@article {423,
	title = {Automatic bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from fMRI data},
	journal = {IEEE Transactions on Biomedical Engineering},
	volume = {57},
	year = {2010},
	pages = {2850-2860},
	abstract = {<p>We present a method for supervised, automatic, and reliable classification of healthy controls, patients with bipolar disorder, and patients with schizophrenia using brain imaging data. The method uses four supervised classification learning machines trained with a stochastic gradient learning rule based on the minimization of KullbackLeibler divergence and an optimal model complexity search through posterior probability estimation. Prior to classification, given the high dimensionality of functional MRI (fMRI) data, a dimension reduction stage comprising two steps is performed: first, a one-sample univariate t-test mean-difference Tscore approach is used to reduce the number of significant discriminative functional activated voxels, and then singular value decomposition is performed to further reduce the dimension of the input patterns to a number comparable to the limited number of subjects available for each of the three classes. Experimental results using functional brain imaging (fMRI) data include receiver operation characteristic curves for the three-way classifier with area under curve values around 0.82, 0.89, and 0.90 for healthy control versus nonhealthy, bipolar disorder versus nonbipolar, and schizophrenia patients versus nonschizophrenia binary problems, respectively. The average three-way correct classification rate (CCR) is in the range of 70\%-72\%, for the test set, remaining close to the estimated Bayesian optimal CCR theoretical upper bound of about 80\%, estimated from the one nearest-neighbor classifier over the same data. {\^A}{\textcopyright} 2010 IEEE.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Bayes Theorem, Bayesian learning, Bayesian networks, Biological, Brain, Case-Control Studies, Classifiers, Computer-Assisted, Diseases, Functional MRI (fMRI), Humans, Learning machines, Learning systems, Magnetic Resonance Imaging, Models, Operation characteristic, Optimization, ROC Curve, Reproducibility of Results, Signal Processing, Singular value decomposition, Statistical tests, Stochastic models, Student t test, area under the curve, article, bipolar disorder, classification, controlled study, functional magnetic resonance imaging, human, machine learning, major clinical study, neuroimaging, patient coding, receiver operating characteristic, reliability, schizophrenia},
	issn = {00189294},
	doi = {10.1109/TBME.2010.2080679},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78649311169\&partnerID=40\&md5=d3b90f1a3ee4ef209d131ef986e142db},
	author = {J I Arribas and V D Calhoun and T Adali}
}
@article {422,
	title = {A radius and ulna TW3 bone age assessment system},
	journal = {IEEE Transactions on Biomedical Engineering},
	volume = {55},
	year = {2008},
	pages = {1463-1476},
	abstract = {<p>An end-to-end system to automate the well-known Tanner - Whitehouse (TW3) clinical procedure to estimate the skeletal age in childhood is proposed. The system comprises the detailed analysis of the two most important bones in TW3: the radius and ulna wrist bones. First, a modified version of an adaptive clustering segmentation algorithm is presented to properly semi-automatically segment the contour of the bones. Second, up to 89 features are defined and extracted from bone contours and gray scale information inside the contour, followed by some well-founded feature selection mathematical criteria, based on the ideas of maximizing the classes{\textquoteright} separability. Third, bone age is estimated with the help of a Generalized Softmax Perceptron (GSP) neural network (NN) that, after supervised learning and optimal complexity estimation via the application of the recently developed Posterior Probability Model Selection (PPMS) algorithm, is able to accurately predict the different development stages in both radius and ulna from which and with the help of the TW3 methodology, we are able to conveniently score and estimate the bone age of a patient in years, in what can be understood as a multiple-class (multiple stages) pattern recognition approach with posterior probability estimation. Finally, numerical results are presented to evaluate the system performance in predicting the bone stages and the final patient bone age over a private hand image database, with the help of the pediatricians and the radiologists expert diagnoses. {\^A}{\textcopyright} 2006 IEEE.</p>
},
	keywords = {Age Determination by Skeleton, Aging, Algorithms, Artificial Intelligence, Automated, Bone, Bone age assessment, Clustering algorithms, Computer-Assisted, Humans, Model selection, Neural networks, Pattern recognition, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity and Specificity, Skeletal maturity, algorithm, article, artificial neural network, automation, bone age, bone maturation, childhood, instrumentation, radius, ulna},
	issn = {00189294},
	doi = {10.1109/TBME.2008.918554},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-42249094547\&partnerID=40\&md5=2cecfea5f75a61b048611f2391b00aed},
	author = {Antonio Trist{\'a}n-Vega and J I Arribas}
}
