Classification of hyperspectral imagery with neural networks: comparison to conventional tools

NASA Astrophysics Data System (ADS)

Merényi, Erzsébet; Farrand, William H.; Taranik, James V.; Minor, Timothy B.

2014-12-01

Efficient exploitation of hyperspectral imagery is of great importance in remote sensing. Artificial intelligence approaches have been receiving favorable reviews for classification of hyperspectral data because the complexity of such data challenges the limitations of many conventional methods. Artificial neural networks (ANNs) were shown to outperform traditional classifiers in many situations. However, studies that use the full spectral dimensionality of hyperspectral images to classify a large number of surface covers are scarce if non-existent. We advocate the need for methods that can handle the full dimensionality and a large number of classes to retain the discovery potential and the ability to discriminate classes with subtle spectral differences. We demonstrate that such a method exists in the family of ANNs. We compare the maximum likelihood, Mahalonobis distance, minimum distance, spectral angle mapper, and a hybrid ANN classifier for real hyperspectral AVIRIS data, using the full spectral resolution to map 23 cover types and using a small training set. Rigorous evaluation of the classification accuracies shows that the ANN outperforms the other methods and achieves ≈90% accuracy on test data.

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms

PubMed Central

2013-01-01

Background Breast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose of improving classification performance in CAD systems. Methods The aim of the proposed SRC framework is to construct separate dictionaries according to the types of mass margins. The underlying idea behind our method is that the separated dictionaries can enhance the sparsity of mass class (true-positive), leading to an improved performance for differentiating mammographic masses from normal tissues (false-positive). When a mass sample is given for classification, the sparse solutions based on corresponding dictionaries are separately solved and combined at score level. Experiments have been performed on both database (DB) named as Digital Database for Screening Mammography (DDSM) and clinical Full Field Digital Mammogram (FFDM) DBs. In our experiments, sparsity concentration in the true class (SCTC) and area under the Receiver operating characteristic (ROC) curve (AUC) were measured for the comparison between the proposed method and a conventional single dictionary based approach. In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass classification. Results Comparing with the conventional single dictionary configuration, the proposed approach is able to improve SCTC of up to 13.9% and 23.6% on DDSM and FFDM DBs, respectively. Moreover, the proposed method is able to improve AUC with 8.2% and 22.1% on DDSM and FFDM DBs, respectively. Comparing to SVM classifier, the proposed method improves AUC with 2.9% and 11.6% on DDSM and FFDM DBs, respectively. Conclusions The proposed dictionary configuration is found to well improve the sparsity of dictionaries, resulting in an enhanced classification performance. Moreover, the results show that the proposed method is better than conventional SVM classifier for classifying breast masses subject to various margins from normal tissues. PMID:24564973

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

PubMed Central

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. PMID:26346558

Computer-aided classification of lung nodules on computed tomography images via deep learning technique.

PubMed

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.

A modified artificial immune system based pattern recognition approach -- an application to clinic diagnostics

PubMed Central

Zhao, Weixiang; Davis, Cristina E.

2011-01-01

Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS-kNN); and (3) the proposed AIS-RBFPLS classification approaches also yielded better diagnosis results than two classical neural network approaches of BPNN and Ortho-RBF network. Conclusion In summary, this paper proposed a new machine learning method for complex systems by integrating the AIS system with RBFPLS. This new method demonstrates its satisfactory effect on classification accuracy for clinical diagnosis, and also indicates its wide potential applications to other diagnosis and detection problems. PMID:21515033

An advanced method for classifying atmospheric circulation types based on prototypes connectivity graph

NASA Astrophysics Data System (ADS)

Zagouras, Athanassios; Argiriou, Athanassios A.; Flocas, Helena A.; Economou, George; Fotopoulos, Spiros

2012-11-01

Classification of weather maps at various isobaric levels as a methodological tool is used in several problems related to meteorology, climatology, atmospheric pollution and to other fields for many years. Initially the classification was performed manually. The criteria used by the person performing the classification are features of isobars or isopleths of geopotential height, depending on the type of maps to be classified. Although manual classifications integrate the perceptual experience and other unquantifiable qualities of the meteorology specialists involved, these are typically subjective and time consuming. Furthermore, during the last years different approaches of automated methods for atmospheric circulation classification have been proposed, which present automated and so-called objective classifications. In this paper a new method of atmospheric circulation classification of isobaric maps is presented. The method is based on graph theory. It starts with an intelligent prototype selection using an over-partitioning mode of fuzzy c-means (FCM) algorithm, proceeds to a graph formulation for the entire dataset and produces the clusters based on the contemporary dominant sets clustering method. Graph theory is a novel mathematical approach, allowing a more efficient representation of spatially correlated data, compared to the classical Euclidian space representation approaches, used in conventional classification methods. The method has been applied to the classification of 850 hPa atmospheric circulation over the Eastern Mediterranean. The evaluation of the automated methods is performed by statistical indexes; results indicate that the classification is adequately comparable with other state-of-the-art automated map classification methods, for a variable number of clusters.

Ground-based cloud classification by learning stable local binary patterns

NASA Astrophysics Data System (ADS)

Wang, Yu; Shi, Cunzhao; Wang, Chunheng; Xiao, Baihua

2018-07-01

Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.

A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis.

PubMed

Bennet, Jaison; Ganaprakasam, Chilambuchelvan Arul; Arputharaj, Kannan

2014-01-01

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach

NASA Astrophysics Data System (ADS)

Tarai, Madhumita; Kumar, Keshav; Divya, O.; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar

2017-09-01

The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.

Multiple Sparse Representations Classification

PubMed Central

Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik

2015-01-01

Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and sparsity level. PMID:26177106

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

NASA Technical Reports Server (NTRS)

Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

1990-01-01

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

Statistical methods and neural network approaches for classification of data from multiple sources

NASA Technical Reports Server (NTRS)

Benediktsson, Jon Atli; Swain, Philip H.

1990-01-01

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Intelligent Automatic Classification of True and Counterfeit Notes Based on Spectrum Analysis

NASA Astrophysics Data System (ADS)

Matsunaga, Shohei; Omatu, Sigeru; Kosaka, Toshohisa

The purpose of this paper is to classify bank notes into “true” or “counterfeit” ones faster and more precisely compared with a conventional method. We note that thin lines are represented by direct lines in the images of true notes while they are represented in the counterfeit notes by dotted lines. This is due to properties of dot printers or scanner levels. To use the properties, we propose two method to classify a note into true or counterfeited one by checking whether there exist thin lines or dotted lines of the note. First, we use Fourier transform of the note to find quantity of features for classification and we classify a note into true or counterfeit one by using the features by Fourier transform. Then we propose a classification method by using wavelet transform in place of Fourier transform. Finally, some classification results are illustrated to show the effectiveness of the proposed methods.

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

NASA Astrophysics Data System (ADS)

Zhang, Yan; Wirkert, Sebastian J.; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T.; Elson, Daniel S.; Maier-Hein, Lena

2016-03-01

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

DNA extraction on bio-chip: history and preeminence over conventional and solid-phase extraction methods.

PubMed

Ayoib, Adilah; Hashim, Uda; Gopinath, Subash C B; Md Arshad, M K

2017-11-01

This review covers a developmental progression on early to modern taxonomy at cellular level following the advent of electron microscopy and the advancement in deoxyribonucleic acid (DNA) extraction for expatiation of biological classification at DNA level. Here, we discuss the fundamental values of conventional chemical methods of DNA extraction using liquid/liquid extraction (LLE) followed by development of solid-phase extraction (SPE) methods, as well as recent advances in microfluidics device-based system for DNA extraction on-chip. We also discuss the importance of DNA extraction as well as the advantages over conventional chemical methods, and how Lab-on-a-Chip (LOC) system plays a crucial role for the future achievements.

Automatic adventitious respiratory sound analysis: A systematic review.

PubMed

Pramono, Renard Xaviero Adhi; Bowyer, Stuart; Rodriguez-Villegas, Esther

2017-01-01

Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases.

Automatic adventitious respiratory sound analysis: A systematic review

PubMed Central

Bowyer, Stuart; Rodriguez-Villegas, Esther

2017-01-01

Background Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. Objective To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. Data sources A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Study selection Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Data extraction Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. Data synthesis A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Limitations Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. Conclusion A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases. PMID:28552969

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Effectiveness of Spectral Similarity Measures to Develop Precise Crop Spectra for Hyperspectral Data Analysis

NASA Astrophysics Data System (ADS)

Chauhan, H.; Krishna Mohan, B.

2014-11-01

The present study was undertaken with the objective to check effectiveness of spectral similarity measures to develop precise crop spectra from the collected hyperspectral field spectra. In Multispectral and Hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to select precise crop spectra from the set of field spectra. Conventionally crop spectra are developed after rejecting outliers based only on broad-spectrum analysis. Here a successful attempt has been made to develop precise crop spectra based on spectral similarity. As unevaluated data usage leads to uncertainty in the image classification, it is very crucial to evaluate the data. Hence, notwithstanding the conventional method, the data precision has been performed effectively to serve the purpose of the present research work. The effectiveness of developed precise field spectra was evaluated by spectral discrimination measures and found higher discrimination values compared to spectra developed conventionally. Overall classification accuracy for the image classified by field spectra selected conventionally is 51.89% and 75.47% for the image classified by field spectra selected precisely based on spectral similarity. KHAT values are 0.37, 0.62 and Z values are 2.77, 9.59 for image classified using conventional and precise field spectra respectively. Reasonable higher classification accuracy, KHAT and Z values shows the possibility of a new approach for field spectra selection based on spectral similarity measure.

Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach.

PubMed

Tarai, Madhumita; Kumar, Keshav; Divya, O; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar

2017-09-05

The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix. Copyright © 2017 Elsevier B.V. All rights reserved.

Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis

NASA Astrophysics Data System (ADS)

Itoh, Hayato; Mori, Yuichi; Misawa, Masashi; Oda, Masahiro; Kudo, Shin-ei; Mori, Kensaku

2018-02-01

This paper presents a new classification method for endocytoscopic images. Endocytoscopy is a new endoscope that enables us to perform conventional endoscopic observation and ultramagnified observation of cell level. This ultramagnified views (endocytoscopic images) make possible to perform pathological diagnosis only on endo-scopic views of polyps during colonoscopy. However, endocytoscopic image diagnosis requires higher experiences for physicians. An automated pathological diagnosis system is required to prevent the overlooking of neoplastic lesions in endocytoscopy. For this purpose, we propose a new automated endocytoscopic image classification method that classifies neoplastic and non-neoplastic endocytoscopic images. This method consists of two classification steps. At the first step, we classify an input image by support vector machine. We forward the image to the second step if the confidence of the first classification is low. At the second step, we classify the forwarded image by convolutional neural network. We reject the input image if the confidence of the second classification is also low. We experimentally evaluate the classification performance of the proposed method. In this experiment, we use about 16,000 and 4,000 colorectal endocytoscopic images as training and test data, respectively. The results show that the proposed method achieves high sensitivity 93.4% with small rejection rate 9.3% even for difficult test data.

Quantitative computed tomography applied to interstitial lung diseases.

PubMed

Obert, Martin; Kampschulte, Marian; Limburg, Rebekka; Barańczuk, Stefan; Krombach, Gabriele A

2018-03-01

To evaluate a new image marker that retrieves information from computed tomography (CT) density histograms, with respect to classification properties between different lung parenchyma groups. Furthermore, to conduct a comparison of the new image marker with conventional markers. Density histograms from 220 different subjects (normal = 71; emphysema = 73; fibrotic = 76) were used to compare the conventionally applied emphysema index (EI), 15 th percentile value (PV), mean value (MV), variance (V), skewness (S), kurtosis (K), with a new histogram's functional shape (HFS) method. Multinomial logistic regression (MLR) analyses was performed to calculate predictions of different lung parenchyma group membership using the individual methods, as well as combinations thereof, as covariates. Overall correct assigned subjects (OCA), sensitivity (sens), specificity (spec), and Nagelkerke's pseudo R 2 (NR 2 ) effect size were estimated. NR 2 was used to set up a ranking list of the different methods. MLR indicates the highest classification power (OCA of 92%; sens 0.95; spec 0.89; NR 2 0.95) when all histogram analyses methods were applied together in the MLR. Highest classification power among individually applied methods was found using the HFS concept (OCA 86%; sens 0.93; spec 0.79; NR 2 0.80). Conventional methods achieved lower classification potential on their own: EI (OCA 69%; sens 0.95; spec 0.26; NR 2 0.52); PV (OCA 69%; sens 0.90; spec 0.37; NR 2 0.57); MV (OCA 65%; sens 0.71; spec 0.58; NR 2 0.61); V (OCA 66%; sens 0.72; spec 0.53; NR 2 0.66); S (OCA 65%; sens 0.88; spec 0.26; NR 2 0.55); and K (OCA 63%; sens 0.90; spec 0.16; NR 2 0.48). The HFS method, which was so far applied to a CT bone density curve analysis, is also a remarkable information extraction tool for lung density histograms. Presumably, being a principle mathematical approach, the HFS method can extract valuable health related information also from histograms from complete different areas. Copyright © 2018 Elsevier B.V. All rights reserved.

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

PubMed

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

PubMed Central

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-01-01

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Forest and range mapping in the Houston area with ERTS-1

NASA Technical Reports Server (NTRS)

Heath, G. R.; Parker, H. D.

1973-01-01

ERTS-1 data acquired over the Houston area has been analyzed for applications to forest and range mapping. In the field of forestry the Sam Houston National Forest (Texas) was chosen as a test site, (Scene ID 1037-16244). Conventional imagery interpretation as well as computer processing methods were used to make classification maps of timber species, condition and land-use. The results were compared with timber stand maps which were obtained from aircraft imagery and checked in the field. The preliminary investigations show that conventional interpretation techniques indicated an accuracy in classification of 63 percent. The computer-aided interpretations made by a clustering technique gave 70 percent accuracy. Computer-aided and conventional multispectral analysis techniques were applied to range vegetation type mapping in the gulf coast marsh. Two species of salt marsh grasses were mapped.

Adaptive skin detection based on online training

NASA Astrophysics Data System (ADS)

Zhang, Ming; Tang, Liang; Zhou, Jie; Rong, Gang

2007-11-01

Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.

Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

PubMed

Li, Qi; Shi, Kaiyang; Gao, Ning; Li, Jian; Bai, Ou

2018-03-27

P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue. This study aimed to develop a method for acquiring more training data based on a collected small training set. A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.

The Use of Fuzzy Set Classification for Pattern Recognition of the Polygraph

DTIC Science & Technology

1993-12-01

actual feature extraction was done, It was decided to use the K-nearest neighbor ( KNN ) the data was preprocessed. The electrocardiogram classifier in...showing heart pulse, and a low frequency not known beforehand, and the KNN classifier does not component showing blood volume. The derivative of...the characteristics of the conventional KNN these six derived signals were detrended and filtered, classification method is that it assigns each

Support Vector Machines to improve physiologic hot flash measures: application to the ambulatory setting.

PubMed

Thurston, Rebecca C; Hernandez, Javier; Del Rio, Jose M; De La Torre, Fernando

2011-07-01

Most midlife women have hot flashes. The conventional criterion (≥2 μmho rise/30 s) for classifying hot flashes physiologically has shown poor performance. We improved this performance in the laboratory with Support Vector Machines (SVMs), a pattern classification method. We aimed to compare conventional to SVM methods to classify hot flashes in the ambulatory setting. Thirty-one women with hot flashes underwent 24 h of ambulatory sternal skin conductance monitoring. Hot flashes were quantified with conventional (≥2 μmho/30 s) and SVM methods. Conventional methods had low sensitivity (sensitivity=.57, specificity=.98, positive predictive value (PPV)=.91, negative predictive value (NPV)=.90, F1=.60), with performance lower with higher body mass index (BMI). SVMs improved this performance (sensitivity=.87, specificity=.97, PPV=.90, NPV=.96, F1=.88) and reduced BMI variation. SVMs can improve ambulatory physiologic hot flash measures. Copyright © 2010 Society for Psychophysiological Research.

8 CFR 204.306 - Classification as an immediate relative based on a Convention adoption.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 8 Aliens and Nationality 1 2011-01-01 2011-01-01 false Classification as an immediate relative....306 Classification as an immediate relative based on a Convention adoption. (a) Unless 8 CFR 204.309 requires the denial of a Form I-800A or Form I-800, a child is eligible for classification as an immediate...

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed

K, Jalal Deen; R, Ganesan; A, Merline

2017-07-27

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. Creative Commons Attribution License

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed Central

K, Jalal Deen; R, Ganesan; A, Merline

2017-01-01

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. PMID:28749127

Extracting land use information from the earth resources technology satellite data by conventional interpretation methods

NASA Technical Reports Server (NTRS)

Vegas, P. L.

1974-01-01

A procedure for obtaining land use data from satellite imagery by the use of conventional interpretation methods is presented. The satellite is described briefly, and the advantages of various scales and multispectral scanner bands are discussed. Methods for obtaining satellite imagery and the sources of this imagery are given. Equipment used in the study is described, and samples of land use maps derived from satellite imagery are included together with the land use classification system used. Accuracy percentages are cited and are compared to those of a previous experiment using small scale aerial photography.

Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease

PubMed Central

Koikkalainen, Juha; Lötjönen, Jyrki; Thurfjell, Lennart; Rueckert, Daniel; Waldemar, Gunhild; Soininen, Hilkka

2012-01-01

In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and comparedtothe conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N=772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86.0% for the classification of control and AD subjects, and 72.1%for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM. PMID:21419228

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

PubMed

Schwartzkopf, Wade C; Bovik, Alan C; Evans, Brian L

2005-12-01

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

Deep multi-scale convolutional neural network for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Zhang, Feng-zhe; Yang, Xia

2018-04-01

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques.

PubMed

Eitrich, T; Kless, A; Druska, C; Meyer, W; Grotendorst, J

2007-01-01

In this paper, we study the classifications of unbalanced data sets of drugs. As an example we chose a data set of 2D6 inhibitors of cytochrome P450. The human cytochrome P450 2D6 isoform plays a key role in the metabolism of many drugs in the preclinical drug discovery process. We have collected a data set from annotated public data and calculated physicochemical properties with chemoinformatics methods. On top of this data, we have built classifiers based on machine learning methods. Data sets with different class distributions lead to the effect that conventional machine learning methods are biased toward the larger class. To overcome this problem and to obtain sensitive but also accurate classifiers we combine machine learning and feature selection methods with techniques addressing the problem of unbalanced classification, such as oversampling and threshold moving. We have used our own implementation of a support vector machine algorithm as well as the maximum entropy method. Our feature selection is based on the unsupervised McCabe method. The classification results from our test set are compared structurally with compounds from the training set. We show that the applied algorithms enable the effective high throughput in silico classification of potential drug candidates.

Criteria for Mitral Regurgitation Classification were inadequate for Dilated Cardiomyopathy

PubMed Central

Mancuso, Frederico José Neves; Moisés, Valdir Ambrosio; Almeida, Dirceu Rodrigues; Oliveira, Wercules Antonio; Poyares, Dalva; Brito, Flavio Souza; de Paola, Angelo Amato Vincenzo; Carvalho, Antonio Carlos Camargo; Campos, Orlando

2013-01-01

Background Mitral regurgitation (MR) is common in patients with dilated cardiomyopathy (DCM). It is unknown whether the criteria for MR classification are inadequate for patients with DCM. Objective We aimed to evaluate the agreement among the four most common echocardiographic methods for MR classification. Methods Ninety patients with DCM were included. Functional MR was classified using four echocardiographic methods: color flow jet area (JA), vena contracta (VC), effective regurgitant orifice area (ERO) and regurgitant volume (RV). MR was classified as mild, moderate or important according to the American Society of Echocardiography criteria and by dividing the values into terciles. The Kappa test was used to evaluate whether the methods agreed, and the Pearson correlation coefficient was used to evaluate the correlation between the absolute values of each method. Results MR classification according to each method was as follows: JA: 26 mild, 44 moderate, 20 important; VC: 12 mild, 72 moderate, 6 important; ERO: 70 mild, 15 moderate, 5 important; RV: 70 mild, 16 moderate, 4 important. The agreement was poor among methods (kappa = 0.11; p < 0.001). It was observed a strong correlation between the absolute values of each method, ranging from 0.70 to 0.95 (p < 0.01) and the agreement was higher when values were divided into terciles (kappa = 0.44; p < 0.01) Conclusion The use of conventional echocardiographic criteria for MR classification seems inadequate in patients with DCM. It is necessary to establish new cutoff values for MR classification in these patients. PMID:24100692

Coniferous forest classification and inventory using Landsat and digital terrain data

NASA Technical Reports Server (NTRS)

Franklin, J.; Logan, T. L.; Woodcock, C. E.; Strahler, A. H.

1986-01-01

Machine-processing techniques were used in a Forest Classification and Inventory System (FOCIS) procedure to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Using FOCIS as a basis for stratified sampling, the softwood timber volumes of the Klamath National Forest and Eldorado National Forest were estimated within standard errors of 4.8 and 4.0 percent, respectively. The accuracy of these large-area inventories is comparable to the accuracy yielded by use of conventional timber inventory methods, but, because of automation, the FOCIS inventories are more rapid (9-12 months compared to 2-3 years for conventional manual photointerpretation, map compilation and drafting, field sampling, and data processing) and are less costly.

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

PubMed Central

2013-01-01

Background Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes. Methods We propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets. Results The performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO. PMID:23617960

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Significance of perceptually relevant image decolorization for scene classification

NASA Astrophysics Data System (ADS)

Viswanathan, Sowmya; Divakaran, Govind; Soman, Kutti Padanyl

2017-11-01

Color images contain luminance and chrominance components representing the intensity and color information, respectively. The objective of this paper is to show the significance of incorporating chrominance information to the task of scene classification. An improved color-to-grayscale image conversion algorithm that effectively incorporates chrominance information is proposed using the color-to-gray structure similarity index and singular value decomposition to improve the perceptual quality of the converted grayscale images. The experimental results based on an image quality assessment for image decolorization and its success rate (using the Cadik and COLOR250 datasets) show that the proposed image decolorization technique performs better than eight existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component for scene classification tasks is demonstrated using a deep belief network-based image classification system developed using dense scale-invariant feature transforms. The amount of chrominance information incorporated into the proposed image decolorization technique is confirmed with the improvement to the overall scene classification accuracy. Moreover, the overall scene classification performance improved by combining the models obtained using the proposed method and conventional decolorization methods.

Criteria for mitral regurgitation classification were inadequate for dilated cardiomyopathy.

PubMed

Mancuso, Frederico José Neves; Moisés, Valdir Ambrosio; Almeida, Dirceu Rodrigues; Oliveira, Wercules Antonio; Poyares, Dalva; Brito, Flavio Souza; Paola, Angelo Amato Vincenzo de; Carvalho, Antonio Carlos Camargo; Campos, Orlando

2013-11-01

Mitral regurgitation (MR) is common in patients with dilated cardiomyopathy (DCM). It is unknown whether the criteria for MR classification are inadequate for patients with DCM. We aimed to evaluate the agreement among the four most common echocardiographic methods for MR classification. Ninety patients with DCM were included. Functional MR was classified using four echocardiographic methods: color flow jet area (JA), vena contracta (VC), effective regurgitant orifice area (ERO) and regurgitant volume (RV). MR was classified as mild, moderate or important according to the American Society of Echocardiography criteria and by dividing the values into terciles. The Kappa test was used to evaluate whether the methods agreed, and the Pearson correlation coefficient was used to evaluate the correlation between the absolute values of each method. MR classification according to each method was as follows: JA: 26 mild, 44 moderate, 20 important; VC: 12 mild, 72 moderate, 6 important; ERO: 70 mild, 15 moderate, 5 important; RV: 70 mild, 16 moderate, 4 important. The agreement was poor among methods (kappa=0.11; p<0.001). It was observed a strong correlation between the absolute values of each method, ranging from 0.70 to 0.95 (p<0.01) and the agreement was higher when values were divided into terciles (kappa = 0.44; p < 0.01) CONCLUSION: The use of conventional echocardiographic criteria for MR classification seems inadequate in patients with DCM. It is necessary to establish new cutoff values for MR classification in these patients.

The Influence of Tactile Perception on Classification of Bone Tissue at Dental Implant Insertion.

PubMed

Linck, Gláucia Kelly Silva Barbosa; Ferreira, Geovane Miranda; De Oliveira, Rubelisa Cândido Gomes; Lindh, Christina; Leles, Cláudio Rodrigues; Ribeiro-Rotta, Rejane Faria

2016-06-01

Various ways of using the Lekholm and Zarb (L&Z) classification have added to the lack of scientific evidence of the effectiveness of this clinical method in the evaluation of implant treatment. The study aims to assess subjective jawbone classifications in patients referred for implant treatment, using L&Z classification with and without surgeon's hand perception at implant insertion. The association between bone type classifications and quantitative parameters of primary implant stability was also assessed. One hundred thirty-five implants were inserted using conventional loading protocol. Three surgeons classified bone quality at implant sites using two methods: one based on periapical and panoramic images (modified L&Z) and one based on the same images associated with the surgeon's tactile perception during drilling (original L&Z). Peak insertion torque and implant stability quotient (ISQ) were recorded. The modified and original L&Z were strongly correlated (rho = 0.79; p < .001); Wilcoxon signed-rank test showed no significant difference in the distribution of bone type classification between pairs using the two methods (p = .538). Spearman correlation tested the association between primary stability parameters and bone type classifications (-0.34 to -0.57 [p < .001]). Tactile surgical perception has a minor influence on rating of subjective bone type for dental implant treatment using the L&Z classification. © 2015 Wiley Periodicals, Inc.

8 CFR 204.306 - Classification as an immediate relative based on a Convention adoption.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 8 Aliens and Nationality 1 2010-01-01 2010-01-01 false Classification as an immediate relative based on a Convention adoption. 204.306 Section 204.306 Aliens and Nationality DEPARTMENT OF HOMELAND SECURITY IMMIGRATION REGULATIONS IMMIGRANT PETITIONS Intercountry Adoption of a Convention Adoptee § 204...

A computer analysis of ERTS data of the Lake Gregory area of South Australia with particular emphasis on its role in terrain classification for engineering. M.S. Thesis

NASA Technical Reports Server (NTRS)

Lodwick, G. D. (Principal Investigator)

1976-01-01

A digital computer and multivariate statistical techniques were used to analyze 4-band multispectral data. A representation of the original data for each of the four bands allows a certain degree of terrain interpretation; however, variations in appearance of sites within and between bands, without additional criteria for deciding which representation should be preferred, create difficulties for classification. Investigation of the video data groups produced by principal components analysis and cluster analysis techniques shows that effective correlations with classifications of terrain produced by conventional methods could be carried out. The analyses also highlighted underlying relationships between the various elements. The approach used allows large areas (185 cm by 185 cm) to be classified into fundamental units within a matter of hours and can be applied to those parts of the Earth where facilities for conventional studies are poor or lacking.

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation.

PubMed

Cong, Rui; Li, Jing; Guo, Song

2017-02-01

To examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. From April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. With pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all P<0.05). When applying Qual1=Color pattern 1 for downgrading and Qual1=Color pattern 5 for upgrading the BI-RADS categories, we obtained the highest Az value (0.951), and achieved a significantly higher specificity (86.56%, P=0.002) than that of the US (81.18%) with the same sensitivity (94.96%). The qualitative classification proposed in this study may be representative of SWE parameters and has potential to be relevant assistance in breast mass diagnoses. Copyright © 2016. Published by Elsevier B.V.

Training sample selection based on self-training for liver cirrhosis classification using ultrasound images

NASA Astrophysics Data System (ADS)

Fujita, Yusuke; Mitani, Yoshihiro; Hamamoto, Yoshihiko; Segawa, Makoto; Terai, Shuji; Sakaida, Isao

2017-03-01

Ultrasound imaging is a popular and non-invasive tool used in the diagnoses of liver disease. Cirrhosis is a chronic liver disease and it can advance to liver cancer. Early detection and appropriate treatment are crucial to prevent liver cancer. However, ultrasound image analysis is very challenging, because of the low signal-to-noise ratio of ultrasound images. To achieve the higher classification performance, selection of training regions of interest (ROIs) is very important that effect to classification accuracy. The purpose of our study is cirrhosis detection with high accuracy using liver ultrasound images. In our previous works, training ROI selection by MILBoost and multiple-ROI classification based on the product rule had been proposed, to achieve high classification performance. In this article, we propose self-training method to select training ROIs effectively. Evaluation experiments were performed to evaluate effect of self-training, using manually selected ROIs and also automatically selected ROIs. Experimental results show that self-training for manually selected ROIs achieved higher classification performance than other approaches, including our conventional methods. The manually ROI definition and sample selection are important to improve classification accuracy in cirrhosis detection using ultrasound images.

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

PubMed Central

Javadi, Mehrdad; Ebrahimpour, Reza; Sajedin, Atena; Faridi, Soheil; Zakernejad, Shokoufeh

2011-01-01

This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization. PMID:22046232

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm.

PubMed

Mao, Yong; Zhou, Xiao-Bo; Pi, Dao-Ying; Sun, You-Xian; Wong, Stephen T C

2005-10-01

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.

Evaluation of 99mTc-MIBI in thyroid gland imaging for the diagnosis of amiodarone-induced thyrotoxicosis

PubMed Central

Zhang, Ruiguo

2017-01-01

Objective: Amiodarone-induced thyrotoxicosis (AIT) is caused by amiodarone as a side effect of cardiovascular disease treatment. Based on the differences in their pathological and physiological mechanisms, many methods have been developed so far to differentiate AIT subtypes such as colour flow Doppler sonography (CFDS) and 24-h radioiodine uptake (RAIU). However, these methods suffer from inadequate accuracy in distinguishing different types of AITs and sometimes lead to misdiagnosis and delayed treatments. Therefore, there is an unmet demand for an efficient method for accurate classification of AIT. Methods: Technetium-99 methoxyisobutylisonitrile (99mTc-MIBI) thyroid imaging was performed on 15 patients for AIT classification, and the results were compared with other conventional methods such as CFDS, RAIU and 99mTcO4 imaging. Results: High uptake and retention of MIBI in thyroid tissue is characteristic in Type I AIT, while in sharp contrast, low uptake of MIBI in the thyroid tissue was observed in Type II AIT. Mixed-type AIT shows uptake value between Types I and II. MIBI imaging outperforms other methods with a lower misdiagnosis rate. Conclusion: Among the methods evaluated, MIBI imaging enables an accurate identification of Type I, II and mixed-type AITs by showing distinct images for different types of AITs. The results obtained from our selected subjects revealed that MIBI imaging is a reliable method for diagnosis and classification of AITs and MIBI imaging has potential in the treatment of thyroid diseases. Advances in knowledge: 99mTc-MIBI imaging is a useful method for the diagnosis of AIT. It can distinguish different types of AITs especially for mixed-type AIT, which is usually difficult to treat. 99mTc-MIBI has potential advantages over conventional methods in the efficient treatment of AIT. PMID:28106465

Spectral-spatial classification of hyperspectral image using three-dimensional convolution network

NASA Astrophysics Data System (ADS)

Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu

2018-01-01

Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

Dermal and inhalation acute toxic class methods: test procedures and biometric evaluations for the Globally Harmonized Classification System.

PubMed

Holzhütter, H G; Genschow, E; Diener, W; Schlede, E

2003-05-01

The acute toxic class (ATC) methods were developed for determining LD(50)/LC(50) estimates of chemical substances with significantly fewer animals than needed when applying conventional LD(50)/LC(50) tests. The ATC methods are sequential stepwise procedures with fixed starting doses/concentrations and a maximum of six animals used per dose/concentration. The numbers of dead/moribund animals determine whether further testing is necessary or whether the test is terminated. In recent years we have developed classification procedures for the oral, dermal and inhalation routes of administration by using biometric methods. The biometric approach assumes a probit model for the mortality probability of a single animal and assigns the chemical to that toxicity class for which the best concordance is achieved between the statistically expected and the observed numbers of dead/moribund animals at the various steps of the test procedure. In previous publications we have demonstrated the validity of the biometric ATC methods on the basis of data obtained for the oral ATC method in two-animal ring studies with 15 participants from six countries. Although the test procedures and biometric evaluations for the dermal and inhalation ATC methods have already been published, there was a need for an adaptation of the classification schemes to the starting doses/concentrations of the Globally Harmonized Classification System (GHS) recently adopted by the Organization for Economic Co-operation and Development (OECD). Here we present the biometric evaluation of the dermal and inhalation ATC methods for the starting doses/concentrations of the GHS and of some other international classification systems still in use. We have developed new test procedures and decision rules for the dermal and inhalation ATC methods, which require significantly fewer animals to provide predictions of toxicity classes, that are equally good or even better than those achieved by using the conventional LD(50)/LC(50) methods. In order to cope with rather narrow dose/concentration classes of the GHS we have, as in our previous publications, combined the outcome of all results that can be obtained during testing for the allocation to one of the defined toxicity classes of the GHS. Our results strongly recommend the deletion of the dermal LD(50) and the inhalation LC(50) test as regulatory tests and the adoption of the dermal and inhalation ATC methods as internationally accepted alternatives.

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

PubMed Central

Cao, Houwei; Verma, Ragini; Nenkova, Ani

2014-01-01

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion. PMID:25422534

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

PubMed

Cao, Houwei; Verma, Ragini; Nenkova, Ani

2015-01-01

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion.

Local Subspace Classifier with Transform-Invariance for Image Classification

NASA Astrophysics Data System (ADS)

Hotta, Seiji

A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.

Speech Enhancement based on the Dominant Classification Between Speech and Noise Using Feature Data in Spectrogram of Observation Signal

NASA Astrophysics Data System (ADS)

Nomura, Yukihiro; Lu, Jianming; Sekiya, Hiroo; Yahagi, Takashi

This paper presents a speech enhancement using the classification between the dominants of speech and noise. In our system, a new classification scheme between the dominants of speech and noise is proposed. The proposed classifications use the standard deviation of the spectrum of observation signal in each band. We introduce two oversubtraction factors for the dominants of speech and noise, respectively. And spectral subtraction is carried out after the classification. The proposed method is tested on several noise types from the Noisex-92 database. From the investigation of segmental SNR, Itakura-Saito distance measure, inspection of spectrograms and listening tests, the proposed system is shown to be effective to reduce background noise. Moreover, the enhanced speech using our system generates less musical noise and distortion than that of conventional systems.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

PubMed

Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

Updated Histologic Classification of Adenomas and Carcinomas in the Colon of Carcinogen-treated Sprague-Dawley Rats.

PubMed

Rubio, Carlos A

2017-12-01

Recent studies have disclosed novel histological phenotypes of colon tumours in carcinogen-treated rats. The aim of this study was to update the current histological classification of colonic neoplasias in Sprague-Dawley (SD) rats. Archival sections from 398 SD rats having 408 neoplasias in previous experiments were re-evaluated. Of the 408 colonic neoplasias, 11% (44/408) were adenomas without invasive growth and 89% (364/408) invasive carcinomas. Out of the 44 adenomas, 82% were conventional (tubular or villous), 14% traditional serrated (TSA; with unlocked serrations or with closed microtubules) and 5% gut-associated lymphoid tissue (GALT)-associated adenomas. Out of 364 carcinomas, 57% were conventional carcinomas, 26% GALT carcinomas, 8% undifferentiated, 6% signet-ring cell carcinomas, and 4% traditional serrated carcinomas (TSC). Thus, conventional adenomas, conventional carcinomas and GALT-associated carcinomas predominated (p<0.05). The updated classification of colonic tumours in SD rats includes conventional adenomas, TSA, GALT-associated adenomas, conventional carcinomas, TSC, GALT-associated carcinomas, signet-ring cell carcinomas and undifferentiated carcinomas. Several of the histological phenotypes reported here are not included in any of the current classifications of colonic tumours in rodents. This updated classification fulfils the requirements for an animal model of human disease, inasmuch as similar histological phenotypes of colon neoplasias have been documented in humans. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people

NASA Astrophysics Data System (ADS)

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Fengkui; Liu, Feixiang

2017-09-01

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral image analysis

USDA-ARS?s Scientific Manuscript database

Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive....

Classification of MR brain images by combination of multi-CNNs for AD diagnosis

NASA Astrophysics Data System (ADS)

Cheng, Danni; Liu, Manhua; Fu, Jianliang; Wang, Yaping

2017-07-01

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for development of future treatment. Magnetic resonance images (MRI) play important role to help understand the brain anatomical changes related to AD. Conventional methods extract the hand-crafted features such as gray matter volumes and cortical thickness and train a classifier to distinguish AD from other groups. Different from these methods, this paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. First, a number of local image patches are extracted from the whole brain image and a 3D-CNN is built upon each local patch to transform the local image into more compact high-level features. Then, the upper convolution and fully connected layers are fine-tuned to combine the multiple 3D-CNNs for image classification. The proposed method can automatically learn the generic features from imaging data for classification. Our method is evaluated using T1-weighted structural MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 87.15% and an AUC (area under the ROC curve) of 92.26% for AD classification, demonstrating the promising classification performances.

Human Vision-Motivated Algorithm Allows Consistent Retinal Vessel Classification Based on Local Color Contrast for Advancing General Diagnostic Exams.

PubMed

Ivanov, Iliya V; Leitritz, Martin A; Norrenberg, Lars A; Völker, Michael; Dynowski, Marek; Ueffing, Marius; Dietter, Johannes

2016-02-01

Abnormalities of blood vessel anatomy, morphology, and ratio can serve as important diagnostic markers for retinal diseases such as AMD or diabetic retinopathy. Large cohort studies demand automated and quantitative image analysis of vascular abnormalities. Therefore, we developed an analytical software tool to enable automated standardized classification of blood vessels supporting clinical reading. A dataset of 61 images was collected from a total of 33 women and 8 men with a median age of 38 years. The pupils were not dilated, and images were taken after dark adaption. In contrast to current methods in which classification is based on vessel profile intensity averages, and similar to human vision, local color contrast was chosen as a discriminator to allow artery vein discrimination and arterial-venous ratio (AVR) calculation without vessel tracking. With 83% ± 1 standard error of the mean for our dataset, we achieved best classification for weighted lightness information from a combination of the red, green, and blue channels. Tested on an independent dataset, our method reached 89% correct classification, which, when benchmarked against conventional ophthalmologic classification, shows significantly improved classification scores. Our study demonstrates that vessel classification based on local color contrast can cope with inter- or intraimage lightness variability and allows consistent AVR calculation. We offer an open-source implementation of this method upon request, which can be integrated into existing tool sets and applied to general diagnostic exams.

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase

NASA Astrophysics Data System (ADS)

Zink, Rob; Hunyadi, Borbála; Van Huffel, Sabine; De Vos, Maarten

2016-04-01

Objective. One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. Approach. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. Main results. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. Significance. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase.

PubMed

Zink, Rob; Hunyadi, Borbála; Huffel, Sabine Van; Vos, Maarten De

2016-04-01

One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

PubMed

Taguchi, Y-h; Iwadate, Mitsuo; Umeyama, Hideaki

2015-04-30

Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

PubMed

Boskamp, Tobias; Lachmund, Delf; Oetjen, Janina; Cordero Hernandez, Yovany; Trede, Dennis; Maass, Peter; Casadonte, Rita; Kriegsmann, Jörg; Warth, Arne; Dienemann, Hendrik; Weichert, Wilko; Kriegsmann, Mark

2017-07-01

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2016 Elsevier B.V. All rights reserved.

Spectral Data Reduction via Wavelet Decomposition

NASA Technical Reports Server (NTRS)

Kaewpijit, S.; LeMoigne, J.; El-Ghazawi, T.; Rood, Richard (Technical Monitor)

2002-01-01

The greatest advantage gained from hyperspectral imagery is that narrow spectral features can be used to give more information about materials than was previously possible with broad-band multispectral imagery. For many applications, the new larger data volumes from such hyperspectral sensors, however, present a challenge for traditional processing techniques. For example, the actual identification of each ground surface pixel by its corresponding reflecting spectral signature is still one of the most difficult challenges in the exploitation of this advanced technology, because of the immense volume of data collected. Therefore, conventional classification methods require a preprocessing step of dimension reduction to conquer the so-called "curse of dimensionality." Spectral data reduction using wavelet decomposition could be useful, as it does not only reduce the data volume, but also preserves the distinctions between spectral signatures. This characteristic is related to the intrinsic property of wavelet transforms that preserves high- and low-frequency features during the signal decomposition, therefore preserving peaks and valleys found in typical spectra. When comparing to the most widespread dimension reduction technique, the Principal Component Analysis (PCA), and looking at the same level of compression rate, we show that Wavelet Reduction yields better classification accuracy, for hyperspectral data processed with a conventional supervised classification such as a maximum likelihood method.

a Single-Exposure Dual-Energy Computed Radiography Technique for Improved Nodule Detection and Classification in Chest Imaging

NASA Astrophysics Data System (ADS)

Zink, Frank Edward

The detection and classification of pulmonary nodules is of great interest in chest radiography. Nodules are often indicative of primary cancer, and their detection is particularly important in asymptomatic patients. The ability to classify nodules as calcified or non-calcified is important because calcification is a positive indicator that the nodule is benign. Dual-energy methods offer the potential to improve both the detection and classification of nodules by allowing the formation of material-selective images. Tissue-selective images can improve detection by virtue of the elimination of obscuring rib structure. Bone -selective images are essentially calcium images, allowing classification of the nodule. A dual-energy technique is introduced which uses a computed radiography system to acquire dual-energy chest radiographs in a single-exposure. All aspects of the dual-energy technique are described, with particular emphasis on scatter-correction, beam-hardening correction, and noise-reduction algorithms. The adaptive noise-reduction algorithm employed improves material-selective signal-to-noise ratio by up to a factor of seven with minimal sacrifice in selectivity. A clinical comparison study is described, undertaken to compare the dual-energy technique to conventional chest radiography for the tasks of nodule detection and classification. Observer performance data were collected using the Free Response Observer Characteristic (FROC) method and the bi-normal Alternative FROC (AFROC) performance model. Results of the comparison study, analyzed using two common multiple observer statistical models, showed that the dual-energy technique was superior to conventional chest radiography for detection of nodules at a statistically significant level (p < .05). Discussion of the comparison study emphasizes the unique combination of data collection and analysis techniques employed, as well as the limitations of comparison techniques in the larger context of technology assessment.

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

PubMed

Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

2007-08-01

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

Selective classification for improved robustness of myoelectric control under nonideal conditions.

PubMed

Scheme, Erik J; Englehart, Kevin B; Hudgins, Bernard S

2011-06-01

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional classification accuracy as well as a form of leave-one-out analysis that may be more representative of real prosthetic use. Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries making it flexible and intuitive for clinical use.

On Sloshing Modes in Equilateral-Polygonal-Section Containers

NASA Astrophysics Data System (ADS)

Hirata, Katsuya; Tanigawa, Hirochika; Yamamoto, Masahiro; Nakashima, Tohru; Funaki, Jiro

Vertical sloshing is the liquid surface motion in a container forced to oscillate in the vertical direction. The present paper concerns the vertical sloshing in various equilateral-polygonal-section containers such as octagonal-, heptagonal-, hexagonal-, pentagonal-, square- and triangular-section containers together with a circular-section container, in order to generalise their sloshing modes. As a result, the authors classify the sloshing modes on the basis of the conventional circular-section-container sloshing modes. It is revealed that this modal classification has some advantages over that based on the conventional square-section-container sloshing modes. Furthermore, the stability diagrams for all the equilateral-polygonal-section containers are investigated by both experiments and computations. The present computation is based on a discrete singularity method. The proposed modal classification is useful to predict the eigen frequencies. Specifically speaking, it is found that the equivalent diameter de1 based on the hydraulic mean depth is the most adequate as a characteristic length scale to classify all the sloshing modes. The authors show a unified formula to predict the eigen frequencies, using de1 together with the proposed modal classification.

Effect of management (organic vs conventional) on volatile profiles of six plum cultivars (Prunus salicina Lindl.). A chemometric approach for varietal classification and determination of potential markers.

PubMed

Cuevas, F J; Moreno-Rojas, J M; Arroyo, F; Daza, A; Ruiz-Moreno, M J

2016-05-15

The volatile profiles of six plum cultivars ('Laetitia', 'Primetime', 'Sapphire', 'Showtime', 'Songold' and 'Souvenir') produced under two management systems (conventional and organic) and harvested in two consecutive years were obtained by HS-SPME-GC-MS. Twenty-five metabolites were determined, five of which (pentanal, (E)-2-heptenal, 1-octanol, eucalyptol and 2-pentylfuran) are reported for the first time in Prunus salicina Lindl. Hexanal stood out as a major volatile compound affected by the management system. In addition, partial least square discriminant analysis (PLS-DA) achieved an effective classification of genotypes based on their volatile profiles. A high classification accuracy model was obtained with a sensitivity of 97.9% and a specificity of 99.6%. Furthermore, the application of a dual criterion, based on a method of variable selection, VIP (variable importance in projection) and the results of a univariate analysis (ANOVA), allowed the identification of potential volatile markers in 'Primetime', 'Showtime' and 'Souvenir' genotypes (cultivars not characterised to date). Copyright © 2015 Elsevier Ltd. All rights reserved.

Object-based methods for individual tree identification and tree species classification from high-spatial resolution imagery

NASA Astrophysics Data System (ADS)

Wang, Le

2003-10-01

Modern forest management poses an increasing need for detailed knowledge of forest information at different spatial scales. At the forest level, the information for tree species assemblage is desired whereas at or below the stand level, individual tree related information is preferred. Remote Sensing provides an effective tool to extract the above information at multiple spatial scales in the continuous time domain. To date, the increasing volume and readily availability of high-spatial-resolution data have lead to a much wider application of remotely sensed products. Nevertheless, to make effective use of the improving spatial resolution, conventional pixel-based classification methods are far from satisfactory. Correspondingly, developing object-based methods becomes a central challenge for researchers in the field of Remote Sensing. This thesis focuses on the development of methods for accurate individual tree identification and tree species classification. We develop a method in which individual tree crown boundaries and treetop locations are derived under a unified framework. We apply a two-stage approach with edge detection followed by marker-controlled watershed segmentation. Treetops are modeled from radiometry and geometry aspects. Specifically, treetops are assumed to be represented by local radiation maxima and to be located near the center of the tree-crown. As a result, a marker image was created from the derived treetop to guide a watershed segmentation to further differentiate overlapping trees and to produce a segmented image comprised of individual tree crowns. The image segmentation method developed achieves a promising result for a 256 x 256 CASI image. Then further effort is made to extend our methods to the multiscales which are constructed from a wavelet decomposition. A scale consistency and geometric consistency are designed to examine the gradients along the scale-space for the purpose of separating true crown boundary from unwanted textures occurring due to branches and twigs. As a result from the inverse wavelet transform, the tree crown boundary is enhanced while the unwanted textures are suppressed. Based on the enhanced image, an improvement is achieved when applying the two-stage methods to a high resolution aerial photograph. To improve tree species classification, we develop a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. The optimal scale parameter is then fed in the process of a region-growing-based segmentation as a break-off value. Our object classification achieves a better accuracy in separating tree species when compared to the conventional Maximum Likelihood Classification (MLC). In summary, we develop two object-based methods for identifying individual trees and classifying tree species from high-spatial resolution imagery. Both methods achieve promising results and will promote integration of Remote Sensing and GIS in forest applications.

How a national vegetation classification can help ecological research and management

USGS Publications Warehouse

Franklin, Scott; Comer, Patrick; Evens, Julie; Ezcurra, Exequiel; Faber-Langendoen, Don; Franklin, Janet; Jennings, Michael; Josse, Carmen; Lea, Chris; Loucks, Orie; Muldavin, Esteban; Peet, Robert K.; Ponomarenko, Serguei; Roberts, David G.; Solomeshch, Ayzik; Keeler-Wolf, Todd; Van Kley, James; Weakley, Alan; McKerrow, Alexa; Burke, Marianne; Spurrier, Carol

2015-01-01

The elegance of classification lies in its ability to compile and systematize various terminological conventions and masses of information that are unattainable during typical research projects. Imagine a discipline without standards for collection, analysis, and interpretation; unfortunately, that describes much of 20th-century vegetation ecology. With differing methods, how do we assess community dynamics over decades, much less centuries? How do we compare plant communities from different areas? The need for a widely applied vegetation classification has long been clear. Now imagine a multi-decade effort to assimilate hundreds of disparate vegetation classifications into one common classification for the US. In this letter, we introduce the US National Vegetation Classification (USNVC; www.usnvc.org) as a powerful tool for research and conservation, analogous to the argument made by Schimel and Chadwick (2013) for soils. The USNVC provides a national framework to classify and describe vegetation; here we describe the USNVC and offer brief examples of its efficacy.

Manifold Regularized Experimental Design for Active Learning.

PubMed

Zhang, Lining; Shum, Hubert P H; Shao, Ling

2016-12-02

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Crowd density estimation based on convolutional neural networks with mixed pooling

NASA Astrophysics Data System (ADS)

Zhang, Li; Zheng, Hong; Zhang, Ying; Zhang, Dongming

2017-09-01

Crowd density estimation is an important topic in the fields of machine learning and video surveillance. Existing methods do not provide satisfactory classification accuracy; moreover, they have difficulty in adapting to complex scenes. Therefore, we propose a method based on convolutional neural networks (CNNs). The proposed method improves performance of crowd density estimation in two key ways. First, we propose a feature pooling method named mixed pooling to regularize the CNNs. It replaces deterministic pooling operations with a parameter that, by studying the algorithm, could combine the conventional max pooling with average pooling methods. Second, we present a classification strategy, in which an image is divided into two cells and respectively categorized. The proposed approach was evaluated on three datasets: two ground truth image sequences and the University of California, San Diego, anomaly detection dataset. The results demonstrate that the proposed approach performs more effectively and easily than other methods.

Design of Passive Power Filter for Hybrid Series Active Power Filter using Estimation, Detection and Classification Method

NASA Astrophysics Data System (ADS)

Swain, Sushree Diptimayee; Ray, Pravat Kumar; Mohanty, K. B.

2016-06-01

This research paper discover the design of a shunt Passive Power Filter (PPF) in Hybrid Series Active Power Filter (HSAPF) that employs a novel analytic methodology which is superior than FFT analysis. This novel approach consists of the estimation, detection and classification of the signals. The proposed method is applied to estimate, detect and classify the power quality (PQ) disturbance such as harmonics. This proposed work deals with three methods: the harmonic detection through wavelet transform method, the harmonic estimation by Kalman Filter algorithm and harmonic classification by decision tree method. From different type of mother wavelets in wavelet transform method, the db8 is selected as suitable mother wavelet because of its potency on transient response and crouched oscillation at frequency domain. In harmonic compensation process, the detected harmonic is compensated through Hybrid Series Active Power Filter (HSAPF) based on Instantaneous Reactive Power Theory (IRPT). The efficacy of the proposed method is verified in MATLAB/SIMULINK domain and as well as with an experimental set up. The obtained results confirm the superiority of the proposed methodology than FFT analysis. This newly proposed PPF is used to make the conventional HSAPF more robust and stable.

Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

2015-12-01

This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data.

PubMed

Wang, Shuaiqun; Aorigele; Kong, Wei; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

PubMed Central

Aorigele; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. PMID:27579323

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

PubMed Central

2014-01-01

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms. PMID:24795876

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

PubMed

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Pulmonary emphysema classification based on an improved texton learning model by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Fujita, Hiroshi

2013-03-01

In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE) subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE and PLE, respectively.

Novel Strength Test Battery to Permit Evidence-Based Paralympic Classification

PubMed Central

Beckman, Emma M.; Newcombe, Peter; Vanlandewijck, Yves; Connick, Mark J.; Tweedy, Sean M.

2014-01-01

Abstract Ordinal-scale strength assessment methods currently used in Paralympic athletics classification prevent the development of evidence-based classification systems. This study evaluated a battery of 7, ratio-scale, isometric tests with the aim of facilitating the development of evidence-based methods of classification. This study aimed to report sex-specific normal performance ranges, evaluate test–retest reliability, and evaluate the relationship between the measures and body mass. Body mass and strength measures were obtained from 118 participants—63 males and 55 females—ages 23.2 years ± 3.7 (mean ± SD). Seventeen participants completed the battery twice to evaluate test–retest reliability. The body mass–strength relationship was evaluated using Pearson correlations and allometric exponents. Conventional patterns of force production were observed. Reliability was acceptable (mean intraclass correlation = 0.85). Eight measures had moderate significant correlations with body size (r = 0.30–61). Allometric exponents were higher in males than in females (mean 0.99 vs 0.30). Results indicate that this comprehensive and parsimonious battery is an important methodological advance because it has psychometric properties critical for the development of evidence-based classification. Measures were interrelated with body size, indicating further research is required to determine whether raw measures require normalization in order to be validly applied in classification. PMID:25068950

Differentiation of Organically and Conventionally Grown Tomatoes by Chemometric Analysis of Combined Data from Proton Nuclear Magnetic Resonance and Mid-infrared Spectroscopy and Stable Isotope Analysis.

PubMed

Hohmann, Monika; Monakhova, Yulia; Erich, Sarah; Christoph, Norbert; Wachter, Helmut; Holzgrabe, Ulrike

2015-11-04

Because the basic suitability of proton nuclear magnetic resonance spectroscopy ((1)H NMR) to differentiate organic versus conventional tomatoes was recently proven, the approach to optimize (1)H NMR classification models (comprising overall 205 authentic tomato samples) by including additional data of isotope ratio mass spectrometry (IRMS, δ(13)C, δ(15)N, and δ(18)O) and mid-infrared (MIR) spectroscopy was assessed. Both individual and combined analytical methods ((1)H NMR + MIR, (1)H NMR + IRMS, MIR + IRMS, and (1)H NMR + MIR + IRMS) were examined using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and common components and specific weight analysis (ComDim). With regard to classification abilities, fused data of (1)H NMR + MIR + IRMS yielded better validation results (ranging between 95.0 and 100.0%) than individual methods ((1)H NMR, 91.3-100%; MIR, 75.6-91.7%), suggesting that the combined examination of analytical profiles enhances authentication of organically produced tomatoes.

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Zeng, Qiu-Yao; Li, Lin-Fang; Zhang, Yan-Jiao; Wan, Ming-Ming; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Liu, Song-Hao

2013-02-01

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

Agreement Among Traditional and RTI-based Definitions of Reading-Related Learning Disability with Preschool Children.

PubMed

Milburn, Trelani F; Lonigan, Christopher J; Allan, Darcey M; Phillips, Beth M

2017-04-01

To investigate approaches for identifying young children who may be at risk for later reading-related learning disabilities, this study compared the use of four contemporary methods of indexing learning disability (LD) with older children (i.e., IQ-achievement discrepancy, low achievement, low growth, and dual-discrepancy) to determine risk status with a large sample of 1,011 preschoolers. These children were classified as at risk or not using each method across three early-literacy skills (i.e., language, phonological awareness, print knowledge) and at three levels of severity (i.e., 5th, 10th, 25th percentiles). Chance-corrected affected-status agreement (CCASA) indicated poor agreement among methods with rates of agreement generally decreasing with greater levels of severity for both single- and two-measure classification, and agreement rates were lower for two-measure classification than for single-measure classification. These low rates of agreement between conventional methods of identifying children at risk for LD represent a significant impediment for identification and intervention for young children considered at-risk.

Agreement Among Traditional and RTI-based Definitions of Reading-Related Learning Disability with Preschool Children

PubMed Central

Milburn, Trelani F.; Lonigan, Christopher J.; Allan, Darcey M.; Phillips, Beth M.

2017-01-01

To investigate approaches for identifying young children who may be at risk for later reading-related learning disabilities, this study compared the use of four contemporary methods of indexing learning disability (LD) with older children (i.e., IQ-achievement discrepancy, low achievement, low growth, and dual-discrepancy) to determine risk status with a large sample of 1,011 preschoolers. These children were classified as at risk or not using each method across three early-literacy skills (i.e., language, phonological awareness, print knowledge) and at three levels of severity (i.e., 5th, 10th, 25th percentiles). Chance-corrected affected-status agreement (CCASA) indicated poor agreement among methods with rates of agreement generally decreasing with greater levels of severity for both single- and two-measure classification, and agreement rates were lower for two-measure classification than for single-measure classification. These low rates of agreement between conventional methods of identifying children at risk for LD represent a significant impediment for identification and intervention for young children considered at-risk. PMID:28670102

Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning.

PubMed

Yang, Yimin; Wu, Q M Jonathan

2016-11-01

The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

46 CFR 8.320 - Classification society authorization to issue international certificates.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 1 2010-10-01 2010-10-01 false Classification society authorization to issue... Classification society authorization to issue international certificates. (a) The Commandant may authorize a recognized classification society to issue certain international convention certificates. Authorization will...

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

A review and analysis of neural networks for classification of remotely sensed multispectral imagery

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1993-01-01

A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

Comparisons and Selections of Features and Classifiers for Short Text Classification

NASA Astrophysics Data System (ADS)

Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

2017-10-01

Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

46 CFR 8.320 - Classification society authorization to issue international certificates.

Code of Federal Regulations, 2011 CFR

2011-10-01

... classification society authorized to issue international convention certificates. This agreement will define the... 46 Shipping 1 2011-10-01 2011-10-01 false Classification society authorization to issue... Classification society authorization to issue international certificates. (a) The Commandant may authorize a...

Comparative Analysis of Haar and Daubechies Wavelet for Hyper Spectral Image Classification

NASA Astrophysics Data System (ADS)

Sharif, I.; Khare, S.

2014-11-01

With the number of channels in the hundreds instead of in the tens Hyper spectral imagery possesses much richer spectral information than multispectral imagery. The increased dimensionality of such Hyper spectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be useful without dimension reduction pre-processing. So dimension reduction has become a significant part of Hyper spectral image processing. This paper presents a comparative analysis of the efficacy of Haar and Daubechies wavelets for dimensionality reduction in achieving image classification. Spectral data reduction using Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. Daubechies wavelets optimally capture the polynomial trends while Haar wavelet is discontinuous and resembles a step function. The performance of these wavelets are compared in terms of classification accuracy and time complexity. This paper shows that wavelet reduction has more separate classes and yields better or comparable classification accuracy. In the context of the dimensionality reduction algorithm, it is found that the performance of classification of Daubechies wavelets is better as compared to Haar wavelet while Daubechies takes more time compare to Haar wavelet. The experimental results demonstrate the classification system consistently provides over 84% classification accuracy.

A manual and an automatic TERS based virus discrimination

NASA Astrophysics Data System (ADS)

Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen

2015-02-01

Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr07033j

Training echo state networks for rotation-invariant bone marrow cell classification.

PubMed

Kainz, Philipp; Burgsteiner, Harald; Asslaber, Martin; Ahammer, Helmut

2017-01-01

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Weighted minimum-norm source estimation of magnetoencephalography utilizing the temporal information of the measured data

NASA Astrophysics Data System (ADS)

Iwaki, Sunao; Ueno, Shoogo

1998-06-01

The weighted minimum-norm estimation (wMNE) is a popular method to obtain the source distribution in the human brain from magneto- and electro- encephalograpic measurements when detailed information about the generator profile is not available. We propose a method to reconstruct current distributions in the human brain based on the wMNE technique with the weighting factors defined by a simplified multiple signal classification (MUSIC) prescanning. In this method, in addition to the conventional depth normalization technique, weighting factors of the wMNE were determined by the cost values previously calculated by a simplified MUSIC scanning which contains the temporal information of the measured data. We performed computer simulations of this method and compared it with the conventional wMNE method. The results show that the proposed method is effective for the reconstruction of the current distributions from noisy data.

Adventitious sounds identification and extraction using temporal-spectral dominance-based features.

PubMed

Jin, Feng; Krishnan, Sridhar Sri; Sattar, Farook

2011-11-01

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-related signal components in joint time-frequency (TF) plane. This paper proposes a new signal identification and extraction method for various ASs based on instantaneous frequency (IF) analysis. The presented TF decomposition method produces a noise-resistant high definition TF representation of RS signals as compared to the conventional linear TF analysis methods, yet preserving the low computational complexity as compared to those quadratic TF analysis methods. The discarded phase information in conventional spectrogram has been adopted for the estimation of IF and group delay, and a temporal-spectral dominance spectrogram has subsequently been constructed by investigating the TF spreads of the computed time-corrected IF components. The proposed dominance measure enables the extraction of signal components correspond to ASs from noisy RS signal at high noise level. A new set of TF features has also been proposed to quantify the shapes of the obtained TF contours, and therefore strongly, enhances the identification of multicomponents signals such as polyphonic wheezes. An overall accuracy of 92.4±2.9% for the classification of real RS recordings shows the promising performance of the presented method.

Use of in Vitro HTS-Derived Concentration-Response Data as ...

EPA Pesticide Factsheets

Background: Quantitative high-throughput screening (qHTS) assays are increasingly being employed to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the NIH Chemical Genomics Center. Objectives: To test a hypothesis that dose-response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, may improve the accuracy of Quantitative Structure-Activity Relationship (QSAR) models applied to prediction of in vivo toxicity endpoints. Methods and Results: The cell viability qHTS concentration-response data for 1,408 substances assayed in 13 cell lines were obtained from PubChem; for a subset of these compounds rodent acute toxicity LD50 data were also available. The classification k Nearest Neighbor and Random Forest QSAR methods were employed for modeling LD50 data using either chemical descriptors alone (conventional models) or in combination with biological descriptors derived from the concentration-response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. We show that both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models was superior to convent

Waveform fitting and geometry analysis for full-waveform lidar feature extraction

NASA Astrophysics Data System (ADS)

Tsai, Fuan; Lai, Jhe-Syuan; Cheng, Yi-Hsiu

2016-10-01

This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

PubMed

Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

2017-03-09

Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence

PubMed Central

Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

2017-01-01

Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults. PMID:28282936

Classification of small lesions in dynamic breast MRI: Eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement over time.

PubMed

Nagarajan, Mahesh B; Huber, Markus B; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2013-10-01

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they don't exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of sixty annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were also used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter , thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented ( p < 0.05). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.

Removal of BCG artifacts using a non-Kirchhoffian overcomplete representation.

PubMed

Dyrholm, Mads; Goldman, Robin; Sajda, Paul; Brown, Truman R

2009-02-01

We present a nonlinear unmixing approach for extracting the ballistocardiogram (BCG) from EEG recorded in an MR scanner during simultaneous acquisition of functional MRI (fMRI). First, an overcomplete basis is identified in the EEG based on a custom multipath EEG electrode cap. Next, the overcomplete basis is used to infer non-Kirchhoffian latent variables that are not consistent with a conservative electric field. Neural activity is strictly Kirchhoffian while the BCG artifact is not, and the representation can hence be used to remove the artifacts from the data in a way that does not attenuate the neural signals needed for optimal single-trial classification performance. We compare our method to more standard methods for BCG removal, namely independent component analysis and optimal basis sets, by looking at single-trial classification performance for an auditory oddball experiment. We show that our overcomplete representation method for removing BCG artifacts results in better single-trial classification performance compared to the conventional approaches, indicating that the derived neural activity in this representation retains the complex information in the trial-to-trial variability.

Increasing accuracy of vehicle detection from conventional vehicle detectors - counts, speeds, classification, and travel time.

DOT National Transportation Integrated Search

2014-09-01

Vehicle classification is an important traffic parameter for transportation planning and infrastructure : management. Length-based vehicle classification from dual loop detectors is among the lowest cost : technologies commonly used for collecting th...

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

PubMed

Mumtaz, Wajid; Saad, Mohamad Naufal B Mohamad; Kamel, Nidal; Ali, Syed Saad Azhar; Malik, Aamir Saeed

2018-01-01

The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics. In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used. The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95. The SL features could be utilized as objective markers to screen the AUD patients and healthy controls. Copyright © 2017 Elsevier B.V. All rights reserved.

Change classification in SAR time series: a functional approach

NASA Astrophysics Data System (ADS)

Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan

2017-10-01

Change detection represents a broad field of research in SAR remote sensing, consisting of many different approaches. Besides the simple recognition of change areas, the analysis of type, category or class of the change areas is at least as important for creating a comprehensive result. Conventional strategies for change classification are based on supervised or unsupervised landuse / landcover classifications. The main drawback of such approaches is that the quality of the classification result directly depends on the selection of training and reference data. Additionally, supervised processing methods require an experienced operator who capably selects the training samples. This training step is not necessary when using unsupervised strategies, but nevertheless meaningful reference data must be available for identifying the resulting classes. Consequently, an experienced operator is indispensable. In this study, an innovative concept for the classification of changes in SAR time series data is proposed. Regarding the drawbacks of traditional strategies given above, it copes without using any training data. Moreover, the method can be applied by an operator, who does not have detailed knowledge about the available scenery yet. This knowledge is provided by the algorithm. The final step of the procedure, which main aspect is given by the iterative optimization of an initial class scheme with respect to the categorized change objects, is represented by the classification of these objects to the finally resulting classes. This assignment step is subject of this paper.

Classification of speech dysfluencies using LPC based parameterization techniques.

PubMed

Hariharan, M; Chee, Lim Sin; Ai, Ooi Chia; Yaacob, Sazali

2012-06-01

The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.

Accounting for both local aquatic community composition and bioavailability in setting site-specific quality standards for zinc.

PubMed

Peters, Adam; Simpson, Peter; Moccia, Alessandra

2014-01-01

Recent years have seen considerable improvement in water quality standards (QS) for metals by taking account of the effect of local water chemistry conditions on their bioavailability. We describe preliminary efforts to further refine water quality standards, by taking account of the composition of the local ecological community (the ultimate protection objective) in addition to bioavailability. Relevance of QS to the local ecological community is critical as it is important to minimise instances where quality classification using QS does not reconcile with a quality classification based on an assessment of the composition of the local ecology (e.g. using benthic macroinvertebrate quality assessment metrics such as River InVertebrate Prediction and Classification System (RIVPACS)), particularly where ecology is assessed to be at good or better status, whilst chemical quality is determined to be failing relevant standards. The alternative approach outlined here describes a method to derive a site-specific species sensitivity distribution (SSD) based on the ecological community which is expected to be present at the site in the absence of anthropogenic pressures (reference conditions). The method combines a conventional laboratory ecotoxicity dataset normalised for bioavailability with field measurements of the response of benthic macroinvertebrate abundance to chemical exposure. Site-specific QSref are then derived from the 5%ile of this SSD. Using this method, site QSref have been derived for zinc in an area impacted by historic mining activities. Application of QSref can result in greater agreement between chemical and ecological metrics of environmental quality compared with the use of either conventional (QScon) or bioavailability-based QS (QSbio). In addition to zinc, the approach is likely to be applicable to other metals and possibly other types of chemical stressors (e.g. pesticides). However, the methodology for deriving site-specific targets requires additional development and validation before they can be robustly applied during surface water classification.

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

PubMed

Kumar, Shiu; Mamun, Kabir; Sharma, Alok

2017-12-01

Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Micro-Raman spectroscopy for identification and classification of UTI bacteria

NASA Astrophysics Data System (ADS)

Yogesha, M.; Chawla, Kiran; Acharya, Mahendra; Chidangil, Santhosh; Bankapur, Aseefhali

2017-07-01

Urinary tract infection (UTI) is one of the major clinical problems known to mankind, especially among adult women. Conventional methods for identification of UTI causing bacteria are time consuming and expensive. Therefore, a rapid and cost-effective method is desired. In the present study, five bacteria (one Gram-positive and four Gram-negative), most commonly known to cause UTI, have been identified and classified using micro-Raman spectroscopy combined with principal component analysis (PCA).

Opacity annotation of diffuse lung diseases using deep convolutional neural network with multi-channel information

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Kido, Shoji; Hashimoto, Noriaki; Hirano, Yasushi; Kuremoto, Takashi

2018-02-01

This research proposes a multi-channel deep convolutional neural network (DCNN) for computer-aided diagnosis (CAD) that classifies normal and abnormal opacities of diffuse lung diseases in Computed Tomography (CT) images. Because CT images are gray scale, DCNN usually uses one channel for inputting image data. On the other hand, this research uses multi-channel DCNN where each channel corresponds to the original raw image or the images transformed by some preprocessing techniques. In fact, the information obtained only from raw images is limited and some conventional research suggested that preprocessing of images contributes to improving the classification accuracy. Thus, the combination of the original and preprocessed images is expected to show higher accuracy. The proposed method realizes region of interest (ROI)-based opacity annotation. We used lung CT images taken in Yamaguchi University Hospital, Japan, and they are divided into 32 × 32 ROI images. The ROIs contain six kinds of opacities: consolidation, ground-glass opacity (GGO), emphysema, honeycombing, nodular, and normal. The aim of the proposed method is to classify each ROI into one of the six opacities (classes). The DCNN structure is based on VGG network that secured the first and second places in ImageNet ILSVRC-2014. From the experimental results, the classification accuracy of the proposed method was better than the conventional method with single channel, and there was a significant difference between them.

Classification of shiga toxin-producing escherichia coli (STEC) serotypes with hyperspectral microscope imagery

USDA-ARS?s Scientific Manuscript database

Non-O157:H7 Shiga toxin-producing Escherichia coli (STEC) strains such as O26, O45, O103, O111, O121 and O145 are recognized as serious outbreak to cause human illness due to their toxicity. Since a conventional microbiological method for cell counting is laborious and time-consuming process, optica...

Classification of non-O157 shiga toxin-producing escherichia coli(STEC) serotypes with hyperspectral microscope imaging

USDA-ARS?s Scientific Manuscript database

Non-O157 Shiga toxin-producing Escherichia coli (STEC) strains such as O26, O45, O103, O111, O121 and O145 are recognized as serious outbreak to cause human illness due to their toxicity. A conventional microbiological method for cell counting is laborious and needs long time for the results. Since ...

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

PubMed Central

Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance. PMID:29209156

Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

NASA Astrophysics Data System (ADS)

Niazmardi, S.; Safari, A.; Homayouni, S.

2017-09-01

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

Assessing a traceability technique in fresh oranges (Citrus sinensis L. Osbeck) with an HS-SPME-GC-MS method. Towards a volatile characterisation of organic oranges.

PubMed

Cuevas, Francisco Julián; Moreno-Rojas, José Manuel; Ruiz-Moreno, María José

2017-04-15

A targeted approach using HS-SPME-GC-MS was performed to compare flavour compounds of 'Navelina' and 'Salustiana' orange cultivars from organic and conventional management systems. Both varieties of conventional oranges showed higher content of ester compounds. On the other hand, higher content of some compounds related with the geranyl-diphosphate pathway (neryl and geranyl acetates) and some terpenoids were found in the organic samples. Furthermore, the partial least square discriminant analysis (PLS-DA) achieved an effective classification for oranges based on the farming system using their volatile profiles (90 and 100% correct classification). To our knowledge, it is the first time that a comparative study dealing with farming systems and orange aroma profile has been performed. These new insights, taking into account local databases, cultivars and advanced analytical tools, highlight the potential of volatile composition for organic orange discrimination. Copyright © 2016 Elsevier Ltd. All rights reserved.

Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification?

PubMed

Boudissa, M; Orfeuvre, B; Chabanas, M; Tonetti, J

2017-09-01

The Letournel classification of acetabular fracture shows poor reproducibility in inexperienced observers, despite the introduction of 3D imaging. We therefore developed a method of semi-automatic segmentation based on CT data. The present prospective study aimed to assess: (1) whether semi-automatic bone-fragment segmentation increased the rate of correct classification; (2) if so, in which fracture types; and (3) feasibility using the open-source itksnap 3.0 software package without incurring extra cost for users. Semi-automatic segmentation of acetabular fractures significantly increases the rate of correct classification by orthopedic surgery residents. Twelve orthopedic surgery residents classified 23 acetabular fractures. Six used conventional 3D reconstructions provided by the center's radiology department (conventional group) and 6 others used reconstructions obtained by semi-automatic segmentation using the open-source itksnap 3.0 software package (segmentation group). Bone fragments were identified by specific colors. Correct classification rates were compared between groups on Chi 2 test. Assessment was repeated 2 weeks later, to determine intra-observer reproducibility. Correct classification rates were significantly higher in the "segmentation" group: 114/138 (83%) versus 71/138 (52%); P<0.0001. The difference was greater for simple (36/36 (100%) versus 17/36 (47%); P<0.0001) than complex fractures (79/102 (77%) versus 54/102 (53%); P=0.0004). Mean segmentation time per fracture was 27±3min [range, 21-35min]. The segmentation group showed excellent intra-observer correlation coefficients, overall (ICC=0.88), and for simple (ICC=0.92) and complex fractures (ICC=0.84). Semi-automatic segmentation, identifying the various bone fragments, was effective in increasing the rate of correct acetabular fracture classification on the Letournel system by orthopedic surgery residents. It may be considered for routine use in education and training. III: prospective case-control study of a diagnostic procedure. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning.

PubMed

Ying, Shihui; Wen, Zhijie; Shi, Jun; Peng, Yaxin; Peng, Jigen; Qiao, Hong

2017-05-18

In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positive-definite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.

Graph pyramids for protein function prediction

PubMed Central

2015-01-01

Background Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Methods Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Results Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data. PMID:26044522

A Unified Fisher's Ratio Learning Method for Spatial Filter Optimization.

PubMed

Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.

Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

PubMed Central

Sedykh, Alexander; Zhu, Hao; Tang, Hao; Zhang, Liying; Richard, Ann; Rusyn, Ivan; Tropsha, Alexander

2011-01-01

Background Quantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center. Objectives Our goal was to test a hypothesis that dose–response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure–activity relationship (QSAR) models applied to prediction of in vivo toxicity end points. Methods We obtained cell viability qHTS concentration–response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration–response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. Results Both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models. Conclusions Concentration–response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points. PMID:20980217

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

PubMed

Zhao, Weixiang; Sankaran, Shankar; Ibáñez, Ana M; Dandekar, Abhaya M; Davis, Cristina E

2009-08-04

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

Weathering Grade Classification of Granite Stone Monument Using Reflectance Spectroscopy

NASA Astrophysics Data System (ADS)

Hyun, C.; Roh, T.; Choi, M.; Park, H.

2009-05-01

Stone monument has been placed in field and exposed to rain and wind. This outdoor environment and air pollution induced weathering of stone monument. Weathering grade classification is necessary to manage and conserve stone monuments. Visual interpretation by geologist and laboratory experiments using specimens fallen off from the monument to avoid damage on the monument have been applied to classify weathering grade conventionally. Rocks and minerals absorb some particular wavelength ranges of electromagnetic energy by electronic process and vibrational process of composing elements and these phenomena produce intrinsic diagnostic spectral reflectance curve. Non-destructive technique for weathering degree assessment measures those diagnostic absorption features of weathering products and converts the depths of features related to abundance of the materials to relative weathering degree. We selected granite outcrop to apply conventional six folded weathering grade classification method using Schmidt hammer rebound teste. The correlations between Schmidt hammer rebound values and absorption depths of iron oxides such as ferric oxide in the vicinity of 0.9 micrometer wavelength and clay minerals such as illite and kaolinite in the vicinity of 2.2 micrometer wavelength, representative weathering products of granite, were analyzed. The Schmidt hammer rebound value decreased according to increase of absorption depths induced from those weathering products. Weathering grade classification on the granite stone monument was conducted by using absorption depths of weathering products This research is supported from National Research Institute of Cultural Heritage and we appreciate for this.

CLASSIFYING MEDICAL IMAGES USING MORPHOLOGICAL APPEARANCE MANIFOLDS.

PubMed

Varol, Erdem; Gaonkar, Bilwaj; Davatzikos, Christos

2013-12-31

Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and use the results as input to the subsequent classification step. The variation in registration results due to choice of parameters thus translates to variation of performance of the classifiers that depend on the registration step for input. Analogous issues have been investigated in the computer vision literature, where image appearance varies with pose and illumination, thereby making classification vulnerable to these confounding parameters. The proposed methodology addresses this issue by sampling image appearances as registration parameters vary, and shows that better classification accuracies can be obtained this way, compared to the conventional approach.

Comparison of three methods for long-term monitoring of boreal lake area using Landsat TM and ETM+ imagery

USGS Publications Warehouse

Roach, Jennifer K.; Griffith, Brad; Verbyla, David

2012-01-01

Programs to monitor lake area change are becoming increasingly important in high latitude regions, and their development often requires evaluating tradeoffs among different approaches in terms of accuracy of measurement, consistency across multiple users over long time periods, and efficiency. We compared three supervised methods for lake classification from Landsat imagery (density slicing, classification trees, and feature extraction). The accuracy of lake area and number estimates was evaluated relative to high-resolution aerial photography acquired within two days of satellite overpasses. The shortwave infrared band 5 was better at separating surface water from nonwater when used alone than when combined with other spectral bands. The simplest of the three methods, density slicing, performed best overall. The classification tree method resulted in the most omission errors (approx. 2x), feature extraction resulted in the most commission errors (approx. 4x), and density slicing had the least directional bias (approx. half of the lakes with overestimated area and half of the lakes with underestimated area). Feature extraction was the least consistent across training sets (i.e., large standard error among different training sets). Density slicing was the best of the three at classifying small lakes as evidenced by its lower optimal minimum lake size criterion of 5850 m2 compared with the other methods (8550 m2). Contrary to conventional wisdom, the use of additional spectral bands and a more sophisticated method not only required additional processing effort but also had a cost in terms of the accuracy and consistency of lake classifications.

Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

2018-04-01

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

Effects of conventional overground gait training and a gait trainer with partial body weight support on spatiotemporal gait parameters of patients after stroke

PubMed Central

Park, Byoung-Sun; Kim, Mee-Young; Lee, Lim-Kyu; Yang, Seung-Min; Lee, Won-Deok; Noh, Ji-Woong; Shin, Yong-Sub; Kim, Ju-Hyun; Lee, Jeong-Uk; Kwak, Taek-Yong; Lee, Tae-Hyun; Kim, Ju-Young; Kim, Junghwan

2015-01-01

[Purpose] The purpose of this study was to confirm the effects of both conventional overground gait training (CGT) and a gait trainer with partial body weight support (GTBWS) on spatiotemporal gait parameters of patients with hemiparesis following chronic stroke. [Subjects and Methods] Thirty stroke patients were alternately assigned to one of two treatment groups, and both groups underwent CGT and GTBWS. [Results] The functional ambulation classification on the affected side improved significantly in the CGT and GTBWS groups. Walking speed also improved significantly in both groups. [Conclusion] These results suggest that the GTBWS in company with CGT may be, in part, an effective method of gait training for restoring gait ability in patients after a stroke. PMID:26157272

Effects of conventional overground gait training and a gait trainer with partial body weight support on spatiotemporal gait parameters of patients after stroke.

PubMed

Park, Byoung-Sun; Kim, Mee-Young; Lee, Lim-Kyu; Yang, Seung-Min; Lee, Won-Deok; Noh, Ji-Woong; Shin, Yong-Sub; Kim, Ju-Hyun; Lee, Jeong-Uk; Kwak, Taek-Yong; Lee, Tae-Hyun; Kim, Ju-Young; Kim, Junghwan

2015-05-01

[Purpose] The purpose of this study was to confirm the effects of both conventional overground gait training (CGT) and a gait trainer with partial body weight support (GTBWS) on spatiotemporal gait parameters of patients with hemiparesis following chronic stroke. [Subjects and Methods] Thirty stroke patients were alternately assigned to one of two treatment groups, and both groups underwent CGT and GTBWS. [Results] The functional ambulation classification on the affected side improved significantly in the CGT and GTBWS groups. Walking speed also improved significantly in both groups. [Conclusion] These results suggest that the GTBWS in company with CGT may be, in part, an effective method of gait training for restoring gait ability in patients after a stroke.

Linear discriminant analysis based on L1-norm maximization.

PubMed

Zhong, Fujin; Zhang, Jiashu

2013-08-01

Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-norm-based within-class dispersion. The proposed method is theoretically proved to be feasible and robust to outliers while overcoming the singular problem of the within-class scatter matrix for conventional LDA. Experiments on artificial datasets, standard classification datasets and three popular image databases demonstrate the efficacy of the proposed method.

Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature.

PubMed

Wang, Xinglong; Rak, Rafal; Restificar, Angelo; Nobata, Chikashi; Rupp, C J; Batista-Navarro, Riza Theresa B; Nawaz, Raheel; Ananiadou, Sophia

2011-10-03

The selection of relevant articles for curation, and linking those articles to experimental techniques confirming the findings became one of the primary subjects of the recent BioCreative III contest. The contest's Protein-Protein Interaction (PPI) task consisted of two sub-tasks: Article Classification Task (ACT) and Interaction Method Task (IMT). ACT aimed to automatically select relevant documents for PPI curation, whereas the goal of IMT was to recognise the methods used in experiments for identifying the interactions in full-text articles. We proposed and compared several classification-based methods for both tasks, employing rich contextual features as well as features extracted from external knowledge sources. For IMT, a new method that classifies pair-wise relations between every text phrase and candidate interaction method obtained promising results with an F1 score of 64.49%, as tested on the task's development dataset. We also explored ways to combine this new approach and more conventional, multi-label document classification methods. For ACT, our classifiers exploited automatically detected named entities and other linguistic information. The evaluation results on the BioCreative III PPI test datasets showed that our systems were very competitive: one of our IMT methods yielded the best performance among all participants, as measured by F1 score, Matthew's Correlation Coefficient and AUC iP/R; whereas for ACT, our best classifier was ranked second as measured by AUC iP/R, and also competitive according to other metrics. Our novel approach that converts the multi-class, multi-label classification problem to a binary classification problem showed much promise in IMT. Nevertheless, on the test dataset the best performance was achieved by taking the union of the output of this method and that of a multi-class, multi-label document classifier, which indicates that the two types of systems complement each other in terms of recall. For ACT, our system exploited a rich set of features and also obtained encouraging results. We examined the features with respect to their contributions to the classification results, and concluded that contextual words surrounding named entities, as well as the MeSH headings associated with the documents were among the main contributors to the performance.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Computer assisted optical biopsy for colorectal polyps

NASA Astrophysics Data System (ADS)

Navarro-Avila, Fernando J.; Saint-Hill-Febles, Yadira; Renner, Janis; Klare, Peter; von Delius, Stefan; Navab, Nassir; Mateus, Diana

2017-03-01

We propose a method for computer-assisted optical biopsy for colorectal polyps, with the final goal of assisting the medical expert during the colonoscopy. In particular, we target the problem of automatic classification of polyp images in two classes: adenomatous vs non-adenoma. Our approach is based on recent advancements in convolutional neural networks (CNN) for image representation. In the paper, we describe and compare four different methodologies to address the binary classification task: a baseline with classical features and a Random Forest classifier, two methods based on features obtained from a pre-trained network, and finally, the end-to-end training of a CNN. With the pre-trained network, we show the feasibility of transferring a feature extraction mechanism trained on millions of natural images, to the task of classifying adenomatous polyps. We then demonstrate further performance improvements when training the CNN for our specific classification task. In our study, 776 polyp images were acquired and histologically analyzed after polyp resection. We report a performance increase of the CNN-based approaches with respect to both, the conventional engineered features and to a state-of-the-art method based on videos and 3D shape features.

Graph pyramids for protein function prediction.

PubMed

Sandhan, Tushar; Yoo, Youngjun; Choi, Jin; Kim, Sun

2015-01-01

Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data.

Ventricular beat classifier using fractal number clustering.

PubMed

Bakardjian, H

1992-09-01

A two-stage ventricular beat 'associative' classification procedure is described. The first stage separates typical beats from extrasystoles on the basis of area and polarity rules. At the second stage, the extrasystoles are classified in self-organised cluster formations of adjacent shape parameter values. This approach avoids the use of threshold values for discrimination between ectopic beats of different shapes, which could be critical in borderline cases. A pattern shape feature conventionally called a 'fractal number', in combination with a polarity attribute, was found to be a good criterion for waveform evaluation. An additional advantage of this pattern classification method is its good computational efficiency, which affords the opportunity to implement it in real-time systems.

Self-adaptive road tracking in hyperspectral data for C-IED

NASA Astrophysics Data System (ADS)

Schilling, Hendrik; Gross, Wolfgang; Middelmann, Wolfgang

2012-09-01

For Counter Improvised Explosive Devices purposes, main routes including their vicinity are surveyed. In future military operations, small hyperspectral sensors will be used for ground covering reconnaissance, complementing images from infrared and high resolution sensors. They will be mounted on unmanned airborne vehicles and are used for on-line monitoring of convoy routes. Depending of the proximity to the road, different regions can be defined for threat assessment. Automatic road tracking can help choosing the correct areas of interest. Often, the exact discrimination between road and surroundings fails in conventional methods due to low contrast in pan-chromatic images at the road boundaries or occlusions. In this contribution, a novel real-time lock-on road tracking algorithm is introduced. It uses hyperspectral data and is specifically designed to address the afore- mentioned deficiencies of conventional methods. Local features are calculated from the high-resolution spectral signatures. They describe the similarity to the actual road cover and to either roadside. Classification is per- formed to discriminate the signatures. To improve robustness against variations in road cover, the classification results are used to progressively adapt the road and roadside classes. Occlusions are treated by predicting the course of the road and comparing the signatures in the target area to previously determined road cover signa- tures. The algorithm can be easily extended to show regions of varying threat, depending on the distance to the road. Thus, complex anomaly detectors and classification algorithms can be applied to a reduced data set. First experiments were performed for AISA Eagle II (400nm - 970nm) and AISA Hawk (970nm - 2450nm) data

An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

NASA Astrophysics Data System (ADS)

Jiang, Li; Shi, Tielin; Xuan, Jianping

2012-05-01

Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

Satellite image analysis using neural networks

NASA Technical Reports Server (NTRS)

Sheldon, Roger A.

1990-01-01

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Proposal of a new radiological classification system for spinal meningiomas as a descriptive tool and surgical guide.

PubMed

Bayoumi, Ahmed B; Laviv, Yosef; Yokus, Burhan; Efe, Ibrahim E; Toktas, Zafer Orkun; Kilic, Turker; Demir, Mustafa K; Konya, Deniz; Kasper, Ekkehard M

2017-11-01

1) To provide neurosurgeons and radiologists with a new quantitative and anatomical method to describe spinal meningiomas (SM) consistently. 2) To provide a guide to the surgical approach needed and amount of bony resection required based on the proposed classification. 3) To report the distribution of our 58 cases of SM over different Stages and Subtypes in correlation to the surgical treatment needed for each case. 4) To briefly review the literature on the rare non-conventional surgical corridors to resect SM. We reviewed the literature to report on previously published cohorts and classifications used to describe the location of the tumor inside the spinal canal. We reviewed the cases that were published prior showing non-conventional surgical approaches to resect spinal meningiomas. We proposed our classification system composed of Staging based on maximal cross-sectional surface area of tumor inside canal, Typing based on number of quadrants occupied by tumor and Subtyping based on location of the tumor bulk to spinal cord. Extradural and extra-spinal growth were also covered by our classification. We then applied it retrospectively on our 58 cases. 12 articles were published illustrating overlapping terms to describe spinal meningiomas. Another 7 articles were published reporting on 23 cases of anteriorly located spinal meningiomas treated with approaches other than laminectomies/laminoplasties. 4 Types, 9 Subtypes and 4 Stages were described in our Classification System. In our series of 58 patients, no midline anterior type was represented. Therefore, all our cases were treated by laminectomies or laminoplasties (with/without facetectomies) except a case with a paraspinal component where a costotransversectomy was needed. Spinal meningiomas can be radiologically described in a precise fashion. Selection of surgical corridor depends mainly on location of tumor bulk inside canal. Copyright © 2017 Elsevier B.V. All rights reserved.

SU-D-207B-02: Early Grade Classification in Meningioma Patients Combining Radiomics and Semantics Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coroller, T; Bi, W; Abedalthagafi, M

Purpose: The clinical management of meningioma is guided by its grade and biologic behavior. Currently, diagnosis of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor behavior are needed. We investigated the association between imaging features extracted from preoperative gadolinium-enhanced T1-weighted MRI and meningioma grade. Methods: We retrospectively examined the pre-operative MRI for 139 patients with de novo WHO grade I (63%) and grade II (37%) meningiomas. We investigated the predictive power of ten semantic radiologic features as determined by a neuroradiologist, fifteen radiomic features, and tumor location. Conventional (volume and diameter) imaging featuresmore » were added for comparison. AUC was computed for continuous and χ{sup 2} for discrete variables. Classification was done using random forest. Performance was evaluated using cross validation (1000 iterations, 75% training and 25% validation). All p-values were adjusted for multiple testing. Results: Significant association was observed between meningioma grade and tumor location (p<0.001) and two semantic features including intra-tumoral heterogeneity (p<0.001) and overt hemorrhage (p=0.01). Conventional (AUC 0.61–0.67) and eleven radiomic (AUC 0.60–0.70) features were significant from random (p<0.05, Noether test). Median AUC values for classification of tumor grade were 0.57, 0.71, 0.72 and 0.77 respectively for conventional, radiomic, location, and semantic features after using random forest. By combining all imaging data (semantic, radiomic, and location), the median AUC was 0.81, which offers superior predicting power to that of conventional imaging descriptors for meningioma as well as radiomic features alone (p<0.05, permutation test). Conclusion: We demonstrate a strong association between radiologic features and meningioma grade. Pre-operative prediction of tumor behavior based on imaging features offers promise for guiding personalized medicine and improving patient management.« less

Authentication of Organically and Conventionally Grown Basils by Gas Chromatography/Mass Spectrometry Chemical Profiles

PubMed Central

Wang, Zhengfang; Chen, Pei; Yu, Liangli; Harrington, Peter de B.

2013-01-01

Basil plants cultivated by organic and conventional farming practices were accurately classified by pattern recognition of gas chromatography/mass spectrometry (GC/MS) data. A novel extraction procedure was devised to extract characteristic compounds from ground basil powders. Two in-house fuzzy classifiers, i.e., the fuzzy rule-building expert system (FuRES) and the fuzzy optimal associative memory (FOAM) for the first time, were used to build classification models. Two crisp classifiers, i.e., soft independent modeling by class analogy (SIMCA) and the partial least-squares discriminant analysis (PLS-DA), were used as control methods. Prior to data processing, baseline correction and retention time alignment were performed. Classifiers were built with the two-way data sets, the total ion chromatogram representation of data sets, and the total mass spectrum representation of data sets, separately. Bootstrapped Latin partition (BLP) was used as an unbiased evaluation of the classifiers. By using two-way data sets, average classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100 ± 0%, 94.4 ± 0.4%, 93.3 ± 0.4%, and 100 ± 0%, respectively, for 100 independent evaluations. The established classifiers were used to classify a new validation set collected 2.5 months later with no parametric changes except that the training set and validation set were individually mean-centered. For the new two-way validation set, classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100%, 83%, 97%, and 100%, respectively. Thereby, the GC/MS analysis was demonstrated as a viable approach for organic basil authentication. It is the first time that a FOAM has been applied to classification. A novel baseline correction method was used also for the first time. The FuRES and the FOAM are demonstrated as powerful tools for modeling and classifying GC/MS data of complex samples and the data pretreatments are demonstrated to be useful to improve the performance of classifiers. PMID:23398171

Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study.

PubMed

Tres, A; van der Veer, G; Perez-Marin, M D; van Ruth, S M; Garrido-Varo, A

2012-08-22

Organic products tend to retail at a higher price than their conventional counterparts, which makes them susceptible to fraud. In this study we evaluate the application of near-infrared spectroscopy (NIRS) as a rapid, cost-effective method to verify the organic identity of feed for laying hens. For this purpose a total of 36 organic and 60 conventional feed samples from The Netherlands were measured by NIRS. A binary classification model (organic vs conventional feed) was developed using partial least squares discriminant analysis. Models were developed using five different data preprocessing techniques, which were externally validated by a stratified random resampling strategy using 1000 realizations. Spectral regions related to the protein and fat content were among the most important ones for the classification model. The models based on data preprocessed using direct orthogonal signal correction (DOSC), standard normal variate (SNV), and first and second derivatives provided the most successful results in terms of median sensitivity (0.91 in external validation) and median specificity (1.00 for external validation of SNV models and 0.94 for DOSC and first and second derivative models). A previously developed model, which was based on fatty acid fingerprinting of the same set of feed samples, provided a higher sensitivity (1.00). This shows that the NIRS-based approach provides a rapid and low-cost screening tool, whereas the fatty acid fingerprinting model can be used for further confirmation of the organic identity of feed samples for laying hens. These methods provide additional assurance to the administrative controls currently conducted in the organic feed sector.

Proposition of novel classification approach and features for improved real-time arrhythmia monitoring.

PubMed

Kim, Yoon Jae; Heo, Jeong; Park, Kwang Suk; Kim, Sungwan

2016-08-01

Arrhythmia refers to a group of conditions in which the heartbeat is irregular, fast, or slow due to abnormal electrical activity in the heart. Some types of arrhythmia such as ventricular fibrillation may result in cardiac arrest or death. Thus, arrhythmia detection becomes an important issue, and various studies have been conducted. Additionally, an arrhythmia detection algorithm for portable devices such as mobile phones has recently been developed because of increasing interest in e-health care. This paper proposes a novel classification approach and features, which are validated for improved real-time arrhythmia monitoring. The classification approach that was employed for arrhythmia detection is based on the concept of ensemble learning and the Taguchi method and has the advantage of being accurate and computationally efficient. The electrocardiography (ECG) data for arrhythmia detection was obtained from the MIT-BIH Arrhythmia Database (n=48). A novel feature, namely the heart rate variability calculated from 5s segments of ECG, which was not considered previously, was used. The novel classification approach and feature demonstrated arrhythmia detection accuracy of 89.13%. When the same data was classified using the conventional support vector machine (SVM), the obtained accuracy was 91.69%, 88.14%, and 88.74% for Gaussian, linear, and polynomial kernels, respectively. In terms of computation time, the proposed classifier was 5821.7 times faster than conventional SVM. In conclusion, the proposed classifier and feature showed performance comparable to those of previous studies, while the computational complexity and update interval were highly reduced. Copyright © 2016 Elsevier Ltd. All rights reserved.

A statistical evaluation of the effects of gender differences in assessment of acute inhalation toxicity

PubMed Central

Price, Charlotte; Stallard, Nigel; Creton, Stuart; Indans, Ian; Guest, Robert; Griffiths, David; Edwards, Philippa

2010-01-01

Acute inhalation toxicity of chemicals has conventionally been assessed by the median lethal concentration (LC50) test (organisation for economic co-operation and development (OECD) TG 403). Two new methods, the recently adopted acute toxic class method (ATC; OECD TG 436) and a proposed fixed concentration procedure (FCP), have recently been considered, but statistical evaluations of these methods did not investigate the influence of differential sensitivity between male and female rats on the outcomes. This paper presents an analysis of data from the assessment of acute inhalation toxicity for 56 substances. Statistically significant differences between the LC50 for males and females were found for 16 substances, with greater than 10-fold differences in the LC50 for two substances. The paper also reports a statistical evaluation of the three test methods in the presence of unanticipated gender differences. With TG 403, a gender difference leads to a slightly greater chance of under-classification. This is also the case for the ATC method, but more pronounced than for TG 403, with misclassification of nearly all substances from Globally Harmonised System (GHS) class 3 into class 4. As the FCP uses females only, if females are more sensitive, the classification is unchanged. If males are more sensitive, the procedure may lead to under-classification. Additional research on modification of the FCP is thus proposed. PMID:20488841

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, M; Craft, D

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchicalmore » clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.« less

Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications.

PubMed

Liu, Chenwei; Shea, Nancy; Rucker, Sally; Harvey, Linda; Russo, Paul; Saul, Richard; Lopez, Mary F; Mikulskis, Alvydas; Kuzdzal, Scott; Golenko, Eva; Fishman, David; Vonderheid, Eric; Booher, Susan; Cowen, Edward W; Hwang, Sam T; Whiteley, Gordon R

2007-11-01

Proteomic patterns as a potential diagnostic technology has been well established for several cancer conditions and other diseases. The use of machine learning techniques such as decision trees, neural networks, genetic algorithms, and other methods has been the basis for pattern determination. Cancer is known to involve signaling pathways that are regulated through PTM of proteins. These modifications are also detectable with high confidence using high-resolution MS. We generated data using a prOTOF mass spectrometer on two sets of patient samples: ovarian cancer and cutaneous t-cell lymphoma (CTCL) with matched normal samples for each disease. Using the knowledge of mass shifts caused by common modifications, we built models using peak pairs and compared this to a conventional technique using individual peaks. The results for each disease showed that a small number of peak pairs gave classification equal to or better than the conventional technique that used multiple individual peaks. This simple peak picking technique could be used to guide identification of important peak pairs involved in the disease process.

EXhype: A tool for mineral classification using hyperspectral data

NASA Astrophysics Data System (ADS)

Adep, Ramesh Nityanand; shetty, Amba; Ramesh, H.

2017-02-01

Various supervised classification algorithms have been developed to classify earth surface features using hyperspectral data. Each algorithm is modelled based on different human expertises. However, the performance of conventional algorithms is not satisfactory to map especially the minerals in view of their typical spectral responses. This study introduces a new expert system named 'EXhype (Expert system for hyperspectral data classification)' to map minerals. The system incorporates human expertise at several stages of it's implementation: (i) to deal with intra-class variation; (ii) to identify absorption features; (iii) to discriminate spectra by considering absorption features, non-absorption features and by full spectra comparison; and (iv) finally takes a decision based on learning and by emphasizing most important features. It is developed using a knowledge base consisting of an Optimal Spectral Library, Segmented Upper Hull method, Spectral Angle Mapper (SAM) and Artificial Neural Network. The performance of the EXhype is compared with a traditional, most commonly used SAM algorithm using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Cuprite, Nevada, USA. A virtual verification method is used to collect samples information for accuracy assessment. Further, a modified accuracy assessment method is used to get a real users accuracies in cases where only limited or desired classes are considered for classification. With the modified accuracy assessment method, SAM and EXhype yields an overall accuracy of 60.35% and 90.75% and the kappa coefficient of 0.51 and 0.89 respectively. It was also found that the virtual verification method allows to use most desired stratified random sampling method and eliminates all the difficulties associated with it. The experimental results show that EXhype is not only producing better accuracy compared to traditional SAM but, can also rightly classify the minerals. It is proficient in avoiding misclassification between target classes when applied on minerals.

3D texture analysis for classification of second harmonic generation images of human ovarian cancer

NASA Astrophysics Data System (ADS)

Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

2016-10-01

Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Active Electro-Location of Objects in the Underwater Environment Based on the Mixed Polarization Multiple Signal Classification Algorithm

PubMed Central

Guo, Lili; Qi, Junwei; Xue, Wei

2018-01-01

This article proposes a novel active localization method based on the mixed polarization multiple signal classification (MP-MUSIC) algorithm for positioning a metal target or an insulator target in the underwater environment by using a uniform circular antenna (UCA). The boundary element method (BEM) is introduced to analyze the boundary of the target by use of a matrix equation. In this method, an electric dipole source as a part of the locating system is set perpendicularly to the plane of the UCA. As a result, the UCA can only receive the induction field of the target. The potential of each electrode of the UCA is used as spatial-temporal localization data, and it does not need to obtain the field component in each direction compared with the conventional fields-based localization method, which can be easily implemented in practical engineering applications. A simulation model and a physical experiment are constructed. The simulation and the experiment results provide accurate positioning performance, with the help of verifying the effectiveness of the proposed localization method in underwater target locating. PMID:29439495

Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

NASA Astrophysics Data System (ADS)

Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

2017-09-01

Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed classification scheme is 94.91 %, while that with the conventional classification scheme is 93.70 %. Moreover, for multi-temporal UAVSAR data, the averaged overall classification accuracy with the proposed classification scheme is up to 97.08 %, which is much higher than the 87.79 % from the conventional classification scheme. Furthermore, for multitemporal PolSAR data, the proposed classification scheme can achieve better robustness. The comparison studies also clearly demonstrate that mining and utilization of hidden polarimetric features and information in the rotation domain can gain the added benefits for PolSAR land cover classification and provide a new vision for PolSAR image interpretation and application.

The synergy between complex channel-specific FIR filter and spatial filter for single-trial EEG classification.

PubMed

Yu, Ke; Wang, Yue; Shen, Kaiquan; Li, Xiaoping

2013-01-01

The common spatial pattern analysis (CSP), a frequently utilized feature extraction method in brain-computer-interface applications, is believed to be time-invariant and sensitive to noises, mainly due to an inherent shortcoming of purely relying on spatial filtering. Therefore, temporal/spectral filtering which can be very effective to counteract the unfavorable influence of noises is usually used as a supplement. This work integrates the CSP spatial filters with complex channel-specific finite impulse response (FIR) filters in a natural and intuitive manner. Each hybrid spatial-FIR filter is of high-order, data-driven and is unique to its corresponding channel. They are derived by introducing multiple time delays and regularization into conventional CSP. The general framework of the method follows that of CSP but performs better, as proven in single-trial classification tasks like event-related potential detection and motor imagery.

A Discriminative Approach to EEG Seizure Detection

PubMed Central

Johnson, Ashley N.; Sow, Daby; Biem, Alain

2011-01-01

Seizures are abnormal sudden discharges in the brain with signatures represented in electroencephalograms (EEG). The efficacy of the application of speech processing techniques to discriminate between seizure and non-seizure states in EEGs is reported. The approach accounts for the challenges of unbalanced datasets (seizure and non-seizure), while also showing a system capable of real-time seizure detection. The Minimum Classification Error (MCE) algorithm, which is a discriminative learning algorithm with wide-use in speech processing, is applied and compared with conventional classification techniques that have already been applied to the discrimination between seizure and non-seizure states in the literature. The system is evaluated on 22 pediatric patients multi-channel EEG recordings. Experimental results show that the application of speech processing techniques and MCE compare favorably with conventional classification techniques in terms of classification performance, while requiring less computational overhead. The results strongly suggests the possibility of deploying the designed system at the bedside. PMID:22195192

15 CFR 310.1 - Background and purpose.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Expositions (BIE) rules. The BIE is an international organization established by the Paris Convention of 1928... ratification of the Paris Convention by the U.S. Senate (114 Cong. Rec. 11012). 1 The BIE defines a General... detailed BIE classification criteria and regulations are contained in the Paris Convention of 1928, as...

Rapid Identification of Candida Species by Using Nuclear Magnetic Resonance Spectroscopy and a Statistical Classification Strategy

PubMed Central

Himmelreich, Uwe; Somorjai, Ray L.; Dolenko, Brion; Lee, Ok Cha; Daniel, Heide-Marie; Murray, Ronan; Mountford, Carolyn E.; Sorrell, Tania C.

2003-01-01

Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a statistical classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms. PMID:12902244

Spotting East African mammals in open savannah from space.

PubMed

Yang, Zheng; Wang, Tiejun; Skidmore, Andrew K; de Leeuw, Jan; Said, Mohammed Y; Freer, Jim

2014-01-01

Knowledge of population dynamics is essential for managing and conserving wildlife. Traditional methods of counting wild animals such as aerial survey or ground counts not only disturb animals, but also can be labour intensive and costly. New, commercially available very high-resolution satellite images offer great potential for accurate estimates of animal abundance over large open areas. However, little research has been conducted in the area of satellite-aided wildlife census, although computer processing speeds and image analysis algorithms have vastly improved. This paper explores the possibility of detecting large animals in the open savannah of Maasai Mara National Reserve, Kenya from very high-resolution GeoEye-1 satellite images. A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space, and therefore provide a complementary and alternative approach to the conventional wildlife survey techniques.

Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach.

PubMed

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Changsen; Liu, Feixiang

2017-02-15

Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naïve Bayesian Classifier (WNBC) for classification. Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. The proposed approach is a promising candidate for future BCI systems. Copyright © 2016 Elsevier B.V. All rights reserved.

Combined chemometric analysis of (1)H NMR, (13)C NMR and stable isotope data to differentiate organic and conventional milk.

PubMed

Erich, Sarah; Schill, Sandra; Annweiler, Eva; Waiblinger, Hans-Ulrich; Kuballa, Thomas; Lachenmeier, Dirk W; Monakhova, Yulia B

2015-12-01

The increased sales of organically produced food create a strong need for analytical methods, which could authenticate organic and conventional products. Combined chemometric analysis of (1)H NMR-, (13)C NMR-spectroscopy data, stable-isotope data (IRMS) and α-linolenic acid content (gas chromatography) was used to differentiate organic and conventional milk. In total 85 raw, pasteurized and ultra-heat treated (UHT) milk samples (52 organic and 33 conventional) were collected between August 2013 and May 2014. The carbon isotope ratios of milk protein and milk fat as well as the α-linolenic acid content of these samples were determined. Additionally, the milk fat was analyzed by (1)H and (13)C NMR spectroscopy. The chemometric analysis of combined data (IRMS, GC, NMR) resulted in more precise authentication of German raw and retail milk with a considerably increased classification rate of 95% compared to 81% for NMR and 90% for IRMS using linear discriminate analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

DOE PAGES

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; ...

2015-07-09

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning

NASA Astrophysics Data System (ADS)

Sun, Yankui; Li, Shan; Sun, Zhongyang

2017-01-01

We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects-15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing-168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

PubMed Central

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-01-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required. PMID:26156000

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

NASA Astrophysics Data System (ADS)

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-07-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.

Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning.

PubMed

Sun, Yankui; Li, Shan; Sun, Zhongyang

2017-01-01

We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects—15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing—168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.

Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature

PubMed Central

2011-01-01

Background The selection of relevant articles for curation, and linking those articles to experimental techniques confirming the findings became one of the primary subjects of the recent BioCreative III contest. The contest’s Protein-Protein Interaction (PPI) task consisted of two sub-tasks: Article Classification Task (ACT) and Interaction Method Task (IMT). ACT aimed to automatically select relevant documents for PPI curation, whereas the goal of IMT was to recognise the methods used in experiments for identifying the interactions in full-text articles. Results We proposed and compared several classification-based methods for both tasks, employing rich contextual features as well as features extracted from external knowledge sources. For IMT, a new method that classifies pair-wise relations between every text phrase and candidate interaction method obtained promising results with an F1 score of 64.49%, as tested on the task’s development dataset. We also explored ways to combine this new approach and more conventional, multi-label document classification methods. For ACT, our classifiers exploited automatically detected named entities and other linguistic information. The evaluation results on the BioCreative III PPI test datasets showed that our systems were very competitive: one of our IMT methods yielded the best performance among all participants, as measured by F1 score, Matthew’s Correlation Coefficient and AUC iP/R; whereas for ACT, our best classifier was ranked second as measured by AUC iP/R, and also competitive according to other metrics. Conclusions Our novel approach that converts the multi-class, multi-label classification problem to a binary classification problem showed much promise in IMT. Nevertheless, on the test dataset the best performance was achieved by taking the union of the output of this method and that of a multi-class, multi-label document classifier, which indicates that the two types of systems complement each other in terms of recall. For ACT, our system exploited a rich set of features and also obtained encouraging results. We examined the features with respect to their contributions to the classification results, and concluded that contextual words surrounding named entities, as well as the MeSH headings associated with the documents were among the main contributors to the performance. PMID:22151769

Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning

PubMed Central

Wang, Zhengxia; Zhu, Xiaofeng; Adeli, Ehsan; Zhu, Yingying; Nie, Feiping; Munsell, Brent

2018-01-01

Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain. To address this limitation, a progressive GTL (pGTL) method is proposed that gradually finds an intrinsic data representation that more accurately aligns imaging features with the phenotype data. In general, optimal feature-to-phenotype alignment is achieved using an iterative approach that: (1) refines inter-subject relationships observed in the feature domain by using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined inter-subject relationships, and (3) verifies the intrinsic data representation on the training data to guarantee an optimal classification when applied to testing data. Additionally, the iterative approach is extended to multi-modal imaging data to further improve pGTL classification accuracy. Using Alzheimer’s disease and Parkinson’s disease study data, the classification accuracy of the proposed pGTL method is compared to several state-of-the-art classification methods, and the results show pGTL can more accurately identify subjects, even at different progression stages, in these two study data sets. PMID:28551556

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

NASA Astrophysics Data System (ADS)

Fotin, Sergei V.; Yin, Yin; Haldankar, Hrishikesh; Hoffmeister, Jeffrey W.; Periaswamy, Senthil

2016-03-01

Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 +/- 0:040 to 0:893 +/- 0:033 for suspicious ROIs; and from 0:852 +/- 0:065 to 0:930 +/- 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events

NASA Astrophysics Data System (ADS)

Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prévôt, André S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael

2017-03-01

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k-means+ + , for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.

46 CFR 2.01-25 - International Convention for Safety of Life at Sea, 1974.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Guard, an authorized classification society may issue international convention certificates as permitted... request of the government of a country in which is registered a vessel engaged in an international voyage... 46 Shipping 1 2012-10-01 2012-10-01 false International Convention for Safety of Life at Sea, 1974...

46 CFR 2.01-25 - International Convention for Safety of Life at Sea, 1974.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Guard, an authorized classification society may issue international convention certificates as permitted... request of the government of a country in which is registered a vessel engaged in an international voyage... 46 Shipping 1 2014-10-01 2014-10-01 false International Convention for Safety of Life at Sea, 1974...

46 CFR 2.01-25 - International Convention for Safety of Life at Sea, 1974.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Guard, an authorized classification society may issue international convention certificates as permitted... request of the government of a country in which is registered a vessel engaged in an international voyage... 46 Shipping 1 2013-10-01 2013-10-01 false International Convention for Safety of Life at Sea, 1974...

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

PubMed Central

Schmitter, Daniel; Roche, Alexis; Maréchal, Bénédicte; Ribes, Delphine; Abdulkadir, Ahmed; Bach-Cuadra, Meritxell; Daducci, Alessandro; Granziera, Cristina; Klöppel, Stefan; Maeder, Philippe; Meuli, Reto; Krueger, Gunnar

2014-01-01

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease. PMID:25429357

Differences between conventional and glyphosate tolerant soybeans and moisture effect in their discrimination by near infrared spectroscopy.

PubMed

Esteve Agelet, Lidia; Armstrong, Paul R; Tallada, Jasper G; Hurburgh, Charles R

2013-12-01

Previous studies showed that Near Infrared Spectroscopy (NIRS) could distinguish between Roundup Ready® (RR) and conventional soybeans at the bulk and single seed sample level, but it was not clear which compounds drove the classification. In this research the varieties used did not show significant differences in major compounds between RR and conventional beans, but moisture content had a big impact on classification accuracies. Four of the five RR samples had slightly higher moistures and had a higher water uptake than their conventional counterparts. This could be linked with differences in their hulls, being either compositional or morphological. Because water absorption occurs in the same region as main compounds in hulls (mainly carbohydrates) and water causes physical changes from swelling, variations in moisture cause a complex interaction resulting in a large impact on discrimination accuracies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Time-resolved contrast-enhanced MRA (TWIST) with gadofosveset trisodium in the classification of soft-tissue vascular anomalies in the head and neck in children following updated 2014 ISSVA classification: first report on systematic evaluation of MRI and TWIST in a cohort of 47 children.

PubMed

Higgins, L J; Koshy, J; Mitchell, S E; Weiss, C R; Carson, K A; Huisman, T A G M; Tekes, A

2016-01-01

To evaluate the relative accuracy of contrast-enhanced time-resolved angiography with interleaved stochastic trajectories versus conventional contrast-enhanced magnetic resonance imaging (MRI) following International Society for the Study of Vascular Anomalies updated 2014-based classification of soft-tissue vascular anomalies in the head and neck in children. Time-resolved angiography with interleaved stochastic trajectories versus conventional contrast-enhanced MRI of children with diagnosis of soft-tissue vascular anomalies in the head and neck referred for MRI between 2008 and 2014 were retrospectively reviewed. Forty-seven children (0-18 years) were evaluated. Two paediatric neuroradiologists evaluated time-resolved MRA and conventional MRI in two different sessions (30 days apart). Blood-pool endovascular MRI contrast agent gadofosveset trisodium was used. The present cohort had the following diagnoses: infantile haemangioma (n=6), venous malformation (VM; n=23), lymphatic malformation (LM; n=16), arteriovenous malformation (AVM; n=2). Time-resolved MRA alone accurately classified 38/47 (81%) and conventional MRI 42/47 (89%), respectively. Although time-resolved MRA alone is slightly superior to conventional MRI alone for diagnosis of infantile haemangioma, conventional MRI is slightly better for diagnosis of venous and LMs. Neither time-resolved MRA nor conventional MRI was sufficient for accurate diagnosis of AVM in this cohort. Conventional MRI combined with time-resolved MRA accurately classified 44/47 cases (94%). Time-resolved MRA using gadofosveset trisodium can accurately classify soft-tissue vascular anomalies in the head and neck in children. The addition of time-resolved MRA to existing conventional MRI protocols provides haemodynamic information, assisting the diagnosis of vascular anomalies in the paediatric population at one-third of the dose of other MRI contrast agents. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

NASA Astrophysics Data System (ADS)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

PubMed Central

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

Intelligent image processing for vegetation classification using multispectral LANDSAT data

NASA Astrophysics Data System (ADS)

Santos, Stewart R.; Flores, Jorge L.; Garcia-Torales, G.

2015-09-01

We propose an intelligent computational technique for analysis of vegetation imaging, which are acquired with multispectral scanner (MSS) sensor. This work focuses on intelligent and adaptive artificial neural network (ANN) methodologies that allow segmentation and classification of spectral remote sensing (RS) signatures, in order to obtain a high resolution map, in which we can delimit the wooded areas and quantify the amount of combustible materials present into these areas. This could provide important information to prevent fires and deforestation of wooded areas. The spectral RS input data, acquired by the MSS sensor, are considered in a random propagation remotely sensed scene with unknown statistics for each Thematic Mapper (TM) band. Performing high-resolution reconstruction and adding these spectral values with neighbor pixels information from each TM band, we can include contextual information into an ANN. The biggest challenge in conventional classifiers is how to reduce the number of components in the feature vector, while preserving the major information contained in the data, especially when the dimensionality of the feature space is high. Preliminary results show that the Adaptive Modified Neural Network method is a promising and effective spectral method for segmentation and classification in RS images acquired with MSS sensor.

Classification of arterial and venous cerebral vasculature based on wavelet postprocessing of CT perfusion data.

PubMed

Havla, Lukas; Schneider, Moritz J; Thierfelder, Kolja M; Beyer, Sebastian E; Ertl-Wagner, Birgit; Reiser, Maximilian F; Sommer, Wieland H; Dietrich, Olaf

2016-02-01

The purpose of this study was to propose and evaluate a new wavelet-based technique for classification of arterial and venous vessels using time-resolved cerebral CT perfusion data sets. Fourteen consecutive patients (mean age 73 yr, range 17-97) with suspected stroke but no pathology in follow-up MRI were included. A CT perfusion scan with 32 dynamic phases was performed during intravenous bolus contrast-agent application. After rigid-body motion correction, a Paul wavelet (order 1) was used to calculate voxelwise the wavelet power spectrum (WPS) of each attenuation-time course. The angiographic intensity A was defined as the maximum of the WPS, located at the coordinates T (time axis) and W (scale/width axis) within the WPS. Using these three parameters (A, T, W) separately as well as combined by (1) Fisher's linear discriminant analysis (FLDA), (2) logistic regression (LogR) analysis, or (3) support vector machine (SVM) analysis, their potential to classify 18 different arterial and venous vessel segments per subject was evaluated. The best vessel classification was obtained using all three parameters A and T and W [area under the curve (AUC): 0.953 with FLDA and 0.957 with LogR or SVM]. In direct comparison, the wavelet-derived parameters provided performance at least equal to conventional attenuation-time-course parameters. The maximum AUC obtained from the proposed wavelet parameters was slightly (although not statistically significantly) higher than the maximum AUC (0.945) obtained from the conventional parameters. A new method to classify arterial and venous cerebral vessels with high statistical accuracy was introduced based on the time-domain wavelet transform of dynamic CT perfusion data in combination with linear or nonlinear multidimensional classification techniques.

Robust Averaging of Covariances for EEG Recordings Classification in Motor Imagery Brain-Computer Interfaces.

PubMed

Uehara, Takashi; Sartori, Matteo; Tanaka, Toshihisa; Fiori, Simone

2017-06-01

The estimation of covariance matrices is of prime importance to analyze the distribution of multivariate signals. In motor imagery-based brain-computer interfaces (MI-BCI), covariance matrices play a central role in the extraction of features from recorded electroencephalograms (EEGs); therefore, correctly estimating covariance is crucial for EEG classification. This letter discusses algorithms to average sample covariance matrices (SCMs) for the selection of the reference matrix in tangent space mapping (TSM)-based MI-BCI. Tangent space mapping is a powerful method of feature extraction and strongly depends on the selection of a reference covariance matrix. In general, the observed signals may include outliers; therefore, taking the geometric mean of SCMs as the reference matrix may not be the best choice. In order to deal with the effects of outliers, robust estimators have to be used. In particular, we discuss and test the use of geometric medians and trimmed averages (defined on the basis of several metrics) as robust estimators. The main idea behind trimmed averages is to eliminate data that exhibit the largest distance from the average covariance calculated on the basis of all available data. The results of the experiments show that while the geometric medians show little differences from conventional methods in terms of classification accuracy in the classification of electroencephalographic recordings, the trimmed averages show significant improvement for all subjects.

GMM-based speaker age and gender classification in Czech and Slovak

NASA Astrophysics Data System (ADS)

Přibil, Jiří; Přibilová, Anna; Matoušek, Jindřich

2017-01-01

The paper describes an experiment with using the Gaussian mixture models (GMM) for automatic classification of the speaker age and gender. It analyses and compares the influence of different number of mixtures and different types of speech features used for GMM gender/age classification. Dependence of the computational complexity on the number of used mixtures is also analysed. Finally, the GMM classification accuracy is compared with the output of the conventional listening tests. The results of these objective and subjective evaluations are in correspondence.

Describing three-class task performance: three-class linear discriminant analysis and three-class ROC analysis

NASA Astrophysics Data System (ADS)

He, Xin; Frey, Eric C.

2007-03-01

Binary ROC analysis has solid decision-theoretic foundations and a close relationship to linear discriminant analysis (LDA). In particular, for the case of Gaussian equal covariance input data, the area under the ROC curve (AUC) value has a direct relationship to the Hotelling trace. Many attempts have been made to extend binary classification methods to multi-class. For example, Fukunaga extended binary LDA to obtain multi-class LDA, which uses the multi-class Hotelling trace as a figure-of-merit, and we have previously developed a three-class ROC analysis method. This work explores the relationship between conventional multi-class LDA and three-class ROC analysis. First, we developed a linear observer, the three-class Hotelling observer (3-HO). For Gaussian equal covariance data, the 3- HO provides equivalent performance to the three-class ideal observer and, under less strict conditions, maximizes the signal to noise ratio for classification of all pairs of the three classes simultaneously. The 3-HO templates are not the eigenvectors obtained from multi-class LDA. Second, we show that the three-class Hotelling trace, which is the figureof- merit in the conventional three-class extension of LDA, has significant limitations. Third, we demonstrate that, under certain conditions, there is a linear relationship between the eigenvectors obtained from multi-class LDA and 3-HO templates. We conclude that the 3-HO based on decision theory has advantages both in its decision theoretic background and in the usefulness of its figure-of-merit. Additionally, there exists the possibility of interpreting the two linear features extracted by the conventional extension of LDA from a decision theoretic point of view.

SVM-based tree-type neural networks as a critic in adaptive critic designs for control.

PubMed

Deb, Alok Kanti; Jayadeva; Gopal, Madan; Chandra, Suresh

2007-07-01

In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in tandem using the failure data. Failure data is binary classification data, where the number of failure states are very few as compared to the number of no-failure states. The difficulty of conventional multilayer feedforward NNs in learning this type of classification data has been overcome by using the SVM-based tree-type NN, which due to its feature to add neurons to learn misclassified data, has the capability to learn any binary classification data without a priori choice of the number of neurons or the structure of the network. The capability of the trained controller to handle unforeseen situations is demonstrated.

Image processing developments and applications for water quality monitoring and trophic state determination

NASA Technical Reports Server (NTRS)

Blackwell, R. J.

1982-01-01

Remote sensing data analysis of water quality monitoring is evaluated. Data anaysis and image processing techniques are applied to LANDSAT remote sensing data to produce an effective operational tool for lake water quality surveying and monitoring. Digital image processing and analysis techniques were designed, developed, tested, and applied to LANDSAT multispectral scanner (MSS) data and conventional surface acquired data. Utilization of these techniques facilitates the surveying and monitoring of large numbers of lakes in an operational manner. Supervised multispectral classification, when used in conjunction with surface acquired water quality indicators, is used to characterize water body trophic status. Unsupervised multispectral classification, when interpreted by lake scientists familiar with a specific water body, yields classifications of equal validity with supervised methods and in a more cost effective manner. Image data base technology is used to great advantage in characterizing other contributing effects to water quality. These effects include drainage basin configuration, terrain slope, soil, precipitation and land cover characteristics.

A statistically harmonized alignment-classification in image space enables accurate and robust alignment of noisy images in single particle analysis.

PubMed

Kawata, Masaaki; Sato, Chikara

2007-06-01

In determining the three-dimensional (3D) structure of macromolecular assemblies in single particle analysis, a large representative dataset of two-dimensional (2D) average images from huge number of raw images is a key for high resolution. Because alignments prior to averaging are computationally intensive, currently available multireference alignment (MRA) software does not survey every possible alignment. This leads to misaligned images, creating blurred averages and reducing the quality of the final 3D reconstruction. We present a new method, in which multireference alignment is harmonized with classification (multireference multiple alignment: MRMA). This method enables a statistical comparison of multiple alignment peaks, reflecting the similarities between each raw image and a set of reference images. Among the selected alignment candidates for each raw image, misaligned images are statistically excluded, based on the principle that aligned raw images of similar projections have a dense distribution around the correctly aligned coordinates in image space. This newly developed method was examined for accuracy and speed using model image sets with various signal-to-noise ratios, and with electron microscope images of the Transient Receptor Potential C3 and the sodium channel. In every data set, the newly developed method outperformed conventional methods in robustness against noise and in speed, creating 2D average images of higher quality. This statistically harmonized alignment-classification combination should greatly improve the quality of single particle analysis.

Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.

PubMed

Li, Jinyan; Fong, Simon; Sung, Yunsick; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2016-01-01

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning.

PubMed

Olesen, Alexander Neergaard; Christensen, Julie A E; Sorensen, Helge B D; Jennum, Poul J

2016-08-01

Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen's kappa of 0.74 indicating substantial agreement between automatic and manual scoring.

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

A semi-automatic traffic sign detection, classification, and positioning system

NASA Astrophysics Data System (ADS)

Creusen, I. M.; Hazelhoff, L.; de With, P. H. N.

2012-01-01

The availability of large-scale databases containing street-level panoramic images offers the possibility to perform semi-automatic surveying of real-world objects such as traffic signs. These inventories can be performed significantly more efficiently than using conventional methods. Governmental agencies are interested in these inventories for maintenance and safety reasons. This paper introduces a complete semi-automatic traffic sign inventory system. The system consists of several components. First, a detection algorithm locates the 2D position of the traffic signs in the panoramic images. Second, a classification algorithm is used to identify the traffic sign. Third, the 3D position of the traffic sign is calculated using the GPS position of the photographs. Finally, the results are listed in a table for quick inspection and are also visualized in a web browser.

Periodontal inflamed surface area as a novel numerical variable describing periodontal conditions

PubMed Central

2017-01-01

Purpose A novel index, the periodontal inflamed surface area (PISA), represents the sum of the periodontal pocket depth of bleeding on probing (BOP)-positive sites. In the present study, we evaluated correlations between PISA and periodontal classifications, and examined PISA as an index integrating the discrete conventional periodontal indexes. Methods This study was a cross-sectional subgroup analysis of data from a prospective cohort study investigating the association between chronic periodontitis and the clinical features of ankylosing spondylitis. Data from 84 patients without systemic diseases (the control group in the previous study) were analyzed in the present study. Results PISA values were positively correlated with conventional periodontal classifications (Spearman correlation coefficient=0.52; P<0.01) and with periodontal indexes, such as BOP and the plaque index (PI) (r=0.94; P<0.01 and r=0.60; P<0.01, respectively; Pearson correlation test). Porphyromonas gingivalis (P. gingivalis) expression and the presence of serum P. gingivalis antibodies were significant factors affecting PISA values in a simple linear regression analysis, together with periodontal classification, PI, bleeding index, and smoking, but not in the multivariate analysis. In the multivariate linear regression analysis, PISA values were positively correlated with the quantity of current smoking, PI, and severity of periodontal disease. Conclusions PISA integrates multiple periodontal indexes, such as probing pocket depth, BOP, and PI into a numerical variable. PISA is advantageous for quantifying periodontal inflammation and plaque accumulation. PMID:29093989

Iatrogenic Bone and Soft Tissue Trauma in Robotic-Arm Assisted Total Knee Arthroplasty Compared With Conventional Jig-Based Total Knee Arthroplasty: A Prospective Cohort Study and Validation of a New Classification System.

PubMed

Kayani, Babar; Konan, Sujith; Pietrzak, Jurek R T; Haddad, Fares S

2018-03-27

The objective of this study was to compare macroscopic bone and soft tissue injury between robotic-arm assisted total knee arthroplasty (RA-TKA) and conventional jig-based total knee arthroplasty (CJ-TKA) and create a validated classification system for reporting iatrogenic bone and periarticular soft tissue injury after TKA. This study included 30 consecutive CJ-TKAs followed by 30 consecutive RA-TKAs performed by a single surgeon. Intraoperative photographs of the femur, tibia, and periarticular soft tissues were taken before implantation of prostheses. Using these outcomes, the macroscopic soft tissue injury (MASTI) classification system was developed to grade iatrogenic bone and soft tissue injuries. Interobserver and Intraobserver validity of the proposed classification system was assessed. Patients undergoing RA-TKA had reduced medial soft tissue injury in both passively correctible (P < .05) and noncorrectible varus deformities (P < .05); more pristine femoral (P < .05) and tibial (P < .05) bone resection cuts; and improved MASTI scores compared to CJ-TKA (P < .05). There was high interobserver (intraclass correlation coefficient 0.92 [95% confidence interval: 0.88-0.96], P < .05) and intraobserver agreement (intraclass correlation coefficient 0.94 [95% confidence interval: 0.92-0.97], P < .05) of the proposed MASTI classification system. There is reduced bone and periarticular soft tissue injury in patients undergoing RA-TKA compared to CJ-TKA. The proposed MASTI classification system is a reproducible grading scheme for describing iatrogenic bone and soft tissue injury in TKA. RA-TKA is associated with reduced bone and soft tissue injury compared with conventional jig-based TKA. The proposed MASTI classification may facilitate further research correlating macroscopic soft tissue injury during TKA to long-term clinical and functional outcomes. Copyright © 2018 Elsevier Inc. All rights reserved.

Multiband tissue classification for ultrasonic transmission tomography using spectral profile detection

NASA Astrophysics Data System (ADS)

Jeong, Jeong-Won; Kim, Tae-Seong; Shin, Dae-Chul; Do, Synho; Marmarelis, Vasilis Z.

2004-04-01

Recently it was shown that soft tissue can be differentiated with spectral unmixing and detection methods that utilize multi-band information obtained from a High-Resolution Ultrasonic Transmission Tomography (HUTT) system. In this study, we focus on tissue differentiation using the spectral target detection method based on Constrained Energy Minimization (CEM). We have developed a new tissue differentiation method called "CEM filter bank". Statistical inference on the output of each CEM filter of a filter bank is used to make a decision based on the maximum statistical significance rather than the magnitude of each CEM filter output. We validate this method through 3-D inter/intra-phantom soft tissue classification where target profiles obtained from an arbitrary single slice are used for differentiation in multiple tomographic slices. Also spectral coherence between target and object profiles of an identical tissue at different slices and phantoms is evaluated by conventional cross-correlation analysis. The performance of the proposed classifier is assessed using Receiver Operating Characteristic (ROC) analysis. Finally we apply our method to classify tiny structures inside a beef kidney such as Styrofoam balls (~1mm), chicken tissue (~5mm), and vessel-duct structures.

Keypoint Density-Based Region Proposal for Fine-Grained Object Detection and Classification Using Regions with Convolutional Neural Network Features

DTIC Science & Technology

2015-12-15

Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network ... Convolutional Neural Networks (CNNs) enable them to outperform conventional techniques on standard object detection and classification tasks, their...detection accuracy and speed on the fine-grained Caltech UCSD bird dataset (Wah et al., 2011). Recently, Convolutional Neural Networks (CNNs), a deep

Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection

PubMed Central

Regeling, Bianca; Thies, Boris; Gerstner, Andreas O. H.; Westermann, Stephan; Müller, Nina A.; Bendix, Jörg; Laffers, Wiebke

2016-01-01

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details. PMID:27529255

Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection.

PubMed

Regeling, Bianca; Thies, Boris; Gerstner, Andreas O H; Westermann, Stephan; Müller, Nina A; Bendix, Jörg; Laffers, Wiebke

2016-08-13

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope's fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

PAIR Comparison between Two Within-Group Conditions of Resting-State fMRI Improves Classification Accuracy

PubMed Central

Zhou, Zhen; Wang, Jian-Bao; Zang, Yu-Feng; Pan, Gang

2018-01-01

Classification approaches have been increasingly applied to differentiate patients and normal controls using resting-state functional magnetic resonance imaging data (RS-fMRI). Although most previous classification studies have reported promising accuracy within individual datasets, achieving high levels of accuracy with multiple datasets remains challenging for two main reasons: high dimensionality, and high variability across subjects. We used two independent RS-fMRI datasets (n = 31, 46, respectively) both with eyes closed (EC) and eyes open (EO) conditions. For each dataset, we first reduced the number of features to a small number of brain regions with paired t-tests, using the amplitude of low frequency fluctuation (ALFF) as a metric. Second, we employed a new method for feature extraction, named the PAIR method, examining EC and EO as paired conditions rather than independent conditions. Specifically, for each dataset, we obtained EC minus EO (EC—EO) maps of ALFF from half of subjects (n = 15 for dataset-1, n = 23 for dataset-2) and obtained EO—EC maps from the other half (n = 16 for dataset-1, n = 23 for dataset-2). A support vector machine (SVM) method was used for classification of EC RS-fMRI mapping and EO mapping. The mean classification accuracy of the PAIR method was 91.40% for dataset-1, and 92.75% for dataset-2 in the conventional frequency band of 0.01–0.08 Hz. For cross-dataset validation, we applied the classifier from dataset-1 directly to dataset-2, and vice versa. The mean accuracy of cross-dataset validation was 94.93% for dataset-1 to dataset-2 and 90.32% for dataset-2 to dataset-1 in the 0.01–0.08 Hz range. For the UNPAIR method, classification accuracy was substantially lower (mean 69.89% for dataset-1 and 82.97% for dataset-2), and was much lower for cross-dataset validation (64.69% for dataset-1 to dataset-2 and 64.98% for dataset-2 to dataset-1) in the 0.01–0.08 Hz range. In conclusion, for within-group design studies (e.g., paired conditions or follow-up studies), we recommend the PAIR method for feature extraction. In addition, dimensionality reduction with strong prior knowledge of specific brain regions should also be considered for feature selection in neuroimaging studies. PMID:29375288

Peripheral vascular tumors and vascular malformations: imaging (magnetic resonance imaging and conventional angiography), pathologic correlation and treatment options.

PubMed

El-Merhi, Fadi; Garg, Deepak; Cura, Marco; Ghaith, Ola

2013-02-01

Vascular anomalies are classified into vascular tumors (infantile hemangioma) and vascular malformations. Vascular malformations are divided into slow flow and high flow subtypes. Magnetic resonance imaging helps in classification and assessing extent and distribution. Conventional angiography also known as digital subtraction angiography is pivotal in assessment of fine vascular details and treatment planning. Imaging correlates well with histopathology. We review recent development in imaging techniques of various vascular anomalies most of which are affecting the peripheral system which potentially may broaden understanding of their diagnosis, classification and treatment.

FOCIS: A forest classification and inventory system using LANDSAT and digital terrain data

NASA Technical Reports Server (NTRS)

Strahler, A. H.; Franklin, J.; Woodcook, C. E.; Logan, T. L.

1981-01-01

Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS). Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine processing techniques to extract and process tonal, textural, and terrain information from registered LANDSAT multispectral and digital terrain data. Comparison of samples from timber strata identified by conventional procedures showed that both have about the same potential to reduce the variance of timber volume estimates over simple random sampling.

Parallel consensual neural networks.

PubMed

Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

1997-01-01

A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

Matching shapes with self-intersections: application to leaf classification.

PubMed

Mokhtarian, Farzin; Abbasi, Sadegh

2004-05-01

We address the problem of two-dimensional (2-D) shape representation and matching in presence of self-intersection for large image databases. This may occur when part of an object is hidden behind another part and results in a darker section in the gray level image of the object. The boundary contour of the object must include the boundary of this part which is entirely inside the outline of the object. The Curvature Scale Space (CSS) image of a shape is a multiscale organization of its inflection points as it is smoothed. The CSS-based shape representation method has been selected for MPEG-7 standardization. We study the effects of contour self-intersection on the Curvature Scale Space image. When there is no self-intersection, the CSS image contains several arch shape contours, each related to a concavity or a convexity of the shape. Self intersections create contours with minima as well as maxima in the CSS image. An efficient shape representation method has been introduced in this paper which describes a shape using the maxima as well as the minima of its CSS contours. This is a natural generalization of the conventional method which only includes the maxima of the CSS image contours. The conventional matching algorithm has also been modified to accommodate the new information about the minima. The method has been successfully used in a real world application to find, for an unknown leaf, similar classes from a database of classified leaf images representing different varieties of chrysanthemum. For many classes of leaves, self-intersection is inevitable during the scanning of the image. Therefore the original contributions of this paper is the generalization of the Curvature Scale Space representation to the class of 2-D contours with self-intersection, and its application to the classification of Chrysanthemum leaves.

Classification of motor intent in transradial amputees using sonomyography and spatio-temporal image analysis

NASA Astrophysics Data System (ADS)

Hariharan, Harishwaran; Aklaghi, Nima; Baker, Clayton A.; Rangwala, Huzefa; Kosecka, Jana; Sikdar, Siddhartha

2016-04-01

In spite of major advances in biomechanical design of upper extremity prosthetics, these devices continue to lack intuitive control. Conventional myoelectric control strategies typically utilize electromyography (EMG) signal amplitude sensed from forearm muscles. EMG has limited specificity in resolving deep muscle activity and poor signal-to-noise ratio. We have been investigating alternative control strategies that rely on real-time ultrasound imaging that can overcome many of the limitations of EMG. In this work, we present an ultrasound image sequence classification method that utilizes spatiotemporal features to describe muscle activity and classify motor intent. Ultrasound images of the forearm muscles were obtained from able-bodied subjects and a trans-radial amputee while they attempted different hand movements. A grid-based approach is used to test the feasibility of using spatio-temporal features by classifying hand motions performed by the subjects. Using the leave-one-out cross validation on image sequences acquired from able-bodied subjects, we observe that the grid-based approach is able to discern four hand motions with 95.31% accuracy. In case of the trans-radial amputee, we are able to discern three hand motions with 80% accuracy. In a second set of experiments, we study classification accuracy by extracting spatio-temporal sub-sequences the depict activity due to the motion of local anatomical interfaces. Short time and space limited cuboidal sequences are initially extracted and assigned an optical flow behavior label, based on a response function. The image space is clustered based on the location of cuboids and features calculated from the cuboids in each cluster. Using sequences of known motions, we extract feature vectors that describe said motion. A K-nearest neighbor classifier is designed for classification experiments. Using the leave-one-out cross validation on image sequences for an amputee subject, we demonstrate that the classifier is able to discern three important hand motions with an accuracy of 93.33% accuracy, 91-100% precision and 80-100% recall rate. We anticipate that ultrasound imaging based methods will address some limitations of conventional myoelectric sensing, while adding advantages inherent to ultrasound imaging.

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

PubMed

Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

2015-01-01

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first. work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method.

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis

PubMed Central

Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

2015-01-01

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method. PMID:26221691

Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

EPA Science Inventory

Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

How a national vegetation classification can help ecological research and management

Treesearch

Scott Franklin; Patrick Comer; Julie Evens; Exequiel Ezcurra; Don Faber-Langendoen; Janet Franklin; Michael Jennings; Carmen Josse; Chris Lea; Orie Loucks; Esteban Muldavin; Robert Peet; Serguei Ponomarenko; David Roberts; Ayzik Solomeshch; Todd Keeler-Wolf; James Van Kley; Alan Weakley; Alexa McKerrow; Marianne Burke; Carol Spurrier

2015-01-01

The elegance of classification lies in its ability to compile and systematize various terminological conventions and masses of information that are unattainable during typical research projects. Imagine a discipline without standards for collection, analysis, and interpretation; unfortunately, that describes much of 20th-century vegetation ecology.

MO-DE-207A-02: A Feature-Preserving Image Reconstruction Method for Improved Pancreaticlesion Classification in Diagnostic CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, J; Tsui, B; Noo, F

Purpose: To develop a feature-preserving model based image reconstruction (MBIR) method that improves performance in pancreatic lesion classification at equal or reduced radiation dose. Methods: A set of pancreatic lesion models was created with both benign and premalignant lesion types. These two classes of lesions are distinguished by their fine internal structures; their delineation is therefore crucial to the task of pancreatic lesion classification. To reduce image noise while preserving the features of the lesions, we developed a MBIR method with curvature-based regularization. The novel regularization encourages formation of smooth surfaces that model both the exterior shape and the internalmore » features of pancreatic lesions. Given that the curvature depends on the unknown image, image reconstruction or denoising becomes a non-convex optimization problem; to address this issue an iterative-reweighting scheme was used to calculate and update the curvature using the image from the previous iteration. Evaluation was carried out with insertion of the lesion models into the pancreas of a patient CT image. Results: Visual inspection was used to compare conventional TV regularization with our curvature-based regularization. Several penalty-strengths were considered for TV regularization, all of which resulted in erasing portions of the septation (thin partition) in a premalignant lesion. At matched noise variance (50% noise reduction in the patient stomach region), the connectivity of the septation was well preserved using the proposed curvature-based method. Conclusion: The curvature-based regularization is able to reduce image noise while simultaneously preserving the lesion features. This method could potentially improve task performance for pancreatic lesion classification at equal or reduced radiation dose. The result is of high significance for longitudinal surveillance studies of patients with pancreatic cysts, which may develop into pancreatic cancer. The Senior Author receives financial support from Siemens GmbH Healthcare.« less

In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging

PubMed Central

Ibrahim, Mohd Firdaus; Ahmad Sa’ad, Fathinul Syahir; Zakaria, Ammar; Md Shakaff, Ali Yeon

2016-01-01

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass. PMID:27801799

In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging.

PubMed

Ibrahim, Mohd Firdaus; Ahmad Sa'ad, Fathinul Syahir; Zakaria, Ammar; Md Shakaff, Ali Yeon

2016-10-27

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t -test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets.

PubMed

Li, Jinyan; Fong, Simon; Wong, Raymond K; Millham, Richard; Wong, Kelvin K L

2017-06-28

Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, 'It is not the strongest of the species that survives, but the most adaptable'. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

PubMed

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Random forests-based differential analysis of gene sets for gene expression data.

PubMed

Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

2013-04-10

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for interpretation of data in complex biological systems. The classifications of biologically defined gene sets can reveal the underlying interactions of gene sets associated with the phenotypes, and provide an insightful complement to conventional gene set analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cavanaugh, J.E.; McQuarrie, A.D.; Shumway, R.H.

Conventional methods for discriminating between earthquakes and explosions at regional distances have concentrated on extracting specific features such as amplitude and spectral ratios from the waveforms of the P and S phases. We consider here an optimum nonparametric classification procedure derived from the classical approach to discriminating between two Gaussian processes with unequal spectra. Two robust variations based on the minimum discrimination information statistic and Renyi's entropy are also considered. We compare the optimum classification procedure with various amplitude and spectral ratio discriminants and show that its performance is superior when applied to a small population of 8 land-based earthquakesmore » and 8 mining explosions recorded in Scandinavia. Several parametric characterizations of the notion of complexity based on modeling earthquakes and explosions as autoregressive or modulated autoregressive processes are also proposed and their performance compared with the nonparametric and feature extraction approaches.« less

Improved signal processing approaches in an offline simulation of a hybrid brain–computer interface

PubMed Central

Brunner, Clemens; Allison, Brendan Z.; Krusienski, Dean J.; Kaiser, Vera; Müller-Putz, Gernot R.; Pfurtscheller, Gert; Neuper, Christa

2012-01-01

In a conventional brain–computer interface (BCI) system, users perform mental tasks that yield specific patterns of brain activity. A pattern recognition system determines which brain activity pattern a user is producing and thereby infers the user’s mental task, allowing users to send messages or commands through brain activity alone. Unfortunately, despite extensive research to improve classification accuracy, BCIs almost always exhibit errors, which are sometimes so severe that effective communication is impossible. We recently introduced a new idea to improve accuracy, especially for users with poor performance. In an offline simulation of a “hybrid” BCI, subjects performed two mental tasks independently and then simultaneously. This hybrid BCI could use two different types of brain signals common in BCIs – event-related desynchronization (ERD) and steady-state evoked potentials (SSEPs). This study suggested that such a hybrid BCI is feasible. Here, we re-analyzed the data from our initial study. We explored eight different signal processing methods that aimed to improve classification and further assess both the causes and the extent of the benefits of the hybrid condition. Most analyses showed that the improved methods described here yielded a statistically significant improvement over our initial study. Some of these improvements could be relevant to conventional BCIs as well. Moreover, the number of illiterates could be reduced with the hybrid condition. Results are also discussed in terms of dual task interference and relevance to protocol design in hybrid BCIs. PMID:20153371

8 CFR 204.300 - Scope of this subpart.

Code of Federal Regulations, 2011 CFR

2011-01-01

... apply to the immigrant visa classification of adopted children, as defined in section 101(b)(1)(E) of the Act. For the procedures that govern classification of adopted children as defined in section 101(b... date, as defined in 8 CFR 204.301. (b) Orphan cases. On or after the Convention effective date, no Form...

Novel chromatin texture features for the classification of pap smears

NASA Astrophysics Data System (ADS)

Bejnordi, Babak E.; Moshavegh, Ramin; Sujathan, K.; Malm, Patrik; Bengtsson, Ewert; Mehnert, Andrew

2013-03-01

This paper presents a set of novel structural texture features for quantifying nuclear chromatin patterns in cells on a conventional Pap smear. The features are derived from an initial segmentation of the chromatin into bloblike texture primitives. The results of a comprehensive feature selection experiment, including the set of proposed structural texture features and a range of different cytology features drawn from the literature, show that two of the four top ranking features are structural texture features. They also show that a combination of structural and conventional features yields a classification performance of 0.954±0.019 (AUC±SE) for the discrimination of normal (NILM) and abnormal (LSIL and HSIL) slides. The results of a second classification experiment, using only normal-appearing cells from both normal and abnormal slides, demonstrates that a single structural texture feature measuring chromatin margination yields a classification performance of 0.815±0.019. Overall the results demonstrate the efficacy of the proposed structural approach and that it is possible to detect malignancy associated changes (MACs) in Papanicoloau stain.

A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju; Bhaduri, Budhendra L

2011-01-01

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of large number of accurate training samples (10 to 30 |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, itmore » is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 25 to 35% improvement in overall classification accuracy over conventional classification schemes.« less

Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms

NASA Astrophysics Data System (ADS)

Gholami, Raoof; Rasouli, Vamegh; Alimoradi, Andisheh

2013-09-01

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures.

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

PubMed

Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup

2017-07-25

Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

PubMed

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Novel gene sets improve set-level classification of prokaryotic gene expression data.

PubMed

Holec, Matěj; Kuželka, Ondřej; Železný, Filip

2015-10-28

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

PubMed Central

Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Aarsvold, John N.; Raghunath, Nivedita; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.; Votaw, John R.

2012-01-01

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [11C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET. PMID:23039679

High resolution vs conventional esophageal manometry in the assessment of esophageal motor disorders in patients with non-cardiac chest pain.

PubMed

Akinsiku, O; Yamasaki, T; Brunner, S; Ganocy, S; Fass, R

2018-06-01

High-resolution esophageal manometry (HREM) has become a leading tool in the assessment of esophageal motor disorders, replacing conventional manometry. However, there is limited data about the contribution of HREM as compared with conventional manometry to the assessment of esophageal motor disorders in patients with non-cardiac chest pain (NCCP). The aim of the study was to compare the distribution of esophageal motor disorders in patients with NCCP using HREM as compared with conventional manometry and to determine if HREM improved diagnosis of these disorders. In this study, we included 300 consecutive patients with NCCP who underwent either HREM or conventional manometry over a period of 10 years. A total of 150 patients had conventional manometry and the other 150 patients HREM. The Chicago 3.0 classification and the Castell and Spechler classification were used to determine the esophageal motor disorder of NCCP patients undergoing HREM and conventional manometry, respectively. In both HREM and the conventional manometry groups, normal esophageal motility was the most frequent finding (47% and 36%; respectively, P = .054). Hypotensive lower esophageal sphincter was the most common motility disorder identified by conventional manometry (27.3%), while ineffective esophageal motility was the most common esophageal motor disorder identified by HREM (25.3%). There is a discrepancy in the type of esophageal motor disorders identified by HREM as compared with conventional manometry in NCCP patients. Hypotensive motility disorders are the most commonly diagnosed by both manometric techniques. © 2017 John Wiley & Sons Ltd.

Dental enamel defect diagnosis through different technology-based devices.

PubMed

Kobayashi, Tatiana Yuriko; Vitor, Luciana Lourenço Ribeiro; Carrara, Cleide Felício Carvalho; Silva, Thiago Cruvinel; Rios, Daniela; Machado, Maria Aparecida Andrade Moreira; Oliveira, Thais Marchini

2018-06-01

Dental enamel defects (DEDs) are faulty or deficient enamel formations of primary and permanent teeth. Changes during tooth development result in hypoplasia (a quantitative defect) and/or hypomineralisation (a qualitative defect). To compare technology-based diagnostic methods for detecting DEDs. Two-hundred and nine dental surfaces of anterior permanent teeth were selected in patients, 6-11 years of age, with cleft lip with/without cleft palate. First, a conventional clinical examination was conducted according to the modified Developmental Defects of Enamel Index (DDE Index). Dental surfaces were evaluated using an operating microscope and a fluorescence-based device. Interexaminer reproducibility was determined using the kappa test. To compare groups, McNemar's test was used. Cramer's V test was used for comparing the distribution of index codes obtained after classification of all dental surfaces. Cramer's V test revealed statistically significant differences (P < .0001) in the distribution of index codes obtained using the different methods; the coefficients were 0.365 for conventional clinical examination versus fluorescence, 0.961 for conventional clinical examination versus operating microscope and 0.358 for operating microscope versus fluorescence. The sensitivity of the operating microscope and fluorescence method was statistically significant (P = .008 and P < .0001, respectively). Otherwise, the results did not show statistically significant differences in accuracy and specificity for either the operating microscope or the fluorescence methods. This study suggests that the operating microscope performed better than the fluorescence-based device and could be an auxiliary method for the detection of DEDs. © 2017 FDI World Dental Federation.

40 CFR 144.6 - Classification of wells.

Code of Federal Regulations, 2010 CFR

2010-07-01

... including: (1) Mining of sulfur by the Frasch process; (2) In situ production of uranium or other metals; this category includes only in-situ production from ore bodies which have not been conventionally mined... are brought to the surface in connection with natural gas storage operations, or conventional oil or...

40 CFR 144.6 - Classification of wells.

Code of Federal Regulations, 2011 CFR

2011-07-01

... including: (1) Mining of sulfur by the Frasch process; (2) In situ production of uranium or other metals; this category includes only in-situ production from ore bodies which have not been conventionally mined... are brought to the surface in connection with natural gas storage operations, or conventional oil or...

40 CFR 144.6 - Classification of wells.

Code of Federal Regulations, 2012 CFR

2012-07-01

... including: (1) Mining of sulfur by the Frasch process; (2) In situ production of uranium or other metals; this category includes only in-situ production from ore bodies which have not been conventionally mined... are brought to the surface in connection with natural gas storage operations, or conventional oil or...

40 CFR 144.6 - Classification of wells.

Code of Federal Regulations, 2013 CFR

2013-07-01

... including: (1) Mining of sulfur by the Frasch process; (2) In situ production of uranium or other metals; this category includes only in-situ production from ore bodies which have not been conventionally mined... are brought to the surface in connection with natural gas storage operations, or conventional oil or...

40 CFR 144.6 - Classification of wells.

Code of Federal Regulations, 2014 CFR

2014-07-01

... including: (1) Mining of sulfur by the Frasch process; (2) In situ production of uranium or other metals; this category includes only in-situ production from ore bodies which have not been conventionally mined... are brought to the surface in connection with natural gas storage operations, or conventional oil or...

The effect of call libraries and acoustic filters on the identification of bat echolocation.

PubMed

Clement, Matthew J; Murray, Kevin L; Solick, Donald I; Gruver, Jeffrey C

2014-09-01

Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys.

The effect of call libraries and acoustic filters on the identification of bat echolocation

PubMed Central

Clement, Matthew J; Murray, Kevin L; Solick, Donald I; Gruver, Jeffrey C

2014-01-01

Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys. PMID:25535563

Classification of teeth in cone-beam CT using deep convolutional neural network.

PubMed

Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

2017-01-01

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification. Copyright © 2016 Elsevier Ltd. All rights reserved.

The effect of call libraries and acoustic filters on the identification of bat echolocation

USGS Publications Warehouse

Clement, Matthew; Murray, Kevin L; Solick, Donald I; Gruver, Jeffrey C

2014-01-01

Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys.

Development of fuzzy air quality index using soft computing approach.

PubMed

Mandal, T; Gorai, A K; Pathak, G

2012-10-01

Proper assessment of air quality status in an atmosphere based on limited observations is an essential task for meeting the goals of environmental management. A number of classification methods are available for estimating the changing status of air quality. However, a discrepancy frequently arises from the quality criteria of air employed and vagueness or fuzziness embedded in the decision making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies like air quality index when describing integrated air quality conditions with respect to various pollutants parameters and time of exposure. In recent years, the fuzzy logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental issues. In the present study, a methodology based on fuzzy inference systems (FIS) to assess air quality is proposed. This paper presents a comparative study to assess status of air quality using fuzzy logic technique and that of conventional technique. The findings clearly indicate that the FIS may successfully harmonize inherent discrepancies and interpret complex conditions.

Determination of toxigenic fungi and aflatoxins in nuts and dried fruits using imaging and spectroscopic techniques.

PubMed

Wu, Qifang; Xie, Lijuan; Xu, Huirong

2018-06-30

Nuts and dried fruits contain rich nutrients and are thus highly vulnerable to contamination with toxigenic fungi and aflatoxins because of poor weather, processing and storage conditions. Imaging and spectroscopic techniques have proven to be potential alternative tools to wet chemistry methods for efficient and non-destructive determination of contamination with fungi and toxins. Thus, this review provides an overview of the current developments and applications in frequently used food safety testing techniques, including near infrared spectroscopy (NIRS), mid-infrared spectroscopy (MIRS), conventional imaging techniques (colour imaging (CI) and hyperspectral imaging (HSI)), and fluorescence spectroscopy and imaging (FS/FI). Interesting classification and determination results can be found in both static and on/in-line real-time detection for contaminated nuts and dried fruits. Although these techniques offer many benefits over conventional methods, challenges remain in terms of heterogeneous distribution of toxins, background constituent interference, model robustness, detection limits, sorting efficiency, as well as instrument development. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluation of airborne lidar data to predict vegetation Presence/Absence

USGS Publications Warehouse

Palaseanu-Lovejoy, M.; Nayegandhi, A.; Brock, J.; Woodman, R.; Wright, C.W.

2009-01-01

This study evaluates the capabilities of the Experimental Advanced Airborne Research Lidar (EAARL) in delineating vegetation assemblages in Jean Lafitte National Park, Louisiana. Five-meter-resolution grids of bare earth, canopy height, canopy-reflection ratio, and height of median energy were derived from EAARL data acquired in September 2006. Ground-truth data were collected along transects to assess species composition, canopy cover, and ground cover. To decide which model is more accurate, comparisons of general linear models and generalized additive models were conducted using conventional evaluation methods (i.e., sensitivity, specificity, Kappa statistics, and area under the curve) and two new indexes, net reclassification improvement and integrated discrimination improvement. Generalized additive models were superior to general linear models in modeling presence/absence in training vegetation categories, but no statistically significant differences between the two models were achieved in determining the classification accuracy at validation locations using conventional evaluation methods, although statistically significant improvements in net reclassifications were observed. ?? 2009 Coastal Education and Research Foundation.

A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

Large Uptake of Titania and Iron Oxide Nanoparticles in the Nucleus of Lung Epithelial Cells as Measured by Raman Imaging and Multivariate Classification

PubMed Central

Ahlinder, Linnea; Ekstrand-Hammarström, Barbro; Geladi, Paul; Österlund, Lars

2013-01-01

It is a challenging task to characterize the biodistribution of nanoparticles in cells and tissue on a subcellular level. Conventional methods to study the interaction of nanoparticles with living cells rely on labeling techniques that either selectively stain the particles or selectively tag them with tracer molecules. In this work, Raman imaging, a label-free technique that requires no extensive sample preparation, was combined with multivariate classification to quantify the spatial distribution of oxide nanoparticles inside living lung epithelial cells (A549). Cells were exposed to TiO2 (titania) and/or α-FeO(OH) (goethite) nanoparticles at various incubation times (4 or 48 h). Using multivariate classification of hyperspectral Raman data with partial least-squares discriminant analysis, we show that a surprisingly large fraction of spectra, classified as belonging to the cell nucleus, show Raman bands associated with nanoparticles. Up to 40% of spectra from the cell nucleus show Raman bands associated with nanoparticles. Complementary transmission electron microscopy data for thin cell sections qualitatively support the conclusions. PMID:23870252

Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems †

PubMed Central

Kim, Kwangsoo; Jin, Jae-Yeon; Jin, Seong-il

2016-01-01

Medical asset tracking systems track a medical device with a mobile node and determine its status as either in or out, because it can leave a monitoring area. Due to a failed node, this system may decide that a mobile asset is outside the area, even though it is within the area. In this paper, an efficient classification method is proposed to separate mobile nodes disconnected from a wireless sensor network between nodes with faults and a node that actually has left the monitoring region. The proposed scheme uses two trends extracted from the neighboring nodes of a disconnected mobile node. First is the trend in a series of the neighbor counts; the second is that of the ratios of the boundary nodes included in the neighbors. Based on such trends, the proposed method separates failed nodes from mobile nodes that are disconnected from a wireless sensor network without failures. The proposed method is evaluated using both real data generated from a medical asset tracking system and also using simulations with the network simulator (ns-2). The experimental results show that the proposed method correctly differentiates between failed nodes and nodes that are no longer in the monitoring region, including the cases that the conventional methods fail to detect. PMID:26901200

Classification of Shiga toxin-producing escherichia coli (STEC) serotypes with hyperspectral microscope imagery

NASA Astrophysics Data System (ADS)

Park, Bosoon; Windham, William R.; Ladely, Scott R.; Gurram, Prudhvi; Kwon, Heesung; Yoon, Seung-Chul; Lawrence, Kurt C.; Narang, Neelam; Cray, William C.

2012-05-01

Non-O157:H7 Shiga toxin-producing Escherichia coli (STEC) strains such as O26, O45, O103, O111, O121 and O145 are recognized as serious outbreak to cause human illness due to their toxicity. A conventional microbiological method for cell counting is laborious and needs long time for the results. Since optical detection method is promising for realtime, in-situ foodborne pathogen detection, acousto-optical tunable filters (AOTF)-based hyperspectral microscopic imaging (HMI) method has been developed for identifying pathogenic bacteria because of its capability to differentiate both spatial and spectral characteristics of each bacterial cell from microcolony samples. Using the AOTF-based HMI method, 89 contiguous spectral images could be acquired within approximately 30 seconds with 250 ms exposure time. From this study, we have successfully developed the protocol for live-cell immobilization on glass slides to acquire quality spectral images from STEC bacterial cells using the modified dry method. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 458, 498, 522, 546, 570, 586, 670 and 690 nm were distinctive for STEC bacteria. With two different classification algorithms, Support Vector Machine (SVM) and Sparse Kernel-based Ensemble Learning (SKEL), a STEC serotype O45 could be classified with 92% detection accuracy.

On the nature of global classification

NASA Technical Reports Server (NTRS)

Wheelis, M. L.; Kandler, O.; Woese, C. R.

1992-01-01

Molecular sequencing technology has brought biology into the era of global (universal) classification. Methodologically and philosophically, global classification differs significantly from traditional, local classification. The need for uniformity requires that higher level taxa be defined on the molecular level in terms of universally homologous functions. A global classification should reflect both principal dimensions of the evolutionary process: genealogical relationship and quality and extent of divergence within a group. The ultimate purpose of a global classification is not simply information storage and retrieval; such a system should also function as an heuristic representation of the evolutionary paradigm that exerts a directing influence on the course of biology. The global system envisioned allows paraphyletic taxa. To retain maximal phylogenetic information in these cases, minor notational amendments in existing taxonomic conventions should be adopted.

Prediction of fatigue-related driver performance from EEG data by deep Riemannian model.

PubMed

Hajinoroozi, Mehdi; Jianqiu Zhang; Yufei Huang

2017-07-01

Prediction of the drivers' drowsy and alert states is important for safety purposes. The prediction of drivers' drowsy and alert states from electroencephalography (EEG) using shallow and deep Riemannian methods is presented. For shallow Riemannian methods, the minimum distance to Riemannian mean (mdm) and Log-Euclidian metric are investigated, where it is shown that Log-Euclidian metric outperforms the mdm algorithm. In addition the SPDNet, a deep Riemannian model, that takes the EEG covariance matrix as the input is investigated. It is shown that SPDNet outperforms all tested shallow and deep classification methods. Performance of SPDNet is 6.02% and 2.86% higher than the best performance by the conventional Euclidian classifiers and shallow Riemannian models, respectively.

A 3D convolutional neural network approach to land cover classification using LiDAR and multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Xu, Z.; Guan, K.; Peng, B.; Casler, N. P.; Wang, S. W.

2017-12-01

Landscape has complex three-dimensional features. These 3D features are difficult to extract using conventional methods. Small-footprint LiDAR provides an ideal way for capturing these features. Existing approaches, however, have been relegated to raster or metric-based (two-dimensional) feature extraction from the upper or bottom layer, and thus are not suitable for resolving morphological and intensity features that could be important to fine-scale land cover mapping. Therefore, this research combines airborne LiDAR and multi-temporal Landsat imagery to classify land cover types of Williamson County, Illinois that has diverse and mixed landscape features. Specifically, we applied a 3D convolutional neural network (CNN) method to extract features from LiDAR point clouds by (1) creating occupancy grid, intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into a 3D CNN feature extractor for many epochs of learning. The learned features (e.g., morphological features, intensity features, etc) were combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. We used photo interpretation for training and testing data generation. The classification results show that our approach outperforms traditional methods using LiDAR derived feature maps, and promises to serve as an effective methodology for creating high-quality land cover maps through fusion of complementary types of remote sensing data.

Improving the performance of univariate control charts for abnormal detection and classification

NASA Astrophysics Data System (ADS)

Yiakopoulos, Christos; Koutsoudaki, Maria; Gryllias, Konstantinos; Antoniadis, Ioannis

2017-03-01

Bearing failures in rotating machinery can cause machine breakdown and economical loss, if no effective actions are taken on time. Therefore, it is of prime importance to detect accurately the presence of faults, especially at their early stage, to prevent sequent damage and reduce costly downtime. The machinery fault diagnosis follows a roadmap of data acquisition, feature extraction and diagnostic decision making, in which mechanical vibration fault feature extraction is the foundation and the key to obtain an accurate diagnostic result. A challenge in this area is the selection of the most sensitive features for various types of fault, especially when the characteristics of failures are difficult to be extracted. Thus, a plethora of complex data-driven fault diagnosis methods are fed by prominent features, which are extracted and reduced through traditional or modern algorithms. Since most of the available datasets are captured during normal operating conditions, the last decade a number of novelty detection methods, able to work when only normal data are available, have been developed. In this study, a hybrid method combining univariate control charts and a feature extraction scheme is introduced focusing towards an abnormal change detection and classification, under the assumption that measurements under normal operating conditions of the machinery are available. The feature extraction method integrates the morphological operators and the Morlet wavelets. The effectiveness of the proposed methodology is validated on two different experimental cases with bearing faults, demonstrating that the proposed approach can improve the fault detection and classification performance of conventional control charts.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture.

PubMed

Yamamoto, Kyosuke; Togami, Takashi; Yamaguchi, Norio

2017-11-06

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture-in cooperation with image processing technologies-for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture

PubMed Central

Togami, Takashi; Yamaguchi, Norio

2017-01-01

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture—in cooperation with image processing technologies—for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis. PMID:29113104

Comparison of several chemometric methods of libraries and classifiers for the analysis of expired drugs based on Raman spectra.

PubMed

Gao, Qun; Liu, Yan; Li, Hao; Chen, Hui; Chai, Yifeng; Lu, Feng

2014-06-01

Some expired drugs are difficult to detect by conventional means. If they are repackaged and sold back into market, they will constitute a new public health challenge. For the detection of repackaged expired drugs within specification, paracetamol tablet from a manufacturer was used as a model drug in this study for comparison of Raman spectra-based library verification and classification methods. Raman spectra of different batches of paracetamol tablets were collected and a library including standard spectra of unexpired batches of tablets was established. The Raman spectrum of each sample was identified by cosine and correlation with the standard spectrum. The average HQI of the suspicious samples and the standard spectrum were calculated. The optimum threshold values were 0.997 and 0.998 respectively as a result of ROC and four evaluations, for which the accuracy was up to 97%. Three supervised classifiers, PLS-DA, SVM and k-NN, were chosen to establish two-class classification models and compared subsequently. They were used to establish a classification of expired batches and an unexpired batch, and predict the suspect samples. The average accuracy was 90.12%, 96.80% and 89.37% respectively. Different pre-processing techniques were tried to find that first derivative was optimal for methods of libraries and max-min normalization was optimal for that of classifiers. The results obtained from these studies indicated both libraries and classifier methods could detect the expired drugs effectively, and they should be used complementarily in the fast-screening. Copyright © 2014 Elsevier B.V. All rights reserved.

A method of classification for multisource data in remote sensing based on interval-valued probabilities

NASA Technical Reports Server (NTRS)

Kim, Hakil; Swain, Philip H.

1990-01-01

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Ying; Li, Hong; Bridges, Denzel

We report that the continuing miniaturization of microelectronics is pushing advanced manufacturing into nanomanufacturing. Nanojoining is a bottom-up assembly technique that enables functional nanodevice fabrication with dissimilar nanoscopic building blocks and/or molecular components. Various conventional joining techniques have been modified and re-invented for joining nanomaterials. Our review surveys recent progress in nanojoining methods, as compared to conventional joining processes. Examples of nanojoining are given and classified by the dimensionality of the joining materials. At each classification, nanojoining is reviewed and discussed according to materials specialties, low dimensional processing features, energy input mechanisms and potential applications. The preparation of new intermetallicmore » materials by reactive nanoscale multilayer foils based on self-propagating high-temperature synthesis is highlighted. This review will provide insight into nanojoining fundamentals and innovative applications in power electronics packaging, plasmonic devices, nanosoldering for printable electronics, 3D printing and space manufacturing.« less

Susceptibility of green and conventional building materials to microbial growth.

PubMed

Mensah-Attipoe, J; Reponen, T; Salmela, A; Veijalainen, A-M; Pasanen, P

2015-06-01

Green building materials are becoming more popular. However, little is known about their ability to support or limit microbial growth. The growth of fungi was evaluated on five building materials. Two green, two conventional building materials and wood as a positive control were selected. The materials were inoculated with Aspergillus versicolor, Cladosporium cladosporioides and Penicillium brevicompactum, in the absence and presence of house dust. Microbial growth was assessed at four different time points by cultivation and determining fungal biomass using the N-acetylhexosaminidase (NAHA) enzyme assay. No clear differences were seen between green and conventional building materials in their susceptibility to support microbial growth. The presence of dust, an external source of nutrients, promoted growth of all the fungal species similarly on green and conventional materials. The results also showed a correlation coefficient ranging from 0.81 to 0.88 between NAHA activity and culturable counts. The results suggest that the growth of microbes on a material surface depends on the availability of organic matter rather than the classification of the material as green or conventional. NAHA activity and culturability correlated well indicating that the two methods used in the experiments gave similar trends for the growth of fungi on material surfaces. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Face recognition using an enhanced independent component analysis approach.

PubMed

Kwak, Keun-Chang; Pedrycz, Witold

2007-03-01

This paper is concerned with an enhanced independent component analysis (ICA) and its application to face recognition. Typically, face representations obtained by ICA involve unsupervised learning and high-order statistics. In this paper, we develop an enhancement of the generic ICA by augmenting this method by the Fisher linear discriminant analysis (LDA); hence, its abbreviation, FICA. The FICA is systematically developed and presented along with its underlying architecture. A comparative analysis explores four distance metrics, as well as classification with support vector machines (SVMs). We demonstrate that the FICA approach leads to the formation of well-separated classes in low-dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression. The comprehensive experiments are completed for the facial-recognition technology (FERET) face database; a comparative analysis demonstrates that FICA comes with improved classification rates when compared with some other conventional approaches such as eigenface, fisherface, and the ICA itself.

Scene text detection via extremal region based double threshold convolutional network classification

PubMed Central

Zhu, Wei; Lou, Jing; Chen, Longtao; Xia, Qingyuan

2017-01-01

In this paper, we present a robust text detection approach in natural images which is based on region proposal mechanism. A powerful low-level detector named saliency enhanced-MSER extended from the widely-used MSER is proposed by incorporating saliency detection methods, which ensures a high recall rate. Given a natural image, character candidates are extracted from three channels in a perception-based illumination invariant color space by saliency-enhanced MSER algorithm. A discriminative convolutional neural network (CNN) is jointly trained with multi-level information including pixel-level and character-level information as character candidate classifier. Each image patch is classified as strong text, weak text and non-text by double threshold filtering instead of conventional one-step classification, leveraging confident scores obtained via CNN. To further prune non-text regions, we develop a recursive neighborhood search algorithm to track credible texts from weak text set. Finally, characters are grouped into text lines using heuristic features such as spatial location, size, color, and stroke width. We compare our approach with several state-of-the-art methods, and experiments show that our method achieves competitive performance on public datasets ICDAR 2011 and ICDAR 2013. PMID:28820891

A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture.

PubMed

Zhong, Yuanhong; Gao, Junyuan; Lei, Qilun; Zhou, Yao

2018-05-09

Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.

A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

PubMed Central

Zhong, Yuanhong; Gao, Junyuan; Lei, Qilun; Zhou, Yao

2018-01-01

Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications. PMID:29747429

An assessment of the effectiveness of a random forest classifier for land-cover classification

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

2012-01-01

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

Sleep staging with movement-related signals.

PubMed

Jansen, B H; Shankar, K

1993-05-01

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

CrossLink: a novel method for cross-condition classification of cancer subtypes.

PubMed

Ma, Chifeng; Sastry, Konduru S; Flore, Mario; Gehani, Salah; Al-Bozom, Issam; Feng, Yusheng; Serpedin, Erchin; Chouchane, Lotfi; Chen, Yidong; Huang, Yufei

2016-08-22

We considered the prediction of cancer classes (e.g. subtypes) using patient gene expression profiles that contain both systematic and condition-specific biases when compared with the training reference dataset. The conventional normalization-based approaches cannot guarantee that the gene signatures in the reference and prediction datasets always have the same distribution for all different conditions as the class-specific gene signatures change with the condition. Therefore, the trained classifier would work well under one condition but not under another. To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink (CL). CL recognizes that there is no universal, condition-independent normalization mapping of signatures. In contrast, it exploits the fact that the signature is unique to its associated class under any condition and thus employs an unsupervised clustering algorithm to discover this unique signature. We assessed the performance of CL for cross-condition predictions of PAM50 subtypes of breast cancer by using a simulated dataset modeled after TCGA BRCA tumor samples with a cross-validation scheme, and datasets with known and unknown PAM50 classification. CL achieved prediction accuracy >73 %, highest among other methods we evaluated. We also applied the algorithm to a set of breast cancer tumors derived from Arabic population to assign a PAM50 classification to each tumor based on their gene expression profiles. A novel algorithm CrossLink for cross-condition prediction of cancer classes was proposed. In all test datasets, CL showed robust and consistent improvement in prediction performance over other state-of-the-art normalization and classification algorithms.

a Point Cloud Classification Approach Based on Vertical Structures of Ground Objects

NASA Astrophysics Data System (ADS)

Zhao, Y.; Hu, Q.; Hu, W.

2018-04-01

This paper proposes a novel method for point cloud classification using vertical structural characteristics of ground objects. Since urbanization develops rapidly nowadays, urban ground objects also change frequently. Conventional photogrammetric methods cannot satisfy the requirements of updating the ground objects' information efficiently, so LiDAR (Light Detection and Ranging) technology is employed to accomplish this task. LiDAR data, namely point cloud data, can obtain detailed three-dimensional coordinates of ground objects, but this kind of data is discrete and unorganized. To accomplish ground objects classification with point cloud, we first construct horizontal grids and vertical layers to organize point cloud data, and then calculate vertical characteristics, including density and measures of dispersion, and form characteristic curves for each grids. With the help of PCA processing and K-means algorithm, we analyze the similarities and differences of characteristic curves. Curves that have similar features will be classified into the same class and point cloud correspond to these curves will be classified as well. The whole process is simple but effective, and this approach does not need assistance of other data sources. In this study, point cloud data are classified into three classes, which are vegetation, buildings, and roads. When horizontal grid spacing and vertical layer spacing are 3 m and 1 m respectively, vertical characteristic is set as density, and the number of dimensions after PCA processing is 11, the overall precision of classification result is about 86.31 %. The result can help us quickly understand the distribution of various ground objects.

Luminance sticker based facial expression recognition using discrete wavelet transform for physically disabled persons.

PubMed

Nagarajan, R; Hariharan, M; Satiyan, M

2012-08-01

Developing tools to assist physically disabled and immobilized people through facial expression is a challenging area of research and has attracted many researchers recently. In this paper, luminance stickers based facial expression recognition is proposed. Recognition of facial expression is carried out by employing Discrete Wavelet Transform (DWT) as a feature extraction method. Different wavelet families with their different orders (db1 to db20, Coif1 to Coif 5 and Sym2 to Sym8) are utilized to investigate their performance in recognizing facial expression and to evaluate their computational time. Standard deviation is computed for the coefficients of first level of wavelet decomposition for every order of wavelet family. This standard deviation is used to form a set of feature vectors for classification. In this study, conventional validation and cross validation are performed to evaluate the efficiency of the suggested feature vectors. Three different classifiers namely Artificial Neural Network (ANN), k-Nearest Neighborhood (kNN) and Linear Discriminant Analysis (LDA) are used to classify a set of eight facial expressions. The experimental results demonstrate that the proposed method gives very promising classification accuracies.

Classification of Swiss cheese starter and adjunct cultures using Fourier transform infrared microspectroscopy.

PubMed

Prabhakar, V; Kocaoglu-Vurma, N; Harper, J; Rodriguez-Saona, L

2011-09-01

The acceptability of Swiss cheese largely depends on the flavor profile, and strain variations of cheese cultures will affect the final quality. Conventional biochemical methods to identify the cultures at the strain level are time-consuming and expensive, and require skilled labor. Our objective was to develop rapid classification methods of starter cultures at the strain level by using a combination of hydrophobic grid membrane filters and Fourier transform infrared (FTIR) spectroscopy. Forty-four pulsed-field gel electrophoresis-verified strains of starter and nonstarter cultures including Streptococcus thermophilus, Propionibacterium freudenreichii, and Lactobacillus spp. were analyzed. The strains were grown on their respective agar media, transferred to broth media, and incubated. Then, cultures were centrifuged and the pellets were resuspended in saline solution (10 μL). Aliquots (2 μL) of the suspended bacterial solution were placed onto a grid of the hydrophobic grid membrane filters, having 6 grids per each strain analyzed. The dried filters were read by FTIR microspectroscopy fitted with an attenuated total reflectance probe. Collected spectra were statistically analyzed by a soft independent modeling of class analogy (SIMCA) for pattern recognition. Classification models were developed for Streptococcus thermophilus, Propionibacterium freudenreichii, and Lactobacillus spp. strains. The models showed major discrimination in the spectral region from 1,200 to 900 cm(-1) associated with signals from phosphate-containing compounds and various polysaccharides in the cell wall. The developed method allowed for rapid classification of several Swiss cheese starter and nonstarter cultures at the strain level. This information provides a detailed overview of microbiological status, which would enable corrective measures to be taken early in the cheese making process, limiting production of inferior quality cheese and minimizing defects. This method could be an effective tool to identify and monitor activity of cheese and other dairy starter cultures. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

General tensor discriminant analysis and gabor features for gait recognition.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

Effectiveness of the polysaccharide hemostatic powder in non-variceal upper gastrointestinal bleeding: Using propensity score matching.

PubMed

Park, Jun Chul; Kim, Yeong Jin; Kim, Eun Hye; Lee, Jinae; Yang, Hyun Su; Kim, Eun Hwa; Hahn, Kyu Yeon; Shin, Sung Kwan; Lee, Sang Kil; Lee, Yong Chan

2018-02-07

Recently, the application of hemostatic powder to the bleeding site has been used to treat active upper gastrointestinal bleeding (UGIB). We aimed to assess the effectiveness of the polysaccharide hemostatic powder (PHP) in patients with non-variceal UGIB. We reviewed prospectively collected 40 patients with UGIB treated with PHP therapy between April 2016 and January 2017 (PHP group) and 303 patients with UGIB treated with conventional therapy between April 2012 and October 2014 (conventional therapy group). We compared the rate of successful hemostasis and the rebleeding between the two groups after as well as before propensity score matching using the Glasgow-Blatchford score and Forrest classification. Thirty patients treated with the PHP and 60 patients treated with conventional therapy were included in the matched groups. Baseline patient characteristics including comorbidities, vital signs, and bleeding scores were similar in the matched groups. The rate of immediate hemostasis and 7-day and 30-day rebleeding were also similar in the two groups before and after matching. In the subgroup analysis, no significant differences in immediate hemostasis or rebleeding rate were noted between PHP in monotherapy and PHP combined with a conventional hemostatic method. At 30 days after the therapy, there were no significant PHP-related complications or mortality. Given its safety, the PHP proved feasible for endoscopic treatment of UGIB, having similar effectiveness as that of conventional therapy. The PHP may become a promising hemostatic method for non-variceal UGIB. © 2018 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Probabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherence.

PubMed

Sankari, Ziad; Adeli, Hojjat

2011-04-15

Recently, the authors presented an EEG (electroencephalogram) coherence study of the Alzheimer's disease (AD) and found statistically significant differences between AD and control groups. In this paper a probabilistic neural network (PNN) model is presented for classification of AD and healthy controls using features extracted in coherence and wavelet coherence studies on cortical connectivity in AD. The model is verified using EEGs obtained from 20 AD probable patients and 7 healthy/control subjects based on a standard 10-20 electrode configuration on the scalp. It is shown that extracting features from EEG sub-bands using coherence, as a measure of cortical connectivity, can discriminate AD patients from healthy controls effectively when a mixed band classification model is applied. For the data set used a classification accuracy of 100% is achieved using the conventional coherence and a spread parameter of the Gaussian function in a particular range found in this research. Copyright © 2011 Elsevier B.V. All rights reserved.

Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features

PubMed Central

Xia, Junfeng; Yue, Zhenyu; Di, Yunqiang; Zhu, Xiaolei; Zheng, Chun-Hou

2016-01-01

The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces. PMID:26934646

A Corpus-Based Approach for Automatic Thai Unknown Word Recognition Using Boosting Techniques

NASA Astrophysics Data System (ADS)

Techo, Jakkrit; Nattee, Cholwich; Theeramunkong, Thanaruk

While classification techniques can be applied for automatic unknown word recognition in a language without word boundary, it faces with the problem of unbalanced datasets where the number of positive unknown word candidates is dominantly smaller than that of negative candidates. To solve this problem, this paper presents a corpus-based approach that introduces a so-called group-based ranking evaluation technique into ensemble learning in order to generate a sequence of classification models that later collaborate to select the most probable unknown word from multiple candidates. Given a classification model, the group-based ranking evaluation (GRE) is applied to construct a training dataset for learning the succeeding model, by weighing each of its candidates according to their ranks and correctness when the candidates of an unknown word are considered as one group. A number of experiments have been conducted on a large Thai medical text to evaluate performance of the proposed group-based ranking evaluation approach, namely V-GRE, compared to the conventional naïve Bayes classifier and our vanilla version without ensemble learning. As the result, the proposed method achieves an accuracy of 90.93±0.50% when the first rank is selected while it gains 97.26±0.26% when the top-ten candidates are considered, that is 8.45% and 6.79% improvement over the conventional record-based naïve Bayes classifier and the vanilla version. Another result on applying only best features show 93.93±0.22% and up to 98.85±0.15% accuracy for top-1 and top-10, respectively. They are 3.97% and 9.78% improvement over naive Bayes and the vanilla version. Finally, an error analysis is given.

The ERTS-1 investigation (ER-600): A compendium of analysis results of the utility of ERTS-1 data for land resources management

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The results of the ERTS-1 investigations conducted by the Earth Observations Division at the NASA Lyndon B. Johnson Space Center are summarized in this report, which is an overview of documents detailing individual investigations. Conventional image interpretation and computer-aided classification procedures were the two basic techniques used in analyzing the data for detecting, identifying, locating, and measuring surface features related to earth resources. Data from the ERTS-1 multispectral scanner system were useful for all applications studied, which included agriculture, coastal and estuarine analysis, forestry, range, land use and urban land use, and signature extension. Percentage classification accuracies are cited for the conventional and computer-aided techniques.

Clinical Application of Esophageal High-resolution Manometry in the Diagnosis of Esophageal Motility Disorders.

PubMed

van Hoeij, Froukje B; Bredenoord, Albert J

2016-01-31

Esophageal high-resolution manometry (HRM) is replacing conventional manometry in the clinical evaluation of patients with esophageal symptoms, especially dysphagia. The introduction of HRM gave rise to new objective metrics and recognizable patterns of esophageal motor function, requiring a new classification scheme: the Chicago classification. HRM measurements are more detailed and more easily performed compared to conventional manometry. The visual presentation of acquired data improved the analysis and interpretation of esophageal motor function. This led to a more sensitive, accurate, and objective analysis of esophageal motility. In this review we discuss how HRM changed the way we define and categorize esophageal motility disorders. Moreover, we discuss the clinical applications of HRM for each esophageal motility disorder separately.

Clinical Application of Esophageal High-resolution Manometry in the Diagnosis of Esophageal Motility Disorders

PubMed Central

van Hoeij, Froukje B; Bredenoord, Albert J

2016-01-01

Esophageal high-resolution manometry (HRM) is replacing conventional manometry in the clinical evaluation of patients with esophageal symptoms, especially dysphagia. The introduction of HRM gave rise to new objective metrics and recognizable patterns of esophageal motor function, requiring a new classification scheme: the Chicago classification. HRM measurements are more detailed and more easily performed compared to conventional manometry. The visual presentation of acquired data improved the analysis and interpretation of esophageal motor function. This led to a more sensitive, accurate, and objective analysis of esophageal motility. In this review we discuss how HRM changed the way we define and categorize esophageal motility disorders. Moreover, we discuss the clinical applications of HRM for each esophageal motility disorder separately. PMID:26631942

Herbal Medicine for Oligomenorrhea and Amenorrhea: A Systematic Review of Ancient and Conventional Medicine

PubMed Central

Tansaz, Mojgan; Nazemiyeh, Hossein; Fazljou, Seyed Mohammad Bagher

2018-01-01

Introduction Menstrual bleeding cessation is one of the most frequent gynecologic disorders among women in reproductive age. The treatment is based on hormone therapy. Due to the increasing request for alternative medicine remedies in the field of women's diseases, in present study, it was tried to overview medicinal plants used to treat oligomenorrhea and amenorrhea according to the pharmaceutical textbooks of traditional Persian medicine (TPM) and review the evidence in the conventional medicine. Methods This systematic review was designed and performed in 2017 in order to gather information regarding herbal medications of oligomenorrhea and amenorrhea in TPM and conventional medicine. This study had several steps as searching Iranian traditional medicine literature and extracting the emmenagogue plants, classifying the plants, searching the electronic databases, and finding evidences. To search traditional Persian medicine references, Noor digital library was used, which includes several ancient traditional medical references. The classification of plants was done based on the repetition and potency of the plants in the ancient literatures. The required data was gathered using databases such as PubMed, Scopus, Google Scholar, Cochrane Library, Science Direct, and web of knowledge. Results In present study of all 198 emmenagogue medicinal plants found in TPM, 87 cases were specified to be more effective in treating oligomenorrhea and amenorrhea. In second part of present study, where a search of conventional medicine was performed, 12 studies were found, which had 8 plants investigated: Vitex agnus-castus, Trigonella foenum-graecum, Foeniculum vulgare, Cinnamomum verum, Paeonia lactiflora, Sesamum indicum, Mentha longifolia, and Urtica dioica. Conclusion. Traditional Persian medicine has proposed many different medicinal plants for treatment of oligomenorrhea and amenorrhea. Although just few plants have been proven to be effective for treatment of menstrual irregularities, the results and the classification in present study can be used as an outline for future studies and treatment. PMID:29744355

Submerged Object Detection and Classification System

DTIC Science & Technology

1993-04-16

example of this type of system is a conventional sonar device wherein a highly directional beam of sonic energy periodically radiates from a...scanning transducer which in turn operates as a receiver to detect echoes reflected from any object within the path of 15 propagation. Sonar devices...classification, which requires relatively high frequency signals. Sonar devices also have the shortcoming of sensing background noise generated by

Improvement of range spatial resolution of medical ultrasound imaging by element-domain signal processing

NASA Astrophysics Data System (ADS)

Hasegawa, Hideyuki

2017-07-01

The range spatial resolution is an important factor determining the image quality in ultrasonic imaging. The range spatial resolution in ultrasonic imaging depends on the ultrasonic pulse length, which is determined by the mechanical response of the piezoelectric element in an ultrasonic probe. To improve the range spatial resolution without replacing the transducer element, in the present study, methods based on maximum likelihood (ML) estimation and multiple signal classification (MUSIC) were proposed. The proposed methods were applied to echo signals received by individual transducer elements in an ultrasonic probe. The basic experimental results showed that the axial half maximum of the echo from a string phantom was improved from 0.21 mm (conventional method) to 0.086 mm (ML) and 0.094 mm (MUSIC).

LANDSAT applications to wetlands classification in the upper Mississippi River Valley. Ph.D. Thesis. Final Report

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Werth, L. F. (Principal Investigator)

1980-01-01

A 25% improvement in average classification accuracy was realized by processing double-date vs. single-date data. Under the spectrally and spatially complex site conditions characterizing the geographical area used, further improvement in wetland classification accuracy is apparently precluded by the spectral and spatial resolution restrictions of the LANDSAT MSS. Full scene analysis of scanning densitometer data extracted from scale infrared photography failed to permit discrimination of many wetland and nonwetland cover types. When classification of photographic data was limited to wetland areas only, much more detailed and accurate classification could be made. The integration of conventional image interpretation (to simply delineate wetland boundaries) and machine assisted classification (to discriminate among cover types present within the wetland areas) appears to warrant further research to study the feasibility and cost of extending this methodology over a large area using LANDSAT and/or small scale photography.

Direction of Arrival Estimation with a Novel Single-Port Smart Antenna

NASA Astrophysics Data System (ADS)

Sun, Chen; Karmakar, Nemai C.

2004-12-01

A novel direction of arrival (DOA) estimation technique that uses the conventional multiple-signal classification (MUSIC) algorithm with periodic signals is applied to a single-port smart antenna. Results show that the proposed method gives a high-resolution (1 degree) DOA estimation in an uncorrelated signal environment. The novelty lies in that the MUSIC algorithm is applied to a simplified antenna configuration. Only 1 analogue-to-digital converter (ADC) is used in this antenna, which features low power consumption, low cost, and ease of fabrication. Modifications to the conventional MUSIC algorithm do not bring much additional complexity. The proposed technique is also free from the negative influence by the mutual coupling among antenna elements. Therefore, it offers an economical way to extensively implement smart antennas into the existing wireless mobile communications systems, especially at the power consumption limited mobile terminals such as laptops in wireless networks.

Zero-dimensional to three-dimensional nanojoining: current status and potential applications

DOE PAGES

Ma, Ying; Li, Hong; Bridges, Denzel; ...

2016-08-01

We report that the continuing miniaturization of microelectronics is pushing advanced manufacturing into nanomanufacturing. Nanojoining is a bottom-up assembly technique that enables functional nanodevice fabrication with dissimilar nanoscopic building blocks and/or molecular components. Various conventional joining techniques have been modified and re-invented for joining nanomaterials. Our review surveys recent progress in nanojoining methods, as compared to conventional joining processes. Examples of nanojoining are given and classified by the dimensionality of the joining materials. At each classification, nanojoining is reviewed and discussed according to materials specialties, low dimensional processing features, energy input mechanisms and potential applications. The preparation of new intermetallicmore » materials by reactive nanoscale multilayer foils based on self-propagating high-temperature synthesis is highlighted. This review will provide insight into nanojoining fundamentals and innovative applications in power electronics packaging, plasmonic devices, nanosoldering for printable electronics, 3D printing and space manufacturing.« less

Water quality assessment of the Li Canal using a functional fuzzy synthetic evaluation model.

PubMed

Feng, Yan; Ling, Liu

2014-07-01

Through introducing functional data analysis (FDA) theory into the conventional fuzzy synthetic evaluation (FSE) method, the functional fuzzy synthetic evaluation (FFSE) model is established. FFSE keeps the property of the conventional FSE that the fuzziness in the water quality condition can be suitably measured. Furthermore, compared with FSE, FFSE has the following advantages: (1) FFSE requires fewer conditions for observation, for example, pollutants can be monitored at different times, and missing data is accepted; (2) the dynamic variation of the water quality condition can be represented more comprehensively and intuitively. The procedure of FFSE is discussed and the water quality of the Li Canal in 2012 is evaluated as an illustration. The synthetic classification of the Li Canal is "II" in January, February and July, and "I" in other months, which can satisfy the requirement of the Chinese South-to-North Water Diversion Project.

Leaf epidermis images for robust identification of plants

PubMed Central

da Silva, Núbia Rosa; Oliveira, Marcos William da Silva; Filho, Humberto Antunes de Almeida; Pinheiro, Luiz Felipe Souza; Rossatto, Davi Rodrigo; Kolb, Rosana Marta; Bruno, Odemir Martinez

2016-01-01

This paper proposes a methodology for plant analysis and identification based on extracting texture features from microscopic images of leaf epidermis. All the experiments were carried out using 32 plant species with 309 epidermal samples captured by an optical microscope coupled to a digital camera. The results of the computational methods using texture features were compared to the conventional approach, where quantitative measurements of stomatal traits (density, length and width) were manually obtained. Epidermis image classification using texture has achieved a success rate of over 96%, while success rate was around 60% for quantitative measurements taken manually. Furthermore, we verified the robustness of our method accounting for natural phenotypic plasticity of stomata, analysing samples from the same species grown in different environments. Texture methods were robust even when considering phenotypic plasticity of stomatal traits with a decrease of 20% in the success rate, as quantitative measurements proved to be fully sensitive with a decrease of 77%. Results from the comparison between the computational approach and the conventional quantitative measurements lead us to discover how computational systems are advantageous and promising in terms of solving problems related to Botany, such as species identification. PMID:27217018

A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Nourani, Vahid; Hrnjica, Bahrudin; Molajou, Amir

2017-12-01

The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies. However, its capability to solve classification problems has not been sufficiently explored so far. This study develops and applies a novel classification-forecasting model, namely Binary GP (BGP), for teleconnection studies between sea surface temperature (SST) variations and maximum monthly rainfall (MMR) events. The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously. The model was trained and tested using SST series of Black Sea, Mediterranean Sea, and Red Sea as potential predictors as well as classified MMR events at two locations in Iran as predictand. Skill of the model was measured in regard to different rainfall thresholds and SST lags and compared to that of the hybrid decision tree-association rule (DTAR) model available in the literature. The results indicated that the proposed model can identify potential teleconnection signals of surrounding seas beneficial to long-term forecasting of the occurrence of the classified MMR events.

Radar fall detection using principal component analysis

NASA Astrophysics Data System (ADS)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

Demonstration of wetland vegetation mapping in Florida from computer-processed satellite and aircraft multispectral scanner data

NASA Technical Reports Server (NTRS)

Butera, M. K.

1979-01-01

The success of remotely mapping wetland vegetation of the southwestern coast of Florida is examined. A computerized technique to process aircraft and LANDSAT multispectral scanner data into vegetation classification maps was used. The cost effectiveness of this mapping technique was evaluated in terms of user requirements, accuracy, and cost. Results indicate that mangrove communities are classified most cost effectively by the LANDSAT technique, with an accuracy of approximately 87 percent and with a cost of approximately 3 cent per hectare compared to $46.50 per hectare for conventional ground survey methods.

Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

PubMed

Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

2018-02-05

Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Educational Implications of Behavioral Disordered Children's Classifications of Moral, Conventional and Personal Issues.

ERIC Educational Resources Information Center

Nucci, Larry P.; Herman, Susan

To investigate how behavioral disordered (BD) children conceptualize moral, conventional, and personal issues, 20 BD and 22 normal fourth graders were asked to respond to a set of cartoon strips in which actions were depicted that violated either an explicitly stated rule or generally held cultural expectation. Ss were asked to rank the acts from…

Introduction to the history and current status of evidence-based korean medicine: a unique integrated system of allopathic and holistic medicine.

PubMed

Yin, Chang Shik; Ko, Seong-Gyu

2014-01-01

Objectives. Korean medicine, an integrated allopathic and traditional medicine, has developed unique characteristics and has been active in contributing to evidence-based medicine. Recent developments in Korean medicine have not been as well disseminated as traditional Chinese medicine. This introduction to recent developments in Korean medicine will draw attention to, and facilitate, the advancement of evidence-based complementary alternative medicine (CAM). Methods and Results. The history of and recent developments in Korean medicine as evidence-based medicine are explored through discussions on the development of a national standard classification of diseases and study reports, ranging from basic research to newly developed clinical therapies. A national standard classification of diseases has been developed and revised serially into an integrated classification of Western allopathic and traditional holistic medicine disease entities. Standard disease classifications offer a starting point for the reliable gathering of evidence and provide a representative example of the unique status of evidence-based Korean medicine as an integration of Western allopathic medicine and traditional holistic medicine. Conclusions. Recent developments in evidence-based Korean medicine show a unique development in evidence-based medicine, adopting both Western allopathic and holistic traditional medicine. It is expected that Korean medicine will continue to be an important contributor to evidence-based medicine, encompassing conventional and complementary approaches.

Classification and source determination of medium petroleum distillates by chemometric and artificial neural networks: a self organizing feature approach.

PubMed

Mat-Desa, Wan N S; Ismail, Dzulkiflee; NicDaeid, Niamh

2011-10-15

Three different medium petroleum distillate (MPD) products (white spirit, paint brush cleaner, and lamp oil) were purchased from commercial stores in Glasgow, Scotland. Samples of 10, 25, 50, 75, 90, and 95% evaporated product were prepared, resulting in 56 samples in total which were analyzed using gas chromatography-mass spectrometry. Data sets from the chromatographic patterns were examined and preprocessed for unsupervised multivariate analyses using principal component analysis (PCA), hierarchical cluster analysis (HCA), and a self organizing feature map (SOFM) artificial neural network. It was revealed that data sets comprised of higher boiling point hydrocarbon compounds provided a good means for the classification of the samples and successfully linked highly weathered samples back to their unevaporated counterpart in every case. The classification abilities of SOFM were further tested and validated for their predictive abilities where one set of weather data in each case was withdrawn from the sample set and used as a test set of the retrained network. This revealed SOFM to be an outstanding mechanism for sample discrimination and linkage over the more conventional PCA and HCA methods often suggested for such data analysis. SOFM also has the advantage of providing additional information through the evaluation of component planes facilitating the investigation of underlying variables that account for the classification. © 2011 American Chemical Society

Superposition of polarized waves at layered media: theoretical modeling and measurement

NASA Astrophysics Data System (ADS)

Finkele, Rolf; Wanielik, Gerd

1997-12-01

The detection of ice layers on road surfaces is a crucial requirement for a system that is designed to warn vehicle drivers of hazardous road conditions. In the millimeter wave regime at 76 GHz the dielectric constant of ice and conventional road surface materials (i.e. asphalt, concrete) is found to be nearly similar. Thus, if the layer of ice is very thin and thus is of the same shape of roughness as the underlying road surface it cannot be securely detected using conventional algorithmic approaches. The method introduced in this paper extents and applies the theoretical work of Pancharatnam on the superposition of polarized waves. The projection of the Stokes vectors onto the Poincare sphere traces a circle due to the variation of the thickness of the ice layer. The paper presents a method that utilizes the concept of wave superposition to detect this trace even if it is corrupted by stochastic variation due to rough surface scattering. Measurement results taken under real traffic conditions prove the validity of the proposed algorithms. Classification results are presented and the results discussed.

Rule Changes Passed at the NCAA Convention.

ERIC Educational Resources Information Center

Chronicle of Higher Education, 1987

1987-01-01

Recent changes in National Collegiate Athletic Association rules concerning academics, recruiting, amateurism, membership and classification, championships, playing and practice seasons, general policies, and eligibility are summarized. (MSE)

Classification of jet fuels by fuzzy rule-building expert systems applied to three-way data by fast gas chromatography--fast scanning quadrupole ion trap mass spectrometry.

PubMed

Sun, Xiaobo; Zimmermann, Carolyn M; Jackson, Glen P; Bunker, Christopher E; Harrington, Peter B

2011-01-30

A fast method that can be used to classify unknown jet fuel types or detect possible property changes in jet fuel physical properties is of paramount interest to national defense and the airline industries. While fast gas chromatography (GC) has been used with conventional mass spectrometry (MS) to study jet fuels, fast GC was combined with fast scanning MS and used to classify jet fuels into lot numbers or origin for the first time by using fuzzy rule-building expert system (FuRES) classifiers. In the process of building classifiers, the data were pretreated with and without wavelet transformation and evaluated with respect to performance. Principal component transformation was used to compress the two-way data images prior to classification. Jet fuel samples were successfully classified with 99.8 ± 0.5% accuracy for both with and without wavelet compression. Ten bootstrapped Latin partitions were used to validate the generalized prediction accuracy. Optimized partial least squares (o-PLS) regression results were used as positively biased references for comparing the FuRES prediction results. The prediction results for the jet fuel samples obtained with these two methods were compared statistically. The projected difference resolution (PDR) method was also used to evaluate the fast GC and fast MS data. Two batches of aliquots of ten new samples were prepared and run independently 4 days apart to evaluate the robustness of the method. The only change in classification parameters was the use of polynomial retention time alignment to correct for drift that occurred during the 4-day span of the two collections. FuRES achieved perfect classifications for four models of uncompressed three-way data. This fast GC/fast MS method furnishes characteristics of high speed, accuracy, and robustness. This mode of measurement may be useful as a monitoring tool to track changes in the chemical composition of fuels that may also lead to property changes. Copyright © 2010 Elsevier B.V. All rights reserved.

Spectral embedding finds meaningful (relevant) structure in image and microarray data

PubMed Central

Higgs, Brandon W; Weller, Jennifer; Solka, Jeffrey L

2006-01-01

Background Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. Results We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. Conclusion Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology. PMID:16483359

Ear recognition from one sample per person.

PubMed

Chen, Long; Mu, Zhichun; Zhang, Baoqing; Zhang, Yi

2015-01-01

Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP) in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP) available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC) ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

Guidelines to classification and nomenclature of Arabian felsic plutonic rocks

USGS Publications Warehouse

Ramsay, C.R.; Stoeser, D.B.; Drysdall, A.R.

1986-01-01

Well-defined procedures for classifying the felsic plutonic rocks of the Arabian Shield on the basis of petrographic, chemical and lithostratigraphic criteria and mineral-resource potential have been adopted and developed in the Saudi Arabian Deputy Ministry for Mineral Resources over the past decade. A number of problems with conventional classification schemes have been identified and resolved; others, notably those arising from difficulties in identifying precise mineral compositions, continue to present difficulties. The petrographic nomenclature used is essentially that recommended by the International Union of Geological Sciences. Problems that have arisen include the definition of: (1) rocks with sodic, zoned or perthitic feldspar, (2) trondhjemites, and (3) alkali granites. Chemical classification has been largely based on relative molar amounts of alumina, lime and alkalis, and the use of conventional variation diagrams, but pilot studies utilizing univariate and multivariate statistical techniques have been made. The classification used in Saudi Arabia for stratigraphic purposes is a hierarchy of formation-rank units, suites and super-suites as defined in the Saudi Arabian stratigraphic code. For genetic and petrological studies, a grouping as 'associations' of similar and genetically related lithologies is commonly used. In order to indicate mineral-resource potential, the felsic plutons are classed as common, precursor, specialized or mineralized, in order of increasing exploration significance. ?? 1986.

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Highly efficient classification and identification of human pathogenic bacteria by MALDI-TOF MS.

PubMed

Hsieh, Sen-Yung; Tseng, Chiao-Li; Lee, Yun-Shien; Kuo, An-Jing; Sun, Chien-Feng; Lin, Yen-Hsiu; Chen, Jen-Kun

2008-02-01

Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health. Conventional work flows are time-consuming, and procedures are multifaceted. MS can be an alternative but is limited by low efficiency for amino acid sequencing as well as low reproducibility for spectrum fingerprinting. We systematically analyzed the feasibility of applying MS for rapid and accurate bacterial identification. Directly applying bacterial colonies without further protein extraction to MALDI-TOF MS analysis revealed rich peak contents and high reproducibility. The MS spectra derived from 57 isolates comprising six human pathogenic bacterial species were analyzed using both unsupervised hierarchical clustering and supervised model construction via the Genetic Algorithm. Hierarchical clustering analysis categorized the spectra into six groups precisely corresponding to the six bacterial species. Precise classification was also maintained in an independently prepared set of bacteria even when the numbers of m/z values were reduced to six. In parallel, classification models were constructed via Genetic Algorithm analysis. A model containing 18 m/z values accurately classified independently prepared bacteria and identified those species originally not used for model construction. Moreover bacteria fewer than 10(4) cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDI-TOF MS in combination with a suitable model construction provides a highly accurate method for bacterial classification and identification. The approach can identify bacteria with low abundance even in mixed flora, suggesting that a rapid and accurate bacterial identification using MS techniques even before culture can be attained in the near future.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

NASA Astrophysics Data System (ADS)

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

PubMed

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-15

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

PubMed Central

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-01-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features. PMID:27079888

Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

PubMed Central

Mandelkow, Hendrik; de Zwart, Jacco A.; Duyn, Jeff H.

2016-01-01

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation. PMID:27065832

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

PubMed Central

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

2017-01-01

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography (FFDM) images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a “scoring fusion” artificial neural network (ANN) classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793±0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions. PMID:27997380

Ethnicity and Population Structure in Personal Naming Networks

PubMed Central

Mateos, Pablo; Longley, Paul A.; O'Sullivan, David

2011-01-01

Personal naming practices exist in all human groups and are far from random. Rather, they continue to reflect social norms and ethno-cultural customs that have developed over generations. As a consequence, contemporary name frequency distributions retain distinct geographic, social and ethno-cultural patterning that can be exploited to understand population structure in human biology, public health and social science. Previous attempts to detect and delineate such structure in large populations have entailed extensive empirical analysis of naming conventions in different parts of the world without seeking any general or automated methods of population classification by ethno-cultural origin. Here we show how ‘naming networks’, constructed from forename-surname pairs of a large sample of the contemporary human population in 17 countries, provide a valuable representation of cultural, ethnic and linguistic population structure around the world. This innovative approach enriches and adds value to automated population classification through conventional national data sources such as telephone directories and electoral registers. The method identifies clear social and ethno-cultural clusters in such naming networks that extend far beyond the geographic areas in which particular names originated, and that are preserved even after international migration. Moreover, one of the most striking findings of this approach is that these clusters simply ‘emerge’ from the aggregation of millions of individual decisions on parental naming practices for their children, without any prior knowledge introduced by the researcher. Our probabilistic approach to community assignment, both at city level as well as at a global scale, helps to reveal the degree of isolation, integration or overlap between human populations in our rapidly globalising world. As such, this work has important implications for research in population genetics, public health, and social science adding new understandings of migration, identity, integration and social interaction across the world. PMID:21909399

Robust gene selection methods using weighting schemes for microarray data analysis.

PubMed

Kang, Suyeon; Song, Jongwoo

2017-09-02

A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

Sediment classification using neural networks: An example from the site-U1344A of IODP Expedition 323 in the Bering Sea

NASA Astrophysics Data System (ADS)

Ojha, Maheswar; Maiti, Saumen

2016-03-01

A novel approach based on the concept of Bayesian neural network (BNN) has been implemented for classifying sediment boundaries using downhole log data obtained during Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. The Bayesian framework in conjunction with Markov Chain Monte Carlo (MCMC)/hybrid Monte Carlo (HMC) learning paradigm has been applied to constrain the lithology boundaries using density, density porosity, gamma ray, sonic P-wave velocity and electrical resistivity at the Hole U1344A. We have demonstrated the effectiveness of our supervised classification methodology by comparing our findings with a conventional neural network and a Bayesian neural network optimized by scaled conjugate gradient method (SCG), and tested the robustness of the algorithm in the presence of red noise in the data. The Bayesian results based on the HMC algorithm (BNN.HMC) resolve detailed finer structures at certain depths in addition to main lithology such as silty clay, diatom clayey silt and sandy silt. Our method also recovers the lithology information from a depth ranging between 615 and 655 m Wireline log Matched depth below Sea Floor of no core recovery zone. Our analyses demonstrate that the BNN based approach renders robust means for the classification of complex lithology successions at the Hole U1344A, which could be very useful for other studies and understanding the oceanic crustal inhomogeneity and structural discontinuities.

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

PubMed Central

He, Jun; Yang, Shixi; Gan, Chunbiao

2017-01-01

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods. PMID:28677638

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

PubMed

He, Jun; Yang, Shixi; Gan, Chunbiao

2017-07-04

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Texture segmentation by genetic programming.

PubMed

Song, Andy; Ciesielski, Vic

2008-01-01

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

A novel bioinformatics method for efficient knowledge discovery by BLSOM from big genomic sequence data.

PubMed

Bai, Yu; Iwasaki, Yuki; Kanaya, Shigehiko; Zhao, Yue; Ikemura, Toshimichi

2014-01-01

With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed for comprehensive analyses of the big sequence data. Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional data such as oligonucleotide composition on one map. By modifying the conventional SOM, we have previously developed Batch-Learning SOM (BLSOM), which allows classification of sequence fragments according to species, solely depending on the oligonucleotide composition. In the present study, we introduce the oligonucleotide BLSOM used for characterization of vertebrate genome sequences. We first analyzed pentanucleotide compositions in 100 kb sequences derived from a wide range of vertebrate genomes and then the compositions in the human and mouse genomes in order to investigate an efficient method for detecting differences between the closely related genomes. BLSOM can recognize the species-specific key combination of oligonucleotide frequencies in each genome, which is called a "genome signature," and the specific regions specifically enriched in transcription-factor-binding sequences. Because the classification and visualization power is very high, BLSOM is an efficient powerful tool for extracting a wide range of information from massive amounts of genomic sequences (i.e., big sequence data).

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Hermann, Thomas; Bukschat, Yannick; Kramer, Tilmann; Schad, Lothar R.; Zöllner, Frank Gerrit

2017-03-01

Electrocardiography (ECG) data are multidimensional temporal data with ubiquitous applications in the clinic. Conventionally, these data are presented visually. It is presently unclear to what degree data sonification (auditory display), can enable the detection of clinically relevant cardiac pathologies in ECG data. In this study, we introduce a method for polyphonic sonification of ECG data, whereby different ECG channels are simultaneously represented by sound of different pitch. We retrospectively applied this method to 12 samples from a publicly available ECG database. We and colleagues from our professional environment then analyzed these data in a blinded way. Based on these analyses, we found that the sonification technique can be intuitively understood after a short training session. On average, the correct classification rate for observers trained in cardiology was 78%, compared to 68% and 50% for observers not trained in cardiology or not trained in medicine at all, respectively. These values compare to an expected random guessing performance of 25%. Strikingly, 27% of all observers had a classification accuracy over 90%, indicating that sonification can be very successfully used by talented individuals. These findings can serve as a baseline for potential clinical applications of ECG sonification.

Flammability Indices for Refrigerants

NASA Astrophysics Data System (ADS)

Kataoka, Osami

This paper introduces a new index to classify flammable refrigerants. A question on flammability indices that ASHRAE employs arose from combustion test results of R152a and ammonia. Conventional methods of not only ASHRAE but also ISO and Japanese High-pressure gas safety law to classify the flammability of refrigerants are evaluated to show why these methods conflict with the test results. The key finding of this paper is that the ratio of stoichiometric concentration to LFL concentration (R factor) represents the test results most precisely. In addition, it has excellent correlation with other flammability parameters such as flame speed and pressure rise coefficient. Classification according to this index gives reasonable flammability order of substances including ammonia, R152a and carbon monoxide. Theoretical background why this index gives good correlation is also discussed as well as the insufficient part of this method.

Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

PubMed Central

Hussain, Shaista; Basu, Arindam

2016-01-01

The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best “k” out of “d” inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike classifiers. We show that our system can achieve classification accuracy within 1 − 2% of other reported spike-based classifiers while using much less synaptic resources (only 7%) compared to that used by other methods. Further, an ensemble classifier created with adaptively learned sizes can attain accuracy of 96.4% which is at par with the best reported performance of spike-based classifiers. Moreover, the proposed method achieves this by using about 20% of the synapses used by other spike algorithms. We also present results of applying our algorithm to classify the MNIST-DVS dataset collected from a real spike-based image sensor and show results comparable to the best reported ones (88.1% accuracy). For VLSI implementations, we show that the reduced synaptic memory can save upto 4X area compared to conventional crossbar topologies. Finally, we also present a biologically realistic spike-based version for calculating the correlations required by the structural learning rule and demonstrate the correspondence between the rate-based and spike-based methods of learning. PMID:27065782

Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees.

PubMed

Chung, Yi-Shih

2013-12-01

Factor complexity is a characteristic of traffic crashes. This paper proposes a novel method, namely boosted regression trees (BRT), to investigate the complex and nonlinear relationships in high-variance traffic crash data. The Taiwanese 2004-2005 single-vehicle motorcycle crash data are used to demonstrate the utility of BRT. Traditional logistic regression and classification and regression tree (CART) models are also used to compare their estimation results and external validities. Both the in-sample cross-validation and out-of-sample validation results show that an increase in tree complexity provides improved, although declining, classification performance, indicating a limited factor complexity of single-vehicle motorcycle crashes. The effects of crucial variables including geographical, time, and sociodemographic factors explain some fatal crashes. Relatively unique fatal crashes are better approximated by interactive terms, especially combinations of behavioral factors. BRT models generally provide improved transferability than conventional logistic regression and CART models. This study also discusses the implications of the results for devising safety policies. Copyright © 2012 Elsevier Ltd. All rights reserved.

Analysis of x-ray hand images for bone age assessment

NASA Astrophysics Data System (ADS)

Serrat, Joan; Vitria, Jordi M.; Villanueva, Juan J.

1990-09-01

In this paper we describe a model-based system for the assessment of skeletal maturity on hand radiographs by the TW2 method. The problem consists in classiflying a set of bones appearing in an image in one of several stages described in an atlas. A first approach consisting in pre-processing segmentation and classification independent phases is also presented. However it is only well suited for well contrasted low noise images without superimposed bones were the edge detection by zero crossing of second directional derivatives is able to extract all bone contours maybe with little gaps and few false edges on the background. Hence the use of all available knowledge about the problem domain is needed to build a rather general system. We have designed a rule-based system for narrow down the rank of possible stages for each bone and guide the analysis process. It calls procedures written in conventional languages for matching stage models against the image and getting features needed in the classification process.

Identification of Enterococcus, Streptococcus, and Staphylococcus by Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

PubMed Central

Bourne, Roger; Himmelreich, Uwe; Sharma, Ansuiya; Mountford, Carolyn; Sorrell, Tania

2001-01-01

A new fingerprinting technique with the potential for rapid identification of bacteria was developed by combining proton magnetic resonance spectroscopy (1H MRS) with multivariate statistical analysis. This resulted in an objective identification strategy for common clinical isolates belonging to the bacterial species Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Streptococcus pneumoniae, Streptococcus pyogenes, Streptococcus agalactiae, and the Streptococcus milleri group. Duplicate cultures of 104 different isolates were examined one or more times using 1H MRS. A total of 312 cultures were examined. An optimized classifier was developed using a bootstrapping process and a seven-group linear discriminant analysis to provide objective classification of the spectra. Identification of isolates was based on consistent high-probability classification of spectra from duplicate cultures and achieved 92% agreement with conventional methods of identification. Fewer than 1% of isolates were identified incorrectly. Identification of the remaining 7% of isolates was defined as indeterminate. PMID:11474013

Raster Vs. Point Cloud LiDAR Data Classification

NASA Astrophysics Data System (ADS)

El-Ashmawy, N.; Shaker, A.

2014-09-01

Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the classification results can be achieved by using the proposed approach.

Classification-Based Spatial Error Concealment for Visual Communications

NASA Astrophysics Data System (ADS)

Chen, Meng; Zheng, Yefeng; Wu, Min

2006-12-01

In an error-prone transmission environment, error concealment is an effective technique to reconstruct the damaged visual content. Due to large variations of image characteristics, different concealment approaches are necessary to accommodate the different nature of the lost image content. In this paper, we address this issue and propose using classification to integrate the state-of-the-art error concealment techniques. The proposed approach takes advantage of multiple concealment algorithms and adaptively selects the suitable algorithm for each damaged image area. With growing awareness that the design of sender and receiver systems should be jointly considered for efficient and reliable multimedia communications, we proposed a set of classification-based block concealment schemes, including receiver-side classification, sender-side attachment, and sender-side embedding. Our experimental results provide extensive performance comparisons and demonstrate that the proposed classification-based error concealment approaches outperform the conventional approaches.

Web-based comparison of historical vs contemporary methods of fetal heart rate interpretation.

PubMed

Epstein, Aaron J; Iriye, Brian K; Hancock, Lyle; Quilligan, Edward J; Rumney, Pamela J; Hancock, Judy; Ghamsary, Mark; Eakin, Cortney M; Smith, Cheryl; Wing, Deborah A

2016-10-01

Contemporary interpretation of fetal heart rate patterns is based largely on the tenets of Drs Quilligan and Hon. This method differs from an older method that was championed by Dr Caldeyro-Barcia in recording speed and classification of decelerations. The latter uses a paper speed of 1 cm/min and classifies decelerations referent to uterine contractions as type I or II dips, compared with conventional classification as early, late, or variable with paper speed of 3 cm/min. We hypothesized that 3 cm/min speed may lead to over-analysis of fetal heart rate and that 1 cm/min may provide adequate information without compromising accuracy or efficiency. The purpose of this study was to compare the Hon-Quilligan method of fetal heart rate interpretation with the Caldeyro-Barcia method among groups of obstetrics care providers with the use of an online interactive testing tool. We deidentified 40 fetal heart rate tracings from the terminal 30 minutes before delivery. A website was created to view these tracings with the use of the standard Hon-Quilligan method and adjusted the same tracings to the 1 cm/min monitoring speed for the Caldeyro-Barcia method. We invited 2-4 caregivers to participate: maternal-fetal medicine experts, practicing maternal-fetal medicine specialists, maternal-fetal medicine fellows, obstetrics nurses, and certified nurse midwives. After completing an introductory tutorial and quiz, they were asked to interpret the fetal heart rate tracings (the order was scrambled) to manage and predict maternal and neonatal outcomes using both methods. Their results were compared with those of our expert, Edward Quilligan, and were compared among groups. Analysis was performed with the use of 3 measures: percent classification, Kappa, and adjusted Gwet-Kappa (P < .05 was considered significant). Overall, our results show from moderate to almost perfect agreement with the expert and both between and within examiners (Gwet-Kappa 0.4-0.8). The agreement at each stratum of practitioner was generally highest for ascertainment of baseline and for management; the least agreement was for assessment of variability. We examined the agreement of fetal heart rate interpretation with a defined set of rules among a number of different obstetrics practitioners using 3 different statistical methods and found moderate-to-substantial agreement among the clinicians for matching the interpretation of the expert. This implies that the simpler Caldeyro-Barcia method may perform as well as the newer classification system. Copyright © 2016 Elsevier Inc. All rights reserved.

Holographic neural networks versus conventional neural networks: a comparative evaluation for the classification of landmine targets in ground-penetrating radar images

NASA Astrophysics Data System (ADS)

Mudigonda, Naga R.; Kacelenga, Ray; Edwards, Mark

2004-09-01

This paper evaluates the performance of a holographic neural network in comparison with a conventional feedforward backpropagation neural network for the classification of landmine targets in ground penetrating radar images. The data used in the study was acquired from four different test sites using the landmine detection system developed by General Dynamics Canada Ltd., in collaboration with the Defense Research and Development Canada, Suffield. A set of seven features extracted for each detected alarm is used as stimulus inputs for the networks. The recall responses of the networks are then evaluated against the ground truth to declare true or false detections. The area computed under the receiver operating characteristic curve is used for comparative purposes. With a large dataset comprising of data from multiple sites, both the holographic and conventional networks showed comparable trends in recall accuracies with area values of 0.88 and 0.87, respectively. By using independent validation datasets, the holographic network"s generalization performance was observed to be better (mean area = 0.86) as compared to the conventional network (mean area = 0.82). Despite the widely publicized theoretical advantages of the holographic technology, use of more than the required number of cortical memory elements resulted in an over-fitting phenomenon of the holographic network.

Nearest patch matching for color image segmentation supporting neural network classification in pulmonary tuberculosis identification

NASA Astrophysics Data System (ADS)

Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

2016-03-01

Pulmonary tuberculosis is a deadly infectious disease which occurs in many countries in Asia and Africa. In Indonesia, many people with tuberculosis disease are examined in the community health center. Examination of pulmonary tuberculosis is done through sputum smear with Ziehl - Neelsen staining using conventional light microscope. The results of Ziehl - Neelsen staining will give effect to the appearance of tuberculosis (TB) bacteria in red color and sputum background in blue color. The first examination is to detect the presence of TB bacteria from its color, then from the morphology of the TB bacteria itself. The results of Ziehl - Neelsen staining in sputum smear give the complex color images, so that the clinicians have difficulty when doing slide examination manually because it is time consuming and needs highly training to detect the presence of TB bacteria accurately. The clinicians have heavy workload to examine many sputum smear slides from the patients. To assist the clinicians when reading the sputum smear slide, this research built computer aided diagnose with color image segmentation, feature extraction, and classification method. This research used K-means clustering with patch technique to segment digital sputum smear images which separated the TB bacteria images from the background images. This segmentation method gave the good accuracy 97.68%. Then, feature extraction based on geometrical shape of TB bacteria was applied to this research. The last step, this research used neural network with back propagation method to classify TB bacteria and non TB bacteria images in sputum slides. The classification result of neural network back propagation are learning time (42.69±0.02) second, the number of epoch 5000, error rate of learning 15%, learning accuracy (98.58±0.01)%, and test accuracy (96.54±0.02)%.

The influence of multispectral scanner spatial resolution on forest feature classification

NASA Technical Reports Server (NTRS)

Sadowski, F. G.; Malila, W. A.; Sarno, J. E.; Nalepka, R. F.

1977-01-01

Inappropriate spatial resolution and corresponding data processing techniques may be major causes for non-optimal forest classification results frequently achieved from multispectral scanner (MSS) data. Procedures and results of empirical investigations are studied to determine the influence of MSS spatial resolution on the classification of forest features into levels of detail or hierarchies of information that might be appropriate for nationwide forest surveys and detailed in-place inventories. Two somewhat different, but related studies are presented. The first consisted of establishing classification accuracies for several hierarchies of features as spatial resolution was progressively coarsened from (2 meters) squared to (64 meters) squared. The second investigated the capabilities for specialized processing techniques to improve upon the results of conventional processing procedures for both coarse and fine resolution data.

Rapid identification of oral Actinomyces species cultivated from subgingival biofilm by MALDI-TOF-MS

PubMed Central

Stingu, Catalina S.; Borgmann, Toralf; Rodloff, Arne C.; Vielkind, Paul; Jentsch, Holger; Schellenberger, Wolfgang; Eschrich, Klaus

2015-01-01

Background Actinomyces are a common part of the residential flora of the human intestinal tract, genitourinary system and skin. Isolation and identification of Actinomyces by conventional methods is often difficult and time consuming. In recent years, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has become a rapid and simple method to identify bacteria. Objective The present study evaluated a new in-house algorithm using MALDI-TOF-MS for rapid identification of different species of oral Actinomyces cultivated from subgingival biofilm. Design Eleven reference strains and 674 clinical strains were used in this study. All the strains were preliminarily identified using biochemical methods and then subjected to MALDI-TOF-MS analysis using both similarity-based analysis and classification methods (support vector machine [SVM]). The genotype of the reference strains and of 232 clinical strains was identified by sequence analysis of the 16S ribosomal RNA (rRNA). Results The sequence analysis of the 16S rRNA gene of all references strains confirmed their previous identification. The MALDI-TOF-MS spectra obtained from the reference strains and the other clinical strains undoubtedly identified as Actinomyces by 16S rRNA sequencing were used to create the mass spectra reference database. Already a visual inspection of the mass spectra of different species reveals both similarities and differences. However, the differences between them are not large enough to allow a reliable differentiation by similarity analysis. Therefore, classification methods were applied as an alternative approach for differentiation and identification of Actinomyces at the species level. A cross-validation of the reference database representing 14 Actinomyces species yielded correct results for all species which were represented by more than two strains in the database. Conclusions Our results suggest that a combination of MALDI-TOF-MS with powerful classification algorithms, such as SVMs, provide a useful tool for the differentiation and identification of oral Actinomyces. PMID:25597306

An Analysis Platform for Multiscale Hydrogeologic Modeling with Emphasis on Hybrid Multiscale Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scheibe, Timothy D.; Murphy, Ellyn M.; Chen, Xingyuan

2015-01-01

One of the most significant challenges facing hydrogeologic modelers is the disparity between those spatial and temporal scales at which fundamental flow, transport and reaction processes can best be understood and quantified (e.g., microscopic to pore scales, seconds to days) and those at which practical model predictions are needed (e.g., plume to aquifer scales, years to centuries). While the multiscale nature of hydrogeologic problems is widely recognized, technological limitations in computational and characterization restrict most practical modeling efforts to fairly coarse representations of heterogeneous properties and processes. For some modern problems, the necessary level of simplification is such that modelmore » parameters may lose physical meaning and model predictive ability is questionable for any conditions other than those to which the model was calibrated. Recently, there has been broad interest across a wide range of scientific and engineering disciplines in simulation approaches that more rigorously account for the multiscale nature of systems of interest. In this paper, we review a number of such approaches and propose a classification scheme for defining different types of multiscale simulation methods and those classes of problems to which they are most applicable. Our classification scheme is presented in terms of a flow chart (Multiscale Analysis Platform or MAP), and defines several different motifs of multiscale simulation. Within each motif, the member methods are reviewed and example applications are discussed. We focus attention on hybrid multiscale methods, in which two or more models with different physics described at fundamentally different scales are directly coupled within a single simulation. Very recently these methods have begun to be applied to groundwater flow and transport simulations, and we discuss these applications in the context of our classification scheme. As computational and characterization capabilities continue to improve, we envision that hybrid multiscale modeling will become more common and may become a viable alternative to conventional single-scale models in the near future.« less

An analysis platform for multiscale hydrogeologic modeling with emphasis on hybrid multiscale methods.

PubMed

Scheibe, Timothy D; Murphy, Ellyn M; Chen, Xingyuan; Rice, Amy K; Carroll, Kenneth C; Palmer, Bruce J; Tartakovsky, Alexandre M; Battiato, Ilenia; Wood, Brian D

2015-01-01

One of the most significant challenges faced by hydrogeologic modelers is the disparity between the spatial and temporal scales at which fundamental flow, transport, and reaction processes can best be understood and quantified (e.g., microscopic to pore scales and seconds to days) and at which practical model predictions are needed (e.g., plume to aquifer scales and years to centuries). While the multiscale nature of hydrogeologic problems is widely recognized, technological limitations in computation and characterization restrict most practical modeling efforts to fairly coarse representations of heterogeneous properties and processes. For some modern problems, the necessary level of simplification is such that model parameters may lose physical meaning and model predictive ability is questionable for any conditions other than those to which the model was calibrated. Recently, there has been broad interest across a wide range of scientific and engineering disciplines in simulation approaches that more rigorously account for the multiscale nature of systems of interest. In this article, we review a number of such approaches and propose a classification scheme for defining different types of multiscale simulation methods and those classes of problems to which they are most applicable. Our classification scheme is presented in terms of a flowchart (Multiscale Analysis Platform), and defines several different motifs of multiscale simulation. Within each motif, the member methods are reviewed and example applications are discussed. We focus attention on hybrid multiscale methods, in which two or more models with different physics described at fundamentally different scales are directly coupled within a single simulation. Very recently these methods have begun to be applied to groundwater flow and transport simulations, and we discuss these applications in the context of our classification scheme. As computational and characterization capabilities continue to improve, we envision that hybrid multiscale modeling will become more common and also a viable alternative to conventional single-scale models in the near future. © 2014, National Ground Water Association.

Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration

NASA Astrophysics Data System (ADS)

Godinho, Robson B.; Santos, Mauricio C.; Poppi, Ronei J.

2016-03-01

An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies.

Improvement in defect classification efficiency by grouping disposition for reticle inspection

NASA Astrophysics Data System (ADS)

Lai, Rick; Hsu, Luke T. H.; Chang, Peter; Ho, C. H.; Tsai, Frankie; Long, Garrett; Yu, Paul; Miller, John; Hsu, Vincent; Chen, Ellison

2005-11-01

As the lithography design rule of IC manufacturing continues to migrate toward more advanced technology nodes, the mask error enhancement factor (MEEF) increases and necessitates the use of aggressive OPC features. These aggressive OPC features pose challenges to reticle inspection due to high false detection, which is time-consuming for defect classification and impacts the throughput of mask manufacturing. Moreover, higher MEEF leads to stricter mask defect capture criteria so that new generation reticle inspection tool is equipped with better detection capability. Hence, mask process induced defects, which were once undetectable, are now detected and results in the increase of total defect count. Therefore, how to review and characterize reticle defects efficiently is becoming more significant. A new defect review system called ReviewSmart has been developed based on the concept of defect grouping disposition. The review system intelligently bins repeating or similar defects into defect groups and thus allows operators to review massive defects more efficiently. Compared to the conventional defect review method, ReviewSmart not only reduces defect classification time and human judgment error, but also eliminates desensitization that is formerly inevitable. In this study, we attempt to explore the most efficient use of ReviewSmart by evaluating various defect binning conditions. The optimal binning conditions are obtained and have been verified for fidelity qualification through inspection reports (IRs) of production masks. The experiment results help to achieve the best defect classification efficiency when using ReviewSmart in the mask manufacturing and development.

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization.

PubMed

Kainz, Philipp; Pfeiffer, Michael; Urschler, Martin

2017-01-01

Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses.

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

PubMed Central

Kainz, Philipp; Pfeiffer, Michael

2017-01-01

Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses. PMID:29018612

Assessment of functional defecation disorders using anorectal manometry

PubMed Central

2018-01-01

Purpose The aim was to evaluate the discriminating accuracy of anorectal manometry (ARM) between nonconstipated (NC) subjects and functionally constipated (FC) subjects, and between FC subjects with and without functional defecation disorder (FDD). Methods Among female patients who visited anorectal physiology unit, those who could be grouped to following categories were included; FC group with FDD (+FDD subgroup), or without FDD (−FDD subgroup) and NC group. ARM was performed and interpreted not only with absolute pressure values, but also pattern classification and quantification of pressure changes in the rectum and anus during attempted defecation. Results There were 76 subjects in NC group and 75 in FC group. Among FC group, 63 subjects were in −FDD subgroup and 12 in +FDD subgroup. In pattern classification of pressure changes, type 0, as ‘normal’ response, was only slightly more prevalent in NC group than in FC group. When all ‘abnormal’ types (types 1–5) were considered together as positive findings, the sensitivity and specificity of pattern classification in diagnosing FC among all subjects were 89.3% and 22.7%. Those values in diagnosing FDD among FC group were 91.7% and 11.1%. Manometric defecation index (MDI) as a quantification parameter was significantly different between −FDD and +FDD subgroups. Other conventional absolute pressures were mostly comparable between the groups. Conclusion Among all parameters of ARM, MDI was useful to diagnose FDD in FC patients. Other parameters including the pattern classification were questionable in their ability to diagnose FDD. PMID:29854711

Waterbodies Extraction from LANDSAT8-OLI Imagery Using Awater Indexs-Guied Stochastic Fully-Connected Conditional Random Field Model and the Support Vector Machine

NASA Astrophysics Data System (ADS)

Wang, X.; Xu, L.

2018-04-01

One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method's performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments.

7 CFR 28.179 - Methods of cotton classification and comparison.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 2 2013-01-01 2013-01-01 false Methods of cotton classification and comparison. 28... STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Classification for Foreign Growth Cotton § 28.179 Methods of cotton classification and comparison. The classification of samples from...

7 CFR 28.179 - Methods of cotton classification and comparison.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Methods of cotton classification and comparison. 28... STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Classification for Foreign Growth Cotton § 28.179 Methods of cotton classification and comparison. The classification of samples from...

7 CFR 28.179 - Methods of cotton classification and comparison.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Methods of cotton classification and comparison. 28... STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Classification for Foreign Growth Cotton § 28.179 Methods of cotton classification and comparison. The classification of samples from...

7 CFR 28.179 - Methods of cotton classification and comparison.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Methods of cotton classification and comparison. 28... STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Classification for Foreign Growth Cotton § 28.179 Methods of cotton classification and comparison. The classification of samples from...

7 CFR 28.179 - Methods of cotton classification and comparison.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 2 2014-01-01 2014-01-01 false Methods of cotton classification and comparison. 28... STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Classification for Foreign Growth Cotton § 28.179 Methods of cotton classification and comparison. The classification of samples from...

Mercury⊕: An evidential reasoning image classifier

NASA Astrophysics Data System (ADS)

Peddle, Derek R.

1995-12-01

MERCURY⊕ is a multisource evidential reasoning classification software system based on the Dempster-Shafer theory of evidence. The design and implementation of this software package is described for improving the classification and analysis of multisource digital image data necessary for addressing advanced environmental and geoscience applications. In the remote-sensing context, the approach provides a more appropriate framework for classifying modern, multisource, and ancillary data sets which may contain a large number of disparate variables with different statistical properties, scales of measurement, and levels of error which cannot be handled using conventional Bayesian approaches. The software uses a nonparametric, supervised approach to classification, and provides a more objective and flexible interface to the evidential reasoning framework using a frequency-based method for computing support values from training data. The MERCURY⊕ software package has been implemented efficiently in the C programming language, with extensive use made of dynamic memory allocation procedures and compound linked list and hash-table data structures to optimize the storage and retrieval of evidence in a Knowledge Look-up Table. The software is complete with a full user interface and runs under Unix, Ultrix, VAX/VMS, MS-DOS, and Apple Macintosh operating system. An example of classifying alpine land cover and permafrost active layer depth in northern Canada is presented to illustrate the use and application of these ideas.

Classification of air quality using fuzzy synthetic multiplication.

PubMed

Abdullah, Lazim; Khalid, Noor Dalina

2012-11-01

Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.

Identification of bacteria isolated from veterinary clinical specimens using MALDI-TOF MS.

PubMed

Pavlovic, Melanie; Wudy, Corinna; Zeller-Peronnet, Veronique; Maggipinto, Marzena; Zimmermann, Pia; Straubinger, Alix; Iwobi, Azuka; Märtlbauer, Erwin; Busch, Ulrich; Huber, Ingrid

2015-01-01

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has recently emerged as a rapid and accurate identification method for bacterial species. Although it has been successfully applied for the identification of human pathogens, it has so far not been well evaluated for routine identification of veterinary bacterial isolates. This study was performed to compare and evaluate the performance of MALDI-TOF MS based identification of veterinary bacterial isolates with commercially available conventional test systems. Discrepancies of both methods were resolved by sequencing 16S rDNA and, if necessary, the infB gene for Actinobacillus isolates. A total of 375 consecutively isolated veterinary samples were collected. Among the 357 isolates (95.2%) correctly identified at the genus level by MALDI-TOF MS, 338 of them (90.1% of the total isolates) were also correctly identified at the species level. Conventional methods offered correct species identification for 319 isolates (85.1%). MALDI-TOF identification therefore offered more accurate identification of veterinary bacterial isolates. An update of the in-house mass spectra database with additional reference spectra clearly improved the identification results. In conclusion, the presented data suggest that MALDI-TOF MS is an appropriate platform for classification and identification of veterinary bacterial isolates.

Analysis of composition-based metagenomic classification.

PubMed

Higashi, Susan; Barreto, André da Motta Salles; Cantão, Maurício Egidio; de Vasconcelos, Ana Tereza Ribeiro

2012-01-01

An essential step of a metagenomic study is the taxonomic classification, that is, the identification of the taxonomic lineage of the organisms in a given sample. The taxonomic classification process involves a series of decisions. Currently, in the context of metagenomics, such decisions are usually based on empirical studies that consider one specific type of classifier. In this study we propose a general framework for analyzing the impact that several decisions can have on the classification problem. Instead of focusing on any specific classifier, we define a generic score function that provides a measure of the difficulty of the classification task. Using this framework, we analyze the impact of the following parameters on the taxonomic classification problem: (i) the length of n-mers used to encode the metagenomic sequences, (ii) the similarity measure used to compare sequences, and (iii) the type of taxonomic classification, which can be conventional or hierarchical, depending on whether the classification process occurs in a single shot or in several steps according to the taxonomic tree. We defined a score function that measures the degree of separability of the taxonomic classes under a given configuration induced by the parameters above. We conducted an extensive computational experiment and found out that reasonable values for the parameters of interest could be (i) intermediate values of n, the length of the n-mers; (ii) any similarity measure, because all of them resulted in similar scores; and (iii) the hierarchical strategy, which performed better in all of the cases. As expected, short n-mers generate lower configuration scores because they give rise to frequency vectors that represent distinct sequences in a similar way. On the other hand, large values for n result in sparse frequency vectors that represent differently metagenomic fragments that are in fact similar, also leading to low configuration scores. Regarding the similarity measure, in contrast to our expectations, the variation of the measures did not change the configuration scores significantly. Finally, the hierarchical strategy was more effective than the conventional strategy, which suggests that, instead of using a single classifier, one should adopt multiple classifiers organized as a hierarchy.

Computer-aided diagnosis of pulmonary diseases using x-ray darkfield radiography

NASA Astrophysics Data System (ADS)

Einarsdóttir, Hildur; Yaroshenko, Andre; Velroyen, Astrid; Bech, Martin; Hellbach, Katharina; Auweter, Sigrid; Yildirim, Önder; Meinel, Felix G.; Eickelberg, Oliver; Reiser, Maximilian; Larsen, Rasmus; Kjær Ersbøll, Bjarne; Pfeiffer, Franz

2015-12-01

In this work we develop a computer-aided diagnosis (CAD) scheme for classification of pulmonary disease for grating-based x-ray radiography. In addition to conventional transmission radiography, the grating-based technique provides a dark-field imaging modality, which utilizes the scattering properties of the x-rays. This modality has shown great potential for diagnosing early stage emphysema and fibrosis in mouse lungs in vivo. The CAD scheme is developed to assist radiologists and other medical experts to develop new diagnostic methods when evaluating grating-based images. The scheme consists of three stages: (i) automatic lung segmentation; (ii) feature extraction from lung shape and dark-field image intensities; (iii) classification between healthy, emphysema and fibrosis lungs. A study of 102 mice was conducted with 34 healthy, 52 emphysema and 16 fibrosis subjects. Each image was manually annotated to build an experimental dataset. System performance was assessed by: (i) determining the quality of the segmentations; (ii) validating emphysema and fibrosis recognition by a linear support vector machine using leave-one-out cross-validation. In terms of segmentation quality, we obtained an overlap percentage (Ω) 92.63  ±  3.65%, Dice Similarity Coefficient (DSC) 89.74  ±  8.84% and Jaccard Similarity Coefficient 82.39  ±  12.62%. For classification, the accuracy, sensitivity and specificity of diseased lung recognition was 100%. Classification between emphysema and fibrosis resulted in an accuracy of 93%, whilst the sensitivity was 94% and specificity 88%. In addition to the automatic classification of lungs, deviation maps created by the CAD scheme provide a visual aid for medical experts to further assess the severity of pulmonary disease in the lung, and highlights regions affected.

Evaluation of a Web-Based App Demonstrating an Exclusionary Algorithmic Approach to TNM Cancer Staging

PubMed Central

2015-01-01

Background TNM staging plays a critical role in the evaluation and management of a range of different types of cancers. The conventional combinatorial approach to the determination of an anatomic stage relies on the identification of distinct tumor (T), node (N), and metastasis (M) classifications to generate a TNM grouping. This process is inherently inefficient due to the need for scrupulous review of the criteria specified for each classification to ensure accurate assignment. An exclusionary approach to TNM staging based on sequential constraint of options may serve to minimize the number of classifications that need to be reviewed to accurately determine an anatomic stage. Objective Our aim was to evaluate the usability and utility of a Web-based app configured to demonstrate an exclusionary approach to TNM staging. Methods Internal medicine residents, surgery residents, and oncology fellows engaged in clinical training were asked to evaluate a Web-based app developed as an instructional aid incorporating (1) an exclusionary algorithm that polls tabulated classifications and sorts them into ranked order based on frequency counts, (2) reconfiguration of classification criteria to generate disambiguated yes/no questions that function as selection and exclusion prompts, and (3) a selectable grid of TNM groupings that provides dynamic graphic demonstration of the effects of sequentially selecting or excluding specific classifications. Subjects were asked to evaluate the performance of this app after completing exercises simulating the staging of different types of cancers encountered during training. Results Survey responses indicated high levels of agreement with statements supporting the usability and utility of this app. Subjects reported that its user interface provided a clear display with intuitive controls and that the exclusionary approach to TNM staging it demonstrated represented an efficient process of assignment that helped to clarify distinctions between tumor, node, and metastasis classifications. High overall usefulness ratings were bolstered by supplementary comments suggesting that this app might be readily adopted for use in clinical practice. Conclusions A Web-based app that utilizes an exclusionary algorithm to prompt the assignment of tumor, node, and metastasis classifications may serve as an effective instructional aid demonstrating an efficient and informative approach to TNM staging. PMID:28410163

Characterization and geographical discrimination of commercial Citrus spp. honeys produced in different Mediterranean countries based on minerals, volatile compounds and physicochemical parameters, using chemometrics.

PubMed

Karabagias, Ioannis K; Louppis, Artemis P; Karabournioti, Sofia; Kontakos, Stavros; Papastephanou, Chara; Kontominas, Michael G

2017-02-15

The objective of the present study was: i) to characterize Mediterranean citrus honeys based on conventional physicochemical parameter values, volatile compounds, and mineral content ii) to investigate the potential of above parameters to differentiate citrus honeys according to geographical origin using chemometrics. Thus, 37 citrus honey samples were collected during harvesting periods 2013 and 2014 from Greece, Egypt, Morocco, and Spain. Conventional physicochemical and CIELAB colour parameters were determined using official methods of analysis and the Commission Internationale de l' Eclairage recommendations, respectively. Minerals were determined using ICP-OES and volatiles using SPME-GC/MS. Results showed that honey samples analyzed, met the standard quality criteria set by the EU and were successfully classified according to geographical origin. Correct classification rates were 97.3% using 8 physicochemical parameter values, 86.5% using 15 volatile compound data and 83.8% using 13 minerals. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

NASA Astrophysics Data System (ADS)

Kim, Kyungmin; Harry, Ian W.; Hodge, Kari A.; Kim, Young-Min; Lee, Chang-Hwan; Lee, Hyun Kyu; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.

2015-12-01

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%-14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs.

The Bipolar Affective Disorder Dimension Scale (BADDS) – a dimensional scale for rating lifetime psychopathology in Bipolar spectrum disorders

PubMed Central

Craddock, Nick; Jones, Ian; Kirov, George; Jones, Lisa

2004-01-01

Background Current operational diagnostic systems have substantial limitations for lifetime diagnostic classification of bipolar spectrum disorders. Issues include: (1) It is difficult to operationalize the integration of diverse episodes of psychopathology, (2) Hierarchies lead to loss of information, (3) Boundaries between diagnostic categories are often arbitrary, (4) Boundaries between categories usually require a major element of subjective interpretation, (5) Available diagnostic categories are relatively unhelpful in distinguishing severity, (6) "Not Otherwise Specified (NOS)" categories are highly heterogeneous, (7) Subclinical cases are not accommodated usefully within the current diagnostic categories. This latter limitation is particularly pertinent in the context of the increasing evidence for the existence of a broader bipolar spectrum than has been acknowledged within existing classifications. Method We have developed a numerical rating system, the Bipolar Affective Disorder Dimension Scale, BADDS, that can be used as an adjunct to conventional best-estimate lifetime diagnostic procedures. The scale definitions were informed by (a) the current concepts of mood syndrome recognized within DSMIV and ICD10, (b) the literature regarding severity of episodes, and (c) our own clinical experience. We undertook an iterative process in which we initially agreed scale definitions, piloted their use on sets of cases and made modifications to improve utility and reliability. Results BADDS has four dimensions, each rated as an integer on a 0 – 100 scale, that measure four key domains of lifetime psychopathology: Mania (M), Depression (D), Psychosis (P) and Incongruence (I). In our experience it is easy to learn, straightforward to use, has excellent inter-rater reliability and retains the key information required to make diagnoses according to DSMIV and ICD10. Conclusions Use of BADDS as an adjunct to conventional categorical diagnosis provides a richer description of lifetime psychopathology that (a) can accommodate sub-clinical features, (b) discriminate between illness severity amongst individuals within a single conventional diagnostic category, and (c) demonstrate the similarity between the illness experience of individuals who have been classified into different disease categories but whose illnesses both fall near the boundaries between the two categories. BADDS may be useful for researchers and clinicians who are interested in description and classification of lifetime psychopathology of individuals with disorders lying on the bipolar spectrum. PMID:15236660

Categorizing biomedicine images using novel image features and sparse coding representation

PubMed Central

2013-01-01

Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance. Conclusions Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study. PMID:24565470

Pattern learning with deep neural networks in EMG-based speech recognition.

PubMed

Wand, Michael; Schultz, Tanja

2014-01-01

We report on classification of phones and phonetic features from facial electromyographic (EMG) data, within the context of our EMG-based Silent Speech interface. In this paper we show that a Deep Neural Network can be used to perform this classification task, yielding a significant improvement over conventional Gaussian Mixture models. Our central contribution is the visualization of patterns which are learned by the neural network. With increasing network depth, these patterns represent more and more intricate electromyographic activity.

Multivariate analysis of the volatile components in tobacco based on infrared-assisted extraction coupled to headspace solid-phase microextraction and gas chromatography-mass spectrometry.

PubMed

Yang, Yanqin; Pan, Yuanjiang; Zhou, Guojun; Chu, Guohai; Jiang, Jian; Yuan, Kailong; Xia, Qian; Cheng, Changhe

2016-11-01

A novel infrared-assisted extraction coupled to headspace solid-phase microextraction followed by gas chromatography with mass spectrometry method has been developed for the rapid determination of the volatile components in tobacco. The optimal extraction conditions for maximizing the extraction efficiency were as follows: 65 μm polydimethylsiloxane-divinylbenzene fiber, extraction time of 20 min, infrared power of 175 W, and distance between the infrared lamp and the headspace vial of 2 cm. Under the optimum conditions, 50 components were found to exist in all ten tobacco samples from different geographical origins. Compared with conventional water-bath heating and nonheating extraction methods, the extraction efficiency of infrared-assisted extraction was greatly improved. Furthermore, multivariate analysis including principal component analysis, hierarchical cluster analysis, and similarity analysis were performed to evaluate the chemical information of these samples and divided them into three classifications, including rich, moderate, and fresh flavors. The above-mentioned classification results were consistent with the sensory evaluation, which was pivotal and meaningful for tobacco discrimination. As a simple, fast, cost-effective, and highly efficient method, the infrared-assisted extraction coupled to headspace solid-phase microextraction technique is powerful and promising for distinguishing the geographical origins of the tobacco samples coupled to suitable chemometrics. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Grain dust originating from organic and conventional farming as a potential source of biological agents causing respiratory diseases in farmers.

PubMed

Zukiewicz-Sobczak, Wioletta A; Cholewa, Grażyna; Krasowska, Ewelina; Chmielewska-Badora, Jolanta; Zwoliński, Jacek; Sobczak, Paweł

2013-12-01

Agricultural producers are exposed to a number of different health risks associated with their work environment. The objective of the study was to assess the degree of colonization by fungi in terms of quantity and in terms of variety of species the samples taken from the settled dust from combine threshing of rye cultivation from organic and conventional farms in the Province of Lublin. This paper is a preliminary quantitative assessment of the species of fungi colonizing the samples of settled dust collected during combine threshing from organic and conventional farms in the Province of Lublin. One of the stages of the project was the classification of biosafety BSL (biosafety level) of selected isolates and API ZYM tests to evaluate the potential ability of isolates to cause adverse health effects. To determine the concentration and composition of fungi in collected samples plate dilution method was used with two media: Malt Agar and Potato Dextrose Agar. MOST COMMONLY ISOLATED FUNGI IN SETTLED DUST SAMPLES COLLECTED DURING COMBINE THRESHING FROM ORGANIC FARMS, ON PDA MEDIUM WERE: Alternaria alternata and Aureobasidium pullulans. Cultures on MA medium were dominated by Alternaria alternata, Mycelia sterilia and Fusarium poae. In samples of dust from conventional crops, the predominant species was Alternaria alternata on PDA medium and on MA medium. The obtained results show a potential risk of people involved in agricultural work.

Land Cover Changes between 1974 and 2008 in Ulaanbaatar, Mongolia

NASA Astrophysics Data System (ADS)

Bagan, H.; Kinoshita, T.; Yamagata, Y.

2009-12-01

In the past 35 years, a combination of human actions and natural causes has led to a significant decline in land quality in Ulaanbaatar, the capital city of Mongolia. Human causes include changes in conventional livestock husbandry, overgrazing, and exploitation for traditional uses. Natural causes include a harsh, dry climate, short growing seasons, and thin soils. Since 1995, many herders left the countryside to come to the city in search of new opportunities, the Ger areas (wooden houses and Ger) have expended, resulting in urban sprawl. Since urbanization usually advance in an uncontrolled or unorganized way in Mongolia, they have destructive effects on the environment, particularly on basic ecosystems, wildlife habitat, and pollution of natural resources (e.g. air and water). Land use and land cover changes occurred in the region are investigated using satellite images acquired in 1974 (Landsat MSS), 1990 (Landsat TM), 2000 (ASTER), 2006 (IKONOS), and 2008 (ALOS). Pre-processing of all data included orthorectification and registration to precisely geolocated imagery. In the detection of changes, classification approaches were employed using a self-organizing map (SOM) neural network classifier (Fig. 1a) and new developed subspace classification method (Fig. 1b). From the time-series classified remote sensing images, we extract the land cover and land cover temporal changes from 1974 to 2008. The results show some important findings regarding the size and nature of the change occurred in the study area. A significant amount of steppe and forest lands have been destroyed or replaced by residential areas; as a result, the total area of urban region doubled in the 35-year period with a higher urbanization rate between 2000 and 2008. Key words: Environment; Land Cover; Urban; Change detection; Classification. References Chinbat,B., Bayantur,M., & Amarsaikhan.D. (2006). Investigation of the internal structure changes of ulaanbaatar city using RS and GIS. ISPRS Commission VII Mid-term Symposium “Remote Sensing: From Pixels to Processes”, Enschede, the Netherlands, 8-11 May 2006. 511-516. Bagan, H., Wang, Q., Watanabe, M., Karneyarna, S., & Bao, Y. (2008). Land-cover classification using ASTER multi-band combinations based on wavelet fusion and SOM neural network. Photogrammetric Engineering and Remote Sensing, 74, 333-342. Bagan, H., Yasuoka, Y., Endo, T., Wang, X., & Feng, Z. (2008). Classification of airborne hyperspectral data based on the average learning subspace method. IEEE Geoscience and Remote Sensing Letters, 5, 368-372. Figure 1. The self-organizing map (SOM) neural network classifier (a) and the subspace classification method (b).

Narrowing of the middle cerebral artery: artificial intelligence methods and comparison of transcranial color coded duplex sonography with conventional TCD.

PubMed

Swiercz, Miroslaw; Swiat, Maciej; Pawlak, Mikolaj; Weigele, John; Tarasewicz, Roman; Sobolewski, Andrzej; Hurst, Robert W; Mariak, Zenon D; Melhem, Elias R; Krejza, Jaroslaw

2010-01-01

The goal of the study was to compare performances of transcranial color-coded duplex sonography (TCCS) and transcranial Doppler sonography (TCD) in the diagnosis of the middle cerebral artery (MCA) narrowing in the same population of patients using statistical and nonstatistical intelligent models for data analysis. We prospectively collected data from 179 consecutive routine digital subtraction angiography (DSA) procedures performed in 111 patients (mean age 54.17+/-14.4 years; 59 women, 52 men) who underwent TCD and TCCS examinations simultaneously. Each patient was examined independently using both ultrasound techniques, 267 M1 segments of MCA were assessed and narrowings were classified as < or =50% and >50% lumen reduction. Diagnostic performance was estimated by two statistical and two artificial neural networks (ANN) classification methods. Separate models were constructed for the TCD and TCCS sonographic data, as well as for detection of "any narrowing" and "severe narrowing" of the MCA. Input for each classifier consisted of the peak-systolic, mean and end-diastolic velocities measured with each sonographic method; the output was MCA narrowing. Arterial narrowings less or equal 50% of lumen reduction were found in 55 and >50% narrowings in 26 out of 267 arteries, as indicated by DSA. In the category of "any narrowing" the rate of correct assignment by all models was 82% to 83% for TCCS and 79% to 81% for TCD. In the diagnosis of >50% narrowing the overall classification accuracy remained in the range of 89% to 90% for TCCS data and 90% to 91% for TCD data. For the diagnosis of any narrowing, the sensitivity of the TCCS was significantly higher than that of the TCD, while for diagnosis of >50% MCA narrowing, sensitivity of the TCCS was similar to sensitivity of the TCD. Our study showed that TCCS outperforms conventional TCD in detection of < or =50% MCA narrowing, whereas no significant difference in accuracy between both methods was found in the diagnosis of >50% MCA narrowing. (E-mail: jaroslaw.krejza@uphs.upenn.edu).

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Supervised DNA Barcodes species classification: analysis, comparisons and results

PubMed Central

2014-01-01

Background Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. Methods In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the classification results are compared with respect to ad-hoc and well-established DNA Barcode classification methods. Results A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the classification procedure. The analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable classification model. Rule-based methods have slightly inferior classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior classification performances with respect to the traditional DNA Barcode classification methods. On empirical data their classification performances are at a comparable level to the other methods. Conclusions The classification analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community. PMID:24721333

Photometric stereo endoscopy.

PubMed

Parot, Vicente; Lim, Daryl; González, Germán; Traverso, Giovanni; Nishioka, Norman S; Vakoc, Benjamin J; Durr, Nicholas J

2013-07-01

While color video endoscopy has enabled wide-field examination of the gastrointestinal tract, it often misses or incorrectly classifies lesions. Many of these missed lesions exhibit characteristic three-dimensional surface topographies. An endoscopic system that adds topographical measurements to conventional color imagery could therefore increase lesion detection and improve classification accuracy. We introduce photometric stereo endoscopy (PSE), a technique which allows high spatial frequency components of surface topography to be acquired simultaneously with conventional two-dimensional color imagery. We implement this technique in an endoscopic form factor and demonstrate that it can acquire the topography of small features with complex geometries and heterogeneous optical properties. PSE imaging of ex vivo human gastrointestinal tissue shows that surface topography measurements enable differentiation of abnormal shapes from surrounding normal tissue. Together, these results confirm that the topographical measurements can be obtained with relatively simple hardware in an endoscopic form factor, and suggest the potential of PSE to improve lesion detection and classification in gastrointestinal imaging.

Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

PubMed

Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

2018-02-01

In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Zheng, Yuejiu; Ouyang, Minggao; Han, Xuebing; Lu, Languang; Li, Jianqiu

2018-02-01

Sate of charge (SOC) estimation is generally acknowledged as one of the most important functions in battery management system for lithium-ion batteries in new energy vehicles. Though every effort is made for various online SOC estimation methods to reliably increase the estimation accuracy as much as possible within the limited on-chip resources, little literature discusses the error sources for those SOC estimation methods. This paper firstly reviews the commonly studied SOC estimation methods from a conventional classification. A novel perspective focusing on the error analysis of the SOC estimation methods is proposed. SOC estimation methods are analyzed from the views of the measured values, models, algorithms and state parameters. Subsequently, the error flow charts are proposed to analyze the error sources from the signal measurement to the models and algorithms for the widely used online SOC estimation methods in new energy vehicles. Finally, with the consideration of the working conditions, choosing more reliable and applicable SOC estimation methods is discussed, and the future development of the promising online SOC estimation methods is suggested.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Optimizing spectral CT parameters for material classification tasks

NASA Astrophysics Data System (ADS)

Rigie, D. S.; La Rivière, P. J.

2016-06-01

In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC’s) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC’s predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies.

Optimizing Spectral CT Parameters for Material Classification Tasks

PubMed Central

Rigie, D. S.; La Rivière, P. J.

2017-01-01

In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC’s) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC’s predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies. PMID:27227430

Enrichment analysis in high-throughput genomics - accounting for dependency in the NULL.

PubMed

Gold, David L; Coombes, Kevin R; Wang, Jing; Mallick, Bani

2007-03-01

Translating the overwhelming amount of data generated in high-throughput genomics experiments into biologically meaningful evidence, which may for example point to a series of biomarkers or hint at a relevant pathway, is a matter of great interest in bioinformatics these days. Genes showing similar experimental profiles, it is hypothesized, share biological mechanisms that if understood could provide clues to the molecular processes leading to pathological events. It is the topic of further study to learn if or how a priori information about the known genes may serve to explain coexpression. One popular method of knowledge discovery in high-throughput genomics experiments, enrichment analysis (EA), seeks to infer if an interesting collection of genes is 'enriched' for a Consortium particular set of a priori Gene Ontology Consortium (GO) classes. For the purposes of statistical testing, the conventional methods offered in EA software implicitly assume independence between the GO classes. Genes may be annotated for more than one biological classification, and therefore the resulting test statistics of enrichment between GO classes can be highly dependent if the overlapping gene sets are relatively large. There is a need to formally determine if conventional EA results are robust to the independence assumption. We derive the exact null distribution for testing enrichment of GO classes by relaxing the independence assumption using well-known statistical theory. In applications with publicly available data sets, our test results are similar to the conventional approach which assumes independence. We argue that the independence assumption is not detrimental.

Utility of Dual-Energy CT-based Monochromatic Imaging in the Assessment of Myocardial Delayed Enhancement in Patients with Cardiomyopathy.

PubMed

Chang, Suyon; Han, Kyunghwa; Youn, Jong-Chan; Im, Dong Jin; Kim, Jin Young; Suh, Young Joo; Hong, Yoo Jin; Hur, Jin; Kim, Young Jin; Choi, Byoung Wook; Lee, Hye-Jeong

2018-05-01

Purpose To investigate the diagnostic utility of dual-energy computed tomography (CT)-based monochromatic imaging for myocardial delayed enhancement (MDE) assessment in patients with cardiomyopathy. Materials and Methods The institutional review board approved this prospective study, and informed consent was obtained from all participants who were enrolled in the study. Forty patients (27 men and 13 women; mean age, 56 years ± 15 [standard deviation]; age range, 22-81 years) with cardiomyopathy underwent cardiac magnetic resonance (MR) imaging and dual-energy CT. Conventional (120-kV) and monochromatic (60-, 70-, and 80-keV) images were reconstructed from the dual-energy CT acquisition. Subjective quality score, contrast-to-noise ratio (CNR), and beam-hardening artifacts were compared pairwise with the Friedman test at post hoc analysis. With cardiac MR imaging as the reference standard, diagnostic performance of dual-energy CT in MDE detection and its predictive ability for pattern classification were compared pairwise by using logistic regression analysis with the generalized estimating equation in a per-segment analysis. The Bland-Altman method was used to find agreement between cardiac MR imaging and CT in MDE quantification. Results Among the monochromatic images, 70-keV CT images resulted in higher subjective quality (mean score, 3.38 ± 0.54 vs 3.15 ± 0.43; P = .0067), higher CNR (mean, 4.26 ± 1.38 vs 3.93 ± 1.33; P = .0047), and a lower value for beam-hardening artifacts (mean, 3.47 ± 1.56 vs 4.15 ± 1.67; P < .0001) when compared with conventional CT. When compared with conventional CT, 70-keV CT showed improved diagnostic performance for MDE detection (sensitivity, 94.6% vs 90.4% [P = .0032]; specificity, 96.0% vs 94.0% [P = .0031]; and accuracy, 95.6% vs 92.7% [P < .0001]) and improved predictive ability for pattern classification (subendocardial, 91.5% vs 84.3% [P = .0111]; epicardial, 94.3% vs 73.5% [P = .0001]; transmural, 93.0% vs 77.7% [P = .0018]; mesocardial, 85.4% vs 69.2% [P = .0047]; and patchy. 84.4% vs 78.4% [P = .1514]). For MDE quantification, 70-keV CT showed a small bias 0.1534% (95% limits of agreement: -4.7013, 5.0080). Conclusion Dual-energy CT-based 70-keV monochromatic images improve MDE assessment in patients with cardiomyopathy via improved image quality and CNR and reduced beam-hardening artifacts when compared with conventional CT images. © RSNA, 2017 Online supplemental material is available for this article.

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Singularity and Nonnormality in the Classification of Compositional Data

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.; Olea, R.A.; Harff, Jan

1998-01-01

Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. Fortunately, discriminant analyses can be calculated using a pseudo-inverse of the singular covariance matrix; this is done automatically by some statistical packages such as SAS. Granulometric data from the Darss Sill region of the Baltic Sea is used to explore how the pseudo-inversion procedure influences discriminant analysis results, comparing the algorithm used by SAS to the more conventional Moore-Penrose algorithm. Logratio transforms have been recommended to overcome problems associated with analysis of compositional data, including singularity. A regionalized classification of the Darss Sill data after logratio transformation is different only slightly from one based on raw granulometric data, suggesting that closure problems do not influence severely regionalized classification of compositional data.

[The informational support of statistical observation related to children disability].

PubMed

Son, I M; Polikarpov, A V; Ogrizko, E V; Golubeva, T Yu

2016-01-01

Within the framework of the Convention on rights of the disabled the revision is specified concerning criteria of identification of disability of children and reformation of system of medical social expertise according international standards of indices of health and indices related to health. In connection with it, it is important to consider the relationship between alterations in forms of the Federal statistical monitoring in the part of registration of disabled children in the Russian Federation and classification of health indices and indices related to health applied at identification of disability. The article presents analysis of relationship between alterations in forms of the Federal statistical monitoring in the part of registration of disabled children in the Russian Federation and applied classifications used at identification of disability (International classification of impairments, disabilities and handicap (ICDH), international classification of functioning, disability and health (ICF), international classification of functioning, disability and health, version for children and youth (ICF-CY). The intersectorial interaction is considered within the framework of statistics of children disability.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

PubMed

Zhengming Li; Zhihui Lai; Yong Xu; Jian Yang; Zhang, David

2017-02-01

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

Instructional Method Classifications Lack User Language and Orientation

ERIC Educational Resources Information Center

Neumann, Susanne; Koper, Rob

2010-01-01

Following publications emphasizing the need of a taxonomy for instructional methods, this article presents a literature review on classifications for learning and teaching in order to identify possible classifications for instructional methods. Data was collected for 37 classifications capturing the origins, theoretical underpinnings, purposes and…

Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement.

PubMed

Lancaster, Cady; Espinoza, Edgard

2012-05-15

International trade of several Dalbergia wood species is regulated by The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). In order to supplement morphological identification of these species, a rapid chemical method of analysis was developed. Using Direct Analysis in Real Time (DART) ionization coupled with Time-of-Flight (TOF) Mass Spectrometry (MS), selected Dalbergia and common trade species were analyzed. Each of the 13 wood species was classified using principal component analysis and linear discriminant analysis (LDA). These statistical data clusters served as reliable anchors for species identification of unknowns. Analysis of 20 or more samples from the 13 species studied in this research indicates that the DART-TOFMS results are reproducible. Statistical analysis of the most abundant ions gave good classifications that were useful for identifying unknown wood samples. DART-TOFMS and LDA analysis of 13 species of selected timber samples and the statistical classification allowed for the correct assignment of unknown wood samples. This method is rapid and can be useful when anatomical identification is difficult but needed in order to support CITES enforcement. Published 2012. This article is a US Government work and is in the public domain in the USA.

Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

PubMed

Bai, Li-Yue; Dai, Hao; Xu, Qin; Junaid, Muhammad; Peng, Shao-Liang; Zhu, Xiaolei; Xiong, Yi; Wei, Dong-Qing

2018-02-05

Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new method for spectral-spatial classification of hyperspectral images is proposed. The method is based on the integration of probabilistic classification within the hierarchical best merge region growing algorithm. For this purpose, preliminary probabilistic support vector machines classification is performed. Then, hierarchical step-wise optimization algorithm is applied, by iteratively merging regions with the smallest Dissimilarity Criterion (DC). The main novelty of this method consists in defining a DC between regions as a function of region statistical and geometrical features along with classification probabilities. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana s vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.

A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation.

PubMed

Tseng, Yi-Ju; Wu, Jung-Hsuan; Lin, Hui-Chi; Chen, Ming-Yuan; Ping, Xiao-Ou; Sun, Chun-Chuan; Shang, Rung-Ji; Sheng, Wang-Huei; Chen, Yee-Chun; Lai, Feipei; Chang, Shan-Chwen

2015-09-21

Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system.

Classification of parotidectomy: a proposed modification to the European Salivary Gland Society classification system.

PubMed

Wong, Wai Keat; Shetty, Subhaschandra

2017-08-01

Parotidectomy remains the mainstay of treatment for both benign and malignant lesions of the parotid gland. There exists a wide range of possible surgical options in parotidectomy in terms of extent of parotid tissue removed. There is increasing need for uniformity of terminology resulting from growing interest in modifications of the conventional parotidectomy. It is, therefore, of paramount importance for a standardized classification system in describing extent of parotidectomy. Recently, the European Salivary Gland Society (ESGS) proposed a novel classification system for parotidectomy. The aim of this study is to evaluate this system. A classification system proposed by the ESGS was critically re-evaluated and modified to increase its accuracy and its acceptability. Modifications mainly focused on subdividing Levels I and II into IA, IB, IIA, and IIB. From June 2006 to June 2016, 126 patients underwent 130 parotidectomies at our hospital. The classification system was tested in that cohort of patient. While the ESGS classification system is comprehensive, it does not cover all possibilities. The addition of Sublevels IA, IB, IIA, and IIB may help to address some of the clinical situations seen and is clinically relevant. We aim to test the modified classification system for partial parotidectomy to address some of the challenges mentioned.

7 CFR 28.35 - Method of classification.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Method of classification. 28.35 Section 28.35 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... Classification § 28.35 Method of classification. All cotton samples shall be classified on the basis of the...

Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

PubMed Central

Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

2014-01-01

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results. PMID:21699217

Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics.

PubMed

Abdolali, Fatemeh; Zoroofi, Reza Aghaeizadeh; Otake, Yoshito; Sato, Yoshinobu

2017-02-01

Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor. Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features. This study demonstrated that our proposed methodology can improve the computer assisted diagnosis (CAD) performance by incorporating more discriminative features. Using orthogonalized SPHARM is promising in computerized cyst detection and may have a significant impact in future CAD systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Use of a modified GreenScreen tool to conduct a screening-level comparative hazard assessment of conventional silver and two forms of nanosilver.

PubMed

Sass, Jennifer; Heine, Lauren; Hwang, Nina

2016-11-08

Increased concern for potential health and environmental impacts of chemicals, including nanomaterials, in consumer products is driving demand for greater transparency regarding potential risks. Chemical hazard assessment is a powerful tool to inform product design, development and procurement and has been integrated into alternative assessment frameworks. The extent to which assessment methods originally designed for conventionally-sized materials can be used for nanomaterials, which have size-dependent physical and chemical properties, have not been well established. We contracted with a certified GreenScreen profiler to conduct three GreenScreen hazard assessments, for conventional silver and two forms of nanosilver. The contractor summarized publicly available literature, and used defined GreenScreen hazard criteria and expert judgment to assign and report hazard classification levels, along with indications of confidence in those assignments. Where data were not available, a data gap (DG) was assigned. Using the individual endpoint scores, an aggregated benchmark score (BM) was applied. Conventional silver and low-soluble nanosilver were assigned the highest possible hazard score and a silica-silver nanocomposite called AGS-20 could not be scored due to data gaps. AGS-20 is approved for use as antimicrobials by the US Environmental Protection Agency. An existing method for chemical hazard assessment and communication can be used - with minor adaptations- to compare hazards across conventional and nano forms of a substance. The differences in data gaps and in hazard profiles support the argument that each silver form should be considered unique and subjected to hazard assessment to inform regulatory decisions and decisions about product design and development. A critical limitation of hazard assessments for nanomaterials is the lack of nano-specific hazard data - where data are available, we demonstrate that existing hazard assessment systems can work. The work is relevant for risk assessors and regulators. We recommend that regulatory agencies and others require more robust data sets on each novel nanomaterial before granting market approval.

Genomic relations among 31 species of Mammillaria haworth (Cactaceae) using random amplified polymorphic DNA.

PubMed

Mattagajasingh, Ilwola; Mukherjee, Arup Kumar; Das, Premananda

2006-01-01

Thirty-one species of Mammillaria were selected to study the molecular phylogeny using random amplified polymorphic DNA (RAPD) markers. High amount of mucilage (gelling polysaccharides) present in Mammillaria was a major obstacle in isolating good quality genomic DNA. The CTAB (cetyl trimethyl ammonium bromide) method was modified to obtain good quality genomic DNA. Twenty-two random decamer primers resulted in 621 bands, all of which were polymorphic. The similarity matrix value varied from 0.109 to 0.622 indicating wide variability among the studied species. The dendrogram obtained from the unweighted pair group method using arithmetic averages (UPGMA) analysis revealed that some of the species did not follow the conventional classification. The present work shows the usefulness of RAPD markers for genetic characterization to establish phylogenetic relations among Mammillaria species.

Identification of Candida lusitaniae as an opportunistic yeast in humans.

PubMed

Holzschu, D L; Presley, H L; Miranda, M; Phaff, H J

1979-08-01

Four yeast strains, causally associated with infection in a patient with acute myelogenous leukemia, were identified by standard methods currently used in yeast taxonomy as representatives of Candida lusitania van Uden et do Carmo-Sousa. Because this species has not been recognized previously as an opportunistic yeast in humans, molecular taxonomic methods were applied to confirm its identity. The nuclear deoxyribonucleic acid (DNA) base composition of two clinical isolates was shown to be 45.1 mol% guanine plus cytosine as compared to 44.7 mol% guanine plus cytosine for the type strain of this species. DNA/DNA reassociation experiments revealed more than 95% complementarity between the DNAs from the clinical isolates and that of the type strain of C. lusitaniae, thus confirming their classification by conventional taxonomy. A key is provided to differentiate C. lusitaniae from two phenotypically similar Candida species.

Recent developments in tissue-type imaging (TTI) for planning and monitoring treatment of prostate cancer.

PubMed

Feleppa, Ernest J; Porter, Christopher R; Ketterling, Jeffrey; Lee, Paul; Dasgupta, Shreedevi; Urban, Stella; Kalisz, Andrew

2004-07-01

Because current methods of imaging prostate cancer are inadequate, biopsies cannot be effectively guided and treatment cannot be effectively planned and targeted. Therefore, our research is aimed at ultrasonically characterizing cancerous prostate tissue so that we can image it more effectively and thereby provide improved means of detecting, treating and monitoring prostate cancer. We base our characterization methods on spectrum analysis of radiofrequency (rf) echo signals combined with clinical variables such as prostate-specific antigen (PSA). Tissue typing using these parameters is performed by artificial neural networks. We employed and evaluated different approaches to data partitioning into training, validation, and test sets and different neural network configuration options. In this manner, we sought to determine what neural network configuration is optimal for these data and also to assess possible bias that might exist due to correlations among different data entries among the data for a given patient. The classification efficacy of each neural network configuration and data-partitioning method was measured using relative-operating-characteristic (ROC) methods. Neural network classification based on spectral parameters combined with clinical data generally produced ROC-curve areas of 0.80 compared to curve areas of 0.64 for conventional transrectal ultrasound imaging combined with clinical data. We then used the optimal neural network configuration to generate lookup tables that translate local spectral parameter values and global clinical-variable values into pixel values in tissue-type images (TTIs). TTIs continue to show cancerous regions successfully, and may prove to be particularly useful clinically in combination with other ultrasonic and nonultrasonic methods, e.g., magnetic-resonance spectroscopy.

Recent Developments in Tissue-type Imaging(TTI) for Planning and Monitoring Treatment of Prostate Cancer

PubMed Central

Feleppa, Ernest J.; Porter, Christopher R.; Ketterling, Jeffrey; Lee, Paul; Dasgupta, Shreedevi; Urban, Stella; Kalisz, Andrew

2006-01-01

Because current methods of imaging prostate cancer are inadequate, biopsies cannot be effectively guided and treatment cannot be effectively planned and targeted. Therefore, our research is aimed at ultrasonically characterizing cancerous prostate tissue so that we can image it more effectively and thereby provide improved means of detecting, treating and monitoring prostate cancer. We base our characterization methods on spectrum analysis of radio frequency (rf) echo signals combined with clinical variables such as prostate-specific antigen (PSA). Tissue typing using these parameters is performed by artificial neural networks. We employedand evaluated different approaches to data partitioning into training, validation, and test sets and different neural network configuration options. In this manner, we sought to determine what neural network configuration is optimal for these data and also to assess possible bias that might exist due to correlations among different data entries among the data for a given patient. The classification efficacy of each neural network configuration and data-partitioning method was measured using relative-operating-characteristic (ROC) methods. Neural network classification based on spectral parameters combined with clinical data generally produced ROC-curve areas of 0.80 compared to curve areas of 0.64 for conventional transrectal ultrasound imaging combined with clinical data. We then used the optimal neural network configuration to generate lookup tables that translate local spectral parameter values and global clinical-variable values into pixel values in tissue-type images (TTIs). TTIs continue to show can cerous regions successfully, and may prove to be particularly useful clinically in combination with other ultrasonic and nonultrasonic methods, e.g., magnetic-resonance spectroscopy. PMID:15754797

Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration.

PubMed

Godinho, Robson B; Santos, Mauricio C; Poppi, Ronei J

2016-03-15

An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies. Copyright © 2015 Elsevier B.V. All rights reserved.

The performance improvement of automatic classification among obstructive lung diseases on the basis of the features of shape analysis, in addition to texture analysis at HRCT

NASA Astrophysics Data System (ADS)

Lee, Youngjoo; Kim, Namkug; Seo, Joon Beom; Lee, JuneGoo; Kang, Suk Ho

2007-03-01

In this paper, we proposed novel shape features to improve classification performance of differentiating obstructive lung diseases, based on HRCT (High Resolution Computerized Tomography) images. The images were selected from HRCT images, obtained from 82 subjects. For each image, two experienced radiologists selected rectangular ROIs with various sizes (16x16, 32x32, and 64x64 pixels), representing each disease or normal lung parenchyma. Besides thirteen textural features, we employed additional seven shape features; cluster shape features, and Top-hat transform features. To evaluate the contribution of shape features for differentiation of obstructive lung diseases, several experiments were conducted with two different types of classifiers and various ROI sizes. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess the performance and cross-validation of the system, 5-folding method was used. In comparison to employing only textural features, adding shape features yields significant enhancement of overall sensitivity(5.9, 5.4, 4.4% in the Bayesian and 9.0, 7.3, 5.3% in the SVM), in the order of ROI size 16x16, 32x32, 64x64 pixels, respectively (t-test, p<0.01). Moreover, this enhancement was largely due to the improvement on class-specific sensitivity of mild centrilobular emphysema and bronchiolitis obliterans which are most hard to differentiate for radiologists. According to these experimental results, adding shape features to conventional texture features is much useful to improve classification performance of obstructive lung diseases in both Bayesian and SVM classifiers.

Automatic target recognition and detection in infrared imagery under cluttered background

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Koç, Aykut; Alatan, A. Aydın.

2017-10-01

Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

[Study of Cervical Exfoliated Cell's DNA Quantitative Analysis Based on Multi-Spectral Imaging Technology].

PubMed

Wu, Zheng; Zeng, Li-bo; Wu, Qiong-shui

2016-02-01

The conventional cervical cancer screening methods mainly include TBS (the bethesda system) classification method and cellular DNA quantitative analysis, however, by using multiple staining method in one cell slide, which is staining the cytoplasm with Papanicolaou reagent and the nucleus with Feulgen reagent, the study of achieving both two methods in the cervical cancer screening at the same time is still blank. Because the difficulty of this multiple staining method is that the absorbance of the non-DNA material may interfere with the absorbance of DNA, so that this paper has set up a multi-spectral imaging system, and established an absorbance unmixing model by using multiple linear regression method based on absorbance's linear superposition character, and successfully stripped out the absorbance of DNA to run the DNA quantitative analysis, and achieved the perfect combination of those two kinds of conventional screening method. Through a series of experiment we have proved that between the absorbance of DNA which is calculated by the absorbance unmixxing model and the absorbance of DNA which is measured there is no significant difference in statistics when the test level is 1%, also the result of actual application has shown that there is no intersection between the confidence interval of the DNA index of the tetraploid cells which are screened by using this paper's analysis method when the confidence level is 99% and the DNA index's judging interval of cancer cells, so that the accuracy and feasibility of the quantitative DNA analysis with multiple staining method expounded by this paper have been verified, therefore this analytical method has a broad application prospect and considerable market potential in early diagnosis of cervical cancer and other cancers.

A Comparison of Two-Group Classification Methods

ERIC Educational Resources Information Center

Holden, Jocelyn E.; Finch, W. Holmes; Kelley, Ken

2011-01-01

The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis,…

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

PubMed Central

2014-01-01

Background The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Methods Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. Conclusions A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice. PMID:24981916

Application of multimedia models for screening assessment of long-range transport potential and overall persistence.

PubMed

Klasmeier, Jörg; Matthies, Michael; Macleod, Matthew; Fenner, Kathrin; Scheringer, Martin; Stroebe, Maximilian; Le Gall, Anne Christine; Mckone, Thomas; Van De Meent, Dik; Wania, Frank

2006-01-01

We propose a multimedia model-based methodology to evaluate whether a chemical substance qualifies as POP-like based on overall persistence (Pov) and potential for long-range transport (LRTP). It relies upon screening chemicals against the Pov and LRTP characteristics of selected reference chemicals with well-established environmental fates. Results indicate that chemicals of high and low concern in terms of persistence and long-range transport can be consistently identified by eight contemporary multimedia models using the proposed methodology. Model results for three hypothetical chemicals illustrate that the model-based classification of chemicals according to Pov and LRTP is not always consistent with the single-media half-life approach proposed by the UNEP Stockholm Convention and thatthe models provide additional insight into the likely long-term hazards associated with chemicals in the environment. We suggest this model-based classification method be adopted as a complement to screening against defined half-life criteria at the initial stages of tiered assessments designed to identify POP-like chemicals and to prioritize further environmental fate studies for new and existing chemicals.

Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: semen and blood mixtures.

PubMed

Sikirzhytski, Vitali; Sikirzhytskaya, Aliaksandra; Lednev, Igor K

2012-10-10

Conventional confirmatory biochemical tests used in the forensic analysis of body fluid traces found at a crime scene are destructive and not universal. Recently, we reported on the application of near-infrared (NIR) Raman microspectroscopy for non-destructive confirmatory identification of pure blood, saliva, semen, vaginal fluid and sweat. Here we expand the method to include dry mixtures of semen and blood. A classification algorithm was developed for differentiating pure body fluids and their mixtures. The classification methodology is based on an effective combination of Support Vector Machine (SVM) regression (data selection) and SVM Discriminant Analysis of preprocessed experimental Raman spectra collected using an automatic mapping of the sample. This extensive cross-validation of the obtained results demonstrated that the detection limit of the minor contributor is as low as a few percent. The developed methodology can be further expanded to any binary mixture of complex solutions, including but not limited to mixtures of other body fluids. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Applications of rule-induction in the derivation of quantitative structure-activity relationships.

PubMed

A-Razzak, M; Glen, R C

1992-08-01

Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.

ROKU: a novel method for identification of tissue-specific genes.

PubMed

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-06-12

One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

Applications of rule-induction in the derivation of quantitative structure-activity relationships

NASA Astrophysics Data System (ADS)

A-Razzak, Mohammed; Glen, Robert C.

1992-08-01

Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

PubMed Central

Lorsakul, Auranuch; Fakhri, Georges El; Worstell, William; Ouyang, Jinsong; Rakvongthai, Yothin; Laine, Andrew F.; Li, Quanzheng

2016-01-01

Abstract. Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dual-energy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre–Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all p<0.01). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all p<0.01) for FBP-Ramp images and 53.2% to 67.7% (all p<0.01) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications. PMID:27429999

Fusion and Gaussian mixture based classifiers for SONAR data

NASA Astrophysics Data System (ADS)

Kotari, Vikas; Chang, KC

2011-06-01

Underwater mines are inexpensive and highly effective weapons. They are difficult to detect and classify. Hence detection and classification of underwater mines is essential for the safety of naval vessels. This necessitates a formulation of highly efficient classifiers and detection techniques. Current techniques primarily focus on signals from one source. Data fusion is known to increase the accuracy of detection and classification. In this paper, we formulated a fusion-based classifier and a Gaussian mixture model (GMM) based classifier for classification of underwater mines. The emphasis has been on sound navigation and ranging (SONAR) signals due to their extensive use in current naval operations. The classifiers have been tested on real SONAR data obtained from University of California Irvine (UCI) repository. The performance of both GMM based classifier and fusion based classifier clearly demonstrate their superior classification accuracy over conventional single source cases and validate our approach.

Automated Decision Tree Classification of Corneal Shape

PubMed Central

Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

2011-01-01

Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification problems. PMID:16357645

Recruitments of trained citizen volunteering for conventional cardiopulmonary resuscitation are necessary to improve the outcome after out-of-hospital cardiac arrests in remote time-distance area: A nationwide population-based study.

PubMed

Takei, Yutaka; Kamikura, Takahisa; Nishi, Taiki; Maeda, Tetsuo; Sakagami, Satoru; Kubo, Minoru; Inaba, Hideo

2016-08-01

To compare the factors associated with survival after out-of-hospital cardiac arrests (OHCAs) among three time-distance areas (defined as interquartile range of time for emergency medical services response to patient's side). From a nationwide, prospectively collected data on 716,608 OHCAs between 2007 and 2012, this study analyzed 193,914 bystander-witnessed OHCAs without pre-hospital physician involvement. Overall neurologically favourable 1-month survival rates were 7.4%, 4.1% and 1.7% for close, intermediate and remote areas, respectively. We classified BCPR by type (compression-only vs. conventional) and by dispatcher-assisted CPR (DA-CPR) (with vs. without); the effects on time-distance area survival were analyzed by BCPR classification. Association of each BCPR classification with survival was affected by time-distance area and arrest aetiology (p<0.05). The survival rates in the remote area were much higher with conventional BCPR than with compression-only BCPR (odds ratio; 95% confidence interval, 1.26; 1.05-1.51) and with BCPR without DA-CPR than with BCPR with DA-CPR (1.54; 1.29-1.82). Accordingly, we classified BCPR into five groups (no BCPR, compression-only with DA-CPR, conventional with DA-CPR, compression-only without DA-CPR, and conventional without DA-CPR) and analyzed for associations with survival, both cardiac and non-cardiac related, in each time-distance area by multivariate logistic regression analysis. In the remote area, conventional BCPR without DA-CPR significantly improved survival after OHCAs of cardiac aetiology, compared with all the other BCPR groups. Other correctable factors associated with survival were short collapse-to-call and call-to-first CPR intervals. Every effort to recruit trained citizens initiating conventional BCPR should be made in remote time-distance areas. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Method of Grassland Information Extraction Based on Multi-Level Segmentation and Cart Model

NASA Astrophysics Data System (ADS)

Qiao, Y.; Chen, T.; He, J.; Wen, Q.; Liu, F.; Wang, Z.

2018-04-01

It is difficult to extract grassland accurately by traditional classification methods, such as supervised method based on pixels or objects. This paper proposed a new method combing the multi-level segmentation with CART (classification and regression tree) model. The multi-level segmentation which combined the multi-resolution segmentation and the spectral difference segmentation could avoid the over and insufficient segmentation seen in the single segmentation mode. The CART model was established based on the spectral characteristics and texture feature which were excavated from training sample data. Xilinhaote City in Inner Mongolia Autonomous Region was chosen as the typical study area and the proposed method was verified by using visual interpretation results as approximate truth value. Meanwhile, the comparison with the nearest neighbor supervised classification method was obtained. The experimental results showed that the total precision of classification and the Kappa coefficient of the proposed method was 95 % and 0.9, respectively. However, the total precision of classification and the Kappa coefficient of the nearest neighbor supervised classification method was 80 % and 0.56, respectively. The result suggested that the accuracy of classification proposed in this paper was higher than the nearest neighbor supervised classification method. The experiment certificated that the proposed method was an effective extraction method of grassland information, which could enhance the boundary of grassland classification and avoid the restriction of grassland distribution scale. This method was also applicable to the extraction of grassland information in other regions with complicated spatial features, which could avoid the interference of woodland, arable land and water body effectively.

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

NASA Technical Reports Server (NTRS)

Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

2013-01-01

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas.

The United Nations Convention on the Law of the Sea: Is It Time for United States Participation

DTIC Science & Technology

1990-02-27

CARLISLE BARRACKS, PA 17013-5050 USAWC MILITARY STUDIES PROGRAM PAPER THE UNITED NATIONS CONVENTION ON THE LAW OF THE SEA: IS IT TIME FOR UNITED STATES...Individual Study Froject -te: 27 February 1990 PAGES: 42 CLASSIFICATION: Unclassified he attempt to achieve an international consensus on law of the sea is a...restrictions of some deqree on navigation, was limited to twelve nautical miles. Finally, the U.S. got to eat its cake too: coastal states, of which

ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple sclerosis lesion heterogeneity, as demonstrated by quantitative MRI

PubMed Central

Tozer, Daniel J; Schmierer, Klaus; Chard, Declan T; Anderson, Valerie M; Altmann, Daniel R; Miller, David H; Wheeler-Kingshott, Claudia AM

2013-01-01

Background: There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans. Objective: To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images. Methods: We obtained conventional PDw and T2w images from 10 patients with relapsing–remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML. Results: Our study’s ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively. Conclusion: ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished. PMID:23037551

Effective classification of the prevalence of Schistosoma mansoni.

PubMed

Mitchell, Shira A; Pagano, Marcello

2012-12-01

To present an effective classification method based on the prevalence of Schistosoma mansoni in the community. We created decision rules (defined by cut-offs for number of positive slides), which account for imperfect sensitivity, both with a simple adjustment of fixed sensitivity and with a more complex adjustment of changing sensitivity with prevalence. To reduce screening costs while maintaining accuracy, we propose a pooled classification method. To estimate sensitivity, we use the De Vlas model for worm and egg distributions. We compare the proposed method with the standard method to investigate differences in efficiency, measured by number of slides read, and accuracy, measured by probability of correct classification. Modelling varying sensitivity lowers the lower cut-off more significantly than the upper cut-off, correctly classifying regions as moderate rather than lower, thus receiving life-saving treatment. The classification method goes directly to classification on the basis of positive pools, avoiding having to know sensitivity to estimate prevalence. For model parameter values describing worm and egg distributions among children, the pooled method with 25 slides achieves an expected 89.9% probability of correct classification, whereas the standard method with 50 slides achieves 88.7%. Among children, it is more efficient and more accurate to use the pooled method for classification of S. mansoni prevalence than the current standard method. © 2012 Blackwell Publishing Ltd.

Classification and Clustering Methods for Multiple Environmental Factors in Gene-Environment Interaction: Application to the Multi-Ethnic Study of Atherosclerosis.

PubMed

Ko, Yi-An; Mukherjee, Bhramar; Smith, Jennifer A; Kardia, Sharon L R; Allison, Matthew; Diez Roux, Ana V

2016-11-01

There has been an increased interest in identifying gene-environment interaction (G × E) in the context of multiple environmental exposures. Most G × E studies analyze one exposure at a time, but we are exposed to multiple exposures in reality. Efficient analysis strategies for complex G × E with multiple environmental factors in a single model are still lacking. Using the data from the Multiethnic Study of Atherosclerosis, we illustrate a two-step approach for modeling G × E with multiple environmental factors. First, we utilize common clustering and classification strategies (e.g., k-means, latent class analysis, classification and regression trees, Bayesian clustering using Dirichlet Process) to define subgroups corresponding to distinct environmental exposure profiles. Second, we illustrate the use of an additive main effects and multiplicative interaction model, instead of the conventional saturated interaction model using product terms of factors, to study G × E with the data-driven exposure subgroups defined in the first step. We demonstrate useful analytical approaches to translate multiple environmental exposures into one summary class. These tools not only allow researchers to consider several environmental exposures in G × E analysis but also provide some insight into how genes modify the effect of a comprehensive exposure profile instead of examining effect modification for each exposure in isolation.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

PubMed

Randhawa, Vinay; Kumar Singh, Anil; Acharya, Vishal

2015-12-01

Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning.

PubMed

Go, Taesik; Byeon, Hyeokjun; Lee, Sang Joon

2018-04-30

Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic. Copyright © 2017 Elsevier B.V. All rights reserved.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation

PubMed Central

O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-01-01

Background Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. Objective The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. Methods We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. Results With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. Conclusions The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. PMID:28778851

Plant species classification using flower images—A comparative study of local feature representations

PubMed Central

Seeland, Marco; Rzanny, Michael; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick

2017-01-01

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification. PMID:28234999

Dynamic surface-enhanced Raman spectroscopy and Chemometric methods for fast detection and intelligent identification of methamphetamine and 3, 4-Methylenedioxy methamphetamine in human urine

NASA Astrophysics Data System (ADS)

Weng, Shizhuang; Dong, Ronglu; Zhu, Zede; Zhang, Dongyan; Zhao, Jinling; Huang, Linsheng; Liang, Dong

2018-01-01

Conventional Surface-Enhanced Raman Spectroscopy (SERS) for fast detection of drugs in urine on the portable Raman spectrometer remains challenges because of low sensitivity and unreliable Raman signal, and spectra process with manual intervention. Here, we develop a novel detection method of drugs in urine using chemometric methods and dynamic SERS (D-SERS) with mPEG-SH coated gold nanorods (GNRs). D-SERS combined with the uniform GNRs can obtain giant enhancement, and the signal is also of high reproducibility. On the basis of the above advantages, we obtained the spectra of urine, urine with methamphetamine (MAMP), urine with 3, 4-Methylenedioxy Methamphetamine (MDMA) using D-SERS. Simultaneously, some chemometric methods were introduced for the intelligent and automatic analysis of spectra. Firstly, the spectra at the critical state were selected through using K-means. Then, the spectra were proposed by random forest (RF) with feature selection and principal component analysis (PCA) to develop the recognition model. And the identification accuracy of model were 100%, 98.7% and 96.7%, respectively. To validate the effect in practical issue further, the drug abusers'urine samples with 0.4, 3, 30 ppm MAMP were detected using D-SERS and identified by the classification model. The high recognition accuracy of > 92.0% can meet the demand of practical application. Additionally, the parameter optimization of RF classification model was simple. Compared with the general laboratory method, the detection process of urine's spectra using D-SERS only need 2 mins and 2 μL samples volume, and the identification of spectra based on chemometric methods can be finish in seconds. It is verified that the proposed approach can provide the accurate, convenient and rapid detection of drugs in urine.

Comparisons of survival predictions using survival risk ratios based on International Classification of Diseases, Ninth Revision and Abbreviated Injury Scale trauma diagnosis codes.

PubMed

Clarke, John R; Ragone, Andrew V; Greenwald, Lloyd

2005-09-01

We conducted a comparison of methods for predicting survival using survival risk ratios (SRRs), including new comparisons based on International Classification of Diseases, Ninth Revision (ICD-9) versus Abbreviated Injury Scale (AIS) six-digit codes. From the Pennsylvania trauma center's registry, all direct trauma admissions were collected through June 22, 1999. Patients with no comorbid medical diagnoses and both ICD-9 and AIS injury codes were used for comparisons based on a single set of data. SRRs for ICD-9 and then for AIS diagnostic codes were each calculated two ways: from the survival rate of patients with each diagnosis and when each diagnosis was an isolated diagnosis. Probabilities of survival for the cohort were calculated using each set of SRRs by the multiplicative ICISS method and, where appropriate, the minimum SRR method. These prediction sets were then internally validated against actual survival by the Hosmer-Lemeshow goodness-of-fit statistic. The 41,364 patients had 1,224 different ICD-9 injury diagnoses in 32,261 combinations and 1,263 corresponding AIS injury diagnoses in 31,755 combinations, ranging from 1 to 27 injuries per patient. All conventional ICD-9-based combinations of SRRs and methods had better Hosmer-Lemeshow goodness-of-fit statistic fits than their AIS-based counterparts. The minimum SRR method produced better calibration than the multiplicative methods, presumably because it did not magnify inaccuracies in the SRRs that might occur with multiplication. Predictions of survival based on anatomic injury alone can be performed using ICD-9 codes, with no advantage from extra coding of AIS diagnoses. Predictions based on the single worst SRR were closer to actual outcomes than those based on multiplying SRRs.

Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.

PubMed

Spertus, Jacob V; T Normand, Sharon-Lise; Wolf, Robert; Cioffi, Matt; Lovett, Ann; Rose, Sherri

2016-11-01

Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly suited to leveraging big data. We assessed hospital all-cause 30-day excess mortality risk among 8952 adults undergoing percutaneous coronary intervention between October 1, 2011, and September 30, 2012, in 24 Massachusetts hospitals using clinical registry data linked with billing data. We compared conventional logistic regression models with augmented inverse probability weighted estimators and targeted maximum likelihood estimators to generate more efficient and unbiased estimates of hospital effects. We also compared a clinically informed and a machine-learning approach to confounder selection, using elastic net penalized regression in the latter case. Hospital excess risk estimates range from -1.4% to 2.0% across methods and confounder sets. Some hospitals were consistently classified as low or as high excess mortality outliers; others changed classification depending on the method and confounder set used. Switching from the clinically selected list of 11 confounders to a full set of 225 confounders increased the estimation uncertainty by an average of 62% across methods as measured by confidence interval length. Agreement among methods ranged from fair, with a κ statistic of 0.39 (SE: 0.16), to perfect, with a κ of 1 (SE: 0.0). Modern causal inference techniques should be more frequently adopted to leverage big data while minimizing bias in hospital performance assessments. © 2016 American Heart Association, Inc.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Enhancing the performance of regional land cover mapping

NASA Astrophysics Data System (ADS)

Wu, Weicheng; Zucca, Claudio; Karam, Fadi; Liu, Guangping

2016-10-01

Different pixel-based, object-based and subpixel-based methods such as time-series analysis, decision-tree, and different supervised approaches have been proposed to conduct land use/cover classification. However, despite their proven advantages in small dataset tests, their performance is variable and less satisfactory while dealing with large datasets, particularly, for regional-scale mapping with high resolution data due to the complexity and diversity in landscapes and land cover patterns, and the unacceptably long processing time. The objective of this paper is to demonstrate the comparatively highest performance of an operational approach based on integration of multisource information ensuring high mapping accuracy in large areas with acceptable processing time. The information used includes phenologically contrasted multiseasonal and multispectral bands, vegetation index, land surface temperature, and topographic features. The performance of different conventional and machine learning classifiers namely Malahanobis Distance (MD), Maximum Likelihood (ML), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Random Forests (RFs) was compared using the same datasets in the same IDL (Interactive Data Language) environment. An Eastern Mediterranean area with complex landscape and steep climate gradients was selected to test and develop the operational approach. The results showed that SVMs and RFs classifiers produced most accurate mapping at local-scale (up to 96.85% in Overall Accuracy), but were very time-consuming in whole-scene classification (more than five days per scene) whereas ML fulfilled the task rapidly (about 10 min per scene) with satisfying accuracy (94.2-96.4%). Thus, the approach composed of integration of seasonally contrasted multisource data and sampling at subclass level followed by a ML classification is a suitable candidate to become an operational and effective regional land cover mapping method.

The use of the decision tree technique and image cytometry to characterize aggressiveness in World Health Organization (WHO) grade II superficial transitional cell carcinomas of the bladder.

PubMed

Decaestecker, C; van Velthoven, R; Petein, M; Janssen, T; Salmon, I; Pasteels, J L; van Ham, P; Schulman, C; Kiss, R

1996-03-01

The aggressiveness of human bladder tumours can be assessed by means of various classification systems, including the one proposed by the World Health Organization (WHO). According to the WHO classification, three levels of malignancy are identified as grades I (low), II (intermediate), and III (high). This classification system operates satisfactorily for two of the three grades in forecasting clinical progression, most grade I tumours being associated with good prognoses and most grade III with bad. In contrast, the grade II group is very heterogeneous in terms of their clinical behaviour. The present study used two computer-assisted methods to investigate whether it is possible to sub-classify grade II tumours: computer-assisted microscope analysis (image cytometry) of Feulgen-stained nuclei and the Decision Tree Technique. This latter technique belongs to the Supervised Learning Algorithm and enables an objective assessment to be made of the diagnostic value associated with a given parameter. The combined use of these two methods in a series of 292 superficial transitional cell carcinomas shows that it is possible to identify one subgroup of grade II tumours which behave clinically like grade I tumours and a second subgroup which behaves clinically like grade III tumours. Of the nine ploidy-related parameters computed by means of image cytometry [the DNA index (DI), DNA histogram type (DHT), and the percentages of diploid, hyperdiploid, triploid, hypertriploid, tetraploid, hypertetraploid, and polyploid cell nuclei], it was the percentage of hyperdiploid and hypertetraploid cell nuclei which enabled identification, rather than conventional parameters such as the DI or the DHT.

Using machine learning to assess covariate balance in matching studies.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

In order to assess the effectiveness of matching approaches in observational studies, investigators typically present summary statistics for each observed pre-intervention covariate, with the objective of showing that matching reduces the difference in means (or proportions) between groups to as close to zero as possible. In this paper, we introduce a new approach to distinguish between study groups based on their distributions of the covariates using a machine-learning algorithm called optimal discriminant analysis (ODA). Assessing covariate balance using ODA as compared with the conventional method has several key advantages: the ability to ascertain how individuals self-select based on optimal (maximum-accuracy) cut-points on the covariates; the application to any variable metric and number of groups; its insensitivity to skewed data or outliers; and the use of accuracy measures that can be widely applied to all analyses. Moreover, ODA accepts analytic weights, thereby extending the assessment of covariate balance to any study design where weights are used for covariate adjustment. By comparing the two approaches using empirical data, we are able to demonstrate that using measures of classification accuracy as balance diagnostics produces highly consistent results to those obtained via the conventional approach (in our matched-pairs example, ODA revealed a weak statistically significant relationship not detected by the conventional approach). Thus, investigators should consider ODA as a robust complement, or perhaps alternative, to the conventional approach for assessing covariate balance in matching studies. © 2016 John Wiley & Sons, Ltd.

Clinical validation of coronal and sagittal spinal curve measurements based on three-dimensional vertebra vector parameters.

PubMed

Somoskeöy, Szabolcs; Tunyogi-Csapó, Miklós; Bogyó, Csaba; Illés, Tamás

2012-10-01

For many decades, visualization and evaluation of three-dimensional (3D) spinal deformities have only been possible by two-dimensional (2D) radiodiagnostic methods, and as a result, characterization and classification were based on 2D terminologies. Recent developments in medical digital imaging and 3D visualization techniques including surface 3D reconstructions opened a chance for a long-sought change in this field. Supported by a 3D Terminology on Spinal Deformities of the Scoliosis Research Society, an approach for 3D measurements and a new 3D classification of scoliosis yielded several compelling concepts on 3D visualization and new proposals for 3D classification in recent years. More recently, a new proposal for visualization and complete 3D evaluation of the spine by 3D vertebra vectors has been introduced by our workgroup, a concept, based on EOS 2D/3D, a groundbreaking new ultralow radiation dose integrated orthopedic imaging device with sterEOS 3D spine reconstruction software. Comparison of accuracy, correlation of measurement values, intraobserver and interrater reliability of methods by conventional manual 2D and vertebra vector-based 3D measurements in a routine clinical setting. Retrospective, nonrandomized study of diagnostic X-ray images created as part of a routine clinical protocol of eligible patients examined at our clinic during a 30-month period between July 2007 and December 2009. In total, 201 individuals (170 females, 31 males; mean age, 19.88 years) including 10 healthy athletes with normal spine and patients with adolescent idiopathic scoliosis (175 cases), adult degenerative scoliosis (11 cases), and Scheuermann hyperkyphosis (5 cases). Overall range of coronal curves was between 2.4 and 117.5°. Analysis of accuracy and reliability of measurements was carried out on a group of all patients and in subgroups based on coronal plane deviation: 0 to 10° (Group 1; n=36), 10 to 25° (Group 2; n=25), 25 to 50° (Group 3; n=69), 50 to 75° (Group 4; n=49), and above 75° (Group 5; n=22). All study subjects were examined by EOS 2D imaging, resulting in anteroposterior (AP) and lateral (LAT) full spine, orthogonal digital X-ray images, in standing position. Conventional coronal and sagittal curvature measurements including sagittal L5 vertebra wedges were determined by 3 experienced examiners, using traditional Cobb methods on EOS 2D AP and LAT images. Vertebra vector-based measurements were performed as published earlier, based on computer-assisted calculations of corresponding spinal curvature. Vertebra vectors were generated by dedicated software from sterEOS 3D spine models reconstructed from EOS 2D images by the same three examiners. Manual measurements were performed by each examiner, thrice for sterEOS 3D reconstructions and twice for vertebra vector-based measurements. Means comparison t test, Pearson bivariate correlation analysis, reliability analysis by intraclass correlation coefficients for intraobserver reproducibility and interrater reliability were performed using SPSS v16.0 software. In comparison with manual 2D methods, only small and nonsignificant differences were detectable in vertebra vector-based curvature data for coronal curves and thoracic kyphosis, whereas the found difference in L1-L5 lordosis values was shown to be strongly related to the magnitude of corresponding L5 wedge. Intraobserver reliability was excellent for both methods, and interrater reproducibility was consistently higher for vertebra vector-based methods that was also found to be unaffected by the magnitude of coronal curves or sagittal plane deviations. Vertebra vector-based angulation measurements could fully substitute conventional manual 2D measurements, with similar accuracy and higher intraobserver reliability and interrater reproducibility. Vertebra vectors represent a truly 3D solution for clear and comprehensible 3D visualization of spinal deformities while preserving crucial parametric information for vertebral size, 3D position, orientation, and rotation. The concept of vertebra vectors may serve as a starting point to a valid and clinically useful alternative for a new 3D classification of scoliosis. Copyright © 2012 Elsevier Inc. All rights reserved.

Real-time classification of vehicles by type within infrared imagery

NASA Astrophysics Data System (ADS)

Kundegorski, Mikolaj E.; Akçay, Samet; Payen de La Garanderie, Grégoire; Breckon, Toby P.

2016-10-01

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios.

Metabolomic fingerprinting employing DART-TOFMS for authentication of tomatoes and peppers from organic and conventional farming.

PubMed

Novotná, H; Kmiecik, O; Gałązka, M; Krtková, V; Hurajová, A; Schulzová, V; Hallmann, E; Rembiałkowska, E; Hajšlová, J

2012-01-01

The rapidly growing demand for organic food requires the availability of analytical tools enabling their authentication. Recently, metabolomic fingerprinting/profiling has been demonstrated as a challenging option for a comprehensive characterisation of small molecules occurring in plants, since their pattern may reflect the impact of various external factors. In a two-year pilot study, concerned with the classification of organic versus conventional crops, ambient mass spectrometry consisting of a direct analysis in real time (DART) ion source and a time-of-flight mass spectrometer (TOFMS) was employed. This novel methodology was tested on 40 tomato and 24 pepper samples grown under specified conditions. To calculate statistical models, the obtained data (mass spectra) were processed by the principal component analysis (PCA) followed by linear discriminant analysis (LDA). The results from the positive ionisation mode enabled better differentiation between organic and conventional samples than the results from the negative mode. In this case, the recognition ability obtained by LDA was 97.5% for tomato and 100% for pepper samples and the prediction abilities were above 80% for both sample sets. The results suggest that the year of production had stronger influence on the metabolomic fingerprints compared with the type of farming (organic versus conventional). In any case, DART-TOFMS is a promising tool for rapid screening of samples. Establishing comprehensive (multi-sample) long-term databases may further help to improve the quality of statistical classification models.

Bayesian Network Structure Learning for Urban Land Use Classification from Landsat ETM+ and Ancillary Data

NASA Astrophysics Data System (ADS)

Park, M.; Stenstrom, M. K.

2004-12-01

Recognizing urban information from the satellite imagery is problematic due to the diverse features and dynamic changes of urban landuse. The use of Landsat imagery for urban land use classification involves inherent uncertainty due to its spatial resolution and the low separability among land uses. To resolve the uncertainty problem, we investigated the performance of Bayesian networks to classify urban land use since Bayesian networks provide a quantitative way of handling uncertainty and have been successfully used in many areas. In this study, we developed the optimized networks for urban land use classification from Landsat ETM+ images of Marina del Rey area based on USGS land cover/use classification level III. The networks started from a tree structure based on mutual information between variables and added the links to improve accuracy. This methodology offers several advantages: (1) The network structure shows the dependency relationships between variables. The class node value can be predicted even with particular band information missing due to sensor system error. The missing information can be inferred from other dependent bands. (2) The network structure provides information of variables that are important for the classification, which is not available from conventional classification methods such as neural networks and maximum likelihood classification. In our case, for example, bands 1, 5 and 6 are the most important inputs in determining the land use of each pixel. (3) The networks can be reduced with those input variables important for classification. This minimizes the problem without considering all possible variables. We also examined the effect of incorporating ancillary data: geospatial information such as X and Y coordinate values of each pixel and DEM data, and vegetation indices such as NDVI and Tasseled Cap transformation. The results showed that the locational information improved overall accuracy (81%) and kappa coefficient (76%), and lowered the omission and commission errors compared with using only spectral data (accuracy 71%, kappa coefficient 62%). Incorporating DEM data did not significantly improve overall accuracy (74%) and kappa coefficient (66%) but lowered the omission and commission errors. Incorporating NDVI did not much improve the overall accuracy (72%) and k coefficient (65%). Including Tasseled Cap transformation reduced the accuracy (accuracy 70%, kappa 61%). Therefore, additional information from the DEM and vegetation indices was not useful as locational ancillary data.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species.

PubMed

Ludeña-Choez, Jimmy; Quispe-Soncco, Raisa; Gallardo-Antolín, Ascensión

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

PubMed Central

Quispe-Soncco, Raisa

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. PMID:28628630

A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy.

PubMed

Gao, Xiang; Lin, Huaiying; Revanna, Kashi; Dong, Qunfeng

2017-05-10

Species-level classification for 16S rRNA gene sequences remains a serious challenge for microbiome researchers, because existing taxonomic classification tools for 16S rRNA gene sequences either do not provide species-level classification, or their classification results are unreliable. The unreliable results are due to the limitations in the existing methods which either lack solid probabilistic-based criteria to evaluate the confidence of their taxonomic assignments, or use nucleotide k-mer frequency as the proxy for sequence similarity measurement. We have developed a method that shows significantly improved species-level classification results over existing methods. Our method calculates true sequence similarity between query sequences and database hits using pairwise sequence alignment. Taxonomic classifications are assigned from the species to the phylum levels based on the lowest common ancestors of multiple database hits for each query sequence, and further classification reliabilities are evaluated by bootstrap confidence scores. The novelty of our method is that the contribution of each database hit to the taxonomic assignment of the query sequence is weighted by a Bayesian posterior probability based upon the degree of sequence similarity of the database hit to the query sequence. Our method does not need any training datasets specific for different taxonomic groups. Instead only a reference database is required for aligning to the query sequences, making our method easily applicable for different regions of the 16S rRNA gene or other phylogenetic marker genes. Reliable species-level classification for 16S rRNA or other phylogenetic marker genes is critical for microbiome research. Our software shows significantly higher classification accuracy than the existing tools and we provide probabilistic-based confidence scores to evaluate the reliability of our taxonomic classification assignments based on multiple database matches to query sequences. Despite its higher computational costs, our method is still suitable for analyzing large-scale microbiome datasets for practical purposes. Furthermore, our method can be applied for taxonomic classification of any phylogenetic marker gene sequences. Our software, called BLCA, is freely available at https://github.com/qunfengdong/BLCA .

Comparing K-mer based methods for improved classification of 16S sequences.

PubMed

Vinje, Hilde; Liland, Kristian Hovde; Almøy, Trygve; Snipen, Lars

2015-07-01

The need for precise and stable taxonomic classification is highly relevant in modern microbiology. Parallel to the explosion in the amount of sequence data accessible, there has also been a shift in focus for classification methods. Previously, alignment-based methods were the most applicable tools. Now, methods based on counting K-mers by sliding windows are the most interesting classification approach with respect to both speed and accuracy. Here, we present a systematic comparison on five different K-mer based classification methods for the 16S rRNA gene. The methods differ from each other both in data usage and modelling strategies. We have based our study on the commonly known and well-used naïve Bayes classifier from the RDP project, and four other methods were implemented and tested on two different data sets, on full-length sequences as well as fragments of typical read-length. The difference in classification error obtained by the methods seemed to be small, but they were stable and for both data sets tested. The Preprocessed nearest-neighbour (PLSNN) method performed best for full-length 16S rRNA sequences, significantly better than the naïve Bayes RDP method. On fragmented sequences the naïve Bayes Multinomial method performed best, significantly better than all other methods. For both data sets explored, and on both full-length and fragmented sequences, all the five methods reached an error-plateau. We conclude that no K-mer based method is universally best for classifying both full-length sequences and fragments (reads). All methods approach an error plateau indicating improved training data is needed to improve classification from here. Classification errors occur most frequent for genera with few sequences present. For improving the taxonomy and testing new classification methods, the need for a better and more universal and robust training data set is crucial.

Probing many-body localization with neural networks

NASA Astrophysics Data System (ADS)

Schindler, Frank; Regnault, Nicolas; Neupert, Titus

2017-06-01

We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime. Specifically, we study the Heisenberg spin-1/2 chain in a random external field. We employ a multilayer perceptron with a single hidden layer, which is trained on labeled entanglement spectra pertaining to the fully localized and fully thermal regimes. We then apply this network to classify spectra belonging to states in the transition region. For training, we use a cost function that contains, in addition to the usual error and regularization parts, a term that favors a confident classification of the transition region states. The resulting phase diagram is in good agreement with the one obtained by more conventional methods and can be computed for small systems. In particular, the neural network outperforms conventional methods in classifying individual eigenstates pertaining to a single disorder realization. It allows us to map out the structure of these eigenstates across the transition with spatial resolution. Furthermore, we analyze the network operation using the dreaming technique to show that the neural network correctly learns by itself the power-law structure of the entanglement spectra in the many-body localized regime.

A Novel Two-Step Hierarchial Quantitative Structure-Activity ...

EPA Pesticide Factsheets

Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC50) and in vivo rodent median lethal dose (LD50) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET ; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments) . The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC50 and LD50. However, a linear IC50 versus LD50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values: One group comprises compounds with linear IC50 versus LD50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models t

Full-polarization radar remote sensing and data mining for tropical crops mapping: a successful SVM-based classification model

NASA Astrophysics Data System (ADS)

Denize, J.; Corgne, S.; Todoroff, P.; LE Mezo, L.

2015-12-01

In Reunion, a tropical island of 2,512 km², 700 km east of Madagascar in the Indian Ocean, constrained by a rugged relief, agricultural sectors are competing in highly fragmented agricultural land constituted by heterogeneous farming systems from corporate to small-scale farming. Policymakers, planners and institutions are in dire need of reliable and updated land use references. Actually conventional land use mapping methods are inefficient under the tropic with frequent cloud cover and loosely synchronous vegetative cycles of the crops due to a constant temperature. This study aims to provide an appropriate method for the identification and mapping of tropical crops by remote sensing. For this purpose, we assess the potential of polarimetric SAR imagery associated with associated with machine learning algorithms. The method has been developed and tested on a study area of 25*25 km thanks to 6 RADARSAT-2 images in 2014 in full-polarization. A set of radar indicators (backscatter coefficient, bands ratios, indices, polarimetric decompositions (Freeman-Durden, Van zyl, Yamaguchi, Cloude and Pottier, Krogager), texture, etc.) was calculated from the coherency matrix. A random forest procedure allowed the selection of the most important variables on each images to reduce the dimension of the dataset and the processing time. Support Vector Machines (SVM), allowed the classification of these indicators based on a learning database created from field observations in 2013. The method shows an overall accuracy of 88% with a Kappa index of 0.82 for the identification of four major crops.

Some new classification methods for hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Du, Pei-jun; Chen, Yun-hao; Jones, Simon; Ferwerda, Jelle G.; Chen, Zhi-jun; Zhang, Hua-peng; Tan, Kun; Yin, Zuo-xia

2006-10-01

Hyperspectral Remote Sensing (HRS) is one of the most significant recent achievements of Earth Observation Technology. Classification is the most commonly employed processing methodology. In this paper three new hyperspectral RS image classification methods are analyzed. These methods are: Object-oriented FIRS image classification, HRS image classification based on information fusion and HSRS image classification by Back Propagation Neural Network (BPNN). OMIS FIRS image is used as the example data. Object-oriented techniques have gained popularity for RS image classification in recent years. In such method, image segmentation is used to extract the regions from the pixel information based on homogeneity criteria at first, and spectral parameters like mean vector, texture, NDVI and spatial/shape parameters like aspect ratio, convexity, solidity, roundness and orientation for each region are calculated, finally classification of the image using the region feature vectors and also using suitable classifiers such as artificial neural network (ANN). It proves that object-oriented methods can improve classification accuracy since they utilize information and features both from the point and the neighborhood, and the processing unit is a polygon (in which all pixels are homogeneous and belong to the class). HRS image classification based on information fusion, divides all bands of the image into different groups initially, and extracts features from every group according to the properties of each group. Three levels of information fusion: data level fusion, feature level fusion and decision level fusion are used to HRS image classification. Artificial Neural Network (ANN) can perform well in RS image classification. In order to promote the advances of ANN used for HIRS image classification, Back Propagation Neural Network (BPNN), the most commonly used neural network, is used to HRS image classification.

Evaluation of different distortion correction methods and interpolation techniques for an automated classification of celiac disease☆

PubMed Central

Gadermayr, M.; Liedlgruber, M.; Uhl, A.; Vécsei, A.

2013-01-01

Due to the optics used in endoscopes, a typical degradation observed in endoscopic images are barrel-type distortions. In this work we investigate the impact of methods used to correct such distortions in images on the classification accuracy in the context of automated celiac disease classification. For this purpose we compare various different distortion correction methods and apply them to endoscopic images, which are subsequently classified. Since the interpolation used in such methods is also assumed to have an influence on the resulting classification accuracies, we also investigate different interpolation methods and their impact on the classification performance. In order to be able to make solid statements about the benefit of distortion correction we use various different feature extraction methods used to obtain features for the classification. Our experiments show that it is not possible to make a clear statement about the usefulness of distortion correction methods in the context of an automated diagnosis of celiac disease. This is mainly due to the fact that an eventual benefit of distortion correction highly depends on the feature extraction method used for the classification. PMID:23981585

Consensus Classification Using Non-Optimized Classifiers.

PubMed

Brownfield, Brett; Lemos, Tony; Kalivas, John H

2018-04-03

Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.

Photometric stereo endoscopy

PubMed Central

Parot, Vicente; Lim, Daryl; González, Germán; Traverso, Giovanni; Nishioka, Norman S.; Vakoc, Benjamin J.

2013-01-01

Abstract. While color video endoscopy has enabled wide-field examination of the gastrointestinal tract, it often misses or incorrectly classifies lesions. Many of these missed lesions exhibit characteristic three-dimensional surface topographies. An endoscopic system that adds topographical measurements to conventional color imagery could therefore increase lesion detection and improve classification accuracy. We introduce photometric stereo endoscopy (PSE), a technique which allows high spatial frequency components of surface topography to be acquired simultaneously with conventional two-dimensional color imagery. We implement this technique in an endoscopic form factor and demonstrate that it can acquire the topography of small features with complex geometries and heterogeneous optical properties. PSE imaging of ex vivo human gastrointestinal tissue shows that surface topography measurements enable differentiation of abnormal shapes from surrounding normal tissue. Together, these results confirm that the topographical measurements can be obtained with relatively simple hardware in an endoscopic form factor, and suggest the potential of PSE to improve lesion detection and classification in gastrointestinal imaging. PMID:23864015

Expanded image database of pistachio x-ray images and classification by conventional methods

NASA Astrophysics Data System (ADS)

Keagy, Pamela M.; Schatzki, Thomas F.; Le, Lan Chau; Casasent, David P.; Weber, David

1996-12-01

In order to develop sorting methods for insect damaged pistachio nuts, a large data set of pistachio x-ray images (6,759 nuts) was created. Both film and linescan sensor images were acquired, nuts dissected and internal conditions coded using the U.S. Grade standards and definitions for pistachios. A subset of 1199 good and 686 insect damaged nuts was used to calculate and test discriminant functions. Statistical parameters of image histograms were evaluated for inclusion by forward stepwise discrimination. Using three variables in the discriminant function, 89% of test set nuts were correctly identified. Comparable data for 6 human subjects ranged from 67 to 92%. If the loss of good nuts is held to 1% by requiring a high probability to discard a nut as insect damaged, approximately half of the insect damage present in clean pistachio nuts may be detected and removed by x-ray inspection.

Classification of human pathogen bacteria for early screening using electronic nose

NASA Astrophysics Data System (ADS)

Zulkifli, Syahida Amani; Mohamad, Che Wan Syarifah Robiah; Abdullah, Abu Hassan

2017-10-01

This paper present human pathogen bacteria for early screening using electronic nose. Electronic nose (E-nose) known as gas sensor array is a device that analyze the odor measurement give the fast response and less time consuming for clinical diagnosis. Many bacterial pathogens could lead to life threatening infections. Accurate and rapid diagnosis is crucial for the successful management of these infections disease. The conventional method need more time to detect the growth of bacterial. Alternatively, the bacteria are Pseudomonas aeruginosa and Shigella cultured on different media agar can be detected and classifies according to the volatile compound in shorter time using electronic nose (E-nose). Then, the data from electronic nose (E-nose) is processed using statistical method which is principal component analysis (PCA). The study shows the capability of electronic nose (E-nose) for early screening for bacterial infection in human stomach.

Identification of Candida lusitaniae as an opportunistic yeast in humans.

PubMed Central

Holzschu, D L; Presley, H L; Miranda, M; Phaff, H J

1979-01-01

Four yeast strains, causally associated with infection in a patient with acute myelogenous leukemia, were identified by standard methods currently used in yeast taxonomy as representatives of Candida lusitania van Uden et do Carmo-Sousa. Because this species has not been recognized previously as an opportunistic yeast in humans, molecular taxonomic methods were applied to confirm its identity. The nuclear deoxyribonucleic acid (DNA) base composition of two clinical isolates was shown to be 45.1 mol% guanine plus cytosine as compared to 44.7 mol% guanine plus cytosine for the type strain of this species. DNA/DNA reassociation experiments revealed more than 95% complementarity between the DNAs from the clinical isolates and that of the type strain of C. lusitaniae, thus confirming their classification by conventional taxonomy. A key is provided to differentiate C. lusitaniae from two phenotypically similar Candida species. PMID:292646

Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

PubMed

Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

2014-02-01

This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening

PubMed Central

Pan, Rui; Wang, Hansheng; Li, Runze

2016-01-01

This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition. PMID:28127109

A Java-based tool for the design of classification microarrays.

PubMed

Meng, Da; Broschat, Shira L; Call, Douglas R

2008-08-04

Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images.

PubMed

Berton, Florian; Cheriet, Farida; Miron, Marie-Claude; Laporte, Catherine

2016-05-01

Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and linear discriminant analysis, each pixel of the image is classified as belonging to one of the three regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84%, 92% and 91% were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis progression. Copyright © 2016 Elsevier Ltd. All rights reserved.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

PubMed

Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-08-04

Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. ©Jose Juan Dominguez Veiga, Martin O'Reilly, Darragh Whelan, Brian Caulfield, Tomas E Ward. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.08.2017.

Cost-sensitive case-based reasoning using a genetic algorithm: application to medical diagnosis.

PubMed

Park, Yoon-Joo; Chun, Se-Hak; Kim, Byung-Chun

2011-02-01

The paper studies the new learning technique called cost-sensitive case-based reasoning (CSCBR) incorporating unequal misclassification cost into CBR model. Conventional CBR is now considered as a suitable technique for diagnosis, prognosis and prescription in medicine. However it lacks the ability to reflect asymmetric misclassification and often assumes that the cost of a positive diagnosis (an illness) as a negative one (no illness) is the same with that of the opposite situation. Thus, the objective of this research is to overcome the limitation of conventional CBR and encourage applying CBR to many real world medical cases associated with costs of asymmetric misclassification errors. The main idea involves adjusting the optimal cut-off classification point for classifying the absence or presence of diseases and the cut-off distance point for selecting optimal neighbors within search spaces based on similarity distribution. These steps are dynamically adapted to new target cases using a genetic algorithm. We apply this proposed method to five real medical datasets and compare the results with two other cost-sensitive learning methods-C5.0 and CART. Our finding shows that the total misclassification cost of CSCBR is lower than other cost-sensitive methods in many cases. Even though the genetic algorithm has limitations in terms of unstable results and over-fitting training data, CSCBR results with GA are better overall than those of other methods. Also the paired t-test results indicate that the total misclassification cost of CSCBR is significantly less than C5.0 and CART for several datasets. We have proposed a new CBR method called cost-sensitive case-based reasoning (CSCBR) that can incorporate unequal misclassification costs into CBR and optimize the number of neighbors dynamically using a genetic algorithm. It is meaningful not only for introducing the concept of cost-sensitive learning to CBR, but also for encouraging the use of CBR in the medical area. The result shows that the total misclassification costs of CSCBR do not increase in arithmetic progression as the cost of false absence increases arithmetically, thus it is cost-sensitive. We also show that total misclassification costs of CSCBR are the lowest among all methods in four datasets out of five and the result is statistically significant in many cases. The limitation of our proposed CSCBR is confined to classify binary cases for minimizing misclassification cost because our proposed CSCBR is originally designed to classify binary case. Our future work extends this method for multi-classification which can classify more than two groups. Copyright © 2010 Elsevier B.V. All rights reserved.

Ultrasonic sensor based defect detection and characterisation of ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh; Zhang, Tonzhua; Crouch, Ian

2014-01-01

Ceramic tiles, used in body armour systems, are currently inspected visually offline using an X-ray technique that is both time consuming and very expensive. The aim of this research is to develop a methodology to detect, locate and classify various manufacturing defects in Reaction Sintered Silicon Carbide (RSSC) ceramic tiles, using an ultrasonic sensing technique. Defects such as free silicon, un-sintered silicon carbide material and conventional porosity are often difficult to detect using conventional X-radiography. An alternative inspection system was developed to detect defects in ceramic components using an Artificial Neural Network (ANN) based signal processing technique. The inspection methodology proposed focuses on pre-processing of signals, de-noising, wavelet decomposition, feature extraction and post-processing of the signals for classification purposes. This research contributes to developing an on-line inspection system that would be far more cost effective than present methods and, moreover, assist manufacturers in checking the location of high density areas, defects and enable real time quality control, including the implementation of accept/reject criteria. Copyright © 2013 Elsevier B.V. All rights reserved.

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

PubMed Central

Wang, Guizhou; Liu, Jianbo; He, Guojin

2013-01-01

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy. PMID:24453808

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

Neural network approaches versus statistical methods in classification of multisource remote sensing data

NASA Technical Reports Server (NTRS)

Benediktsson, Jon A.; Swain, Philip H.; Ersoy, Okan K.

1990-01-01

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

Ankle impingement syndromes: an imaging review

PubMed Central

Tafur, Monica; Ahmed, Sonya S; Huang, Brady K; Chang, Eric Y

2017-01-01

Ankle impingement syndromes encompass a broad spectrum of post-traumatic and chronic degenerative changes that present with pain on specific movements about the ankle joint. Both amateur and professional athletes are disproportionately affected by these conditions, and while conservative measures can potentially treat an impingement syndrome, definitive therapy is often alleviated surgically. Imaging (including conventional radiography, ultrasound, CT and MRI) plays an invaluable role in the diagnosis and pre-surgical work-up. An anatomically based classification system is useful in these syndromes, as the aetiology, sites of pathology and preferred treatment methods are similarly based on anatomic locations about the ankle. This review focuses on the anatomic locations, pathophysiology, imaging considerations and brief discussion of therapies for each of the major anatomic ankle impingement syndromes. PMID:27885856

Computer aided exercise electrocardiographic testing and coronary arteriography in patients with angina pectoris and with myocardial infarction.

PubMed Central

Angelhed, J E; Bjurö, T I; Ejdebäck, J; Selin, K; Schlossman, D; Griffith, L S; Bergstrand, R; Vedin, A; Wilhelmsson, C

1984-01-01

A set of electrocardiographic criteria for the diagnosis of coronary artery disease was evaluated in two different groups of patients examined by computer aided 12 lead exercise electrocardiographic stress testing and coronary arteriography. One group consisted of patients with severe angina pectoris and the other of patients who had suffered a myocardial infarction three years before the study. Angiographically determined categories of patients could be identified with satisfactory precision by the electrocardiographic criteria under test in the patients with angina pectoris but not in those with infarction. A new method of classifying patients on the basis of data from coronary arteriography improved the correlation with ST segment analysis compared with conventional classification. PMID:6743432

A comparison of digital multi-spectral imagery versus conventional photography for mapping seagrass in Indian River Lagoon, Florida

DOE Office of Scientific and Technical Information (OSTI.GOV)

Virnstein, R.; Tepera, M.; Beazley, L.

1997-06-01

A pilot study is very briefly summarized in the article. The study tested the potential of multi-spectral digital imagery for discrimination of seagrass densities and species, algae, and bottom types. Imagery was obtained with the Compact Airborne Spectral Imager (casi) and two flight lines flown with hyper-spectral mode. The photogrammetric method used allowed interpretation of the highest quality product, eliminating limitations caused by outdated or poor quality base maps and the errors associated with transfer of polygons. Initial image analysis indicates that the multi-spectral imagery has several advantages, including sophisticated spectral signature recognition and classification, ease of geo-referencing, and rapidmore » mosaicking.« less

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

PubMed

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

PubMed Central

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method. PMID:24820966

Average Likelihood Methods of Classification of Code Division Multiple Access (CDMA)

DTIC Science & Technology

2016-05-01

case of cognitive radio applications. Modulation classification is part of a broader problem known as blind or uncooperative demodulation the goal of...Introduction 2 2.1 Modulation Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Research Objectives...6 3 Modulation Classification Methods 7 3.0.1 Ad Hoc

Fouling on ion-exchange membranes: Classification, characterization and strategies of prevention and control.

PubMed

Mikhaylin, Sergey; Bazinet, Laurent

2016-03-01

The environmentally friendly ion-exchange membrane (IEM) processes find more and more applications in the modern industries in order to demineralize, concentrate and modify products. Moreover, these processes may be applied for the energy conversion and storage. However, the main drawback of the IEM processes is a formation of fouling, which significantly decreases the process efficiency and increases the process cost. The present review is dedicated to the problematic of IEM fouling phenomena. Firstly, the major types of IEM fouling such as colloidal fouling, organic fouling, scaling and biofouling are discussed along with consideration of the main factors affecting fouling formation and development. Secondly, the review of the possible methods of IEM fouling characterization is provided. This section includes the methods of fouling visualization and characterization as well as methods allowing investigations of characteristics of the fouled IEMs. Eventually, the reader will find the conventional and modern strategies of prevention and control of different fouling types. Copyright © 2015 Elsevier B.V. All rights reserved.

Vibrational biospectroscopy: from plants to animals to humans. A historical perspective

NASA Astrophysics Data System (ADS)

Shaw, R. Anthony; Mantsch, Henry H.

1999-05-01

Today, more than ever, vibrational spectroscopy means different things to different people. From their roots as molecular fingerprinting techniques, both infrared and Raman spectroscopy have evolved to the point where they play roles in a staggering variety of scientific endeavors. This survey focuses upon biological and medical applications. The past 40 years have witnessed enormous advances in our understanding of the building blocks of life, and vibrational spectroscopy has played an important role. That role is reviewed briefly here. In parallel with these efforts, the near-IR community developed powerful 'chemometric' methods to extract a wealth of information from spectra that appeared superficially featureless. As vibrational spectroscopy is finding new niches in the medical and clinical realms, these chemometric methods are proving to be a valuable (but not infallible!) adjunct to conventional spectral interpretation. This survey includes a brief outline of biomedical vibrational spectroscopy and imaging, including several representative examples to illustrate the strengths and pitfalls of a growing reliance upon multivariate quantitation and classification methods.

EEG character identification using stimulus sequences designed to maximize mimimal hamming distance.

PubMed

Fukami, Tadanori; Shimada, Takamasa; Forney, Elliott; Anderson, Charles W

2012-01-01

In this study, we have improved upon the P300 speller Brain-Computer Interface paradigm by introducing a new character encoding method. Our concept in detection of the intended character is not based on a classification of target and nontarget responses, but based on an identifaction of the character which maximize the difference between P300 amplitudes in target and nontarget stimuli. Each bit included in the code corresponds to flashing character, '1', and non-flashing, '0'. Here, the codes were constructed in order to maximize the minimum hamming distance between the characters. Electroencephalography was used to identify the characters using a waveform calculated by adding and subtracting the response of the target and non-target stimulus according the codes respectively. This stimulus presentation method was applied to a 3×3 character matrix, and the results were compared with that of a conventional P300 speller of the same size. Our method reduced the time until the correct character was obtained by 24%.

An information-based network approach for protein classification

PubMed Central

Wan, Xiaogeng; Zhao, Xin; Yau, Stephen S. T.

2017-01-01

Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method. PMID:28350835

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

PubMed

Akbar, Shahid; Hayat, Maqsood; Iqbal, Muhammad; Jan, Mian Ahmad

2017-06-01

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Satellite altimetry in sea ice regions - detecting open water for estimating sea surface heights

NASA Astrophysics Data System (ADS)

Müller, Felix L.; Dettmering, Denise; Bosch, Wolfgang

2017-04-01

The Greenland Sea and the Farm Strait are transporting sea ice from the central Arctic ocean southwards. They are covered by a dynamic changing sea ice layer with significant influences on the Earth climate system. Between the sea ice there exist various sized open water areas known as leads, straight lined open water areas, and polynyas exhibiting a circular shape. Identifying these leads by satellite altimetry enables the extraction of sea surface height information. Analyzing the radar echoes, also called waveforms, provides information on the surface backscatter characteristics. For example waveforms reflected by calm water have a very narrow and single-peaked shape. Waveforms reflected by sea ice show more variability due to diffuse scattering. Here we analyze altimeter waveforms from different conventional pulse-limited satellite altimeters to separate open water and sea ice waveforms. An unsupervised classification approach employing partitional clustering algorithms such as K-medoids and memory-based classification methods such as K-nearest neighbor is used. The classification is based on six parameters derived from the waveform's shape, for example the maximum power or the peak's width. The open-water detection is quantitatively compared to SAR images processed while accounting for sea ice motion. The classification results are used to derive information about the temporal evolution of sea ice extent and sea surface heights. They allow to provide evidence on climate change relevant influences as for example Arctic sea level rise due to enhanced melting rates of Greenland's glaciers and an increasing fresh water influx into the Arctic ocean. Additionally, the sea ice cover extent analyzed over a long-time period provides an important indicator for a globally changing climate system.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

PubMed Central

ChariDingari, Narahara; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real world applications, e.g. quality assurance and process monitoring. Specifically, variability in sample, system and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a non-linear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), due to its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data – highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples as well as in related areas of forensic and biological sample analysis. PMID:22292496

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability.

PubMed

Dingari, Narahara Chari; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P; Kumar Gundawar, Manoj

2012-03-20

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Ensemble Sparse Classification of Alzheimer’s Disease

PubMed Central

Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

2012-01-01

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

Hierarchic Agglomerative Clustering Methods for Automatic Document Classification.

ERIC Educational Resources Information Center

Griffiths, Alan; And Others

1984-01-01

Considers classifications produced by application of single linkage, complete linkage, group average, and word clustering methods to Keen and Cranfield document test collections, and studies structure of hierarchies produced, extent to which methods distort input similarity matrices during classification generation, and retrieval effectiveness…

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

PubMed

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

A matched comparison of perioperative outcomes of a single laparoscopic surgeon versus a multisurgeon robot-assisted cohort for partial nephrectomy.

PubMed

Ellison, Jonathan S; Montgomery, Jeffrey S; Wolf, J Stuart; Hafez, Khaled S; Miller, David C; Weizer, Alon Z

2012-07-01

Minimally invasive nephron sparing surgery is gaining popularity for small renal masses. Few groups have evaluated robot-assisted partial nephrectomy compared to other approaches using comparable patient populations. We present a matched pair analysis of a heterogeneous group of surgeons who performed robot-assisted partial nephrectomy and a single experienced laparoscopic surgeon who performed conventional laparoscopic partial nephrectomy. Perioperative outcomes and complications were compared. All 249 conventional laparoscopic and robot-assisted partial nephrectomy cases from January 2007 to June 2010 were reviewed from our prospectively maintained institutional database. Groups were matched 1:1 (108 matched pairs) by R.E.N.A.L. (radius, exophytic/endophytic properties, nearness of tumor to collecting system or sinus, anterior/posterior, location relative to polar lines) nephrometry score, transperitoneal vs retroperitoneal approach, patient age and hilar nature of the tumor. Statistical analysis was done to compare operative outcomes and complications. Matched analysis revealed that nephrometry score, age, gender, tumor side and American Society of Anesthesia physical status classification were similar. Operative time favored conventional laparoscopic partial nephrectomy. During the study period robot-assisted partial nephrectomy showed significant improvements in estimated blood loss and warm ischemia time compared to those of the experienced conventional laparoscopic group. Postoperative complication rates, and complication distributions by Clavien classification and type were similar for conventional laparoscopic and robot-assisted partial nephrectomy (41.7% and 35.0%, respectively). Robot-assisted partial nephrectomy has a noticeable but rapid learning curve. After it is overcome the robotic procedure results in perioperative outcomes similar to those achieved with conventional laparoscopic partial nephrectomy done by an experienced surgeon. Robot-assisted partial nephrectomy likely improves surgeon and patient accessibility to minimally invasive nephron sparing surgery. Copyright © 2012 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Commentary on "a matched comparison of perioperative outcomes of a single laparoscopic surgeon versus a multisurgeon robot-assisted cohort for partial nephrectomy." Ellison JS, Montgomery JS, Wolf Jr JS, Hafez KS, Miller DC, Weizer AZ, Department of Urology, University of Michigan, Ann Arbor, MI, USA: J Urol 2012;188(1):45-50.

PubMed

Kane, Christopher

2013-02-01

Minimally invasive nephron sparing surgery is gaining popularity for small renal masses. Few groups have evaluated robot-assisted partial nephrectomy compared to other approaches using comparable patient populations. We present a matched pair analysis of a heterogeneous group of surgeons who performed robot-assisted partial nephrectomy and a single experienced laparoscopic surgeon who performed conventional laparoscopic partial nephrectomy. Perioperative outcomes and complications were compared. All 249 conventional laparoscopic and robot-assisted partial nephrectomy cases from January 2007 to June 2010 were reviewed from our prospectively maintained institutional database. Groups were matched 1:1 (108 matched pairs) by R.E.N.A.L. (radius, exophytic/endophytic properties, nearness of tumor to collecting system or sinus, anterior/posterior, location relative to polar lines) nephrometry score, transperitoneal vs retroperitoneal approach, patient age and hilar nature of the tumor. Statistical analysis was done to compare operative outcomes and complications. Matched analysis revealed that nephrometry score, age, gender, tumor side and American Society of Anesthesia physical status classification were similar. Operative time favored conventional laparoscopic partial nephrectomy. During the study period robot-assisted partial nephrectomy showed significant improvements in estimated blood loss and warm ischemia time compared to those of the experienced conventional laparoscopic group. Postoperative complication rates, and complication distributions by Clavien classification and type were similar for conventional laparoscopic and robot-assisted partial nephrectomy (41.7% and 35.0%, respectively). Robot-assisted partial nephrectomy has a noticeable but rapid learning curve. After it is overcome the robotic procedure results in perioperative outcomes similar to those achieved with conventional laparoscopic partial nephrectomy done by an experienced surgeon. Robot-assisted partial nephrectomy likely improves surgeon and patient accessibility to minimally invasive nephron sparing surgery. Copyright © 2013 Elsevier Inc. All rights reserved.

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

Behavior Based Social Dimensions Extraction for Multi-Label Classification

PubMed Central

Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin

2016-01-01

Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849

Site Classification using Multichannel Channel Analysis of Surface Wave (MASW) method on Soft and Hard Ground

NASA Astrophysics Data System (ADS)

Ashraf, M. A. M.; Kumar, N. S.; Yusoh, R.; Hazreek, Z. A. M.; Aziman, M.

2018-04-01

Site classification utilizing average shear wave velocity (Vs(30) up to 30 meters depth is a typical parameter. Numerous geophysical methods have been proposed for estimation of shear wave velocity by utilizing assortment of testing configuration, processing method, and inversion algorithm. Multichannel Analysis of Surface Wave (MASW) method is been rehearsed by numerous specialist and professional to geotechnical engineering for local site characterization and classification. This study aims to determine the site classification on soft and hard ground using MASW method. The subsurface classification was made utilizing National Earthquake Hazards Reduction Program (NERHP) and international Building Code (IBC) classification. Two sites are chosen to acquire the shear wave velocity which is in the state of Pulau Pinang for soft soil and Perlis for hard rock. Results recommend that MASW technique can be utilized to spatially calculate the distribution of shear wave velocity (Vs(30)) in soil and rock to characterize areas.

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

Improved wetland classification using eight-band high-resolution satellite imagery and a hybrid approach

EPA Science Inventory

Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of derived wetland maps were limited or often unsatisfactory largely due to the relatively coarse spatial resolution of conventional satellite imagery. This re...

Disability, Disorder, and Identity

ERIC Educational Resources Information Center

Wehmeyer, Michael L.

2013-01-01

The World Health Organization's "International Classification of Diseases" ("ICD") is the most important diagnostic tool, worldwide, to ensure that people with intellectual and developmental disabilities receive the supports they need to live richer, fuller lives. And yet, the "ICD" has naming conventions that create a conundrum for the field,…

Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images.

PubMed

Quirós, Elia; Felicísimo, Angel M; Cuartero, Aurora

2009-01-01

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

Evaluation of gene expression classification studies: factors associated with classification performance.

PubMed

Novianti, Putri W; Roes, Kit C B; Eijkemans, Marinus J C

2014-01-01

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.

PubMed

Algamal, Z Y; Lee, M H

2017-01-01

A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

PubMed

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-12-16

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.