and the observed target, t. We review some usual error functions, analyze their mathematical properties for data classification purposes, and introduce a new one, E                        Exp, inspired by the Z-EDM algorithm that we have recently proposed. An important property of E
                        Exp is its ability to emulate the behavior of other error functions by the sole adjustment of a real-valued parameter. In other words, E
                        Exp is a sort of generalized error function embodying complementary features of other functions. The experimental results show that the flexibility of the new, generalized, error function allows one to obtain the best results achievable with the other functions with a performance improvement in some cases. {\textcopyright} 2008 Elsevier Ltd. All rights reserved.
},
	keywords = {algorithm, Algorithms, analytical error, article, calculation, Classification, Complementary features, Cybernetics, data analysis, Data classification, Data classifications, Data Interpretation, Statistical, Electric loads, entropy, Error functions, learning, Learning processes, mathematical analysis, Mathematical properties, Models, Statistical, Multilayer neural networks, Multilayer perceptron, Multilayer perceptrons, Multilayers, Neural networks, Neural Networks (Computer), Pattern recognition systems, perceptron, Performance improvements, priority journal, Probability density function},
	isbn = {08936080 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-54449100428\&partnerID=40\&md5=30f8de18c0272f4a440395045bd3c164},
	author = {Silva, L.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
@article {2365,
	title = {LEGClust - A clustering algorithm based on layered entropic subgraphs},
	journal = {IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Trans Pattern Anal Mach Intell},
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	year = {2008},
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},
	month = {2008///},
	pages = {62 - 75},
	abstract = {Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix, and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones. {\textcopyright} 2007 IEEE.},
	keywords = {algorithm, Algorithms, article, artificial intelligence, automated pattern recognition, Cluster Analysis, Clustering, Clustering algorithms, computer assisted diagnosis, Distance measurement, Entropic subgraphs, entropy, Graph theory, Graphs, Hierarchical clustering, Hierarchical systems, Image enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, methodology, Pattern Recognition, Automated, reproducibility, Reproducibility of Results, sensitivity and specificity, three dimensional imaging},
	isbn = {01628828 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-36448984894\&partnerID=40\&md5=f549049e4ae5bff1f3a95aaae22cefb8},
	author = {Santos, J.M. and De S{\'a}, J.M. and Alexandre, L.A.}
}
@book {2395,
	title = {Error entropy minimization for LSTM training},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4131 LNCS - I},
	year = {2006},
	note = {Conference code: 68317Cited By (since 1996): 3Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; Department of Informatics, IT-Networks and Multimedia Group, University of Beira Interior, Covilh{\~a}, Portugal; email: lfbaa@di.ubi.ptReferences: Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780;Gers, F., Schmidhuber, J., Cummins, F., Learning to forget: Continual prediction with LSTM (2000) Neural Computation, 12 (10), pp. 2451-2471; 
Gers, F., Schmidhuber, J., Recurrent nets that time and count (2000) Proc. IJCNN{\textquoteright}2000, Int. Joint Conf. on Neural Networks, , Como, Italy; 
P{\'e}rez-Ortiz, J., Gers, F., Eck, D., Schmidhuber, J., Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets (2003) Neural Networks, 16 (2), pp. 241-250; 
Haykin, S., (1999) Neural Networks: A Comprehensive Foundation, 2nd Edition, , Prentice Hall; 
Erdogmus, D., Principe, J., An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems (2002) IEEE Trans. Signal Processing, 50 (7), pp. 1780-1786; 
Santos, J., Alexandre, L., Sereno, F., Marques De S{\'a}, J., Optimization of the error entropy minimization algorithm for neural network classification (2004) ANNIE 2004, Intelligent Engineering Systems Through Artificial Neural Networks, 14, pp. 81-86. , St.Louis, USA, ASME Press Series; 
Santos, J., Alexandre, L., Marques De S{\'a}, J., The error entropy minimization algorithm for neural network classification (2004) Proceedings of the 5th International Conference on Recent Advances in Soft Computing, pp. 92-97. , Lofti, A., ed.: Nottingham, United Kingdom; 
Silva, L., Marques De S{\'a}, J., Alexandre, L., Neural network classification using Shannon{\textquoteright}s entropy (2005) 13th European Symposium on Artificial Neural Networks - ESANN 2005, pp. 217-222. , Bruges, Belgium
},
	month = {2006///},
	pages = {244 - 253},
	address = {Athens},
	abstract = {In this paper we present a new training algorithm for the Long Short-Term Memory (LSTM) recurrent neural network. This algorithm uses entropy instead of the usual mean squared error as the cost function for the weight update. More precisely we use the Error Entropy Minimization approach, were the entropy of the error is minimized after each symbol is present to the network. Our experiments show that this approach enables the convergence of the LSTM more frequently than with the traditional learning algorithm. This in turn relaxes the burden of parameter tuning since learning is achieved for a wider range of parameter values. The use of EEM also reduces, in some cases, the number of epochs needed for convergence. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {Algorithms, Convergence of numerical methods, Cost function, entropy, Error Entropy Minimization, Errors, Long Short-Term Memory (LSTM), Neural networks, Optimization},
	isbn = {03029743 (ISSN); 3540386254 (ISBN); 9783540386254 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749837658\&partnerID=40\&md5=5f40159a3f1faf4760405dd581e79bf7},
	author = {Alexandre, L.A. and Marques De S{\'a}, J.P.}
}
@article {2419,
	title = {Internal versus external intrapartum foetal heart rate monitoring: The effect on linear and nonlinear parameters},
	journal = {Physiological MeasurementPhysiol. Meas.},
	volume = {27},
	year = {2006},
	note = {Cited By (since 1996): 15Export Date: 21 November 2012Source: ScopusCODEN: PMEAEdoi: 10.1088/0967-3334/27/3/008PubMed ID: 16462016Language of Original Document: EnglishCorrespondence Address: Gon{\c c}alves, H.; Departamento de Matem{\'a}tica Aplicada, Faculdade de Ci{\^e}ncias, Universidade Do Porto, Porto, Portugal; email: hernani.goncalves@fc.up.ptReferences: Ayres-De-Campos, D., Bernardes, J., Garrido, A., Marques-De-S{\'a}, J., Pereira-Leite, L., SisPorto 2.0: A program for automated analysis of cardiotocograms (2000) J. Matern. Fetal Med., 9 (5), pp. 311-318;Ayres-De-Campos, D., Bernardes, J., Reynolds, A., Saraiva, J.P., Santos, C., Pereira-Leite, L., Computer evaluation of internal versus external FHR monitoring in the minutes preceding delivery (2000) Int. J. Gynecol. Obstet., 70, pp. 60-B61; 
Ayres-De-Campos, D., Santos, C., Bernardes, J., Prediction of neonatal state by computer analysis of fetal heart rate tracings: The antepartum arm of the SisPorto {\textregistered}multicentre validation study (2005) Eur. J. Obstet. Gynecol. Reprod. Biol., 118 (1), pp. 52-60; 
Bakker, P.C., Colenbrander, G.J., Verstraeten, A.A., Geijn, V., The quality of intrapartum fetal heart rate monitoring (2004) Eur. J. Obstet. Gynecol. Reprod. Biol., 116 (1), pp. 22-27; 
Bernardes, J., Ayres-De-Campos, D., Costa-Pereira, A., Pereira-Leite, L., Garrido, A., Objective computerized fetal heart rate analysis (1998) Int. J. Gynecol. Obstet., 62 (2), pp. 141-147; 
Bernardes, J., Costa-Pereira, A., The effect of different sampling intervals on the measurement of intrapartum fetal heart rate variability (1997) Obstet. Gynecol., 90 (2), pp. 318-319; 
Bernardes, J., Garrido, A., Computerized fetal heart rate analysis in labour based on 2-s sampling. Can it proceed with confidence? (1995) Eur. J. Obstet. Gynecol. Reprod. Biol., 63 (1), pp. 105-107; 
Bernardes, J., Moura, C., Marques-De-S{\'a}, J.P., Pereira-Leite, L., The Porto system for automated cardiotocographic signal analysis (1991) J. Perinat. Med., 19, pp. 61-65; 
Bernardes, J., Moura, C., Marques-De-S{\'a}, J.P., Van Geijn, H.P., Pereira-Leite, L., The Porto system (1994) A Critical Appraisal of Fetal Surveillance, pp. 315-324; 
Boog, G., Analyse informatis{\'e}e du rythme cardiaque fetal au cours de la grosssesse et de l{\textquoteright}accouchement (2004) Encyclop{\'e}die M{\'e}dico-Chirurgicale, 148; 
Boos, A., Jagger, M.H., Paret, G.W., Hausmann, J.W., A new, lightweight fetal telemetry system (1995) Hewlett-Packard J., pp. 82-93; 
Carter, M.C., Signal processing and display-cardiotocographs (1993) Br. J. Obstet. Gynaecol., 100, pp. 21-23; 
Chaffin, D.G., Barnard, J.M., Phernetton, T., Reed, K.L., Decreased approximate entropy of heart rate variability in the hypoxic ovine fetus (1998) J. Matern. Fetal Invest., 8, pp. 23-26; 
Chang, A., Sahota, D.S., Reed, N.N., James, D.K., Mohajer, M.P., Computerised fetal heart rate analysis in labour-effect of sampling rate (1995) Eur. J. Obstet. Gynecol. Reprod. Biol., 59 (2), pp. 125-129; 
Court, D.J., Parer, J.T., Experimental studies of fetal asphyxia and fetal heart rate interpretation (1984) Research in Perinatal Medicine, pp. 113-169; 
Dawes, G., Computerized fetal heart rate analysis (1994) A Critical Appraisal of Fetal Surveillance, pp. 311-314; 
Dawes, G.S., Moulden, M., Sheil, O., Redman, C.W.G., Approximate entropy, a statistic of regularity, applied to fetal heart rate data before and during labour (1992) Obstet. Gynecol., 80, pp. 763-768; 
Dawes, G.S., Moulden, M., Redman, C.W., Criteria for the design of fetal heart rate analysis systems (1990) Int. J. Biomed. Comput., 25 (4), pp. 287-294; 
Di Renzo, G.C., Montani, M., Fioriti, V., Clerici, G., Branconi, F., Pardini, A., Indraccolo, R., Cosmi, E.V., Fractal analysis: A new method for evaluating fetal heart rate variability (1996) J. Perinat. Med., 24, pp. 261-269; 
Echeverria, J.C., Hayes-Gill, B.R., Crowe, J.A., Woolfson, M.S., Croaker, G.D., Detrended fluctuation analysis: A suitable method for studying fetal heart rate variability? (2004) Physiol. Meas., 25 (3), pp. 763-774; 
Felgueiras, C., Marques-De-S{\'a}, J.P., Bernardes, J., Gama, S., Fractal characterisation of fetal heart rate sequences (1998) Biomed. Eng., 36, pp. 197-201; 
Felgueiras, C.S., Marques-De-S{\'a}, J.P., Bernardes, J., Gama, S., On the reconstruction of fetal heart rate sequences (1996) Actas Do 4{\textdegree} Congresso Portugu{\^e}s de Engenharia Biom{\'e}dica-BioEng{\textquoteright}96, pp. 61-vii65; 
Fukushima, T., Flores, C.A., Hon, E.H., Davidson Jr., E.C., Limitations of autocorrelation in fetal heart rate monitoring (1985) Am. J. Obstet. Gynecol., 153, pp. 685-692; 
Fusheng, Y., Bo, H., Qingyu, T., Approximate entropy and its application in biosignal analysis (1998) Nonlinear Biomedical Signal Processing-Volume II: Dynamical Analysis and Modelling, pp. 72-91; 
Gough, N.A., Fractal analysis of fetal heart rate variability (1993) Physiol. Meas., 14 (3), pp. 309-315; 
Hayes, M., (1996) Statistical Digital Signal Processing and Modelling; 
Hewlett-Packard, (1993) Hewlett-Packard Series 50 Service Manual, M1351A and 1353A; 
Hirsch, M., Karin, J., Akselrod, S., Heart rate variability in the fetus (1995) Heart Rate Variability, pp. 517-531; 
Lawson, G.W., Belcher, R., Dawes, G.S., Redman, C.W., A comparison of ultrasound (with autocorrelation) and direct electrocardiogram fetal heart rate detector systems (1983) Am. J. Obstet. Gynecol., 147, pp. 721-722; 
Li, X., Tang, D., Zhou, S., Zhou, G., Wang, C., Zhuang, Y., Wu, G., Shen, L., Redistribution of power spectrum of heart rate variability during acute umbilical artery embolism and hypoxemia in late-gestation fetal sheep (2004) Eur. J. Obstet. Gynecol. Reprod. Biol., 114 (2), pp. 137-143; 
Lunghi, F., Magenes, G., Pedrinazzi, L., Signorini, M.G., Detection of fetal distress through a support vector machine based on fetal heart rate parameters (2005) Comput. Cardiol., 32, pp. 247-250; 
Magenes, G., Signorini, M.G., Arduini, D., Classification of cardiotocographic records by neural networks (2000) IJCNN 2000: Proc. IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks, pp. 637-641; 
Electronic fetal heart rate monitoring: Research guidelines for interpretation (1997) Am. J. Obstet. Gynecol., 177 (6), pp. 1385-1390; 
Nijhuis, I., Ten Hof, J., Development of fetal heart rate and behavior: Indirect measures to assess the fetal nervous system (1999) Eur. J. Obstet. Gynecol. Reprod. Biol., 87 (1), pp. 1-2; 
Oppenheimer, L.W., Lewinsky, R.M., Power spectral analysis of fetal heart rate (1994) Baillieres Clin. Obstet. Gynaecol., 8, pp. 643-661; 
Peters, C.H., Ten Broeke, E.D., Andriessen, P., Vermeulen, B., Berendsen, R.C., Wijn, P.F., Oei, S.G., Beat-to-beat detection of fetal heart rate: Doppler ultrasound cardiotocography compared to direct ECG cardiotocography in time and frequency domain (2004) Physiol. Meas., 25 (2), pp. 585-593; 
Pincus, S., Approximate entropy as a measure of system complexity (1991) Proc. Natl. Acad. Sci. USA, 88, pp. 2297-2301; 
Pincus, S., Approximate entropy (ApEn) as a complexity measure (1995) Chaos, 5 (1), pp. 110-117; 
Pincus, S., Viscarello, R., Approximate entropy: A regularity measure for fetal heart rate analysis (1992) Obstet. Gynecol., 79, pp. 249-255; 
Richman, J.S., Moorman, J.R., Physiological time-series analysis using approximate entropy and sample entropy (2000) Am. J. Physiol. Heart Circ. Physiol., 278, pp. 2039-H2049; 
Salamalekis, E., Thomopoulos, P., Giannaris, D., Salloum, I., Vasios, G., Prentza, A., Koutsouris, D., Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks (2002) BJOG, 109 (10), pp. 1137-1142; 
Signorini, M., Cerutti, S., Nonlinear properties of Cardiovascular Time Series (1999) Methodology and Clinical Applications of Blood Pressure and Heart Rate Analysis, pp. 73-89; 
Signorini, M.G., Magenes, G., Cerrutti, S., Arduini, D., Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotacographic recordings (2003) IEEE Trans. Biomed. Eng., 50 (3), pp. 365-373; 
Suzuki, T., Okamura, K., Kimura, Y., Watanabe, T., Yaegashi, N., Murotsuki, J., Uehara, S., Yajima, A., Power spectral analysis of R-R interval variability before and during the sinusoidal heart rate pattern in fetal lambs (2000) Am. J. Obstet. Gynecol., 182 (5), pp. 1227-1232; 
Heart rate variability. Standards of measurement, physiological interpretation, and clinical use (1996) Eur. Heart J., 17, pp. 354-381; 
Wilcox, M.A., Wang, W., Sahota, D.S., Dawkins, R.R., Chung, T.K., Chang, A.M., The effect of different sampling intervals on the measurement of intrapartum fetal heart rate variability (1997) Obstet. Gynecol., 89 (4), pp. 577-580; 
Yum, M.K., Kim, K., Kim, J.H., Park, E.Y., A consistent abnormality in the average local smoothness of fetal heart rate in growth-restricted fetuses affected by severe pre-eclampsia (2004) Hypertens. Res., 27 (12), pp. 911-918
},
	month = {2006///},
	pages = {307 - 319},
	abstract = {The effect of foetal heart rate (FHR) acquisition mode on linear and nonlinear parameters is still largely unknown. In 33 normal labouring women, FHR signals were acquired simultaneously by an external ultrasound sensor applied to the maternal abdomen and an internal scalp electrode, in the minutes preceding delivery. For each case, the initial and final 5, 10 and 20 min segments were analysed, considering FHR signals at a frequency of 4 Hz (the frequency at which they are transmitted by the majority of commercialized foetal monitors). Several time and frequency domain linear and nonlinear FHR indices were computed in these segments, namely mean FHR, very low frequency (VLF), low frequency (LF), high frequency (HF), approximate entropy (ApEn) and sample entropy (SampEn). Parametric confidence intervals, statistical tests and correlation coefficients were calculated in order to evaluate the effect of internal versus external FHR monitoring modes on the considered indices. The whole evaluation was repeated using FHR signals at a frequency of 2 Hz. Most time domain linear indices were similar with external and internal monitoring in the initial and final segments of the tracings. However, linear frequency domain indices were poorly correlated in the final segments and had significantly different mean values in the initial segments. Nonlinear indices were significantly different in both initial and final segments. The correlation between 4 and 2 Hz sampled parameters was high for both linear and nonlinear indices (most correlation coefficient values ranging between 0.95 and 1) but nonlinear index values were significantly higher at 2 Hz. In conclusion, the mode used to acquire FHR signals and the sampling rate employed can significantly affect most FHR indices. {\textcopyright} 2006 IOP Publishing Ltd.},
	keywords = {abdomen, Algorithms, article, Cardiotocography, commercial phenomena, Confidence interval, controlled study, correlation coefficient, delivery, Diagnosis, Computer-Assisted, Electrocardiography, electrode, entropy, External and internal monitoring, Female, fetus electrocardiography, fetus monitoring, Foetal heart rate (FHR), Frequency domain analysis, Heart, Heart rate, human, intermethod comparison, intrapartum care, Linear and nonlinear analysis, Linear Models, mathematical computing, Nonlinear Dynamics, normal human, priority journal, Reproducibility of Results, sensitivity and specificity, sensor, Signal Processing, Computer-Assisted, Statistical significance, ultrasound, Variability},
	isbn = {09673334 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-32144438860\&partnerID=40\&md5=dd6dbb9f364b7f974c7048c4cf08d53f},
	author = {Gon{\c c}alves, H. and Rocha, A.P. and Ayres-de-Campos, D. and Bernardes, J.}
}
@conference {2389,
	title = {New developments of the Z-EDM algorithm},
	booktitle = {Proceedings - ISDA 2006: Sixth International Conference on Intelligent Systems Design and ApplicationsProc. ISDA Sixth Int. Conf. Intelligent Syst. Design Applic.},
	series = {ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications},
	volume = {1},
	year = {2006},
	note = {Conference code: 69992Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusArt. No.: 4021587doi: 10.1109/ISDA.2006.204Language of Original Document: EnglishCorrespondence Address: Silva, L.M.; INEB - Instituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: lmsilva@fe.up.ptReferences: Bishop, C., (1995) Neural Networks for Pattern Recognition, , Oxford University Press;Blake, C., Merz, C., (1998) UCI Repository of machine learning databases University of California, , http://www.ics.uci.edu/~mlearn/MLRepository.html, Irvine, Dept. of Information and Computer Sciences; 
Erdogmus, D., Principe, J.C., An Error-Entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive Systems (2002) IEEE Transactions on Signal Processing, 50 (7), pp. 1780-1786; 
Forina, M., Armanino, C., Eigenvector projection and simplified non-linear mapping of fatty acid content of Italian olive oils (1981) Ann. Chim. (Rome), 50, pp. 127-155; 
Marques de S{\'a}, J., (2001) Pattern Recognition: Concepts, Methods and Applications, , Springer Verlag; 
J. Santos, L. Alexandre, and J. Marques de S{\'a}. The Error Entropy Minimization Algorithm for Neural Network Classification. In Int. Conf. on Recent Advances in Soft Computing, Nottingham, United Kingdom, 2004Silva, L., Alexandre, L., Marques de S{\'a}, J., Neural network classification: Maximizing zero-error density (2005) LNCS, 3686, pp. 127-135. , ICAPR 2005; 
Silva, L., Marques de S{\'a}, J., Alexandre, L., Neural Network Classification using Shannon{\textquoteright}s Entropy (2005) European Symposium on Artificial Neural Networks; 
Silverman, B., (1986) Density Estimation for Statistics and Data Analysis, , Chapman \& Hall
},
	month = {2006///},
	pages = {1067 - 1072},
	address = {Jinan},
	abstract = {In this paper we address some open questions on the recently proposed Zero-Error Density Maximization algorithm for MLP training. We propose a new version of the cost function that solves a training problem encountered in previous work and prove that the use of a nonparametric density estimator preserves the optimal solution. Some experiments are reported comparing this cost function to the usual mean-square error and cross entropy cost junctions. {\textcopyright} 2006 IEEE.},
	keywords = {Algorithms, Cost functions, Mean square error, MLP training, Nonparametric density estimators, Optimization, Personnel training, Problem solving, Zero-Error Density Maximization algorithm},
	isbn = {0769525288 (ISBN); 9780769525280 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-34547496988\&partnerID=40\&md5=65cadef393b0843f5b21f6534bc122ff},
	author = {Silva, L.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
@conference {2442,
	title = {Neural network classification: Maximizing zero-error density},
	booktitle = {Lecture Notes in Computer ScienceLect. Notes Comput. Sci.},
	series = {Third International Conference on Advances in Patten Recognition, ICAPR 2005},
	volume = {3686},
	year = {2005},
	note = {Conference code: 65875Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Silva, L.M.; Instituto de Engenharia Biom{\'e}dica, Lab. de Sinai e Imagem Biom{\'e}dica, Campus da FEUP, Rua Dr. Roberto Frias, 4200 - 465 Porto, PortugalReferences: Erdogmus, D., Principe, J.C., An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems (2002) IEEE Transactions on Signal Processing, 50 (7), pp. 1780-1786;Erdogmus, D., Principe, J., Generalized information potential criterion for adaptive system training (2002) IEEE Transactions on Neural Networks, 13 (5), pp. 1035~1044; 
Santos, J.M., Alexandre, L.A., Marques De S{\'a}, J., The error entropy minimization algorithm for neural network classification (2004) Int. Conf. on Recent Advances in Soft Computing, , Nottingham, United Kingdom; 
Santos, J.M., Alexandre, L.A., De Sa, J.M., Optimization of the error entropy minimization algorithm for neural network classification (2004) Intelligent Engineering Systems Through Artificial Neural Networks, 14, pp. 81-86. , C. Dagli, A. Buczak, D. Enke, M. Embrechts, and O. Ersoy, editors, ASME Press Series; 
Silva, L.M., De Sa, J.M., Alexandre, L.A., Neural network classification using shannon{\textquoteright}s entropy (2005) European Symposium on Artificial Neural Networks; 
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixtures of local experts (1991) Neural Computation, 3, pp. 79-87; 
Blake, C.L., Merz, C.J., (1998) UCI Repository of Machine Learning Databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html, University of California, Irvine, Dept. of Information and Computer Sciences
},
	month = {2005///},
	pages = {127 - 135},
	address = {Bath},
	abstract = {We propose a new cost function for neural network classification: the error density at the origin. This method provides a simple objective function that can be easily plugged in the usual backpropagation algorithm, giving a simple and efficient learning scheme. Experimental work shows the effectiveness and superiority of the proposed method when compared to the usual mean square error criteria in four well known datasets. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Algorithms, Backpropagation, Backpropagation algorithm, Classification (of information), Costs, Error analysis, Function evaluation, Learning systems, Mean square error, Neural network classification, Neural networks, Zero-error density},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-27244450344\&partnerID=40\&md5=73ee3c0c073fb3bc7bc85013bf7dcce6},
	author = {Silva, L.M. and Alexandre, L.A. and De S{\'a}, J.M.},
	editor = {Singh S. and Singh M. and Apte C. and Perner P.}
}
@book {2432,
	title = {Segmentation of ultrasonic images of the carotid},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3656 LNCS},
	year = {2005},
	note = {Conference code: 67124Export Date: 21 November 2012Source: Scopusdoi: 10.1007/11559573_115Language of Original Document: EnglishCorrespondence Address: Rocha, R.; INEB - Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinai e Imagem Biom{\'e}dica, Porto, PortugalReferences: Gee, A., Prager, R., Treece, G., Berman, L., Engineering a freehand 3D ultrasound system (2003) Pattern Recogn. Lett., 24, pp. 757-777;Sonka, M., Liang, W., Stefancik, R., Stolpen, A., (2000) Handbook of Medical Imaging: Medical Image Processing and Analysis, 2. , SPIE; 
Christodoulou, C.I., Pattichis, C.S., Pantziaris, M., Nicolaides, A., Texture based classification of atherosclerotic carotid plaques (2003) IEEE Trans. Med. Imaging, 22, pp. 902-912; 
Barratt, D., Ariff, B., Humphries, K., Thom, S., Hughes, A., Reconstruction and quantification of the carotid artery bifurcation from 3-d ultrasound images (2004) IEEE Trans. Med. Imaging, 23, pp. 567-583; 
Li, C., Lee, C., Minimum cross entropy thresholding (1993) Pattern Recogn, 26, pp. 617-625; 
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Vese, L.A., Chan, T.F., A multiphase level set framework for image segmentation using the Mumford and Shah model (2002) Int. J. Comput. Vision, 50, pp. 271-293; 
Touboul, P., Prati, P., Abd, V., Adrai, P.Y.S., Thibout, E., Ducimetiere, P., Use of monitoring software to improve the measurement of carotid wall thickness by B-mode imaging (1992) J. Hypertension, 10, pp. 37-41; 
Liang, Q., Wendelhag, I., Wikstrand, J., Gustavsson, T., A multiscale dynamic programming procedure for boundary detection in ultrasound artery images (2000) IEEE Trans. Med. Imaging, 19, pp. 127-142; 
Schmidt-Trucksass, A., Cheng, D., Sandrock, M., Schulte-Monting, J., Rauramaa, R., Huonker, M., Burkhardt, H., Computerized analysing system using the active contour in ultrasound measurement of carotid artery intima-media thickness (2001) Clinical Physiology, 5, pp. 561-569; 
Cheng, D., Schmidt-Trucksass, A., Cheng, K., Sandrock, M., Pu, Q., Burkhardt, H., Automatic detection of the intimal and the adventitial layers of the common carotid artery wall in ultrasound B-mode images using snakes (1999) International Conference on Image Analysis and Processing, pp. 452-457. , B. Werner; 
Cheng, D., Schmidt-Trucksass, A., Cheng, K., Burkhardt, H., Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images (2002) Computer Methods and Programs in Biomedicine, 67, pp. 27-37; 
Baillard, C., Barillot, C., Bouthemy, P., Robust adaptive segmentation of 3D medical images with level sets (2000) Technical Report, , IRISA, Campus Universitaire de Beaulieu, Prance; 
Tsai, A., Yezzi III, A.J., W., W.M., Tempany, C., Tucker, D., Fan, A., Grimson, W.E.L., Willsky, A.S., A shape-based approach to the segmentation of medical imagery using level sets (2003) IEEE Trans. Med. Imaging, 22, pp. 137-154; 
Perona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion (1990) IEEE Trans. Pattern Anal. Mach. Intell., 12, pp. 629-639; 
Sethian, J.A., (1999) Level Set Methods and Fast Marching Methods, , Cambridge University Press; 
Osher, S., Fedkiw, R., (2003) Level Set Methods and Dynamic Implicit Surfaces, , Springer-Verlag. New York; 
Mumford, D., Shah, J., Optimal approximations by piecewise smooth functions and associated variational problems (1989) Communications on Pure and Applied Mathematics, 42, pp. 577-685; 
Gao, S., Bui, T.D., Image segmentation and selective smoothing by using the Mumford-Shah model (2005) IEEE Trans. on Image Processing, , To be published in
},
	month = {2005///},
	pages = {949 - 957},
	address = {Toronto},
	abstract = {A new algorithm for an effective and automatic segmentation of the carotid wall in ultrasonic images is proposed. It combines the speed of thresholding algorithms with the accuracy, flexibility and robustness of a successful geometric active contour model which incorporates an optimal image segmentation model in a level set framework. Due to the multiphase nature of these images, a sequential minimum cross entropy thresholding is used to get a first approximation of the segments, reducing the problem to a two phase segmentation. This thresholding solution is then used as a starting point for a two phase piecewise constant version of a geometric active contour model to reduce noise, smooth contours, improve their position accuracy and close eventual gaps in the carotid wall. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Active contour model, Algorithms, Approximation theory, Carotid wall, Image segmentation, Image segmentation model, Mathematical models, Optimization, Ultrasonic imaging},
	isbn = {03029743 (ISSN); 3540290699 (ISBN); 9783540290698 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33646014096\&partnerID=40\&md5=42380a0fabe35bd2e682b403ff68e79f},
	author = {Rocha, R. and Campilho, A. and Silva, J.}
}
@conference {2446,
	title = {Spectral methods in image segmentation: A combined approach},
	booktitle = {Lecture Notes in Computer ScienceLect. Notes Comput. Sci.},
	series = {Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005},
	volume = {3523},
	year = {2005},
	note = {Conference code: 65649Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Monteiro, F.C.; INEB - Instituto de Engenharia Biom{\'e}dica, Campus de Santa Apol{\'o}nia, Apartado 134, 5301-857 Bragan{\c c}a, Portugal; email: monteiro@ipb.ptReferences: Fowlkes, C., Belongie, S., Chung, F., Malik, J., Spectral grouping using nystr{\"o}m method (2004) Trans. on Pattern Analysis and Machine Intelligence, 26 (2), pp. 214-225;Malik, J., Belongie, S., Leung, T., Shi, T., Contour and texture analysis for image segmentation (2001) International Journal of Computer Vision, 43 (1), pp. 7-27; 
Martin, D., Fowlkes, C., Tal, D., Malik, J., A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics (2001) IEEE Int. Conf. on Computer Vision, 2, pp. 416-424; 
Meila, M., Shi, J., Learning segmentation by random walks (2000) Neural Information Processing Systems, pp. 873-879; 
Ng, A., Jordan, M., Weiss, Y., On spectral clustering: Analysis and an algorithm (2002) Advances in Neural Information Processing Systems, 14, pp. 849-856; 
Scott, D.S., Solving sparse symmetric generalized eigenvalue without factorization (1981) SIAM Journal of Numerical Analisys, 18, pp. 102-110. , http://www.netlib.org/laso/snlaso.f; 
Scott, G.L., Longuet-Higgins, H.C., Feature grouping by "relocalisation" of eigenvectors of the proximity matrix (1990) Proc. British Machine Vision Conf., pp. 103-108; 
Shi, J., Malik, J., Normalized cuts and image segmentation (2000) IEEE Trans. on Pattern Analysis and Machine Intelligence, 22 (8), pp. 888-905; 
Weiss, Y., Segmentation using eigenvectors: A unifying view (1999) International Conference on Computer Vision, pp. 975-982; 
Wertheimer, M., Laws of organization in perceptual forms (1938) A Sourcebook of Gestalt Psychology, pp. 71-88. , W. B. Ellis Editor; 
Wu, Z., Leahy, R., An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation (1993) IEEE Trans. on Pattern Analysis and Machine Intelligence, 15, pp. 1101-1113; 
Yu, S., Shi, J., Multiclass spectral clustering (2003) International Conference on Computer Vision, pp. 313-319. , Nice, France
},
	month = {2005///},
	pages = {191 - 198},
	address = {Estoril},
	abstract = {Grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated a good performance on this task using spectral methods that are based on the eigensolution of a similarity matrix. In this paper, we implement a variation of the existing methods that combines aspects from several of the best-known eigenvector segmentation algorithms to produce a discrete optimal solution of the relaxed continuous eigensolution. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Algorithms, Computer vision, Discrete optimal solution, Eigensolution, Eigenvalues and eigenfunctions, Image segmentation, Matrix algebra, Problem solving, Similarity matrix, Spectral methods},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-25144437374\&partnerID=40\&md5=5b6351af4ac58a16c41b5558a48ae9e2},
	author = {Monteiro, F.C. and Campilho, A.C.},
	editor = {Marques J.S. and Perez de la Blanca N. and Pina P.}
}
@conference {2463,
	title = {Automatic delimitation of lung fields on chest radiographs},
	booktitle = {2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to NanoIEEE Int. Symp. Biomed. Imaging Macro Nano},
	series = {2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano},
	volume = {2},
	year = {2004},
	note = {Conference code: 64593Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Mendon{\c c}a, A.M.; Inst. de Engenharia Biom{\'e}dica, Lab. de Sinai e Imagem Biomedica, Campus da FEUP, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, PortugalReferences: Tsujii, O., Freedman, M., Mun, S., Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network (1998) Medical Physics, 25, pp. 998-1007;Armato III, S., Giger, M., MacMahon, H., Computerized detection of abnormal asymetry in digital lateral chest radiographs (1994) Medical Physics, 21, pp. 1761-1768; 
Armato III, S., Giger, M., Ashizawa, K., MacMahon, H., Automated lung segmentation in digital lateral chest radiographs (1998) Medical Physics, 25, pp. 1507-1520; 
Carrascal, F., Carreira, J., Souto, M., Automatic calculation of total lung capacity from automatically traced lung boundaries in posterior-anterior and lateral digital chest radiographs (1998) Medical Physics, 25, pp. 1118-1131; 
Ginneken, B., Romeny, B., Automatic segmentation of lung fields in chest radiographs (2000) Medical Physics, 27, pp. 2445-2455; 
Li, L., Zheng, Y., Kallergi, M., Clark, R., Improved method for automatic identification of lung regions on chest radiographs (2001) Academic Radiology, 8, pp. 629-638; 
Monteiro, M., Mendon{\c c}a, A.M., Campilho, A., Lung field detection on chest radiographs (2002) Proceedings of VIIP02, IASTED International Conference on Visualization, Imaging and Image Processing, pp. 367-372. , J. Villanueva (eds.), Spain
Sponsors: IEEE},
	month = {2004///},
	pages = {1287 - 1290},
	address = {Arlington, VA},
	abstract = {The purpose of the research herein presented is the automatic delimitation of lung fields in posterior-anterior digital chest radiographs. In a computer-aided diagnosis system the precise location of the lungs is important as it allows the reduction of the region under analysis, decreasing the computation time and facilitating data compression. Furthermore, it allows the delimitation of the search area, easing the selective tuning of the abnormalities detection algorithms. The results produced by the automatic method were validated by comparison with manual contours traced by experienced radiologists. Two programs with friendly interfaces were developed for this purpose. {\textcopyright} 2004 IEEE.},
	keywords = {Algorithms, Chest radiographs, Computer aided diagnosis, Contour detection, Data compression, Edge detection, Image segmentation, Lung fields, Pulmonary diseases, Radiography, X ray analysis, X ray imaging},
	isbn = {0780383885 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-17144373368\&partnerID=40\&md5=4f13427d02a5013364f4d761ccf5a7b8},
	author = {Mendon{\c c}a, A.M. and Da Silva, J.A. and Campilho, A.}
}
@article {2466,
	title = {A model for educational simulation of infant cardiovascular physiology},
	journal = {Anesthesia and AnalgesiaAnesth. Analg.},
	volume = {99},
	year = {2004},
	note = {Cited By (since 1996): 19Export Date: 21 November 2012Source: ScopusCODEN: AACRAdoi: 10.1213/01.ANE.0000134797.52793.AFPubMed ID: 15562049Language of Original Document: EnglishCorrespondence Address: Goodwin, J.A.; Nemours Children{\textquoteright}s C., 807 Children{\textquoteright}s Way, Jacksonville, FL 32207, United States; email: jgoodwin@nemours.orgReferences: Good, M.L., Gravenstein, J.S., Training for safety in an anesthesia simulator (1993) Semin Anesth, 12, pp. 235-250;Gaba, D.M., DeAnda, A., The response of anesthesia trainees to simulated critical incidents (1989) Anesth Analg, 68, pp. 444-451; 
Howard, S.K., Gaba, D.M., Fish, K.J., Anesthesia crisis resource management training: Teaching anesthesiologists to handle critical incidents (1992) Aviat Space Environ Med, 63, pp. 763-770; 
Euliano, T.Y., Caton, D., Van Meurs, W., Good, M.L., Modeling obstetric cardiovascular physiology on a full-scale patient simulator (1997) J Clin Monit, 13, pp. 293-297; 
Halamek, L.P., Kaegi, D.M., Gaba, D.M., Time for a new paradigm in pediatric medical education: Teaching neonatal resuscitation in a simulated delivery room environment (2000) Pediatrics, 106, pp. E45; 
Beneken, J.E.W., (1965) A Mathematical Approach to Cardiovascular Function: The Uncontrolled Human System, , [PhD thesis]. Medisch Fysisch Institut TNO, Utrecht, The Netherlands; 
Beneken, J.E.W., DeWit, B., A physical approach to hemodynamic aspects of the human cardiovascular system (1967) Physical Bases of Circulatory Transport: Regulation and Exchange, pp. 1-45. , Reeve EB, Guyton AC, eds. Philadelphia: Saunders; 
Wesseling, K.H., Settels, J.J., Baromodulation explains short-term blood pressure variability (1985) Psychophysiology of Cardiovascular Control, pp. 69-97. , Orlebeke JF, Mulder G, van Doornen LJP, eds. New York: Plenum Press; 
Van Meurs, W.L., Good, M.L., Lampotang, S., Functional anatomy of full-scale patient simulators (1997) J Clin Monit, 13, pp. 317-324; 
Pruitt, A.W., Gersony, W.M., The cardiovascular system (1992) Nelson Textbook of Pediatrics. 14th Ed., pp. 1125-1227. , Behrman RE, ed. Philadelphia: Saunders; 
Gregory, G.A., Monitoring during surgery (1994) Pediatric Anesthesia, pp. 261-279. , Gregory GA, ed. New York: Churchill Livingstone; 
Graham Jr., T.P., Jarmakani, M.M., Evaluation of ventricular function in infants and children (1971) Pediatr Clin North Am, 18, pp. 1109-1132; 
Wodey, E., Plady, P., Copin, C., Comparative hemodynamic depression of sevoflurane versus halothane in infants (1997) Anesthesiology, 87, pp. 795-800; 
Suga, H., Sagawa, K., Shoukas, A.A., Load independence of the instantaneous pressure-volume ratio of the canine left ventricle and effects of epinephrine and heart rate on the ratio (1973) Circ Res, 2, pp. 314-322; 
Ten Voorde, B., (1992) Modeling the Baroreflex: A System Analysis Approach, , [PhD thesis]. Vakgroep Medische Fysica, Amsterdam, The Netherlands; 
Cunningham, M.D., Bedside pulmonary function testing of infants (1990) Essentials of Pediatric Intensive Care, pp. 878-883. , Levin DL, Morriss FC, eds. St. Louis: Quality Medical; 
Strafford, M.A., Cardiovascular physiology (1996) Smith{\textquoteright}s Anesthesia for Infants and Children, pp. 69-104. , Motoyama EK, Davis PJ, eds. St. Louis: Mosby; 
Lake, C.L., Cardiovascular anatomy and physiology (1992) Clinical Anesthesia, pp. 989-1020. , Barash PG, Cullen BF, Stoelting RK, eds. Philadelphia: Lippincott; 
Hsieh, K., Chen, P., Fu, S., A simple, noninvasive method to investigate vascular characteristics in children (1996) Angiology, 47, pp. 361-367; 
Stoelting, R.K., Miller, R.D., (2000) Basics of Anesthesia, pp. 364-375. , Philadelphia: Churchill Livingstone; 
Thilenius, O.G., Argilla, R.A., Angiographic right and left ventricular volume determination in normal infants and children (1974) Pediatr Res, 8, pp. 67-74; 
Drouin, E., Gournay, V., Calamel, J., Assessment of spontaneous baroreflex sensitivity in neonates (1997) Arch Dis Child Fetal Neonatal Ed, 76, pp. F108-12
},
	month = {2004///},
	pages = {1655 - 1664},
	abstract = {Full-body patient simulators provide the technology and the environment necessary for excellent clinical education while eliminating risk to the patient. The extension of simulator-based training into management of basic and critical situations in complex patient populations is natural. We describe the derivation of an infant cardiovascular model through the redefinition of a complete set of parameters for an existing adult model. Specifically, we document a stepwise parameter estimation process, explicit simplifying assumptions, and sources for these parameters. The simulated vital signs are within the target hemodynamic variables, and the simulated systemic arterial pressure wave form and left ventricular pressure volume loop are realistic. The system reacts appropriately to blood loss, and incorporation of aortic stenosis is straightforward. This infant cardiovascular model can form the basis for screen-based educational simulations. The model is also an essential step in attaining a full-body, model-driven infant simulator.},
	keywords = {aging, Algorithms, Anesthesiology, aorta stenosis, Aortic Valve Stenosis, arterial pressure, article, Baroreflex, biological model, bleeding, Blood Loss, Surgical, cardiovascular function, Cardiovascular physiology, Computer simulation, education, heart ventricle pressure, hemodynamic parameters, Hemodynamic Processes, Humans, Infant, Models, Biological, priority journal, Pulmonary Circulation, simulation, Ventricular Function, Left, Ventricular Function, Right},
	isbn = {00032999 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-9244231824\&partnerID=40\&md5=afb81c88d315abadfc3022fc3eba159a},
	author = {Goodwin, J.A. and van Meurs, W.L. and S{\'a} Couto, C.D. and Beneken, J.E.W. and Graves, S.A.}
}
@conference {2503,
	title = {Detection and validation of lung field contours on chest radiographs},
	booktitle = {Proceedings of the IASTED International Conference on Biomedical EngineeringProc. IASTED Internat. Conf. Biomed. Eng.},
	series = {Proceedings of the IASTED International Conference on Biomedical Engineering},
	year = {2003},
	note = {Conference code: 63063Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Mendon{\c c}a, A.M.; INEB, Inst. de Engenharia Biom{\'e}dica, Campus da FEUP, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal; email: amendon@fe.up.ptReferences: Tsujii, O., Freedman, M., Mun, S., Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network (1998) Medical Physics, 25, pp. 998-1007;Armato III, S., Giger, M., MacMahon, H., Computerized detection of abnormal asymetry in digital lateral chest radiographs (1994) Medical Physics, 21, pp. 1761-1768; 
Armato III, S., Giger, M., Ashizawa, K., MacMahon, H., Automated lung segmentation in digital lateral chest radiographs (1998) Medical Physics, 25, pp. 1507-1520; 
Carrascal, F., Carreira, J., Souto, M., Automatic calculation of total lung capacity from automatically traced lung boundaries in posterior-anterior and lateral digital chest radiographs (1998) Medical Physics, 25, pp. 1118-1131; 
Ginneken, B., Romeny, B., Automatic segmentation of lung fields in chest radiographs (2000) Medical Physics, 27, pp. 2445-2455; 
Li, L., Zheng, Y., Kallergi, M., Clark, R., Improved method for automatic identification of lung regions on chest radiographs (2001) Academic Radiology, 8, pp. 629-638; 
Monteiro, M., Mendon{\c c}a, A.M., Campilho, A., Lung field detection on chest radiographs (2002) Proceedings of VIIP02, IASTED International Conference on Visualization, Imaging and Image Processing, pp. 367-372. , J. Villanueva (ed): Spain
Sponsors: IASTED},
	month = {2003///},
	pages = {183 - 188},
	address = {Salzburg},
	abstract = {The purpose of the research herein presented is the automatic detection of lung boundaries in posterior-anterior digital chest radiographs. The precise location of the two lungs is important in a computer-aided diagnosis system as it allows the reduction of the region under analysis, decreasing the computation time and facilitating data compression. Furthermore, it allows the delimitation of the search area, easing the selective tuning of the abnormalities detection algorithms. The results produced by the automatic method were validated by comparison with manual contours traced by an experienced radiologist. For this particular purpose, two programs with friendly interfaces were developed. The achieved comparison results demonstrate the good performance of the automatic method.},
	keywords = {Algorithms, automation, Biological organs, Chest radiographs, Computational time, Computer aided diagnosis, Computer-aided diagnosis, Data compression, Image analysis, Medical image processing, Medical imaging, Radiography, Regions of interest (ROI)},
	isbn = {0889863539 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-2642575173\&partnerID=40\&md5=83abd9849c03da498665141b65efa36c},
	author = {Mendon{\c c}a, A.M. and Alves Da Silva, J. and Campilho, A.},
	editor = {Hamza M.H.}
}
@conference {2525,
	title = {Real-time implementation of an optical flow algorithm},
	booktitle = {Proceedings - International Conference on Pattern RecognitionProc. Int. Conf. Pattern Recognit.},
	volume = {16},
	year = {2002},
	note = {Cited By (since 1996): 29Export Date: 21 November 2012Source: ScopusCODEN: PICRELanguage of Original Document: EnglishCorrespondence Address: Correia, M.V.; Instituto de Engenharia Biom{\'e}dica, Faculdade de Engenharia, Dept. Eng. Electrot{\'e}cnica e de Computadores, R. Dr. Roberto Frias, 4200-465 Porto, Portugal; email: mcorreia@fe.up.ptReferences: Barron, J.L., Fleet, D.J., Beauchemin, S.S., Performance of optical flow techniques (1994) International Journal of Computer Vision, 12 (1), pp. 43-77;Correia, M.V., Campilho, A.C., Santos, J.A., Nunes, L.B., Optical flow techniques applied to the calibration of visual perception experiments (1996) Proceedings of 13th International Conference on Pattern Recognition, ICPR {\textquoteright}96, pp. 498-502. , Vienna, Austria, August; 
Fleet, D.J., Langley, K., Recursive filters for optical flow (1995) IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (1), pp. 61-67. , January; 
Fleury, M., Clark, A.F., Downton, A.C., Evaluating optical-flow algorithms on a parallel machine (2001) Image and Vision Computing, 19, pp. 131-143; 
Lin, T., Barron, J.L., Image reconstruction error for optical flow (1994) Proceedings of Vision Interface{\textquoteright}94, pp. 73-80. , Banff National Park, Canada, May; 
Liu, H., Hong, T.-H., Herman, M., Camus, T., Accuracy vs efficiency trade-offs in optical flow algorithms (1998) Computer Vision and Image Understanding, 72 (3), pp. 271-286. , December; 
Lucas, B.D., (1984) Generalized Image Matching by the Method of Differences, , PhD thesis, Carnegie Mellon University; 
Olson, T.J., Taylor, J.R., Lockwood, R.J., Programming a pipelined image processor (1996) Computer Vision and Image Understanding, 64 (3), pp. 351-367. , November; 
Santos, J., Noriega, P., Correia, M., Campilho, A., Albuquerque, P., (2000) Object Motion Segmentation and Optical Flow Contrast, , Relat{\'o}rios de investiga{\c c}{\~a}o C.E.E.P., Universidade do Minho, Braga; 
Simoncelli, E.P., (1993) Distributed Representation and Analysis of Visual Motion, , PhD thesis, Massachusetts Institute of Technology, January
},
	month = {2002///},
	pages = {247 - 250},
	abstract = {This paper presents the implementation of an optical flow algorithm on a pipeline image processor. The overall optical flow computation method is presented and evaluated on a common set of image sequences. Results are compared to other implementations according to two different error measures. Due to its deterministic architecture, this implementation achieves very low computation delays that allow it to operate at standard video frame-rate and resolutions. It compares favorably to recent implementations in parallel hardware. {\textcopyright} 2002 IEEE.},
	keywords = {Algorithms, Computational methods, Image analysis, Image processors, Image sequences, Optical flow algorithm, Optical flows, Real time systems, Video frame-rate, Video signal processing},
	isbn = {10514651 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33645769017\&partnerID=40\&md5=3ee6601ed756c942925be919a4e34178},
	author = {Correia, M.V. and Campilho, A.C.}
}
@conference {2566,
	title = {ECG noise filtering using wavelets with soft-thresholding methods},
	booktitle = {Computers in CardiologyComput Cardiol},
	series = {The 26th Annual Meeting: Computers in Cardiology 1999},
	year = {1999},
	note = {Conference code: 56519Cited By (since 1996): 29Export Date: 21 November 2012Source: ScopusCODEN: COCADLanguage of Original Document: EnglishCorrespondence Address: Agante, P.M.; Inst Nacional de Engenharia, Biomedica, Oporto, PortugalReferences: Graps, A., An introduction to wavelets (1995) IEEE Computational Science and Engineering, 2 (2);Donoho, D.L., De-noising by soft-thresholding (1992) Symposium on Wavelet Theory, , Vanderbilt; 
Daubechies, I., The wavelet transform, time-frequency localization and signal analysis (1990) IEEE Transactions on Information Theory, 36 (5), pp. 961-1005; 
Chui, C.K., (1992) An Introduction to Wavelets, , Academic Press; 
Willems, J.L., CSE multilead atlas (1988) Common Standards for Quantitative Electrocardiography (European Concerted Action Project II.2.2), Div. Medical Informatics, , CSE Project Leader, Univ. Hosp. Gasthuisberg, Leuven; 
Coifman, R., Meyer, Y., Wickerhauser, V., Wavelet Analysis and Signal Processing, , ceres.math.yale.edu, Yale University, available by anonymous ftp at; 
},
	month = {1999///},
	pages = {535 - 538},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Hannover, Ger},
	abstract = {In this paper we analyze algorithms for filtering white gaussian noise and 50 Hz power line noise from ECG signals. We used several wavelets to study their effect and efficiency in the filtering process. To deal with these different kinds of noises we used two distinct soft-thresholding techniques: the Donoho{\textquoteright}s statistical threshold estimator and a method developed by us. This last method exploits one of the wavelet processing main features: time-frequency relation. The de-noising methods led to good filtering results: noise reduction with only minor change of the ECG waveforms as confirmed by the high correlation values between the processed signal and the original one. These good results are due to a wavelet advantage over classical filtering - time-frequency relation - enabling the possibility of filtering noise in the same frequency band of the ECG signal with minimal interference.},
	keywords = {Algorithms, Electrocardiography, Signal filtering and prediction, Signal interference, Wavelet denoising, White noise},
	isbn = {02766574 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0342854378\&partnerID=40\&md5=11e3b634e77d95bc3486d33a9f2a4b2d},
	author = {Agante Da Silva, P.M.G. and Marques De S{\'a}, J.P.}
}
@article {2575,
	title = {Hydrophobicity, surface tension, and zeta potential measurements of glass-reinforced hydroxyapatite composites},
	journal = {Journal of Biomedical Materials ResearchJ. Biomed. Mater. Res.},
	volume = {45},
	year = {1999},
	note = {Cited By (since 1996): 46Export Date: 21 November 2012Source: ScopusCODEN: JBMRBdoi: 10.1002/(SICI)1097-4636(19990615)45:4<370::AID-JBM12>3.0.CO;2-0PubMed ID: 10321710Language of Original Document: EnglishCorrespondence Address: Santos, J.D.; INEB, University of Porto, Rua do Campo Alegre, 823, 4150 Porto, PortugalChemicals/CAS: Biocompatible Materials; Calcium Phosphates; Hydroxyapatites; Proteins; VitronectinReferences: Yamamuro, Y., Hench, L.L., Wilson, J., (1990) CRC Handbook of Bioactive Ceramics, , Boca Raton, FL: CRC Press;Santos, J.D., Knowles, J.C., Reis, R.L., Monteiro, F.J., Hastings, G.W., Microstructural characterization of glass reinforced hydroxyapatite composites (1994) Biomaterials, 15, pp. 5-10; 
Aoki, H., Medical applications of hydroxyapatite (1991) Science and Medical Applications of Hydroxyapatite, pp. 1-214. , Aoki H, Sawai K, editors. Tokyo: Takyama Press System Center; 
Akao, M., Aoki, H., Kato, K., Mechanical properties of sintered hydroxyapatite for prosthetic applications (1981) J Mater Sci, 16, pp. 809-812; 
Davies, J.E., The importance and measurement of surface charge species in cell behaviour at the biomaterial interface (1988) Surface Characterization of Biomaterials, pp. 219-234. , Ratner BD. New York: Elsevier; 
Ratner, B.D., Biomaterial surfaces (1987) J Biomed Mater Res Appl Biomat, 21, pp. 59-90; 
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Grinnell, F., Milamand, M., Srere, P.A., Studies on cell adhesion (1972) Arch biochem biophys, 153, pp. 193-198; 
Chang, S.K., Hum, O.S., Moscarello, Ma., Neumann, A.W., Zing, W., Leutheusser, M.J., Ruegsegger, B., Platelet adhesion to solid surfaces: The effect of plasma proteins and substrate wettability (1997) Med Progr Technol, 5, pp. 57-66; 
Agathopoulos, S., Nikolopoulos, P., Determination of the energy of the interactions oxides and biological liquids (1995) Fourth Euro Ceramics, 8, pp. 319-324. , Ravaglioli A, editor; 
Lapin, M., Warocquier-Cl{\'e}rout, R., Legris, C., Degrange, M., Luizard, M.F.S., Correlation between roughness and wettability, cell adhesion and cell migration (1997) J Biomed Mater Res, 36, pp. 99-108; 
Williams, D.F., Bagnall, R.D., Adsorption of proteins on polymers and its role in the response of soft tissue (1998) Fundamental Aspects of Biocompatibility, 2, pp. 113-127. , Williams DF, editor. Boca Raton, FL: CRC Press; 
Dimitrov, S., Krachevsky, P.A., Nikolov, A.D., Noshi, H., Matsumoto, M., Contact angle measurements with sessile drops and bubbles (1991) J Colloid Interface Sci, 145, pp. 279-282; 
Adamson, W., (1990) Physical chemistry of surfaces. 5th edition, pp. 4-52. , New York: Wiley; 
Young, T., (1835) Miscellaneous Works, 1, p. 418. , Peacock G, editor London: Murray; 
Young, T., (1805) Philos Trans R Soc Lond, 95, p. 65; 
Correia, N.T., Ramos, J.M., Saramago, B., Calado, J., Estimation of the surface tension of a solid: Application to a liquid crystalline (1997) J Colloid Interface Sci, 189, pp. 361-369; 
Owens, D.K., Wendt, R.D., Estimation of the surface free energy of polymers (1969) J Appl Polym Sci, 13, p. 1741; 
Schrader, M.E., Young dupr{\'e} revisited (1995) Langmuir, 11, p. 3585; 
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},
	month = {1999///},
	pages = {370 - 375},
	publisher = {John Wiley \& Sons Inc},
	abstract = {Wettability and zeta potential studies were performed to characterize the hydrophobicity, surface tension, and surface charge of P                        2O
                        5-glass- reinforced hydroxyapatite composites. Quantitative phase analysis was performed by the Rietveld method using GSAS software applied to X-ray diffractograms. Surface charge was assessed by zeta potential measurements. Protein adsorption studies were performed using vitronectin. Contact angles and surface tensions variation with time were determined by the sessile and pendent drop techniques, respectively, using ADSA-P software. The highest (- 18.1 mV) and lowest (-28.7 mV) values of zeta potential were found for hydroxyapatite (HA) and β-tricalcium phosphate (β-TCP), respectively, with composite materials presenting values in between. All studied bioceramic materials showed similar solid surface tension. For HA and β-TCP, solid surface tensions of 46.7 and 45.3 mJ/m
                        2, respectively, were obtained, while composites presented intermediate surface tension values. The dispersive component of surface tension was the predominant one for all materials studied. Adhesion work values between the vitronectin solution and HA and β- TCP were found to be 79.8 and 88.0 mJ/m
                        2, respectively, while the 4.0 wt \% glass composites showed slightly lower values than the 2.5 wt \% ones. The presence of β-TCP influenced surface charge, hydrophobicity, and protein adsorption of the glass-reinforced HA composites, and therefore indirectly affected cell-biomaterial interactions.
The hydrophobicity, surface tension, and surface charge of P
                        2O
                        5-glass-reinforced hydroxyapatite (HA) composites were characterized through wettability and zeta potential studies. The highest and lowest values of zeta potential were found for HA and β-TCP. All studied bioceramic materials showed similar solid surface tension. The presence of β-TCP indirectly affected cell-biomaterial interactions.
},
	keywords = {Adhesion, Adsorption, Algorithms, article, Biocompatible Materials, biomaterial, Biomaterials, Bone, Calcium Compounds, calcium phosphate, Calcium phosphates, Cells, Chemical analysis, Chemistry, Physical, Composite materials, Contact angle, glass, Glass reinforced hydroxyapatite composites, hydrophobicity, hydroxyapatite, Hydroxyapatite-glass composites, Hydroxyapatites, implant, Implants (surgical), Interfacial energy, Phosphorus Compounds, Protein adsorption, Proteins, Surface charge, Surface Properties, surface property, Surface tension, Tricalcium phosphate, Vitronectin, wettability, X-Ray Diffraction, Zeta potential},
	isbn = {00219304 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0033563979\&partnerID=40\&md5=3807a20c608ec3dcf8cce1d48213c1a3},
	author = {Lopes, M.A. and Monteiro, F.J. and Santos, J.D. and Serro, A.P. and Saramago, B.}
}
