Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Statistical segmentation methods and color variance analysis of retinal images

In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.

Development of a new galaxy classification technique and its application to the zCOSMOS survey

The purpose of this Thesis is to develop a robust and powerful method to classify galaxies from large surveys, in order to establish and confirm the connections between the principal observational parameters of the galaxies (spectral features, colours, morphological indices), and help unveil the evolution of these parameters from $z \sim 1$ to the local Universe. Within the framework of zCOSMOS-bright survey, and making use of its large database of objects ($\sim 10\,000$ galaxies in the redshift range $0 < z \lesssim 1.2$) and its great reliability in redshift and spectral properties determinations, first we adopt and extend the \emph{classification cube method}, as developed by Mignoli et al. (2009), to exploit the bimodal properties of galaxies (spectral, photometric and morphologic) separately, and then combining together these three subclassifications. We use this classification method as a test for a newly devised statistical classification, based on Principal Component Analysis and Unsupervised Fuzzy Partition clustering method (PCA+UFP), which is able to define the galaxy population exploiting their natural global bimodality, considering simultaneously up to 8 different properties. The PCA+UFP analysis is a very powerful and robust tool to probe the nature and the evolution of galaxies in a survey. It allows to define with less uncertainties the classification of galaxies, adding the flexibility to be adapted to different parameters: being a fuzzy classification it avoids the problems due to a hard classification, such as the classification cube presented in the first part of the article. The PCA+UFP method can be easily applied to different datasets: it does not rely on the nature of the data and for this reason it can be successfully employed with others observables (magnitudes, colours) or derived properties (masses, luminosities, SFRs, etc.). The agreement between the two classification cluster definitions is very high. ``Early'' and ``late'' type galaxies are well defined by the spectral, photometric and morphological properties, both considering them in a separate way and then combining the classifications (classification cube) and treating them as a whole (PCA+UFP cluster analysis). Differences arise in the definition of outliers: the classification cube is much more sensitive to single measurement errors or misclassifications in one property than the PCA+UFP cluster analysis, in which errors are ``averaged out'' during the process. This method allowed us to behold the \emph{downsizing} effect taking place in the PC spaces: the migration between the blue cloud towards the red clump happens at higher redshifts for galaxies of larger mass. The determination of $M_{\mathrm{cross}}$ the transition mass is in significant agreement with others values in literature.

Pollutant concentrations and Meteorological data classification by Neural Networks

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved.

Causas mal definidas de morte e óbitos sem assistência

OBJETIVOS: Este trabalho estuda a distribuição dos óbitos por causas mal definidas no Brasil, no ano de 2003, entre as quais identifica a proporção de mortes sem assistência. MÉTODOS: Os dados provieram do Sistema de Informações Sobre Mortalidade, coordenado pelo Ministério da Saúde. As causas mal definidas de morte compreenderam as incluídas no "Capítulo XVIII - Sintomas, sinais e achados anormais de exames clínicos e de laboratório não classificados em outra parte" da Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde, décima revisão, capítulo este no qual a categoria R98 identificava a "morte sem assistência". RESULTADOS: No Brasil, em 2003, a causa básica de 13,3% dos óbitos foi identificada como mal definida, sendo que as proporções maiores ocorreram nas Regiões Nordeste e Norte. Do total de causas mal definidas no país, 53,3% corresponderam a mortes sem assistência, proporção esta que superou 70% nos Estados do Maranhão, Piauí, Rio Grande do Norte, Pernambuco, Bahia, Paraíba e Alagoas. CONCLUSÃO: Dada a estrutura descentralizada para o levantamento dos óbitos no país, identifica-se a maior responsabilidade dos municípios e, em seguida, dos Estados para o aprimoramento da qualidade das estatísticas de mortalidade.

O papel das Classificações da OMS - CID e CIF nas definições de deficiência e incapacidade

A Organização Mundial de Saúde tem hoje duas classificações de referência para a descrição dos estados de saúde: a Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde, que corresponde à décima revisão da Classificação Internacional de Doenças (CID-10) e a Classificação Internacional de Funcionalidade, Incapacidade e Saúde (CIF). A utilização da CIF vem sendo aguardada com grande expectativa pelas organizações de pessoas com deficiências e instituições relacionadas. A falta de definição clara de "deficiência" ou "incapacidade" tem sido apontada como um impedimento para a promoção de saúde de pessoas com deficiência. É importante que essas definições, especialmente no âmbito legislativo e regulamentar, sejam consistentes e se fundamentem num modelo coerente sobre o processo que origina as situações de incapacidade. Este artigo tem como objetivo apresentar elementos da CID-10 e da CIF, e o papel que desempenham para definir deficiência e incapacidade. Os componentes da CIF podem contribuir para diferentes campos de aplicabilidade no que diz respeito ao entendimento das definições de deficiência ou incapacidade a partir do conceito de funcionalidade e dos fatores contextuais.

O papel das Classificações da OMS-CID e CIF nas definições de deficiência e incapacidade

A Organização Mundial de Saúde tem hoje duas classificações de referência para a descrição dos estados de saúde: a Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde, que corresponde à décima revisão da Classificação Internacional de Doenças (CID-10) e a Classificação Internacional de Funcionalidade, Incapacidade e Saúde (CIF). A utilização da CIF vem sendo aguardada com grande expectativa pelas organizações de pessoas com deficiências e instituições relacionadas. A falta de definição clara de "deficiência" ou "incapacidade" tem sido apontada como um impedimento para a promoção de saúde de pessoas com deficiência. É importante que essas definições, especialmente no âmbito legislativo e regulamentar, sejam consistentes e se fundamentem num modelo coerente sobre o processo que origina as situações de incapacidade. Este artigo tem como objetivo apresentar elementos da CID-10 e da CIF, e o papel que desempenham para definir deficiência e incapacidade. Os componentes da CIF podem contribuir para diferentes campos de aplicabilidade no que diz respeito ao entendimento das definições de deficiência ou incapacidade a partir do conceito de funcionalidade e dos fatores contextuais

A mortalidade de idosos no Brasil: a questão das causas mal definidas

O envelhecimento populacional é um fato marcante da transição demográfica. O estudo das causas básicas em idosos permite visualizar seu perfil epidemiológico, embora possa ser prejudicado pela alta proporção de causas mal definidas. O objetivo deste trabalho é descrever a mortalidade dos idosos por essas causas no Brasil. A fonte dos dados foi o Sistema de Informações sobre Mortalidade do Ministério da Saúde.Entre as variáveis, a principal modalidade foi a causa básica mal definida [ Capítulo XVIII da Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde-Décima Revisão (CID-10)]. O decréscimo desses óbitos em idosos foi de 35 por cento entre 1996 e 2005.Considerando os óbitos de 60 a 69 anos e os de 80 e mais anos, as proporções de mal definidos aumentaram em 9,9 por cento e 14,8 por cento, respectivamente, no ano de 2005. Métodos visando a sua diminuição são sugeridos, salientando-se que o fato mais importante é o de os médicos preencherem adequadamente as declarações de óbito- com as reais causas básicas, conseqüênciais e terminais-, objetivo maior dos estudiosos

Mortes maternas e mortes por causas maternas

Ao mensurar-se mortalidade materna, é necessário distinguir ' mortes por causas maternas' e 'mortes maternas' Para a Organização Mundial da Saúde-OMS-,mortes maternas são as que ocorrem na gestação, no parto e até 42 dias após o parto; e mortes por causas maternas englobam as causas classificadas no Capítulo XV da Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde, Décima Revisão (CID-10), incluindo as ocorridas quando passados 42 dias do parto. Apresentam-se resultados da investigação de mortes femininas em idade fértil-10 a 49 anos- nas capitais de Estados e no Distrito Federal do Brasil, em 2002. Adotou-se a metodologia RAMOS, comparando-se as causas básicas das declarações de óbito originais com as das declarações preenchidas após o resgate de informações, obtidas em entrevistas domiciliares e prontuários. Entre as mortes por causas maternas originais, 15,9 por cento não eram mortes maternas, de acordo com a definição da OMS. Houve, concomitantemente, subenumeração de mortes maternas. Sugestões são feitas para melhorar o preenchimento das declarações de óbito e inclusão de novas categorias na CID-10, visando melhorar a informação das causas maternas

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Motivation. The study of human brain development in itsearly stage is today possible thanks to in vivo fetalmagnetic resonance imaging (MRI) techniques. Aquantitative analysis of fetal cortical surfacerepresents a new approach which can be used as a markerof the cerebral maturation (as gyration) and also forstudying central nervous system pathologies [1]. However,this quantitative approach is a major challenge forseveral reasons. First, movement of the fetus inside theamniotic cavity requires very fast MRI sequences tominimize motion artifacts, resulting in a poor spatialresolution and/or lower SNR. Second, due to the ongoingmyelination and cortical maturation, the appearance ofthe developing brain differs very much from thehomogenous tissue types found in adults. Third, due tolow resolution, fetal MR images considerably suffer ofpartial volume (PV) effect, sometimes in large areas.Today extensive efforts are made to deal with thereconstruction of high resolution 3D fetal volumes[2,3,4] to cope with intra-volume motion and low SNR.However, few studies exist related to the automatedsegmentation of MR fetal imaging. [5] and [6] work on thesegmentation of specific areas of the fetal brain such asposterior fossa, brainstem or germinal matrix. Firstattempt for automated brain tissue segmentation has beenpresented in [7] and in our previous work [8]. Bothmethods apply the Expectation-Maximization Markov RandomField (EM-MRF) framework but contrary to [7] we do notneed from any anatomical atlas prior. Data set &Methods. Prenatal MR imaging was performed with a 1-Tsystem (GE Medical Systems, Milwaukee) using single shotfast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms,FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatialresolution 1.09mm). Each fetus has 6 axial volumes(around 15 slices per volume), each of them acquired inabout 1 min. Each volume is shifted by 1 mm with respectto the previous one. Gestational age (GA) ranges from 29to 32 weeks. Mother is under sedation. Each volume ismanually segmented to extract fetal brain fromsurrounding maternal tissues. Then, in-homogeneityintensity correction is performed using [9] and linearintensity normalization is performed to have intensityvalues that range from 0 to 255. Note that due tointra-tissue variability of developing brain someintensity variability still remains. For each fetus, ahigh spatial resolution image of isotropic voxel size of1.09 mm is created applying [2] and using B-splines forthe scattered data interpolation [10] (see Fig. 1). Then,basal ganglia (BS) segmentation is performed on thissuper reconstructed volume. Active contour framework witha Level Set (LS) implementation is used. Our LS follows aslightly different formulation from well-known Chan-Vese[11] formulation. In our case, the LS evolves forcing themean of the inside of the curve to be the mean intensityof basal ganglia. Moreover, we add local spatial priorthrough a probabilistic map created by fitting anellipsoid onto the basal ganglia region. Some userinteraction is needed to set the mean intensity of BG(green dots in Fig. 2) and the initial fitting points forthe probabilistic prior map (blue points in Fig. 2). Oncebasal ganglia are removed from the image, brain tissuesegmentation is performed as described in [8]. Results.The case study presented here has 29 weeks of GA. Thehigh resolution reconstructed volume is presented in Fig.1. The steps of BG segmentation are shown in Fig. 2.Overlap in comparison with manual segmentation isquantified by the Dice similarity index (DSI) equal to0.829 (values above 0.7 are considered a very goodagreement). Such BG segmentation has been applied on 3other subjects ranging for 29 to 32 GA and the DSI hasbeen of 0.856, 0.794 and 0.785. Our segmentation of theinner (red and blue contours) and outer cortical surface(green contour) is presented in Fig. 3. Finally, torefine the results we include our WM segmentation in theFreesurfer software [12] and some manual corrections toobtain Fig.4. Discussion. Precise cortical surfaceextraction of fetal brain is needed for quantitativestudies of early human brain development. Our workcombines the well known statistical classificationframework with the active contour segmentation forcentral gray mater extraction. A main advantage of thepresented procedure for fetal brain surface extraction isthat we do not include any spatial prior coming fromanatomical atlases. The results presented here arepreliminary but promising. Our efforts are now in testingsuch approach on a wider range of gestational ages thatwe will include in the final version of this work andstudying as well its generalization to different scannersand different type of MRI sequences. References. [1]Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau,Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4]Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio.Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7]Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009.[9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans.Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEETrans. Img. Proc, 10(2), 2001 [12] Freesurfer,http://surfer.nmr.mgh.harvard.edu.

Brain tissue segmentation of fetal MR images

We present a segmentation method for fetal brain tissuesof T2w MR images, based on the well known ExpectationMaximization Markov Random Field (EM- MRF) scheme. Ourmain contribution is an intensity model composed of 7Gaussian distribution designed to deal with the largeintensity variability of fetal brain tissues. The secondmain contribution is a 3-steps MRF model that introducesboth local spatial and anatomical priors given by acortical distance map. Preliminary results on 4 subjectsare presented and evaluated in comparison to manualsegmentations showing that our methodology cansuccessfully be applied to such data, dealing with largeintensity variability within brain tissues and partialvolume (PV).

Sistema expert en anàlisi de patrons socials

Creació d'un sistema d'anàlisi de patrons socials a partir de comportaments predictius de la nostra base de coneixement.

Decreasing mortality and changing patterns of causes of death in the Swiss HIV Cohort Study.

BACKGROUND: Mortality among HIV-infected persons is decreasing, and causes of death are changing. Classification of deaths is hampered because of low autopsy rates, frequent deaths outside of hospitals, and shortcomings of International Statistical Classification of Diseases and Related Health Problems (ICD-10) coding. METHODS: We studied mortality among Swiss HIV Cohort Study (SHCS) participants (1988-2010) and causes of death using the Coding Causes of Death in HIV (CoDe) protocol (2005-2009). Furthermore, we linked the SHCS data to the Swiss National Cohort (SNC) cause of death registry. RESULTS: AIDS-related mortality peaked in 1992 [11.0/100 person-years (PY)] and decreased to 0.144/100 PY (2006); non-AIDS-related mortality ranged between 1.74 (1993) and 0.776/100 PY (2006); mortality of unknown cause ranged between 2.33 and 0.206/100 PY. From 2005 to 2009, 459 of 9053 participants (5.1%) died. Underlying causes of deaths were: non-AIDS malignancies [total, 85 (19%) of 446 deceased persons with known hepatitis C virus (HCV) status; HCV-negative persons, 59 (24%); HCV-coinfected persons, 26 (13%)]; AIDS [73 (16%); 50 (21%); 23 (11%)]; liver failure [67 (15%); 12 (5%); 55 (27%)]; non-AIDS infections [42 (9%); 13 (5%); 29 (14%)]; substance use [31 (7%); 9 (4%); 22 (11%)]; suicide [28 (6%); 17 (7%), 11 (6%)]; myocardial infarction [28 (6%); 24 (10%), 4 (2%)]. Characteristics of deceased persons differed in 2005 vs. 2009: median age (45 vs. 49 years, respectively); median CD4 count (257 vs. 321 cells/μL, respectively); the percentage of individuals who were antiretroviral therapy-naïve (13 vs. 5%, respectively); the percentage of deaths that were AIDS-related (23 vs. 9%, respectively); and the percentage of deaths from non-AIDS-related malignancies (13 vs. 24%, respectively). Concordance in the classification of deaths was 72% between CoDe and ICD-10 coding in the SHCS; and 60% between the SHCS and the SNC registry. CONCLUSIONS: Mortality in HIV-positive persons decreased to 1.33/100 PY in 2010. Hepatitis B or C virus coinfections increased the risk of death. Between 2005 and 2009, 84% of deaths were non-AIDS-related. Causes of deaths varied according to data source and coding system.