A non-pixel image reader for continuous image detection based on tandem heterostructures

An optically addressed read-write sensor based on two stacked p-i-n heterojunctions is analyzed. The device is a two terminal image sensing structure. The charge packets are injected optically into the p-i-n writer and confined at the illuminated regions changing locally the electrical field profile across the p-i-n reader. An optical scanner is used for charge readout. The design allows a continuous readout without the need for pixel-level patterning. The role of light pattern and scanner wavelengths on the readout parameters is analyzed. The optical-to-electrical transfer characteristics show high quantum efficiency, broad spectral response, and reciprocity between light and image signal. A numerical simulation supports the imaging process. A black and white image is acquired with a resolution around 20 mum showing the potentiality of these devices for imaging applications.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

ECG-based biometrics: A real time classification approach

Behavioral biometrics is one of the areas with growing interest within the biosignal research community. A recent trend in the field is ECG-based biometrics, where electrocardiographic (ECG) signals are used as input to the biometric system. Previous work has shown this to be a promising trait, with the potential to serve as a good complement to other existing, and already more established modalities, due to its intrinsic characteristics. In this paper, we propose a system for ECG biometrics centered on signals acquired at the subject's hand. Our work is based on a previously developed custom, non-intrusive sensing apparatus for data acquisition at the hands, and involved the pre-processing of the ECG signals, and evaluation of two classification approaches targeted at real-time or near real-time applications. Preliminary results show that this system leads to competitive results both for authentication and identification, and further validate the potential of ECG signals as a complementary modality in the toolbox of the biometric system designer.

An overview of the higher level classification of Pucciniomycotina based on combined analyses of nuclear large and small subunit rDNA sequences

Mycologia, Vol. 98, nº6

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

An automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference

In the last few years the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how good is a voice when the application is a speech based interface. In this paper we present a new automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference. Our study is based on a multi-language database composed by female voices. In the objective performance evaluation the system achieved a 7.3% error rate.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI.

The application of support vector machine classification (SVM) to combined information from magnetic resonance imaging (MRI) and [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) has been shown to improve detection and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration. To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data, we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort. SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes revealed a higher discrimination accuracy in comparison to single modality classification. For the ADNI dataset accuracy rates of up to 88% and for the Leipzig cohort of up to 100% were obtained. Classifiers trained on the ADNI data discriminated the Leipzig cohorts with an accuracy of 91%. In conclusion, our results suggest SVM classification based on quantitative meta-analyses of multicenter data as a valid method for individual AD diagnosis. Furthermore, combining imaging information from MRI and FDG-PET might substantially improve the accuracy of AD diagnosis.

Classification of sags according to their origin based on the waveform similarity

A statistical method for classification of sags their origin downstream or upstream from the recording point is proposed in this work. The goal is to obtain a statistical model using the sag waveforms useful to characterise one type of sags and to discriminate them from the other type. This model is built on the basis of multi-way principal component analysis an later used to project the available registers in a new space with lower dimension. Thus, a case base of diagnosed sags is built in the projection space. Finally classification is done by comparing new sags against the existing in the case base. Similarity is defined in the projection space using a combination of distances to recover the nearest neighbours to the new sag. Finally the method assigns the origin of the new sag according to the origin of their neighbours

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Introducing a new MRI classification of lumbar spinal stenosis based on cross-sectional morphology of the dural sac : 37

Introduction: Measures of the degree of lumbar spinal stenosis (LSS) such as antero-posterior diameter of the canal, and dural sac cross sectional area vary, and do not correlate with symptoms or results of surgery. We created a grading system, comprised of seven categories, based on the morphology of the dural sac and its contents as seen on T2 axial images. The categories take into account the ratio of rootlet/ CSF content. Grade A indicates no significant compression, grade D is equivalent to a total myelograhic block. We compared this classification with commonly used criteria of severity of stenosis. Methods: Fifty T2 axial MRI images taken at disc level from 27 symptomatic LSS patients undergoing decompressive surgery were classified twice by two radiologists and three spinal surgeons working at different institutions and countries. Dural sac cross-sectional surface area and AP diameter of the canal were measured both at disc and pedicle level from DICOM images using OsiriX software. Intraand inter-observer reliability were assessed using Cohen's, Fleiss' kappa statistics, and t test. Results: For the morphological grading the average intra-and inter observer kappas were 0.76 and 0.69+, respectively, for physicians working in the study originating country. Combining all observers the kappa values were 0.57 ± 0.19. and 0.44 ± 0.19, respectively. AP diameter and dural sac cross-sectional area measurements showed no statistically significant differences between observers. No correlation between morphological grading and AP diameter or dural sac crosssectional areawas observed in 13 (26%) and 8 cases (16%), respectively. Discussion: The proposed morphological grading relies on the identification of the dural sac and CSF better seen on full MRI series. This was not available to the external observers, which might explain the lower overall kappa values. Since no specific measurement tools are needed the grading suits everyday clinical practice and favours communication of degree of stenosis between practising physicians. The absence of a strict correlation with the dural sac surface suggests that measuring the surface alone might be insufficient in defining LSS as it is essentially a mismatch between the spinal canal and its contents. This grading is now adopted in our unit and further studies concentrating on relation between morphology, clinical symptoms and surgical results are underway.

The AO comprehensive classification of pediatric long-bone fractures: a web-based multicenter agreement study.

The first AO comprehensive pediatric long-bone fracture classification system has been proposed following a structured path of development and validation with experienced pediatric surgeons. A Web-based multicenter agreement study involving 70 surgeons in 15 clinics and 5 countries was conducted to assess the reliability and accuracy of this classification when used by a wide range of surgeons with various levels of experience. Training was provided at each clinic before the session. Using the Internet, participants could log in at any time and classify 275 supracondylar, radius, and tibia fractures at their own pace. The fracture diagnosis was made following the hierarchy of the classification system using both clinical terminology and codes. kappa coefficients for the single-surgeon diagnosis of epiphyseal, metaphyseal, or diaphyseal fracture type were 0.66, 0.80, and 0.91, respectively. Median accuracy estimates for each bone and type were all greater than 80%. Depending on their experience and specialization, surgeons greatly varied in their ability to classify fractures. Pediatric training and at least 2 years of experience were associated with significant improvement in reliability and accuracy. Kappa coefficients for diagnosis of specific child patterns were 0.51, 0.63, and 0.48 for epiphyseal, metaphyseal, and diaphyseal fractures, respectively. Identified reasons for coding discrepancies were related to different understandings of terminology and definitions, as well as poor quality radiographic images. Results supported some minor adjustments in the coding of fracture type and child patterns. This classification system received wide acceptance and support among the surgeons involved. As long as appropriate training could be performed, the system classification was reliable, especially among surgeons with a minimum of 2 years of clinical experience. We encourage broad-based consultation between surgeons' international societies and the use of this classification system in the context of clinical practice as well as prospectively for clinical studies.

Assessment of a new MRI classification of spinal stenosis based on cross-sectional morphology of the dural sac : FM2

Introduction: Quantitative measures of degree of lumbar spinal stenosis (LSS) such as antero-posterior diameter of the canal or dural sac cross sectional area vary widely and do not correlate with clinical symptoms or results of surgical decompression. In an effort to improve quantification of stenosis we have developed a grading system based on the morphology of the dural sac and its contents as seen on T2 axial images. The grading comprises seven categories ranging form normal to the most severe stenosis and takes into account the ratio of rootlet/CSF content. Material and methods: Fifty T2 axial MRI images taken at disc level from twenty seven symptomatic lumbar spinal stenosis patients who underwent decompressive surgery were classified into seven categories by five observers and reclassified 2 weeks later by the same investigators. Intra- and inter-observer reliability of the classification were assessed using Cohen's and Fleiss' kappa statistics, respectively. Results: Generally, the morphology grading system itself was well adopted by the observers. Its success in application is strongly influenced by the identification of the dural sac. The average intraobserver Cohen's kappa was 0.53 ± 0.2. The inter-observer Fleiss' kappa was 0.38 ± 0.02 in the first rating and 0.3 ± 0.03 in the second rating repeated after two weeks. Discussion: In this attempt, the teaching of the observers was limited to an introduction to the general idea of the morphology grading system and one example MRI image per category. The identification of the dimension of the dural sac may be a difficult issue in absence of complete T1 T2 MRI image series as it was the case here. The similarity of the CSF to possibly present fat on T2 images was the main reason of mismatch in the assignment of the cases to a category. The Fleiss correlation factors of the five observers are fair and the proposed morphology grading system is promising.

Low noise pixel detectors based on gated geiger mode avalanche photodiodes

The gated operation is proposed as an effective method to reduce the noise in pixel detectors based on Geiger mode avalanche photodiodes. A prototype with the sensor and the front-end electronics monolithically integrated has been fabricated with a conventional HV-CMOS process. Experimental results demonstrate the increase of the dynamic range of the sensor by applying this technique.