RAP3DF - One shoot 3D face dataset.

Develop researches in the 3D biometrics field requires a large set of images, whether for training the algorithms or during the recognition test. Several datasets can be found in the literature. In an analysis of these datasets it was observed that a single dataset does not have the types of infrared images, visible and three-dimensional light, for the same sample. Given this context, the present work conceived this 3D facial dataset, with its respective visible light image and infrared spectrum, providing the entire image acquisition process from the Kinect One device. The work consists of 267 samples from 64 volunteers, where each volunteer has a frontal facial image and 3 images in arbitrary positions.

Meta-Analysis of the Sensitivity of Decision Support Systems in Diagnosing Diabetic Retinopathy.

Diabetic Retinopathy (DR) is one of the most common microvascular complications presenting by patients diagnosticated with diabetic diseases. Uncontrolled hyperglycemia may manifest as visual impairment and blindness. The early detection of DR is essential to minimize the risk and consequence of visual diminishing. The standard gold diagnoses tool relies on different imaging modalities and requires a judgment of expert photographers, which are not available in most of the primary care centers or remote location. In that scenario, an automate or semiautomated DR screening systems can contribute to improving the accuracy of the diagnostic. Thus, we performed a Systematic Review and Meta-Analysis to evaluate the Decision Support Systems (DSS) in diagnosing DR. The overall Diagnostic Odds Ratio was 73.15 (95%CI: 37.54-142.50), sensitivity was 97.70 (95%CI: 97.50-97.90) and specificity was 90.30 (95%CI: 90.00-90.60). Our results corroborate with the concept of usefulness of DSSs in early diagnosis, screening and preliminary evaluation of suspicious images of DR.

Data Mining Applied to Analysis of Contraceptive Methods Among College Students.

The aim of this study was to use the Data Mining to analyze the profile of the use of contraceptive methods in a university population. We used a database about sexuality performed on a university population in southern Brazil. The results obtained by the generated rules are largely in line with the literature and epidemiology worldwide, showing significant points of vulnerability in the university population. Validation measures of the study, as such, accuracy, sensitivity, specificity, and area under the ROC curve were higher or at least similar as compared to recent studies using the same methodology.

Accuracy of Decision Support Systems for Breast Cancer - Initial Results.

The aim of this study was to evaluate the accuracy of decision support systems in the diagnosis of breast cancer by means of a systematic review and meta-analysis of studies of diagnostic accuracy. The first step of the research, which consisted of the initial research of abstracts and titles identified from the research strategy in the databases was performed by two researchers independently. In this stage, 622 references were retrieved in the databases and, through a consensus meeting, 183 articles were selected for full reading.

Use of Data Mining to Predict the Risk Factors Associated With Osteoporosis and Osteopenia in Women.

Osteoporosis has recently been acknowledged as a major public health issue in developed countries because of the decrease in the quality of life of the affected person and the increase in public costs due to complete or partial physical disability. The aim of this study was to use the J48 algorithm as a classification task for data from women exhibiting changes in bone densitometry. The study population included all patients treated at the diagnostic center for bone densitometry since 2010. Census sample data collection was conducted as all elements of the population were included in the sample. The service in question provides care to patients via the Brazilian Unified Health System and private plans. The results of the classification task were analyzed using the J48 algorithm, and among the dichotomized variables associated with a diagnosis of osteoporosis, the mean accuracy was 74.0 (95% confidence interval [CI], 61.0-68.0) and the mean area under the curve of the receiver operating characteristic (ROC) curve was 0.65 (95% CI, 0.64-0.66), with a mean sensitivity of 76.0 (95% CI, 76.0-76.0) and a mean specificity of 48.0 (95% CI, 46.0-49.0). The analyzed results showed higher values of sensitivity, accuracy, and curve of the ROC area in experiments conducted with individuals with osteoporosis. Most of the generated rules were consistent with the literature, and the few differences might serve as hypotheses for further studies.

Classificação de coloração imuno-histoquímica combinando cor e textura como descritor / Staining Immunohistochemistry classification blending color and texture as descriptor

O presente trabalho teve por objetivo demonstrar a melhora no desempenho da classificação de coloração imuno-histoquímica em imagens microscópicas, utilizando a abordagem de aprendizado supervisionada que emprega a projeção polinomial da distância de Mahalanobis. Foi definido um descritor de características híbrido, combinando core textura baseada no método Local Binary Pattern, proporcionado inicialmente um descritor 23-dimensional para cada píxel. Uma análise de componentes principais foi realizada e um segundo descritor 12-dimensional foi empregado na avaliação. Os testes foram realizados em imagens e metadados obtidos no The Human Protein Atlas, avaliando uma série de medidas de acerto e erro. Com os resultados encontrados percebeu-se que a utilização do descritor híbrido tornou o processo de classificação mais específico e restritivo nas predições positivas.

This study aimed to demonstrate the improvement in performance of immunohistochemical staining classification in microscopic images using a supervised learning approach that employs the polynomial projection of the Mahalanobis distance. A hybrid feature descriptor was defined by combining color and texture based on Local Binary Pattern method, initially provided a 23-dimensional descriptor, for each pixel. A principal component analysis was performed and a second 12-dimensional descriptor was used in the assay. The tests were performed on images and metadata, obtained on The Human Protein Atlas. With the results it can be seen that the use of hybrid descriptor has made the classification process more specific and restrictive on the positive predictions.

A Data Mining Approach to Identify Sexuality Patterns in a Brazilian University Population.

This paper presents the profile and experience of sexuality generated from a data mining classification task. We used a database about sexuality and gender violence performed on a university population in southern Brazil. The data mining task identified two relationships between the variables, which enabled the distinction of subgroups that better detail the profile and experience of sexuality. The identification of the relationships between the variables define behavioral models and factors of risk that will help define the algorithms being implemented in the data mining classification task.

Distributed Parallel Computing in Data Analysis of Osteoporosis.

This research aimed to compare the performance of two models of load balancing (Proportional and Autotuned algorithms) of the JPPF platform in the processing of data mining from a database with osteoporosis and osteopenia. When performing the analysis of execution times, it was observed that the Proportional algorithm performed better in all cases.

Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution.

In this paper, an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame processing. The samples are illuminated by a nearly coherent illumination system, where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging sensor. To increase the resolution, a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene, with small planar displacements. Displacements are resolved by a hybrid approach: (i) alignment of the LR images by a fast feature-based registration method, and (ii) fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum solution. Numerical method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed sample. The presented approach was evaluated with various biological samples including sperm and platelets, whose dimensions are in the order of a few microns. The obtained results demonstrate a spatial resolution of 1.55 µm on a field-of-view of ≈30 mm2.

Classification of images acquired with colposcopy using artificial neural networks.

OBJECTIVE: To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE: Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. RESULTS: After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. CONCLUSION: Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.

Fundus image mosaicking for information augmentation in computer-assisted slit-lamp imaging.

Laser photocoagulation is currently the standard treatment for sight-threatening diseases worldwide, namely diabetic retinopathy and retinal vein occlusions. The slit lamp biomicroscope is the most commonly used device for this procedure, specially for the treatment of the eye periphery. However, only a small portion of the retina can be visualized through the biomicroscope, complicating the task of localizing and identifying surgical targets, increasing treatment duration and patient discomfort. In order to assist surgeons, we propose a method for creating intraoperative retina maps for view expansion using a slit-lamp device. Based on the mosaicking method described by Richa et al, 2012, the proposed method is a combination of direct and feature-based methods, suitable for the textured nature of the human retina. In this paper, we describe three major enhancements to the original formulation. The first is a visual tracking method using local illumination compensation to cope with the challenging visualization conditions. The second is an efficient pixel selection scheme for increased computational efficiency. The third is an entropy-based mosaic update method to dynamically improve the retina map during exploration. To evaluate the performance of the proposed method, we conducted several experiments on human subjects with a computer-assisted slit-lamp prototype. We also demonstrate the practical value of the system for photo documentation, diagnosis and intraoperative navigation.

Performing real-time interactive fiber tracking.

Fiber tracking is a technique that, based on a diffusion tensor magnetic resonance imaging dataset, locates the fiber bundles in the human brain. Because it is a computationally expensive process, the interactivity of current fiber tracking tools is limited. We propose a new approach, which we termed real-time interactive fiber tracking, which aims at providing a rich and intuitive environment for the neuroradiologist. In this approach, fiber tracking is executed automatically every time the user acts upon the application. Particularly, when the volume of interest from which fiber trajectories are calculated is moved on the screen, fiber tracking is executed, even while it is being moved. We present our fiber tracking tool, which implements the real-time fiber tracking concept by using the video card's graphics processing units to execute the fiber tracking algorithm. Results show that real-time interactive fiber tracking is feasible on computers equipped with common, low-cost video cards.

Reconhecimento e quantificação de expressão de imunoistoquímica empregando aprendizado de métricas de distância / Recognition and quantification of immunohistochemistry expression distance metric learning

Este artigo apresenta uma abordagem de segmentação para o reconhecimento e quantificação de expressão de imunoistoquímica (IHC) através do aprendizado de uma métrica de distância. Este método é baseado em duas etapas: treinamento e segmentação. A etapa de treinamento é realizada pela seleção supervisionada de algumas áreas típicas de expressão de IHC na imagem. Nesta etapa o padrão esperado de IHC é estatisticamente caracterizado, onde ocorre o aprendizado da métrica de distância e um espaço característico é modelado. Através desse espaço são obtidos os mapas de similaridade para cada imagem de IHC, com os níveis de intensidade correspondendo ao grau da reação do biomarcador sobre o tecido. A etapa de segmentação é guiada por um parâmetro de escala que controla a quantidade de áreas marcadas com base nos valores de intensidade dos mapas de similaridade. O método é baseado no aprendizado da distância de Mahalanobis para produzir um espaço característico, para posteriormente ser utilizado na distinção entre marcações positivas de expressão de IHC e tecidos normais, bem como quantificar o grau de intensidade da reação. Os resultados obtidos pelo método proposto foram comparados com a classificação linear no espaço de cores HSV (Hue, Saturation and Value) utilizando diferentes categorias de biomarcadores. Os resultados mostram que os limites da fronteira da distribuição dos padrões são mais bem definidos no método proposto, permitindo assim uma melhor discriminação entre tecidos normais e expressão de IHC.

This paper presents a segmentation approach to the recognition and quantification of immunohistochemistry (IHC) expression employing a distance metric learning method. This method is based in a two-step procedure, training and segmentation. The training step is performed by the supervised selection of a few IHC typical stained areas on image. In that step the desired IHC pattern is statistically characterized, where a distance metric is learned and a featured space is created. With this space, similarity maps are obtained by each IHC image with its intensity levels corresponding to degrees of reaction provided by the biomarker over the tissue. The segmentation step is guided by a scale-space parameter that controls the amount of labeled areas based on intensity values of the similarity maps. This method learns a Mahalanobis distance metric to produce a featured space used to distinguish between IHC positive staining and normal tissues, as well as quantifying the reaction intensity degrees. The results obtained by the proposed method were compared to the linear classification on HSV (Hue, Saturation and Value) color space using different biomarkers categories. The comparison results show that the boundary limits of the pattern distributions are better defined in the proposed method, allowing better discrimination between normal tissues and IHC expression.

A computational method for the semi-automated quantitative analysis of tympanic membrane perforations and tympanosclerosis.

Tympanic membrane pathological findings such as perforations and tympanosclerotic plaques (along with conductive hearing loss and purulent drainage) are the most typical trademarks of either active or burned-out chronic otitis media. These findings are normally evaluated by an expert using visual subjective analysis. In this work, we present a computational semi-automated method to quantify these pathologies. We also present a validation study of the method using a subset of 39 cases randomly extracted from a set of more than 2000 cases (perforations and tympanosclerosis) of the Hospital de Clínicas de Porto Alegre. The validation was performed using Ground Truth images, generated by experts, and has shown promising results.

Recording and reenactment of collaborative diagnosis sessions using DICOM.

This paper presents a radiological collaborative tool capable of direct manipulation of Digital Imaging and Communications in Medicine (DICOM) images on both sides, and also recording and reenacting of a recorded session. A special collaborative application protocol formerly developed was extended and used as basis for the development of collaborative session recording and playback processes. The protocol is used today for real-time radiological meetings through the Internet. This new standard for collaborative sessions makes possible other uses for the protocol, such as asynchronous collaborative sessions, decision regulation, auditing, and educational applications. Experimental results are given which compare this protocol with other popular collaborative approaches. Comparison of these results shows that the proposed protocol performs much better than other approaches when run under controlled conditions.

Diffusion tensor fiber tracking on graphics processing units.

Diffusion tensor magnetic resonance imaging has been successfully applied to the process of fiber tracking, which determines the location of fiber bundles within the human brain. This process, however, can be quite lengthy when run on a regular workstation. We present a means of executing this process by making use of the graphics processing units of computers' video cards, which provide a low-cost parallel execution environment that algorithms like fiber tracking can benefit from. With this method we have achieved performance gains varying from 14 to 40 times on common computers. Because of accuracy issues inherent to current graphics processing units, we define a variation index in order to assess how close the results obtained with our method are to those generated by programs running on the central processing units of computers. This index shows that results produced by our method are acceptable when compared to those of traditional programs.

Antiulcerogenic activity of fractions and 3,15-dioxo-21alpha-hydroxy friedelane isolated from Maytenus robusta (Celastraceae).

The hexane, chloroform, ethyl acetate and aqueous-soluble fractions from leaves of Maytenus robusta (Celastraceae) were evaluated for their protective actions against ethanol-induced gastric lesions in rats. The treatment with all fractions (150 mg/kg) and omeprazol (30 mg/kg) significantly reduced the lesion index, the total lesion area, and the percentage of lesion, in comparison with the control group (p<0.05). Since the ethyl acetate-soluble fraction was found to be most active in the pylorus ligated model, this fraction was further investigated and resulted in the isolation of triterpene 3,15-dioxo-21alpha-hydroxy friedelane. The triterpene was evaluated in the HCl/ethanol-induced ulcer model in mice. In this assay, both the groups treated with 3,15-dioxo-21alpha-hydroxy friedelane and omeprazol, at a dose of 30 mg/kg, presented a significant reduction in lesion index, total lesion area, and in the percentage of the lesion, when compared with the control group (p<0.05). The result suggests that the antiulcer effect observed in the extract and fractions may be attributed, at least in part, to this compound. Further experiments are underway to determine which antiulcer mechanisms involved in gastroprotection.

Nerolidol, an antiulcer constituent from the essential oil of Baccharis dracunculifolia DC (Asteraceae).

In this study, the antiulcerogenic effect of essential oil from Baccharis dracunculifolia was evaluated using the model of acute gastric lesions induced by ethanol. The ulcerative lesion index (ULI) was significantly reduced by oral administration of the essential oil of B. dracunculifolia at doses of 50, 250 and 500 mg/kg which reduced the lesions by 42.79, 45.70 and 61.61%, respectively. The analysis of the chemical composition of the essential oil from B. dracunculifolia by GC showed that this was composed mainly of mono- and sesquiterpenes and the majority compound was nerolidol. Therefore, antiulcerogenic activity of nerolidol (50, 250 and 500 mg/kg) was investigated using ethanol-, indomethacin- and stress-induced ulcer models in rat. In the stress-induced ulcer model, a significant reduction of the ULI in animals treated with nerolidol (50, 250 and 500 mg/kg) and cimetidine (100 mg/kg) was observed, compared to the control group (p < 0.05). The percentage of inhibition of ulcer was 41.22, 51.31, 56.57 and 53.50% in groups treated with 50, 250, 500 mg/kg of nerolidol and 100 mg/kg of cimetidine (positive control), respectively. Regarding ethanol- and indomethacin-induced ulcer models, it was observed that the treatment with nerolidol (250 and 500 mg/ kg) significantly reduced the ULI in comparison with the control group (p < 0.05). The dose of 50 mg/kg reduced the parameters analyzed but this was not statistically significant. In the ethanol-induced model percentage of inhibition of ulcer was 34.20, 52.63, 87.63 and 50.87% in groups treated with 50, 250, 500 mg/kg of nerolidol and 30 mg/kg of omeprazol (positive control), respectively. In indomethacin-ulcer the percentage of inhibition of ulcer was 34.69, 40.80, 51.02 and 46.93% in groups treated with 50, 250, 500 mg/kg of nerolidol and 100 mg/ kg of cimetidine (positive control), respectively. The results of this study show that nerolidol displays antiulcer activity, as it significantly inhibited the formation of ulcers induced in different animal models. However, further pharmacological and toxicological investigations, to delineate the mechanism(s) of action and the toxic effects, are required to allow the use of nerolidol for the treatment of gastric ulcer.

Evaluation of the antiulcerogenic activity of Maytenus robusta (Celastraceae) in different experimental ulcer models.

Maytenus robusta (Celastraceae) is used in folk medicine for the treatment of stomach ulcers and is very well adapted to the South of Brazil. Maytenus ilicifolia is the main species of the Celastraceae family, and is used in the treatment of gastric ulcers. However, Maytenus ilicifolia is presently at the stage of extinction, due to indiscriminate use in Brazil. Thus, the use of Maytenus robusta in phytotherapeutic preparations, instead of Maytenus ilicifolia, is suggested. However, there have been no reports regarding the antiulcer activity of Maytenus robusta extract. Therefore, this study was carried out to evaluate the antiulcerogenic property of the hydroalcoholic extract of aerial parts of Maytenus robusta. The antiulcer assays were performed using the following protocols: nonsteroidal anti-inflammatory drug (NSAID)-induced ulcer, ethanol-induced ulcer, and stress-induced ulcer. The effects of the extract on gastric content volume, pH and total acidity, using the pylorus ligated model, were also evaluated. In the ethanol-induced ulcer model, it was observed that the treatment with Maytenus robusta extract significantly reduced the lesion index in 75.1 +/- 8.6, 85.0 +/- 9.2, 86.6 +/- 7.4 and 75.5 +/- 5.3 for the groups treated with 50, 250 and 500 mg/kg of Maytenus robusta and positive control (omeprazole 30 mg/kg), respectively. Also were observed significant inhibition in lesion index in the indomethacin-induced ulcer model, being the decrease of the 62.5 +/- 7.1, 62.5 +/- 6.1, 63.6 +/- 5.5 and 96.2 +/- 3.6 for groups treated with 50, 250 and 500 mg/kg of Maytenus robusta and positive control (cimetidine 100 mg/kg), respectively. Results similar were observed in the stress-induced ulcer model, where the inhibition of ulcer lesions were 71.3 +/- 5.5, 72.7 +/- 6.3, 76.5 +/- 7.1 and 92.3 +/- 7.5 for the groups treated with 50, 250 and 500 mg/kg of Maytenus robusta and positive control (cimetidine 100 mg/kg), respectively. Regarding the model of gastric secretion, a reduction in the volume of gastric juice volume and total acidity was observed, as well as an increase in gastric pH. The results of the present study showed that Maytenus robusta hydroalcoholic extract displays gastroprotective activity. These results were similar to those obtained in studies carried out with Maytenus ilicifolia, which indicate that this species could be used in phytotherapeutic preparations as a substitute for Maytenus ilicifolia. This work also corroborates the traditional indication of Maytenus robusta, contributing to its pharmacological validation.

Antiulcerogenic activity of crude extract, fractions and populnoic acid isolated from Austroplenckia populnea (Celastraceae).

Many plant crude extracts and their isolated compounds are the most attractive sources of new drugs and show promising results for the treatment of gastric ulcers. Austroplenckia populnea is commonly known as "marmelinho-do campo, mangabeira-brava, mangabarana and vime" and it has been used in folk medicine as anti-dysenteric and anti-rheumatic. Powdered bark wood (3.25 kg) was macerated with aqueous ethanol (96%) and the extract was concentrated under reduced pressure to yield 406 g of crude hydralcoholic extract. The hydralcoholic extract was suspended in aqueous methanol and partitioned with hexane, chloroform and ethyl acetate (EtOAc) in sequence, yielding 8.0 g, 9.5 g and 98.17 g of crude extracts, respectively. Chromatography of the hexane extract over a silica gel column led to the isolation of the triterpene populnoic acid. The oral administration of hydralcoholic, hexane, chloroform and EtOAc extracts (200 mg/kg) decreased the ulcer lesion index (ULI) by 83.15%, 46.87%, 32.2%, 68.12%, respectively. Oral administration of populnoic acid (100 mg/kg) diminished the ULI by 55.29%. All the obtained results were significant in comparison with the negative control, with exception of the chloroform extract.