



@article {2242,
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},
	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 {2253,
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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.}
}
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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 {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},
	volume = {30},
	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.}
}
