Morphological Feature Extraction for Automatic Registration of Multispectral Images

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2007-01-01

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

Low-power coprocessor for Haar-like feature extraction with pixel-based pipelined architecture

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Fujita, Yuki; Zhang, Xiangyu; Chen, Lei; Jürgen Mattausch, Hans

2017-04-01

Intelligent analysis of image and video data requires image-feature extraction as an important processing capability for machine-vision realization. A coprocessor with pixel-based pipeline (CFEPP) architecture is developed for real-time Haar-like cell-based feature extraction. Synchronization with the image sensor’s pixel frequency and immediate usage of each input pixel for the feature-construction process avoids the dependence on memory-intensive conventional strategies like integral-image construction or frame buffers. One 180 nm CMOS prototype can extract the 1680-dimensional Haar-like feature vectors, applied in the speeded up robust features (SURF) scheme, using an on-chip memory of only 96 kb (kilobit). Additionally, a low power dissipation of only 43.45 mW at 1.8 V supply voltage is achieved during VGA video procession at 120 MHz frequency with more than 325 fps. The Haar-like feature-extraction coprocessor is further evaluated by the practical application of vehicle recognition, achieving the expected high accuracy which is comparable to previous work.

Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

2018-02-01

This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.

PubMed

Hwang, Wonjun; Wang, Haitao; Kim, Hyunwoo; Kee, Seok-Cheol; Kim, Junmo

2011-04-01

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.

A multistage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on CT images: preliminary investigation.

PubMed

Park, Sang Cheol; Chapman, Brian E; Zheng, Bin

2011-06-01

This study developed a computer-aided detection (CAD) scheme for pulmonary embolism (PE) detection and investigated several approaches to improve CAD performance. In the study, 20 computed tomography examinations with various lung diseases were selected, which include 44 verified PE lesions. The proposed CAD scheme consists of five basic steps: 1) lung segmentation; 2) PE candidate extraction using an intensity mask and tobogganing region growing; 3) PE candidate feature extraction; 4) false-positive (FP) reduction using an artificial neural network (ANN); and 5) a multifeature-based k-nearest neighbor for positive/negative classification. In this study, we also investigated the following additional methods to improve CAD performance: 1) grouping 2-D detected features into a single 3-D object; 2) selecting features with a genetic algorithm (GA); and 3) limiting the number of allowed suspicious lesions to be cued in one examination. The results showed that 1) CAD scheme using tobogganing, an ANN, and grouping method achieved the maximum detection sensitivity of 79.2%; 2) the maximum scoring method achieved the superior performance over other scoring fusion methods; 3) GA was able to delete "redundant" features and further improve CAD performance; and 4) limiting the maximum number of cued lesions in an examination reduced FP rate by 5.3 times. Combining these approaches, CAD scheme achieved 63.2% detection sensitivity with 18.4 FP lesions per examination. The study suggested that performance of CAD schemes for PE detection depends on many factors that include 1) optimizing the 2-D region grouping and scoring methods; 2) selecting the optimal feature set; and 3) limiting the number of allowed cueing lesions per examination.

Comparison of wavelet based denoising schemes for gear condition monitoring: An Artificial Neural Network based Approach

NASA Astrophysics Data System (ADS)

Ahmed, Rounaq; Srinivasa Pai, P.; Sriram, N. S.; Bhat, Vasudeva

2018-02-01

Vibration Analysis has been extensively used in recent past for gear fault diagnosis. The vibration signals extracted is usually contaminated with noise and may lead to wrong interpretation of results. The denoising of extracted vibration signals helps the fault diagnosis by giving meaningful results. Wavelet Transform (WT) increases signal to noise ratio (SNR), reduces root mean square error (RMSE) and is effective to denoise the gear vibration signals. The extracted signals have to be denoised by selecting a proper denoising scheme in order to prevent the loss of signal information along with noise. An approach has been made in this work to show the effectiveness of Principal Component Analysis (PCA) to denoise gear vibration signal. In this regard three selected wavelet based denoising schemes namely PCA, Empirical Mode Decomposition (EMD), Neighcoeff Coefficient (NC), has been compared with Adaptive Threshold (AT) an extensively used wavelet based denoising scheme for gear vibration signal. The vibration signals acquired from a customized gear test rig were denoised by above mentioned four denoising schemes. The fault identification capability as well as SNR, Kurtosis and RMSE for the four denoising schemes have been compared. Features extracted from the denoised signals have been used to train and test artificial neural network (ANN) models. The performances of the four denoising schemes have been evaluated based on the performance of the ANN models. The best denoising scheme has been identified, based on the classification accuracy results. PCA is effective in all the regards as a best denoising scheme.

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

PubMed Central

Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

2012-01-01

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.

PubMed

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

2017-03-01

Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge. In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely - Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors' knowledge. The proposed feature extraction scheme's performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature. The performance of the proposed method is comparable or better than that of the state-of-the-art ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM. Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery

NASA Astrophysics Data System (ADS)

Wang, Ke; Guo, Ping; Luo, A.-Li

2017-03-01

Spectral feature extraction is a crucial procedure in automated spectral analysis. This procedure starts from the spectral data and produces informative and non-redundant features, facilitating the subsequent automated processing and analysis with machine-learning and data-mining techniques. In this paper, we present a new automated feature extraction method for astronomical spectra, with application in spectral classification and defective spectra recovery. The basic idea of our approach is to train a deep neural network to extract features of spectra with different levels of abstraction in different layers. The deep neural network is trained with a fast layer-wise learning algorithm in an analytical way without any iterative optimization procedure. We evaluate the performance of the proposed scheme on real-world spectral data. The results demonstrate that our method is superior regarding its comprehensive performance, and the computational cost is significantly lower than that for other methods. The proposed method can be regarded as a new valid alternative general-purpose feature extraction method for various tasks in spectral data analysis.

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

Liu, Richen; Guo, Hanqi; Yuan, Xiaoru

Most of the existing approaches to visualize vector field ensembles are to reveal the uncertainty of individual variables, for example, statistics, variability, etc. However, a user-defined derived feature like vortex or air mass is also quite significant, since they make more sense to domain scientists. In this paper, we present a new framework to extract user-defined derived features from different simulation runs. Specially, we use a detail-to-overview searching scheme to help extract vortex with a user-defined shape. We further compute the geometry information including the size, the geo-spatial location of the extracted vortexes. We also design some linked views tomore » compare them between different runs. At last, the temporal information such as the occurrence time of the feature is further estimated and compared. Results show that our method is capable of extracting the features across different runs and comparing them spatially and temporally.« less

Face-iris multimodal biometric scheme based on feature level fusion

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

LWT Based Sensor Node Signal Processing in Vehicle Surveillance Distributed Sensor Network

NASA Astrophysics Data System (ADS)

Cha, Daehyun; Hwang, Chansik

Previous vehicle surveillance researches on distributed sensor network focused on overcoming power limitation and communication bandwidth constraints in sensor node. In spite of this constraints, vehicle surveillance sensor node must have signal compression, feature extraction, target localization, noise cancellation and collaborative signal processing with low computation and communication energy dissipation. In this paper, we introduce an algorithm for light-weight wireless sensor node signal processing based on lifting scheme wavelet analysis feature extraction in distributed sensor network.

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PubMed

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Investigation of a Plasma Edge Cathode Under High Current Density Electron Extraction

DTIC Science & Technology

1991-12-05

simu- lation using the MAGIC code confirmed the expected features of the scheme. SLTMMARY .. . . . . . . . . . .. . . . . . . . . . . 1 I...description. An electron temperature of 1 eV was mea- sured in the extraction region without extraction turned on. The plasma from the plasma gun was...jet is reduced if the time between shots is reduced to below I min. The numerical simulation with MAGIC gave confirming results. The simulated current

Coding visual features extracted from video sequences.

PubMed

Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2014-05-01

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering.

PubMed

Rodríguez-Sotelo, J L; Peluffo-Ordoñez, D; Cuesta-Frau, D; Castellanos-Domínguez, G

2012-10-01

The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Average combination difference morphological filters for fault feature extraction of bearing

NASA Astrophysics Data System (ADS)

Lv, Jingxiang; Yu, Jianbo

2018-02-01

In order to extract impulse components from vibration signals with much noise and harmonics, a new morphological filter called average combination difference morphological filter (ACDIF) is proposed in this paper. ACDIF constructs firstly several new combination difference (CDIF) operators, and then integrates the best two CDIFs as the final morphological filter. This design scheme enables ACIDF to extract positive and negative impacts existing in vibration signals to enhance accuracy of bearing fault diagnosis. The length of structure element (SE) that affects the performance of ACDIF is determined adaptively by a new indicator called Teager energy kurtosis (TEK). TEK further improves the effectiveness of ACDIF for fault feature extraction. Experimental results on the simulation and bearing vibration signals demonstrate that ACDIF can effectively suppress noise and extract periodic impulses from bearing vibration signals.

Adaptive video-based vehicle classification technique for monitoring traffic.

DOT National Transportation Integrated Search

2015-08-01

This report presents a methodology for extracting two vehicle features, vehicle length and number of axles in order : to classify the vehicles from video, based on Federal Highway Administration (FHWA)s recommended vehicle : classification scheme....

Surface EMG signals based motion intent recognition using multi-layer ELM

NASA Astrophysics Data System (ADS)

Wang, Jianhui; Qi, Lin; Wang, Xiao

2017-11-01

The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human's intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.

Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

A Fingerprint Encryption Scheme Based on Irreversible Function and Secure Authentication

PubMed Central

Yu, Jianping; Zhang, Peng; Wang, Shulan

2015-01-01

A fingerprint encryption scheme based on irreversible function has been designed in this paper. Since the fingerprint template includes almost the entire information of users' fingerprints, the personal authentication can be determined only by the fingerprint features. This paper proposes an irreversible transforming function (using the improved SHA1 algorithm) to transform the original minutiae which are extracted from the thinned fingerprint image. Then, Chinese remainder theorem is used to obtain the biokey from the integration of the transformed minutiae and the private key. The result shows that the scheme has better performance on security and efficiency comparing with other irreversible function schemes. PMID:25873989

A fingerprint encryption scheme based on irreversible function and secure authentication.

PubMed

Yang, Yijun; Yu, Jianping; Zhang, Peng; Wang, Shulan

2015-01-01

A fingerprint encryption scheme based on irreversible function has been designed in this paper. Since the fingerprint template includes almost the entire information of users' fingerprints, the personal authentication can be determined only by the fingerprint features. This paper proposes an irreversible transforming function (using the improved SHA1 algorithm) to transform the original minutiae which are extracted from the thinned fingerprint image. Then, Chinese remainder theorem is used to obtain the biokey from the integration of the transformed minutiae and the private key. The result shows that the scheme has better performance on security and efficiency comparing with other irreversible function schemes.

A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.

PubMed

Wang, Huafeng; Zhao, Tingting; Li, Lihong Connie; Pan, Haixia; Liu, Wanquan; Gao, Haoqi; Han, Fangfang; Wang, Yuehai; Qi, Yifan; Liang, Zhengrong

2018-01-01

The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702.

Efficient moving target analysis for inverse synthetic aperture radar images via joint speeded-up robust features and regular moment

NASA Astrophysics Data System (ADS)

Yang, Hongxin; Su, Fulin

2018-01-01

We propose a moving target analysis algorithm using speeded-up robust features (SURF) and regular moment in inverse synthetic aperture radar (ISAR) image sequences. In our study, we first extract interest points from ISAR image sequences by SURF. Different from traditional feature point extraction methods, SURF-based feature points are invariant to scattering intensity, target rotation, and image size. Then, we employ a bilateral feature registering model to match these feature points. The feature registering scheme can not only search the isotropic feature points to link the image sequences but also reduce the error matching pairs. After that, the target centroid is detected by regular moment. Consequently, a cost function based on correlation coefficient is adopted to analyze the motion information. Experimental results based on simulated and real data validate the effectiveness and practicability of the proposed method.

Hierarchical content-based image retrieval by dynamic indexing and guided search

NASA Astrophysics Data System (ADS)

You, Jane; Cheung, King H.; Liu, James; Guo, Linong

2003-12-01

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

Effective traffic features selection algorithm for cyber-attacks samples

NASA Astrophysics Data System (ADS)

Li, Yihong; Liu, Fangzheng; Du, Zhenyu

2018-05-01

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

Speech sound classification and detection of articulation disorders with support vector machines and wavelets.

PubMed

Georgoulas, George; Georgopoulos, Voula C; Stylios, Chrysostomos D

2006-01-01

This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented. The use of support vector machines (SVMs), for the classification of speech sounds and detection of articulation disorders is introduced. The proposed method is implemented on a data set where different sets of features and different schemes of SVMs are tested leading to satisfactory performance.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

PubMed

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

PubMed Central

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

Significance of MPEG-7 textural features for improved mass detection in mammography.

PubMed

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

PubMed

Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S

2007-09-01

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.

Stationary Wavelet Transform and AdaBoost with SVM Based Pathological Brain Detection in MRI Scanning.

PubMed

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

2017-01-01

This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the diseased region in brain MR images. Two-dimensional stationary wavelet transform is harnessed to extract features from the preprocessed images. The feature vector is constructed using the energy and entropy values, computed from the level- 2 SWT coefficients. Then, the relevant and uncorrelated features are selected using symmetric uncertainty ranking filter. Subsequently, the selected features are given input to the proposed AdaBoost with support vector machine classifier, where SVM is used as the base classifier of AdaBoost algorithm. To validate the proposed system, three standard MR image datasets, Dataset-66, Dataset-160, and Dataset- 255 have been utilized. The 5 runs of k-fold stratified cross validation results indicate the suggested scheme offers better performance than other existing schemes in terms of accuracy and number of features. The proposed system earns ideal classification over Dataset-66 and Dataset-160; whereas, for Dataset- 255, an accuracy of 99.45% is achieved. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Predominant information quality scheme for the essential amino acids: an information-theoretical analysis.

PubMed

Esquivel, Rodolfo O; Molina-Espíritu, Moyocoyani; López-Rosa, Sheila; Soriano-Correa, Catalina; Barrientos-Salcedo, Carolina; Kohout, Miroslav; Dehesa, Jesús S

2015-08-24

In this work we undertake a pioneer information-theoretical analysis of 18 selected amino acids extracted from a natural protein, bacteriorhodopsin (1C3W). The conformational structures of each amino acid are analyzed by use of various quantum chemistry methodologies at high levels of theory: HF, M062X and CISD(Full). The Shannon entropy, Fisher information and disequilibrium are determined to grasp the spatial spreading features of delocalizability, order and uniformity of the optimized structures. These three entropic measures uniquely characterize all amino acids through a predominant information-theoretic quality scheme (PIQS), which gathers all chemical families by means of three major spreading features: delocalization, narrowness and uniformity. This scheme recognizes four major chemical families: aliphatic (delocalized), aromatic (delocalized), electro-attractive (narrowed) and tiny (uniform). All chemical families recognized by the existing energy-based classifications are embraced by this entropic scheme. Finally, novel chemical patterns are shown in the information planes associated with the PIQS entropic measures. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

A triangle voting algorithm based on double feature constraints for star sensors

NASA Astrophysics Data System (ADS)

Fan, Qiaoyun; Zhong, Xuyang

2018-02-01

A novel autonomous star identification algorithm is presented in this study. In the proposed algorithm, each sensor star constructs multi-triangle with its bright neighbor stars and obtains its candidates by triangle voting process, in which the triangle is considered as the basic voting element. In order to accelerate the speed of this algorithm and reduce the required memory for star database, feature extraction is carried out to reduce the dimension of triangles and each triangle is described by its base and height. During the identification period, the voting scheme based on double feature constraints is proposed to implement triangle voting. This scheme guarantees that only the catalog star satisfying two features can vote for the sensor star, which improves the robustness towards false stars. The simulation and real star image test demonstrate that compared with the other two algorithms, the proposed algorithm is more robust towards position noise, magnitude noise and false stars.

Improved initial guess with semi-subpixel level accuracy in digital image correlation by feature-based method

NASA Astrophysics Data System (ADS)

Zhang, Yunlu; Yan, Lei; Liou, Frank

2018-05-01

The quality initial guess of deformation parameters in digital image correlation (DIC) has a serious impact on convergence, robustness, and efficiency of the following subpixel level searching stage. In this work, an improved feature-based initial guess (FB-IG) scheme is presented to provide initial guess for points of interest (POIs) inside a large region. Oriented FAST and Rotated BRIEF (ORB) features are semi-uniformly extracted from the region of interest (ROI) and matched to provide initial deformation information. False matched pairs are eliminated by the novel feature guided Gaussian mixture model (FG-GMM) point set registration algorithm, and nonuniform deformation parameters of the versatile reproducing kernel Hilbert space (RKHS) function are calculated simultaneously. Validations on simulated images and real-world mini tensile test verify that this scheme can robustly and accurately compute initial guesses with semi-subpixel level accuracy in cases with small or large translation, deformation, or rotation.

Competitive region orientation code for palmprint verification and identification

NASA Astrophysics Data System (ADS)

Tang, Wenliang

2015-11-01

Orientation features of the palmprint have been widely investigated in coding-based palmprint-recognition methods. Conventional orientation-based coding methods usually used discrete filters to extract the orientation feature of palmprint. However, in real operations, the orientations of the filter usually are not consistent with the lines of the palmprint. We thus propose a competitive region orientation-based coding method. Furthermore, an effective weighted balance scheme is proposed to improve the accuracy of the extracted region orientation. Compared with conventional methods, the region orientation of the palmprint extracted using the proposed method can precisely and robustly describe the orientation feature of the palmprint. Extensive experiments on the baseline PolyU and multispectral palmprint databases are performed and the results show that the proposed method achieves a promising performance in comparison to conventional state-of-the-art orientation-based coding methods in both palmprint verification and identification.

Statistical Approach To Extraction Of Texture In SAR

NASA Technical Reports Server (NTRS)

Rignot, Eric J.; Kwok, Ronald

1992-01-01

Improved statistical method of extraction of textural features in synthetic-aperture-radar (SAR) images takes account of effects of scheme used to sample raw SAR data, system noise, resolution of radar equipment, and speckle. Treatment of speckle incorporated into overall statistical treatment of speckle, system noise, and natural variations in texture. One computes speckle auto-correlation function from system transfer function that expresses effect of radar aperature and incorporates range and azimuth resolutions.

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

NASA Astrophysics Data System (ADS)

He, Wei; He, Yigang; Luo, Qiwu; Zhang, Chaolong

2018-04-01

This paper proposes a novel scheme for analog circuit fault diagnosis utilizing features extracted from the time-frequency representations of signals and an improved vector-valued regularized kernel function approximation (VVRKFA). First, the cross-wavelet transform is employed to yield the energy-phase distribution of the fault signals over the time and frequency domain. Since the distribution is high-dimensional, a supervised dimensionality reduction technique—the bilateral 2D linear discriminant analysis—is applied to build a concise feature set from the distributions. Finally, VVRKFA is utilized to locate the fault. In order to improve the classification performance, the quantum-behaved particle swarm optimization technique is employed to gradually tune the learning parameter of the VVRKFA classifier. The experimental results for the analog circuit faults classification have demonstrated that the proposed diagnosis scheme has an advantage over other approaches.

Comparison of two schemes for automatic keyword extraction from MEDLINE for functional gene clustering.

PubMed

Liu, Ying; Ciliax, Brian J; Borges, Karin; Dasigi, Venu; Ram, Ashwin; Navathe, Shamkant B; Dingledine, Ray

2004-01-01

One of the key challenges of microarray studies is to derive biological insights from the unprecedented quatities of data on gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the nature of the functional links among genes within the derived clusters. However, the quality of the keyword lists extracted from biomedical literature for each gene significantly affects the clustering results. We extracted keywords from MEDLINE that describes the most prominent functions of the genes, and used the resulting weights of the keywords as feature vectors for gene clustering. By analyzing the resulting cluster quality, we compared two keyword weighting schemes: normalized z-score and term frequency-inverse document frequency (TFIDF). The best combination of background comparison set, stop list and stemming algorithm was selected based on precision and recall metrics. In a test set of four known gene groups, a hierarchical algorithm correctly assigned 25 of 26 genes to the appropriate clusters based on keywords extracted by the TDFIDF weighting scheme, but only 23 og 26 with the z-score method. To evaluate the effectiveness of the weighting schemes for keyword extraction for gene clusters from microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle were used as a second test set. Using established measures of cluster quality, the results produced from TFIDF-weighted keywords had higher purity, lower entropy, and higher mutual information than those produced from normalized z-score weighted keywords. The optimized algorithms should be useful for sorting genes from microarray lists into functionally discrete clusters.

BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

NASA Astrophysics Data System (ADS)

Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

2006-01-01

Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

Fused man-machine classification schemes to enhance diagnosis of breast microcalcifications

NASA Astrophysics Data System (ADS)

Andreadis, Ioannis; Sevastianos, Chatzistergos; George, Spyrou; Konstantina, Nikita

2017-11-01

Computer aided diagnosis (CAD x ) approaches are developed towards the effective discrimination between benign and malignant clusters of microcalcifications. Different sources of information are exploited, such as features extracted from the image analysis of the region of interest, features related to the location of the cluster inside the breast, age of the patient and descriptors provided by the radiologists while performing their diagnostic task. A series of different CAD x schemes are implemented, each of which uses a different category of features and adopts a variety of machine learning algorithms and alternative image processing techniques. A novel framework is introduced where these independent diagnostic components are properly combined according to features critical to a radiologist in an attempt to identify the most appropriate CAD x schemes for the case under consideration. An open access database (Digital Database of Screening Mammography (DDSM)) has been elaborated to construct a large dataset with cases of varying subtlety, in order to ensure the development of schemes with high generalization ability, as well as extensive evaluation of their performance. The obtained results indicate that the proposed framework succeeds in improving the diagnostic procedure, as the achieved overall classification performance outperforms all the independent single diagnostic components, as well as the radiologists that assessed the same cases, in terms of accuracy, sensitivity, specificity and area under the curve following receiver operating characteristic analysis.

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

PubMed

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection. The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features. Copyright © 2017 Elsevier B.V. All rights reserved.

Relevance feedback-based building recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Allinson, Nigel M.

2010-07-01

Building recognition is a nontrivial task in computer vision research which can be utilized in robot localization, mobile navigation, etc. However, existing building recognition systems usually encounter the following two problems: 1) extracted low level features cannot reveal the true semantic concepts; and 2) they usually involve high dimensional data which require heavy computational costs and memory. Relevance feedback (RF), widely applied in multimedia information retrieval, is able to bridge the gap between the low level visual features and high level concepts; while dimensionality reduction methods can mitigate the high-dimensional problem. In this paper, we propose a building recognition scheme which integrates the RF and subspace learning algorithms. Experimental results undertaken on our own building database show that the newly proposed scheme appreciably enhances the recognition accuracy.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease.

PubMed

Segovia, F; Górriz, J M; Ramírez, J; Phillips, C

2016-01-01

Neuroimaging data as (18)F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

PubMed

Mohammed, Ameer; Zamani, Majid; Bayford, Richard; Demosthenous, Andreas

2017-12-01

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

Hepatic CT image query using Gabor features

NASA Astrophysics Data System (ADS)

Zhao, Chenguang; Cheng, Hongyan; Zhuang, Tiange

2004-07-01

A retrieval scheme for liver computerize tomography (CT) images based on Gabor texture is presented. For each hepatic CT image, we manually delineate abnormal regions within liver area. Then, a continuous Gabor transform is utilized to analyze the texture of the pathology bearing region and extract the corresponding feature vectors. For a given sample image, we compare its feature vector with those of other images. Similar images with the highest rank are retrieved. In experiments, 45 liver CT images are collected, and the effectiveness of Gabor texture for content based retrieval is verified.

Transverse beam splitting made operational: Key features of the multiturn extraction at the CERN Proton Synchrotron

NASA Astrophysics Data System (ADS)

Huschauer, A.; Blas, A.; Borburgh, J.; Damjanovic, S.; Gilardoni, S.; Giovannozzi, M.; Hourican, M.; Kahle, K.; Le Godec, G.; Michels, O.; Sterbini, G.; Hernalsteens, C.

2017-06-01

Following a successful commissioning period, the multiturn extraction (MTE) at the CERN Proton Synchrotron (PS) has been applied for the fixed-target physics programme at the Super Proton Synchrotron (SPS) since September 2015. This exceptional extraction technique was proposed to replace the long-serving continuous transfer (CT) extraction, which has the drawback of inducing high activation in the ring. MTE exploits the principles of nonlinear beam dynamics to perform loss-free beam splitting in the horizontal phase space. Over multiple turns, the resulting beamlets are then transferred to the downstream accelerator. The operational deployment of MTE was rendered possible by the full understanding and mitigation of different hardware limitations and by redesigning the extraction trajectories and nonlinear optics, which was required due to the installation of a dummy septum to reduce the activation of the magnetic extraction septum. This paper focuses on these key features including the use of the transverse damper and the septum shadowing, which allowed a transition from the MTE study to a mature operational extraction scheme.

Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.

PubMed

Lim, Meng-Hui; Teoh, Andrew Beng Jin; Toh, Kar-Ann

2013-06-01

Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.

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.

A malware detection scheme based on mining format information.

PubMed

Bai, Jinrong; Wang, Junfeng; Zou, Guozhong

2014-01-01

Malware has become one of the most serious threats to computer information system and the current malware detection technology still has very significant limitations. In this paper, we proposed a malware detection approach by mining format information of PE (portable executable) files. Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. When the selected features were trained using classification algorithms, the results of our experiments indicate that the accuracy of the top classification algorithm is 99.1% and the value of the AUC is 0.998. We designed three experiments to evaluate the performance of our detection scheme and the ability of detecting unknown and new malware. Although the experimental results of identifying new malware are not perfect, our method is still able to identify 97.6% of new malware with 1.3% false positive rates.

A Malware Detection Scheme Based on Mining Format Information

PubMed Central

Bai, Jinrong; Wang, Junfeng; Zou, Guozhong

2014-01-01

Malware has become one of the most serious threats to computer information system and the current malware detection technology still has very significant limitations. In this paper, we proposed a malware detection approach by mining format information of PE (portable executable) files. Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. When the selected features were trained using classification algorithms, the results of our experiments indicate that the accuracy of the top classification algorithm is 99.1% and the value of the AUC is 0.998. We designed three experiments to evaluate the performance of our detection scheme and the ability of detecting unknown and new malware. Although the experimental results of identifying new malware are not perfect, our method is still able to identify 97.6% of new malware with 1.3% false positive rates. PMID:24991639

CAD scheme for detection of hemorrhages and exudates in ocular fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Nakagawa, Toshiaki; Hayashi, Yoshinori; Mizukusa, Yutaka; Fujita, Akihiro; Kakogawa, Masakatsu; Kawase, Kazuhide; Hara, Takeshi; Fujita, Hiroshi

2007-03-01

This paper describes a method for detecting hemorrhages and exudates in ocular fundus images. The detection of hemorrhages and exudates is important in order to diagnose diabetic retinopathy. Diabetic retinopathy is one of the most significant factors contributing to blindness, and early detection and treatment are important. In this study, hemorrhages and exudates were automatically detected in fundus images without using fluorescein angiograms. Subsequently, the blood vessel regions incorrectly detected as hemorrhages were eliminated by first examining the structure of the blood vessels and then evaluating the length-to-width ratio. Finally, the false positives were eliminated by checking the following features extracted from candidate images: the number of pixels, contrast, 13 features calculated from the co-occurrence matrix, two features based on gray-level difference statistics, and two features calculated from the extrema method. The sensitivity of detecting hemorrhages in the fundus images was 85% and that of detecting exudates was 77%. Our fully automated scheme could accurately detect hemorrhages and exudates.

Latest Progress of Fault Detection and Localization in Complex Electrical Engineering

NASA Astrophysics Data System (ADS)

Zhao, Zheng; Wang, Can; Zhang, Yagang; Sun, Yi

2014-01-01

In the researches of complex electrical engineering, efficient fault detection and localization schemes are essential to quickly detect and locate faults so that appropriate and timely corrective mitigating and maintenance actions can be taken. In this paper, under the current measurement precision of PMU, we will put forward a new type of fault detection and localization technology based on fault factor feature extraction. Lots of simulating experiments indicate that, although there are disturbances of white Gaussian stochastic noise, based on fault factor feature extraction principal, the fault detection and localization results are still accurate and reliable, which also identifies that the fault detection and localization technology has strong anti-interference ability and great redundancy.

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

Development of a morphological convolution operator for bearing fault detection

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

2018-05-01

This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

Urban-area extraction from polarimetric SAR image using combination of target decomposition and orientation angle

NASA Astrophysics Data System (ADS)

Zou, Bin; Lu, Da; Wu, Zhilu; Qiao, Zhijun G.

2016-05-01

The results of model-based target decomposition are the main features used to discriminate urban and non-urban area in polarimetric synthetic aperture radar (PolSAR) application. Traditional urban-area extraction methods based on modelbased target decomposition usually misclassified ground-trunk structure as urban-area or misclassified rotated urbanarea as forest. This paper introduces another feature named orientation angle to improve urban-area extraction scheme for the accurate mapping in urban by PolSAR image. The proposed method takes randomness of orientation angle into account for restriction of urban area first and, subsequently, implements rotation angle to improve results that oriented urban areas are recognized as double-bounce objects from volume scattering. ESAR L-band PolSAR data of the Oberpfaffenhofen Test Site Area was used to validate the proposed algorithm.

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.

Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images

NASA Astrophysics Data System (ADS)

Liu, Xiyao; Lou, Jieting; Wang, Yifan; Du, Jingyu; Zou, Beiji; Chen, Yan

2018-03-01

Authentication and copyright identification are two critical security issues for medical images. Although zerowatermarking schemes can provide durable, reliable and distortion-free protection for medical images, the existing zerowatermarking schemes for medical images still face two problems. On one hand, they rarely considered the distinguishability for medical images, which is critical because different medical images are sometimes similar to each other. On the other hand, their robustness against geometric attacks, such as cropping, rotation and flipping, is insufficient. In this study, a novel discriminative and robust zero-watermarking (DRZW) is proposed to address these two problems. In DRZW, content-based features of medical images are first extracted based on completed local binary pattern (CLBP) operator to ensure the distinguishability and robustness, especially against geometric attacks. Then, master shares and ownership shares are generated from the content-based features and watermark according to (2,2) visual cryptography. Finally, the ownership shares are stored for authentication and copyright identification. For queried medical images, their content-based features are extracted and master shares are generated. Their watermarks for authentication and copyright identification are recovered by stacking the generated master shares and stored ownership shares. 200 different medical images of 5 types are collected as the testing data and our experimental results demonstrate that DRZW ensures both the accuracy and reliability of authentication and copyright identification. When fixing the false positive rate to 1.00%, the average value of false negative rates by using DRZW is only 1.75% under 20 common attacks with different parameters.

A novel scheme for abnormal cell detection in Pap smear images

NASA Astrophysics Data System (ADS)

Zhao, Tong; Wachman, Elliot S.; Farkas, Daniel L.

2004-07-01

Finding malignant cells in Pap smear images is a "needle in a haystack"-type problem, tedious, labor-intensive and error-prone. It is therefore desirable to have an automatic screening tool in order that human experts can concentrate on the evaluation of the more difficult cases. Most research on automatic cervical screening tries to extract morphometric and texture features at the cell level, in accordance with the NIH "The Bethesda System" rules. Due to variances in image quality and features, such as brightness, magnification and focus, morphometric and texture analysis is insufficient to provide robust cervical cancer detection. Using a microscopic spectral imaging system, we have produced a set of multispectral Pap smear images with wavelengths from 400 nm to 690 nm, containing both spectral signatures and spatial attributes. We describe a novel scheme that combines spatial information (including texture and morphometric features) with spectral information to significantly improve abnormal cell detection. Three kinds of wavelet features, orthogonal, bi-orthogonal and non-orthogonal, are carefully chosen to optimize recognition performance. Multispectral feature sets are then extracted in the wavelet domain. Using a Back-Propagation Neural Network classifier that greatly decreases the influence of spurious events, we obtain a classification error rate of 5%. Cell morphometric features, such as area and shape, are then used to eliminate most remaining small artifacts. We report initial results from 149 cells from 40 separate image sets, in which only one abnormal cell was missed (TPR = 97.6%) and one normal cell was falsely classified as cancerous (FPR = 1%).

An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images.

PubMed

Shanthi, C; Pappa, N

2017-05-01

Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are recorded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

NASA Astrophysics Data System (ADS)

He, Xin Yi; Zhao, Bo; Tan, Shu Run; Zhou, Xiao Yang; Jiang, Zhong Jin; Cui, Tie Jun

2009-10-01

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

Shift-invariant discrete wavelet transform analysis for retinal image classification.

PubMed

Khademi, April; Krishnan, Sridhar

2007-12-01

This work involves retinal image classification and a novel analysis system was developed. From the compressed domain, the proposed scheme extracts textural features from wavelet coefficients, which describe the relative homogeneity of localized areas of the retinal images. Since the discrete wavelet transform (DWT) is shift-variant, a shift-invariant DWT was explored to ensure that a robust feature set was extracted. To combat the small database size, linear discriminant analysis classification was used with the leave one out method. 38 normal and 48 abnormal (exudates, large drusens, fine drusens, choroidal neovascularization, central vein and artery occlusion, histoplasmosis, arteriosclerotic retinopathy, hemi-central retinal vein occlusion and more) were used and a specificity of 79% and sensitivity of 85.4% were achieved (the average classification rate is 82.2%). The success of the system can be accounted to the highly robust feature set which included translation, scale and semi-rotational, features. Additionally, this technique is database independent since the features were specifically tuned to the pathologies of the human eye.

Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

NASA Astrophysics Data System (ADS)

Bianconi, Francesco; Bello-Cerezo, Raquel; Napoletano, Paolo

2018-01-01

Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images.

A portable foot-parameter-extracting system

NASA Astrophysics Data System (ADS)

Zhang, MingKai; Liang, Jin; Li, Wenpan; Liu, Shifan

2016-03-01

In order to solve the problem of automatic foot measurement in garment customization, a new automatic footparameter- extracting system based on stereo vision, photogrammetry and heterodyne multiple frequency phase shift technology is proposed and implemented. The key technologies applied in the system are studied, including calibration of projector, alignment of point clouds, and foot measurement. Firstly, a new projector calibration algorithm based on plane model has been put forward to get the initial calibration parameters and a feature point detection scheme of calibration board image is developed. Then, an almost perfect match of two clouds is achieved by performing a first alignment using the Sampled Consensus - Initial Alignment algorithm (SAC-IA) and refining the alignment using the Iterative Closest Point algorithm (ICP). Finally, the approaches used for foot-parameterextracting and the system scheme are presented in detail. Experimental results show that the RMS error of the calibration result is 0.03 pixel and the foot parameter extracting experiment shows the feasibility of the extracting algorithm. Compared with the traditional measurement method, the system can be more portable, accurate and robust.

Research on Signature Verification Method Based on Discrete Fréchet Distance

NASA Astrophysics Data System (ADS)

Fang, J. L.; Wu, W.

2018-05-01

This paper proposes a multi-feature signature template based on discrete Fréchet distance, which breaks through the limitation of traditional signature authentication using a single signature feature. It solves the online handwritten signature authentication signature global feature template extraction calculation workload, signature feature selection unreasonable problem. In this experiment, the false recognition rate (FAR) and false rejection rate (FRR) of the statistical signature are calculated and the average equal error rate (AEER) is calculated. The feasibility of the combined template scheme is verified by comparing the average equal error rate of the combination template and the original template.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

Coding Local and Global Binary Visual Features Extracted From Video Sequences.

PubMed

Baroffio, Luca; Canclini, Antonio; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2015-11-01

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the bag-of-visual word model. Several applications, including, for example, visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget while attaining a target level of efficiency. In this paper, we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can conveniently be adopted to support the analyze-then-compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs the visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the compress-then-analyze (CTA) paradigm. In this paper, we experimentally compare the ATC and the CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: 1) homography estimation and 2) content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with the CTA, especially in bandwidth limited scenarios.

Coding Local and Global Binary Visual Features Extracted From Video Sequences

NASA Astrophysics Data System (ADS)

Baroffio, Luca; Canclini, Antonio; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2015-11-01

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks, while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW) model. Several applications, including for example visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget, while attaining a target level of efficiency. In this paper we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the Compress-Then-Analyze (CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: homography estimation and content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with CTA, especially in bandwidth limited scenarios.

Evaluation of chemotherapy response in ovarian cancer treatment using quantitative CT image biomarkers: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Tan, Maxine; McMeekin, Scott; Thai, Theresa; Moore, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2015-03-01

The purpose of this study is to identify and apply quantitative image biomarkers for early prediction of the tumor response to the chemotherapy among the ovarian cancer patients participated in the clinical trials of testing new drugs. In the experiment, we retrospectively selected 30 cases from the patients who participated in Phase I clinical trials of new drug or drug agents for ovarian cancer treatment. Each case is composed of two sets of CT images acquired pre- and post-treatment (4-6 weeks after starting treatment). A computer-aided detection (CAD) scheme was developed to extract and analyze the quantitative image features of the metastatic tumors previously tracked by the radiologists using the standard Response Evaluation Criteria in Solid Tumors (RECIST) guideline. The CAD scheme first segmented 3-D tumor volumes from the background using a hybrid tumor segmentation scheme. Then, for each segmented tumor, CAD computed three quantitative image features including the change of tumor volume, tumor CT number (density) and density variance. The feature changes were calculated between the matched tumors tracked on the CT images acquired pre- and post-treatments. Finally, CAD predicted patient's 6-month progression-free survival (PFS) using a decision-tree based classifier. The performance of the CAD scheme was compared with the RECIST category. The result shows that the CAD scheme achieved a prediction accuracy of 76.7% (23/30 cases) with a Kappa coefficient of 0.493, which is significantly higher than the performance of RECIST prediction with a prediction accuracy and Kappa coefficient of 60% (17/30) and 0.062, respectively. This study demonstrated the feasibility of analyzing quantitative image features to improve the early predicting accuracy of the tumor response to the new testing drugs or therapeutic methods for the ovarian cancer patients.

A complete passive blind image copy-move forensics scheme based on compound statistics features.

PubMed

Peng, Fei; Nie, Yun-ying; Long, Min

2011-10-10

Since most sensor pattern noise based image copy-move forensics methods require a known reference sensor pattern noise, it generally results in non-blinded passive forensics, which significantly confines the application circumstances. In view of this, a novel passive-blind image copy-move forensics scheme is proposed in this paper. Firstly, a color image is transformed into a grayscale one, and wavelet transform based de-noising filter is used to extract the sensor pattern noise, then the variance of the pattern noise, the signal noise ratio between the de-noised image and the pattern noise, the information entropy and the average energy gradient of the original grayscale image are chosen as features, non-overlapping sliding window operations are done to the images to divide them into different sub-blocks. Finally, the tampered areas are detected by analyzing the correlation of the features between the sub-blocks and the whole image. Experimental results and analysis show that the proposed scheme is completely passive-blind, has a good detection rate, and is robust against JPEG compression, noise, rotation, scaling and blurring. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization.

PubMed

Fang, Chunying; Li, Haifeng; Ma, Lin; Zhang, Mancai

2017-01-01

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S -transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S -transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F -score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.

A novel approach for SEMG signal classification with adaptive local binary patterns.

PubMed

Ertuğrul, Ömer Faruk; Kaya, Yılmaz; Tekin, Ramazan

2016-07-01

Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.

FacetGist: Collective Extraction of Document Facets in Large Technical Corpora.

PubMed

Siddiqui, Tarique; Ren, Xiang; Parameswaran, Aditya; Han, Jiawei

2016-10-01

Given the large volume of technical documents available, it is crucial to automatically organize and categorize these documents to be able to understand and extract value from them. Towards this end, we introduce a new research problem called Facet Extraction. Given a collection of technical documents, the goal of Facet Extraction is to automatically label each document with a set of concepts for the key facets ( e.g. , application, technique, evaluation metrics, and dataset) that people may be interested in. Facet Extraction has numerous applications, including document summarization, literature search, patent search and business intelligence. The major challenge in performing Facet Extraction arises from multiple sources: concept extraction, concept to facet matching, and facet disambiguation. To tackle these challenges, we develop FacetGist, a framework for facet extraction. Facet Extraction involves constructing a graph-based heterogeneous network to capture information available across multiple local sentence-level features, as well as global context features. We then formulate a joint optimization problem, and propose an efficient algorithm for graph-based label propagation to estimate the facet of each concept mention. Experimental results on technical corpora from two domains demonstrate that Facet Extraction can lead to an improvement of over 25% in both precision and recall over competing schemes.

FacetGist: Collective Extraction of Document Facets in Large Technical Corpora

PubMed Central

Siddiqui, Tarique; Ren, Xiang; Parameswaran, Aditya; Han, Jiawei

2017-01-01

Given the large volume of technical documents available, it is crucial to automatically organize and categorize these documents to be able to understand and extract value from them. Towards this end, we introduce a new research problem called Facet Extraction. Given a collection of technical documents, the goal of Facet Extraction is to automatically label each document with a set of concepts for the key facets (e.g., application, technique, evaluation metrics, and dataset) that people may be interested in. Facet Extraction has numerous applications, including document summarization, literature search, patent search and business intelligence. The major challenge in performing Facet Extraction arises from multiple sources: concept extraction, concept to facet matching, and facet disambiguation. To tackle these challenges, we develop FacetGist, a framework for facet extraction. Facet Extraction involves constructing a graph-based heterogeneous network to capture information available across multiple local sentence-level features, as well as global context features. We then formulate a joint optimization problem, and propose an efficient algorithm for graph-based label propagation to estimate the facet of each concept mention. Experimental results on technical corpora from two domains demonstrate that Facet Extraction can lead to an improvement of over 25% in both precision and recall over competing schemes. PMID:28210517

Exploring Sampling in the Detection of Multicategory EEG Signals

PubMed Central

Siuly, Siuly; Kabir, Enamul; Wang, Hua; Zhang, Yanchun

2015-01-01

The paper presents a structure based on samplings and machine leaning techniques for the detection of multicategory EEG signals where random sampling (RS) and optimal allocation sampling (OS) are explored. In the proposed framework, before using the RS and OS scheme, the entire EEG signals of each class are partitioned into several groups based on a particular time period. The RS and OS schemes are used in order to have representative observations from each group of each category of EEG data. Then all of the selected samples by the RS from the groups of each category are combined in a one set named RS set. In the similar way, for the OS scheme, an OS set is obtained. Then eleven statistical features are extracted from the RS and OS set, separately. Finally this study employs three well-known classifiers: k-nearest neighbor (k-NN), multinomial logistic regression with a ridge estimator (MLR), and support vector machine (SVM) to evaluate the performance for the RS and OS feature set. The experimental outcomes demonstrate that the RS scheme well represents the EEG signals and the k-NN with the RS is the optimum choice for detection of multicategory EEG signals. PMID:25977705

xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data.

PubMed

Uppal, Karan; Soltow, Quinlyn A; Strobel, Frederick H; Pittard, W Stephen; Gernert, Kim M; Yu, Tianwei; Jones, Dean P

2013-01-16

Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

Distorted Character Recognition Via An Associative Neural Network

NASA Astrophysics Data System (ADS)

Messner, Richard A.; Szu, Harold H.

1987-03-01

The purpose of this paper is two-fold. First, it is intended to provide some preliminary results of a character recognition scheme which has foundations in on-going neural network architecture modeling, and secondly, to apply some of the neural network results in a real application area where thirty years of effort has had little effect on providing the machine an ability to recognize distorted objects within the same object class. It is the author's belief that the time is ripe to start applying in ernest the results of over twenty years of effort in neural modeling to some of the more difficult problems which seem so hard to solve by conventional means. The character recognition scheme proposed utilizes a preprocessing stage which performs a 2-dimensional Walsh transform of an input cartesian image field, then sequency filters this spectrum into three feature bands. Various features are then extracted and organized into three sets of feature vectors. These vector patterns that are stored and recalled associatively. Two possible associative neural memory models are proposed for further investigation. The first being an outer-product linear matrix associative memory with a threshold function controlling the strength of the output pattern (similar to Kohonen's crosscorrelation approach [1]). The second approach is based upon a modified version of Grossberg's neural architecture [2] which provides better self-organizing properties due to its adaptive nature. Preliminary results of the sequency filtering and feature extraction preprocessing stage and discussion about the use of the proposed neural architectures is included.

Dictionary learning-based CT detection of pulmonary nodules

NASA Astrophysics Data System (ADS)

Wu, Panpan; Xia, Kewen; Zhang, Yanbo; Qian, Xiaohua; Wang, Ge; Yu, Hengyong

2016-10-01

Segmentation of lung features is one of the most important steps for computer-aided detection (CAD) of pulmonary nodules with computed tomography (CT). However, irregular shapes, complicated anatomical background and poor pulmonary nodule contrast make CAD a very challenging problem. Here, we propose a novel scheme for feature extraction and classification of pulmonary nodules through dictionary learning from training CT images, which does not require accurately segmented pulmonary nodules. Specifically, two classification-oriented dictionaries and one background dictionary are learnt to solve a two-category problem. In terms of the classification-oriented dictionaries, we calculate sparse coefficient matrices to extract intrinsic features for pulmonary nodule classification. The support vector machine (SVM) classifier is then designed to optimize the performance. Our proposed methodology is evaluated with the lung image database consortium and image database resource initiative (LIDC-IDRI) database, and the results demonstrate that the proposed strategy is promising.

Data-Mining-Based Intelligent Differential Relaying for Transmission Lines Including UPFC and Wind Farms.

PubMed

Jena, Manas Kumar; Samantaray, Subhransu Ranjan

2016-01-01

This paper presents a data-mining-based intelligent differential relaying scheme for transmission lines, including flexible ac transmission system device, such as unified power flow controller (UPFC) and wind farms. Initially, the current and voltage signals are processed through extended Kalman filter phasor measurement unit for phasor estimation, and 21 potential features are computed at both ends of the line. Once the features are extracted at both ends, the corresponding differential features are derived. These differential features are fed to a data-mining model known as decision tree (DT) to provide the final relaying decision. The proposed technique has been extensively tested for single-circuit transmission line, including UPFC and wind farms with in-feed, double-circuit line with UPFC on one line and wind farm as one of the substations with wide variations in operating parameters. The test results obtained from simulation as well as in real-time digital simulator testing indicate that the DT-based intelligent differential relaying scheme is highly reliable and accurate with a response time of 2.25 cycles from the fault inception.

Objective grading of facial paralysis using Local Binary Patterns in video processing.

PubMed

He, Shu; Soraghan, John J; O'Reilly, Brian F

2008-01-01

This paper presents a novel framework for objective measurement of facial paralysis in biomedial videos. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the Local Binary Patterns (LBP) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of block schemes. A multi-resolution extension of uniform LBP is proposed to efficiently combine the micro-patterns and large-scale patterns into a feature vector, which increases the algorithmic robustness and reduces noise effects while still retaining computational simplicity. The symmetry of facial movements is measured by the Resistor-Average Distance (RAD) between LBP features extracted from the two sides of the face. Support Vector Machine (SVM) is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) Scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

Digital auscultation analysis for heart murmur detection.

PubMed

Delgado-Trejos, Edilson; Quiceno-Manrique, A F; Godino-Llorente, J I; Blanco-Velasco, M; Castellanos-Dominguez, G

2009-02-01

This work presents a comparison of different approaches for the detection of murmurs from phonocardiographic signals. Taking into account the variability of the phonocardiographic signals induced by valve disorders, three families of features were analyzed: (a) time-varying & time-frequency features; (b) perceptual; and (c) fractal features. With the aim of improving the performance of the system, the accuracy of the system was tested using several combinations of the aforementioned families of parameters. In the second stage, the main components extracted from each family were combined together with the goal of improving the accuracy of the system. The contribution of each family of features extracted was evaluated by means of a simple k-nearest neighbors classifier, showing that fractal features provide the best accuracy (97.17%), followed by time-varying & time-frequency (95.28%), and perceptual features (88.7%). However, an accuracy around 94% can be reached just by using the two main features of the fractal family; therefore, considering the difficulties related to the automatic intrabeat segmentation needed for spectral and perceptual features, this scheme becomes an interesting alternative. The conclusion is that fractal type features were the most robust family of parameters (in the sense of accuracy vs. computational load) for the automatic detection of murmurs. This work was carried out using a database that contains 164 phonocardiographic recordings (81 normal and 83 records with murmurs). The database was segmented to extract 360 representative individual beats (180 per class).

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

NASA Astrophysics Data System (ADS)

Ison, Mark; Artemiadis, Panagiotis

2014-10-01

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

A Robust Zero-Watermarking Algorithm for Audio

NASA Astrophysics Data System (ADS)

Chen, Ning; Zhu, Jie

2007-12-01

In traditional watermarking algorithms, the insertion of watermark into the host signal inevitably introduces some perceptible quality degradation. Another problem is the inherent conflict between imperceptibility and robustness. Zero-watermarking technique can solve these problems successfully. Instead of embedding watermark, the zero-watermarking technique extracts some essential characteristics from the host signal and uses them for watermark detection. However, most of the available zero-watermarking schemes are designed for still image and their robustness is not satisfactory. In this paper, an efficient and robust zero-watermarking technique for audio signal is presented. The multiresolution characteristic of discrete wavelet transform (DWT), the energy compression characteristic of discrete cosine transform (DCT), and the Gaussian noise suppression property of higher-order cumulant are combined to extract essential features from the host audio signal and they are then used for watermark recovery. Simulation results demonstrate the effectiveness of our scheme in terms of inaudibility, detection reliability, and robustness.

When Interpolation-Induced Reflection Artifact Meets Time-Frequency Analysis.

PubMed

Lin, Yu-Ting; Flandrin, Patrick; Wu, Hau-Tieng

2016-10-01

While extracting the temporal dynamical features based on the time-frequency analyses, like the reassignment and synchrosqueezing transform, attracts more and more interest in biomedical data analysis, we should be careful about artifacts generated by interpolation schemes, in particular when the sampling rate is not significantly higher than the frequency of the oscillatory component we are interested in. We formulate the problem called the reflection effect and provide a theoretical justification of the statement. We also show examples in the anesthetic depth analysis with clear but undesirable artifacts. The artifact associated with the reflection effect exists not only theoretically but practically as well. Its influence is pronounced when we apply the time-frequency analyses to extract the time-varying dynamics hidden inside the signal. We have to carefully deal with the artifact associated with the reflection effect by choosing a proper interpolation scheme.

Hyperspectral remote sensing image retrieval system using spectral and texture features.

PubMed

Zhang, Jing; Geng, Wenhao; Liang, Xi; Li, Jiafeng; Zhuo, Li; Zhou, Qianlan

2017-06-01

Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Karahaliou, A.; Vassiou, K.; Skiadopoulos, S.; Kanavou, T.; Yiakoumelos, A.; Costaridou, L.

2009-07-01

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.

Automatic recognition of emotions from facial expressions

NASA Astrophysics Data System (ADS)

Xue, Henry; Gertner, Izidor

2014-06-01

In the human-computer interaction (HCI) process it is desirable to have an artificial intelligent (AI) system that can identify and categorize human emotions from facial expressions. Such systems can be used in security, in entertainment industries, and also to study visual perception, social interactions and disorders (e.g. schizophrenia and autism). In this work we survey and compare the performance of different feature extraction algorithms and classification schemes. We introduce a faster feature extraction method that resizes and applies a set of filters to the data images without sacrificing the accuracy. In addition, we have enhanced SVM to multiple dimensions while retaining the high accuracy rate of SVM. The algorithms were tested using the Japanese Female Facial Expression (JAFFE) Database and the Database of Faces (AT&T Faces).

On the feasibility of interoperable schemes in hand biometrics.

PubMed

Morales, Aythami; González, Ester; Ferrer, Miguel A

2012-01-01

Personal recognition through hand-based biometrics has attracted the interest of many researchers in the last twenty years. A significant number of proposals based on different procedures and acquisition devices have been published in the literature. However, comparisons between devices and their interoperability have not been thoroughly studied. This paper tries to fill this gap by proposing procedures to improve the interoperability among different hand biometric schemes. The experiments were conducted on a database made up of 8,320 hand images acquired from six different hand biometric schemes, including a flat scanner, webcams at different wavelengths, high quality cameras, and contactless devices. Acquisitions on both sides of the hand were included. Our experiment includes four feature extraction methods which determine the best performance among the different scenarios for two of the most popular hand biometrics: hand shape and palm print. We propose smoothing techniques at the image and feature levels to reduce interdevice variability. Results suggest that comparative hand shape offers better performance in terms of interoperability than palm prints, but palm prints can be more effective when using similar sensors.

On the Feasibility of Interoperable Schemes in Hand Biometrics

PubMed Central

Morales, Aythami; González, Ester; Ferrer, Miguel A.

2012-01-01

Personal recognition through hand-based biometrics has attracted the interest of many researchers in the last twenty years. A significant number of proposals based on different procedures and acquisition devices have been published in the literature. However, comparisons between devices and their interoperability have not been thoroughly studied. This paper tries to fill this gap by proposing procedures to improve the interoperability among different hand biometric schemes. The experiments were conducted on a database made up of 8,320 hand images acquired from six different hand biometric schemes, including a flat scanner, webcams at different wavelengths, high quality cameras, and contactless devices. Acquisitions on both sides of the hand were included. Our experiment includes four feature extraction methods which determine the best performance among the different scenarios for two of the most popular hand biometrics: hand shape and palm print. We propose smoothing techniques at the image and feature levels to reduce interdevice variability. Results suggest that comparative hand shape offers better performance in terms of interoperability than palm prints, but palm prints can be more effective when using similar sensors. PMID:22438714

An Investigation of Aggregation in Synergistic Solvent Extraction Systems

NASA Astrophysics Data System (ADS)

Jackson, Andy Steven

With an increasing focus on anthropogenic climate change, nuclear reactors present an attractive option for base load power generation with regard to air pollution and carbon emissions, especially when compared with traditional fossil fuel based options. However, used nuclear fuel (UNF) is highly radiotoxic and contains minor actinides (americium and curium) which remain more radiotoxic than natural uranium ore for hundreds of thousands of years, presenting a challenge for long-term storage . Advanced nuclear fuel recycling can reduce this required storage time to thousands of years by removing the highly radiotoxic minor actinides. Many advanced separation schemes have been proposed to achieve this separation but none have been implemented to date. A key feature among many proposed schemes is the use of more than one extraction reagent in a single extraction phase, which can lead to the phenomenon known as "synergism" in which the extraction efficiency for a combination of the reagents is greater than that of the individual extractants alone. This feature is not well understood for many systems and a comprehensive picture of the mechanism behind synergism does not exist. There are several proposed mechanisms for synergism though none have been used to model multiple extraction systems. This work examines several proposed advanced extractant combinations which exhibit synergism: 2-bromodecanoic acid (BDA) with 2,2':6',2"-terpyridine (TERPY), tri-n-butylphosphine oxide (TPBO) with 2-thenoyltrifluoro acetone (HTTA), and dinonylnaphthalene sulfonic acid (HDNNS) with 5,8-diethyl-7-hydroxy-dodecan-6-oxime (LIX). We examine two proposed synergistic mechanisms involving and attempt to verify the ability of these mechanisms to predict the extraction behavior of the chosen systems. These are a reverse micellar catalyzed extraction model and a mixed complex formation model. Neither was able to effectively predict the synergistic behavior of the systems. We further examine these systems for the presence of large reverse micellar aggregates and thermodynamic signatures of aggregation. Behaviors differed widely from system to system, suggesting the possibility of more than one mechanism being responsible for similar observed extraction trends.

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

Semi-Supervised Geographical Feature Detection

NASA Astrophysics Data System (ADS)

Yu, H.; Yu, L.; Kuo, K. S.

2016-12-01

Extraction and tracking geographical features is a fundamental requirement in many geoscience fields. However, this operation has become an increasingly challenging task for domain scientists when tackling a large amount of geoscience data. Although domain scientists may have a relatively clear definition of features, it is difficult to capture the presence of features in an accurate and efficient fashion. We propose a semi-supervised approach to address large geographical feature detection. Our approach has two main components. First, we represent a heterogeneous geoscience data in a unified high-dimensional space, which can facilitate us to evaluate the similarity of data points with respect to geolocation, time, and variable values. We characterize the data from these measures, and use a set of hash functions to parameterize the initial knowledge of the data. Second, for any user query, our approach can automatically extract the initial results based on the hash functions. To improve the accuracy of querying, our approach provides a visualization interface to display the querying results and allow users to interactively explore and refine them. The user feedback will be used to enhance our knowledge base in an iterative manner. In our implementation, we use high-performance computing techniques to accelerate the construction of hash functions. Our design facilitates a parallelization scheme for feature detection and extraction, which is a traditionally challenging problem for large-scale data. We evaluate our approach and demonstrate the effectiveness using both synthetic and real world datasets.

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

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

Lee, Juhun, E-mail: leej15@upmc.edu; Nishikawa, Robert M.; Reiser, Ingrid

2015-09-15

Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimalmore » feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C{sub T}) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C{sub T} yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C{sub T} were conducted. The results showed that C{sub T} was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets.« less

Target identification using Zernike moments and neural networks

NASA Astrophysics Data System (ADS)

Azimi-Sadjadi, Mahmood R.; Jamshidi, Arta A.; Nevis, Andrew J.

2001-10-01

The development of an underwater target identification algorithm capable of identifying various types of underwater targets, such as mines, under different environmental conditions pose many technical problems. Some of the contributing factors are: targets have diverse sizes, shapes and reflectivity properties. Target emplacement environment is variable; targets may be proud or partially buried. Environmental properties vary significantly from one location to another. Bottom features such as sand, rocks, corals, and vegetation can conceal a target whether it is partially buried or proud. Competing clutter with responses that closely resemble those of the targets may lead to false positives. All the problems mentioned above contribute to overly difficult and challenging conditions that could lead to unreliable algorithm performance with existing methods. In this paper, we developed and tested a shape-dependent feature extraction scheme that provides features invariant to rotation, size scaling and translation; properties that are extremely useful for any target classification problem. The developed schemes were tested on an electro-optical imagery data set collected under different environmental conditions with variable background, range and target types. The electro-optic data set was collected using a Laser Line Scan (LLS) sensor by the Coastal Systems Station (CSS), located in Panama City, Florida. The performance of the developed scheme and its robustness to distortion, rotation, scaling and translation was also studied.

Machine learning approach for automated screening of malaria parasite using light microscopic images.

PubMed

Das, Dev Kumar; Ghosh, Madhumala; Pal, Mallika; Maiti, Asok K; Chakraborty, Chandan

2013-02-01

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification. Copyright © 2012 Elsevier Ltd. All rights reserved.

An image understanding system using attributed symbolic representation and inexact graph-matching

NASA Astrophysics Data System (ADS)

Eshera, M. A.; Fu, K.-S.

1986-09-01

A powerful image understanding system using a semantic-syntactic representation scheme consisting of attributed relational graphs (ARGs) is proposed for the analysis of the global information content of images. A multilayer graph transducer scheme performs the extraction of ARG representations from images, with ARG nodes representing the global image features, and the relations between features represented by the attributed branches between corresponding nodes. An efficient dynamic programming technique is employed to derive the distance between two ARGs and the inexact matching of their respective components. Noise, distortion and ambiguity in real-world images are handled through modeling in the transducer mapping rules and through the appropriate cost of error-transformation for the inexact matching of the representation. The system is demonstrated for the case of locating objects in a scene composed of complex overlapped objects, and the case of target detection in noisy and distorted synthetic aperture radar image.

Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

PubMed Central

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-01-01

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

PubMed

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-07-24

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Smoke detection using GLCM, wavelet, and motion

NASA Astrophysics Data System (ADS)

Srisuwan, Teerasak; Ruchanurucks, Miti

2014-01-01

This paper presents a supervised smoke detection method that uses local and global features. This framework integrates and extends notions of many previous works to generate a new comprehensive method. First chrominance detection is used to screen areas that are suspected to be smoke. For these areas, local features are then extracted. The features are among homogeneity of GLCM and energy of wavelet. Then, global feature of motion of the smoke-color areas are extracted using a space-time analysis scheme. Finally these features are used to train an artificial intelligent. Here we use neural network, experiment compares importance of each feature. Hence, we can really know which features among those used by many previous works are really useful. The proposed method outperforms many of the current methods in the sense of correctness, and it does so in a reasonable computation time. It even has less limitation than conventional smoke sensors when used in open space. Best method for the experimental results is to use all the mentioned features as expected, to insure which is the best experiment result can be achieved. The achieved with high accuracy of result expected output is high value of true positive and low value of false positive. And show that our algorithm has good robustness for smoke detection.

Extraction and LOD control of colored interval volumes

NASA Astrophysics Data System (ADS)

Miyamura, Hiroko N.; Takeshima, Yuriko; Fujishiro, Issei; Saito, Takafumi

2005-03-01

Interval volume serves as a generalized isosurface and represents a three-dimensional subvolume for which the associated scalar filed values lie within a user-specified closed interval. In general, it is not an easy task for novices to specify the scalar field interval corresponding to their ROIs. In order to extract interval volumes from which desirable geometric features can be mined effectively, we propose a suggestive technique which extracts interval volumes automatically based on the global examination of the field contrast structure. Also proposed here is a simplification scheme for decimating resultant triangle patches to realize efficient transmission and rendition of large-scale interval volumes. Color distributions as well as geometric features are taken into account to select best edges to be collapsed. In addition, when a user wants to selectively display and analyze the original dataset, the simplified dataset is restructured to the original quality. Several simulated and acquired datasets are used to demonstrate the effectiveness of the present methods.

A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

NASA Technical Reports Server (NTRS)

Turso, James A.; Litt, Jonathan S.

2004-01-01

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID:29209190

A phase angle based diagnostic scheme to planetary gear faults diagnostics under non-stationary operational conditions

NASA Astrophysics Data System (ADS)

Feng, Ke; Wang, Kesheng; Ni, Qing; Zuo, Ming J.; Wei, Dongdong

2017-11-01

Planetary gearbox is a critical component for rotating machinery. It is widely used in wind turbines, aerospace and transmission systems in heavy industry. Thus, it is important to monitor planetary gearboxes, especially for fault diagnostics, during its operational conditions. However, in practice, operational conditions of planetary gearbox are often characterized by variations of rotational speeds and loads, which may bring difficulties for fault diagnosis through the measured vibrations. In this paper, phase angle data extracted from measured planetary gearbox vibrations is used for fault detection under non-stationary operational conditions. Together with sample entropy, fault diagnosis on planetary gearbox is implemented. The proposed scheme is explained and demonstrated in both simulation and experimental studies. The scheme proves to be effective and features advantages on fault diagnosis of planetary gearboxes under non-stationary operational conditions.

Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification

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

Chen Sheng; Suzuki, Kenji; MacMahon, Heber

2011-04-15

Purpose: To develop a computer-aided detection (CADe) scheme for nodules in chest radiographs (CXRs) with a high sensitivity and a low false-positive (FP) rate. Methods: The authors developed a CADe scheme consisting of five major steps, which were developed for improving the overall performance of CADe schemes. First, to segment the lung fields accurately, the authors developed a multisegment active shape model. Then, a two-stage nodule-enhancement technique was developed for improving the conspicuity of nodules. Initial nodule candidates were detected and segmented by using the clustering watershed algorithm. Thirty-one shape-, gray-level-, surface-, and gradient-based features were extracted from each segmentedmore » candidate for determining the feature space, including one of the new features based on the Canny edge detector to eliminate a major FP source caused by rib crossings. Finally, a nonlinear support vector machine (SVM) with a Gaussian kernel was employed for classification of the nodule candidates. Results: To evaluate and compare the scheme to other published CADe schemes, the authors used a publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs. The CADe scheme based on the SVM classifier achieved sensitivities of 78.6% (110/140) and 71.4% (100/140) with averages of 5.0 (1165/233) FPs/image and 2.0 (466/233) FPs/image, respectively, in a leave-one-out cross-validation test, whereas the CADe scheme based on a linear discriminant analysis classifier had a sensitivity of 60.7% (85/140) at an FP rate of 5.0 FPs/image. For nodules classified as ''very subtle'' and ''extremely subtle,'' a sensitivity of 57.1% (24/42) was achieved at an FP rate of 5.0 FPs/image. When the authors used a database developed at the University of Chicago, the sensitivities was 83.3% (40/48) and 77.1% (37/48) at an FP rate of 5.0 (240/48) FPs/image and 2.0 (96/48) FPs /image, respectively. Conclusions: These results compare favorably to those described for other commercial and noncommercial CADe nodule detection systems.« less

Quantitative analysis of facial paralysis using local binary patterns in biomedical videos.

PubMed

He, Shu; Soraghan, John J; O'Reilly, Brian F; Xing, Dongshan

2009-07-01

Facial paralysis is the loss of voluntary muscle movement of one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents a novel framework for objective measurement of facial paralysis. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBPs) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of novel block processing schemes. A multiresolution extension of uniform LBP is proposed to efficiently combine the micropatterns and large-scale patterns into a feature vector. The symmetry of facial movements is measured by the resistor-average distance (RAD) between LBP features extracted from the two sides of the face. Support vector machine is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM.

PubMed

Raj, Sandeep; Ray, Kailash Chandra; Shankar, Om

2016-11-01

The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature. The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automated image segmentation-assisted flattening of atomic force microscopy images.

PubMed

Wang, Yuliang; Lu, Tongda; Li, Xiaolai; Wang, Huimin

2018-01-01

Atomic force microscopy (AFM) images normally exhibit various artifacts. As a result, image flattening is required prior to image analysis. To obtain optimized flattening results, foreground features are generally manually excluded using rectangular masks in image flattening, which is time consuming and inaccurate. In this study, a two-step scheme was proposed to achieve optimized image flattening in an automated manner. In the first step, the convex and concave features in the foreground were automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images. Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method.

A multimodal biometric authentication system based on 2D and 3D palmprint features

NASA Astrophysics Data System (ADS)

Aggithaya, Vivek K.; Zhang, David; Luo, Nan

2008-03-01

This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palmprint features. Here, we aim to improve the accuracy and robustness of existing palmprint authentication systems using 3D palmprint features. The proposed system uses an active stereo technique, structured light, to capture 3D image or range data of the palm and a registered intensity image simultaneously. The surface curvature based method is employed to extract features from 3D palmprint and Gabor feature based competitive coding scheme is used for 2D representation. We individually analyze these representations and attempt to combine them with score level fusion technique. Our experiments on a database of 108 subjects achieve significant improvement in performance (Equal Error Rate) with the integration of 3D features as compared to the case when 2D palmprint features alone are employed.

Single-Grasp Object Classification and Feature Extraction with Simple Robot Hands and Tactile Sensors.

PubMed

Spiers, Adam J; Liarokapis, Minas V; Calli, Berk; Dollar, Aaron M

2016-01-01

Classical robotic approaches to tactile object identification often involve rigid mechanical grippers, dense sensor arrays, and exploratory procedures (EPs). Though EPs are a natural method for humans to acquire object information, evidence also exists for meaningful tactile property inference from brief, non-exploratory motions (a 'haptic glance'). In this work, we implement tactile object identification and feature extraction techniques on data acquired during a single, unplanned grasp with a simple, underactuated robot hand equipped with inexpensive barometric pressure sensors. Our methodology utilizes two cooperating schemes based on an advanced machine learning technique (random forests) and parametric methods that estimate object properties. The available data is limited to actuator positions (one per two link finger) and force sensors values (eight per finger). The schemes are able to work both independently and collaboratively, depending on the task scenario. When collaborating, the results of each method contribute to the other, improving the overall result in a synergistic fashion. Unlike prior work, the proposed approach does not require object exploration, re-grasping, grasp-release, or force modulation and works for arbitrary object start positions and orientations. Due to these factors, the technique may be integrated into practical robotic grasping scenarios without adding time or manipulation overheads.

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition.

PubMed

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-03-27

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K -nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.

Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Lu, Xianglan; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Li, Shibo; Liu, Hong; Zheng, Bin

2016-03-01

Automated high throughput scanning microscopy is a fast developing screening technology used in cytogenetic laboratories for the diagnosis of leukemia or other genetic diseases. However, one of the major challenges of using this new technology is how to efficiently detect the analyzable metaphase chromosomes during the scanning process. The purpose of this investigation is to develop a computer aided detection (CAD) scheme based on deep learning technology, which can identify the metaphase chromosomes with high accuracy. The CAD scheme includes an eight layer neural network. The first six layers compose of an automatic feature extraction module, which has an architecture of three convolution-max-pooling layer pairs. The 1st, 2nd and 3rd pair contains 30, 20, 20 feature maps, respectively. The seventh and eighth layers compose of a multiple layer perception (MLP) based classifier, which is used to identify the analyzable metaphase chromosomes. The performance of new CAD scheme was assessed by receiver operation characteristic (ROC) method. A number of 150 regions of interest (ROIs) were selected to test the performance of our new CAD scheme. Each ROI contains either interphase cell or metaphase chromosomes. The results indicate that new scheme is able to achieve an area under the ROC curve (AUC) of 0.886+/-0.043. This investigation demonstrates that applying a deep learning technique may enable to significantly improve the accuracy of the metaphase chromosome detection using a scanning microscopic imaging technology in the future.

Distillation and purification of symmetric entangled Gaussian states

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

Fiurasek, Jaromir

2010-10-15

We propose an entanglement distillation and purification scheme for symmetric two-mode entangled Gaussian states that allows to asymptotically extract a pure entangled Gaussian state from any input entangled symmetric Gaussian state. The proposed scheme is a modified and extended version of the entanglement distillation protocol originally developed by Browne et al. [Phys. Rev. A 67, 062320 (2003)]. A key feature of the present protocol is that it utilizes a two-copy degaussification procedure that involves a Mach-Zehnder interferometer with single-mode non-Gaussian filters inserted in its two arms. The required non-Gaussian filtering operations can be implemented by coherently combining two sequences ofmore » single-photon addition and subtraction operations.« less

Iris recognition using possibilistic fuzzy matching on local features.

PubMed

Tsai, Chung-Chih; Lin, Heng-Yi; Taur, Jinshiuh; Tao, Chin-Wang

2012-02-01

In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.

Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier

NASA Astrophysics Data System (ADS)

Rasti, Reza; Mehridehnavi, Alireza; Rabbani, Hossein; Hajizadeh, Fedra

2018-03-01

The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.

Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier.

PubMed

Rasti, Reza; Mehridehnavi, Alireza; Rabbani, Hossein; Hajizadeh, Fedra

2018-03-01

The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Extracting Tree Height from Repeat-Pass PolInSAR Data : Experiments with JPL and ESA Airborne Systems

NASA Technical Reports Server (NTRS)

Lavalle, Marco; Ahmed, Razi; Neumann, Maxim; Hensley, Scott

2013-01-01

In this paper we present our latest developments and experiments with the random-motion-over-ground (RMoG) model used to extract canopy height and other important forest parameters from repeat-pass polarimetricinterferometric SAR (Pol-InSAR) data. More specifically, we summarize the key features of the RMoG model in contrast with the random-volume-over-ground (RVoG) model, describe in detail a possible inversion scheme for the RMoG model and illustrate the results of the RMoG inversion using airborne data collected by the Jet Propulsion Laboratory (JPL) and the European Space Agency (ESA).

Discovery of Predicate-Oriented Relations among Named Entities Extracted from Thai Texts

NASA Astrophysics Data System (ADS)

Tongtep, Nattapong; Theeramunkong, Thanaruk

Extracting named entities (NEs) and their relations is more difficult in Thai than in other languages due to several Thai specific characteristics, including no explicit boundaries for words, phrases and sentences; few case markers and modifier clues; high ambiguity in compound words and serial verbs; and flexible word orders. Unlike most previous works which focused on NE relations of specific actions, such as work_for, live_in, located_in, and kill, this paper proposes more general types of NE relations, called predicate-oriented relation (PoR), where an extracted action part (verb) is used as a core component to associate related named entities extracted from Thai Texts. Lacking a practical parser for the Thai language, we present three types of surface features, i.e. punctuation marks (such as token spaces), entity types and the number of entities and then apply five alternative commonly used learning schemes to investigate their performance on predicate-oriented relation extraction. The experimental results show that our approach achieves the F-measure of 97.76%, 99.19%, 95.00% and 93.50% on four different types of predicate-oriented relation (action-location, location-action, action-person and person-action) in crime-related news documents using a data set of 1,736 entity pairs. The effects of NE extraction techniques, feature sets and class unbalance on the performance of relation extraction are explored.

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.

PubMed

Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

2017-04-01

In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data. Copyright © 2016. Published by Elsevier Ltd.

Toward privacy-preserving JPEG image retrieval

NASA Astrophysics Data System (ADS)

Cheng, Hang; Wang, Jingyue; Wang, Meiqing; Zhong, Shangping

2017-07-01

This paper proposes a privacy-preserving retrieval scheme for JPEG images based on local variance. Three parties are involved in the scheme: the content owner, the server, and the authorized user. The content owner encrypts JPEG images for privacy protection by jointly using permutation cipher and stream cipher, and then, the encrypted versions are uploaded to the server. With an encrypted query image provided by an authorized user, the server may extract blockwise local variances in different directions without knowing the plaintext content. After that, it can calculate the similarity between the encrypted query image and each encrypted database image by a local variance-based feature comparison mechanism. The authorized user with the encryption key can decrypt the returned encrypted images with plaintext content similar to the query image. The experimental results show that the proposed scheme not only provides effective privacy-preserving retrieval service but also ensures both format compliance and file size preservation for encrypted JPEG images.

Improvement on Gabor order tracking and objective comparison with Vold Kalman filtering order tracking

NASA Astrophysics Data System (ADS)

Pan, Min-Chun; Liao, Shiu-Wei; Chiu, Chun-Chin

2007-02-01

The waveform-reconstruction schemes of order tracking (OT) such as the Gabor and the Vold-Kalman filtering (VKF) techniques can extract specific order and/or spectral components in addition to characterizing the processed signal in rpm-frequency domain. The study first improves the Gabor OT (GOT) technique to handle the order-crossing problem, and then objectively compares the features of the improved GOT scheme and the angular-displacement VKF OT technique. It is numerically observed the improved method performs less accurately than the VKF_OT scheme at the crossing occurrences, but without end effect in the reconstructed waveform. As OT is not exact science, it may well be that the decrease in computation time can justify the reduced accuracy. The characterisation and discrimination of riding noise with crossing orders emitted by an electrical scooter are conducted as an example of the application.

Extraction of Urban Trees from Integrated Airborne Based Digital Image and LIDAR Point Cloud Datasets - Initial Results

NASA Astrophysics Data System (ADS)

Dogon-yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

2016-10-01

Timely and accurate acquisition of information on the condition and structural changes of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting tree features include; ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraint, such as labour intensive field work, a lot of financial requirement, influences by weather condition and topographical covers which can be overcome by means of integrated airborne based LiDAR and very high resolution digital image datasets. This study presented a semi-automated approach for extracting urban trees from integrated airborne based LIDAR and multispectral digital image datasets over Istanbul city of Turkey. The above scheme includes detection and extraction of shadow free vegetation features based on spectral properties of digital images using shadow index and NDVI techniques and automated extraction of 3D information about vegetation features from the integrated processing of shadow free vegetation image and LiDAR point cloud datasets. The ability of the developed algorithms shows a promising result as an automated and cost effective approach to estimating and delineated 3D information of urban trees. The research also proved that integrated datasets is a suitable technology and a viable source of information for city managers to be used in urban trees management.

A hybrid feature selection and health indicator construction scheme for delay-time-based degradation modelling of rolling element bearings

NASA Astrophysics Data System (ADS)

Zhang, Bin; Deng, Congying; Zhang, Yi

2018-03-01

Rolling element bearings are mechanical components used frequently in most rotating machinery and they are also vulnerable links representing the main source of failures in such systems. Thus, health condition monitoring and fault diagnosis of rolling element bearings have long been studied to improve operational reliability and maintenance efficiency of rotatory machines. Over the past decade, prognosis that enables forewarning of failure and estimation of residual life attracted increasing attention. To accurately and efficiently predict failure of the rolling element bearing, the degradation requires to be well represented and modelled. For this purpose, degradation of the rolling element bearing is analysed with the delay-time-based model in this paper. Also, a hybrid feature selection and health indicator construction scheme is proposed for extraction of the bearing health relevant information from condition monitoring sensor data. Effectiveness of the presented approach is validated through case studies on rolling element bearing run-to-failure experiments.

A multi-view face recognition system based on cascade face detector and improved Dlib

NASA Astrophysics Data System (ADS)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Note: Fully integrated 3.2 Gbps quantum random number generator with real-time extraction

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

Zhang, Xiao-Guang; Nie, You-Qi; Liang, Hao

2016-07-15

We present a real-time and fully integrated quantum random number generator (QRNG) by measuring laser phase fluctuations. The QRNG scheme based on laser phase fluctuations is featured for its capability of generating ultra-high-speed random numbers. However, the speed bottleneck of a practical QRNG lies on the limited speed of randomness extraction. To close the gap between the fast randomness generation and the slow post-processing, we propose a pipeline extraction algorithm based on Toeplitz matrix hashing and implement it in a high-speed field-programmable gate array. Further, all the QRNG components are integrated into a module, including a compact and actively stabilizedmore » interferometer, high-speed data acquisition, and real-time data post-processing and transmission. The final generation rate of the QRNG module with real-time extraction can reach 3.2 Gbps.« less

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

PubMed

Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

2015-07-01

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

Extensions of algebraic image operators: An approach to model-based vision

NASA Technical Reports Server (NTRS)

Lerner, Bao-Ting; Morelli, Michael V.

1990-01-01

Researchers extend their previous research on a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets. Addition and multiplication are defined for the set of all grey-level images, which can then be described as polynomials of two variables. Utilizing this new algebraic structure, researchers devised an innovative, efficient edge detection scheme. An accurate method for deriving gradient component information from this edge detector is presented. Based upon this new edge detection system researchers developed a robust method for linear feature extraction by combining the techniques of a Hough transform and a line follower. The major advantage of this feature extractor is its general, object-independent nature. Target attributes, such as line segment lengths, intersections, angles of intersection, and endpoints are derived by the feature extraction algorithm and employed during model matching. The algebraic operators are global operations which are easily reconfigured to operate on any size or shape region. This provides a natural platform from which to pursue dynamic scene analysis. A method for optimizing the linear feature extractor which capitalizes on the spatially reconfiguration nature of the edge detector/gradient component operator is discussed.

Visualizing and enhancing a deep learning framework using patients age and gender for chest x-ray image retrieval

NASA Astrophysics Data System (ADS)

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2016-03-01

We explore the combination of text metadata, such as patients' age and gender, with image-based features, for X-ray chest pathology image retrieval. We focus on a feature set extracted from a pre-trained deep convolutional network shown in earlier work to achieve state-of-the-art results. Two distance measures are explored: a descriptor-based measure, which computes the distance between image descriptors, and a classification-based measure, which performed by a comparison of the corresponding SVM classification probabilities. We show that retrieval results increase once the age and gender information combined with the features extracted from the last layers of the network, with best results using the classification-based scheme. Visualization of the X-ray data is presented by embedding the high dimensional deep learning features in a 2-D dimensional space while preserving the pairwise distances using the t-SNE algorithm. The 2-D visualization gives the unique ability to find groups of X-ray images that are similar to the query image and among themselves, which is a characteristic we do not see in a 1-D traditional ranking.

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

PubMed Central

Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

2013-01-01

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568

Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity.

PubMed

Bichler, Olivier; Querlioz, Damien; Thorpe, Simon J; Bourgoin, Jean-Philippe; Gamrat, Christian

2012-08-01

A biologically inspired approach to learning temporally correlated patterns from a spiking silicon retina is presented. Spikes are generated from the retina in response to relative changes in illumination at the pixel level and transmitted to a feed-forward spiking neural network. Neurons become sensitive to patterns of pixels with correlated activation times, in a fully unsupervised scheme. This is achieved using a special form of Spike-Timing-Dependent Plasticity which depresses synapses that did not recently contribute to the post-synaptic spike activation, regardless of their activation time. Competitive learning is implemented with lateral inhibition. When tested with real-life data, the system is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway, after only 10 min of traffic learning. Complete trajectories can be learned with a 98% detection rate using a second layer, still with unsupervised learning, and the system may be used as a car counter. The proposed neural network is extremely robust to noise and it can tolerate a high degree of synaptic and neuronal variability with little impact on performance. Such results show that a simple biologically inspired unsupervised learning scheme is capable of generating selectivity to complex meaningful events on the basis of relatively little sensory experience. Copyright © 2012 Elsevier Ltd. All rights reserved.

Application of Multi-task Sparse Lasso Feature Extraction and Support Vector Machine Regression in the Stellar Atmospheric Parameterization

NASA Astrophysics Data System (ADS)

Gao, Wei; Li, Xiang-ru

2017-07-01

The multi-task learning takes the multiple tasks together to make analysis and calculation, so as to dig out the correlations among them, and therefore to improve the accuracy of the analyzed results. This kind of methods have been widely applied to the machine learning, pattern recognition, computer vision, and other related fields. This paper investigates the application of multi-task learning in estimating the stellar atmospheric parameters, including the surface temperature (Teff), surface gravitational acceleration (lg g), and chemical abundance ([Fe/H]). Firstly, the spectral features of the three stellar atmospheric parameters are extracted by using the multi-task sparse group Lasso algorithm, then the support vector machine is used to estimate the atmospheric physical parameters. The proposed scheme is evaluated on both the Sloan stellar spectra and the theoretical spectra computed from the Kurucz's New Opacity Distribution Function (NEWODF) model. The mean absolute errors (MAEs) on the Sloan spectra are: 0.0064 for lg (Teff /K), 0.1622 for lg (g/(cm · s-2)), and 0.1221 dex for [Fe/H]; the MAEs on the synthetic spectra are 0.0006 for lg (Teff /K), 0.0098 for lg (g/(cm · s-2)), and 0.0082 dex for [Fe/H]. Experimental results show that the proposed scheme has a rather high accuracy for the estimation of stellar atmospheric parameters.

Three-dimensional tracking for efficient fire fighting in complex situations

NASA Astrophysics Data System (ADS)

Akhloufi, Moulay; Rossi, Lucile

2009-05-01

Each year, hundred millions hectares of forests burn causing human and economic losses. For efficient fire fighting, the personnel in the ground need tools permitting the prediction of fire front propagation. In this work, we present a new technique for automatically tracking fire spread in three-dimensional space. The proposed approach uses a stereo system to extract a 3D shape from fire images. A new segmentation technique is proposed and permits the extraction of fire regions in complex unstructured scenes. It works in the visible spectrum and combines information extracted from YUV and RGB color spaces. Unlike other techniques, our algorithm does not require previous knowledge about the scene. The resulting fire regions are classified into different homogenous zones using clustering techniques. Contours are then extracted and a feature detection algorithm is used to detect interest points like local maxima and corners. Extracted points from stereo images are then used to compute the 3D shape of the fire front. The resulting data permits to build the fire volume. The final model is used to compute important spatial and temporal fire characteristics like: spread dynamics, local orientation, heading direction, etc. Tests conducted on the ground show the efficiency of the proposed scheme. This scheme is being integrated with a fire spread mathematical model in order to predict and anticipate the fire behaviour during fire fighting. Also of interest to fire-fighters, is the proposed automatic segmentation technique that can be used in early detection of fire in complex scenes.

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

PubMed

Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

2014-06-01

To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition

PubMed Central

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-01-01

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction. PMID:28346385

View subspaces for indexing and retrieval of 3D models

NASA Astrophysics Data System (ADS)

Dutagaci, Helin; Godil, Afzal; Sankur, Bülent; Yemez, Yücel

2010-02-01

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms.

Robust feature extraction for rapid classification of damage in composites

NASA Astrophysics Data System (ADS)

Coelho, Clyde K.; Reynolds, Whitney; Chattopadhyay, Aditi

2009-03-01

The ability to detect anomalies in signals from sensors is imperative for structural health monitoring (SHM) applications. Many of the candidate algorithms for these applications either require a lot of training examples or are very computationally inefficient for large sample sizes. The damage detection framework presented in this paper uses a combination of Linear Discriminant Analysis (LDA) along with Support Vector Machines (SVM) to obtain a computationally efficient classification scheme for rapid damage state determination. LDA was used for feature extraction of damage signals from piezoelectric sensors on a composite plate and these features were used to train the SVM algorithm in parts, reducing the computational intensity associated with the quadratic optimization problem that needs to be solved during training. SVM classifiers were organized into a binary tree structure to speed up classification, which also reduces the total training time required. This framework was validated on composite plates that were impacted at various locations. The results show that the algorithm was able to correctly predict the different impact damage cases in composite laminates using less than 21 percent of the total available training data after data reduction.

A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.

PubMed

Fan, Ming; Zheng, Bin; Li, Lihua

2015-10-01

Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.

Illumination-invariant and deformation-tolerant inner knuckle print recognition using portable devices.

PubMed

Xu, Xuemiao; Jin, Qiang; Zhou, Le; Qin, Jing; Wong, Tien-Tsin; Han, Guoqiang

2015-02-12

We propose a novel biometric recognition method that identifies the inner knuckle print (IKP). It is robust enough to confront uncontrolled lighting conditions, pose variations and low imaging quality. Such robustness is crucial for its application on portable devices equipped with consumer-level cameras. We achieve this robustness by two means. First, we propose a novel feature extraction scheme that highlights the salient structure and suppresses incorrect and/or unwanted features. The extracted IKP features retain simple geometry and morphology and reduce the interference of illumination. Second, to counteract the deformation induced by different hand orientations, we propose a novel structure-context descriptor based on local statistics. To our best knowledge, we are the first to simultaneously consider the illumination invariance and deformation tolerance for appearance-based low-resolution hand biometrics. Settings in previous works are more restrictive. They made strong assumptions either about the illumination condition or the restrictive hand orientation. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of recognition accuracy, especially under uncontrolled lighting conditions and the flexible hand orientation requirement.

Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices

PubMed Central

Xu, Xuemiao; Jin, Qiang; Zhou, Le; Qin, Jing; Wong, Tien-Tsin; Han, Guoqiang

2015-01-01

We propose a novel biometric recognition method that identifies the inner knuckle print (IKP). It is robust enough to confront uncontrolled lighting conditions, pose variations and low imaging quality. Such robustness is crucial for its application on portable devices equipped with consumer-level cameras. We achieve this robustness by two means. First, we propose a novel feature extraction scheme that highlights the salient structure and suppresses incorrect and/or unwanted features. The extracted IKP features retain simple geometry and morphology and reduce the interference of illumination. Second, to counteract the deformation induced by different hand orientations, we propose a novel structure-context descriptor based on local statistics. To our best knowledge, we are the first to simultaneously consider the illumination invariance and deformation tolerance for appearance-based low-resolution hand biometrics. Settings in previous works are more restrictive. They made strong assumptions either about the illumination condition or the restrictive hand orientation. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of recognition accuracy, especially under uncontrolled lighting conditions and the flexible hand orientation requirement. PMID:25686317

Predicting the Valence of a Scene from Observers’ Eye Movements

PubMed Central

R.-Tavakoli, Hamed; Atyabi, Adham; Rantanen, Antti; Laukka, Seppo J.; Nefti-Meziani, Samia; Heikkilä, Janne

2015-01-01

Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that ‘saliency map’, ‘fixation histogram’, ‘histogram of fixation duration’, and ‘histogram of saccade slope’ are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images. PMID:26407322

Automated identification of abnormal metaphase chromosome cells for the detection of chronic myeloid leukemia using microscopic images

NASA Astrophysics Data System (ADS)

Wang, Xingwei; Zheng, Bin; Li, Shibo; Mulvihill, John J.; Chen, Xiaodong; Liu, Hong

2010-07-01

Karyotyping is an important process to classify chromosomes into standard classes and the results are routinely used by the clinicians to diagnose cancers and genetic diseases. However, visual karyotyping using microscopic images is time-consuming and tedious, which reduces the diagnostic efficiency and accuracy. Although many efforts have been made to develop computerized schemes for automated karyotyping, no schemes can get be performed without substantial human intervention. Instead of developing a method to classify all chromosome classes, we develop an automatic scheme to detect abnormal metaphase cells by identifying a specific class of chromosomes (class 22) and prescreen for suspicious chronic myeloid leukemia (CML). The scheme includes three steps: (1) iteratively segment randomly distributed individual chromosomes, (2) process segmented chromosomes and compute image features to identify the candidates, and (3) apply an adaptive matching template to identify chromosomes of class 22. An image data set of 451 metaphase cells extracted from bone marrow specimens of 30 positive and 30 negative cases for CML is selected to test the scheme's performance. The overall case-based classification accuracy is 93.3% (100% sensitivity and 86.7% specificity). The results demonstrate the feasibility of applying an automated scheme to detect or prescreen the suspicious cancer cases.

Automatic movie skimming with general tempo analysis

NASA Astrophysics Data System (ADS)

Lee, Shih-Hung; Yeh, Chia-Hung; Kuo, C. C. J.

2003-11-01

Story units are extracted by general tempo analysis including tempos analysis including tempos of audio and visual information in this research. Although many schemes have been proposed to successfully segment video data into shots using basic low-level features, how to group shots into meaningful units called story units is still a challenging problem. By focusing on a certain type of video such as sport or news, we can explore models with the specific application domain knowledge. For movie contents, many heuristic rules based on audiovisual clues have been proposed with limited success. We propose a method to extract story units using general tempo analysis. Experimental results are given to demonstrate the feasibility and efficiency of the proposed technique.

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

NASA Astrophysics Data System (ADS)

Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

2017-04-01

Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

Respiratory Artefact Removal in Forced Oscillation Measurements: A Machine Learning Approach.

PubMed

Pham, Thuy T; Thamrin, Cindy; Robinson, Paul D; McEwan, Alistair L; Leong, Philip H W

2017-08-01

Respiratory artefact removal for the forced oscillation technique can be treated as an anomaly detection problem. Manual removal is currently considered the gold standard, but this approach is laborious and subjective. Most existing automated techniques used simple statistics and/or rejected anomalous data points. Unfortunately, simple statistics are insensitive to numerous artefacts, leading to low reproducibility of results. Furthermore, rejecting anomalous data points causes an imbalance between the inspiratory and expiratory contributions. From a machine learning perspective, such methods are unsupervised and can be considered simple feature extraction. We hypothesize that supervised techniques can be used to find improved features that are more discriminative and more highly correlated with the desired output. Features thus found are then used for anomaly detection by applying quartile thresholding, which rejects complete breaths if one of its features is out of range. The thresholds are determined by both saliency and performance metrics rather than qualitative assumptions as in previous works. Feature ranking indicates that our new landmark features are among the highest scoring candidates regardless of age across saliency criteria. F1-scores, receiver operating characteristic, and variability of the mean resistance metrics show that the proposed scheme outperforms previous simple feature extraction approaches. Our subject-independent detector, 1IQR-SU, demonstrated approval rates of 80.6% for adults and 98% for children, higher than existing methods. Our new features are more relevant. Our removal is objective and comparable to the manual method. This is a critical work to automate forced oscillation technique quality control.

Detrended fluctuation analysis for major depressive disorder.

PubMed

Mumtaz, Wajid; Malik, Aamir Saeed; Ali, Syed Saad Azhar; Yasin, Mohd Azhar Mohd; Amin, Hafeezullah

2015-01-01

Clinical utility of Electroencephalography (EEG) based diagnostic studies is less clear for major depressive disorder (MDD). In this paper, a novel machine learning (ML) scheme was presented to discriminate the MDD patients and healthy controls. The proposed method inherently involved feature extraction, selection, classification and validation. The EEG data acquisition involved eyes closed (EC) and eyes open (EO) conditions. At feature extraction stage, the de-trended fluctuation analysis (DFA) was performed, based on the EEG data, to achieve scaling exponents. The DFA was performed to analyzes the presence or absence of long-range temporal correlations (LRTC) in the recorded EEG data. The scaling exponents were used as input features to our proposed system. At feature selection stage, 3 different techniques were used for comparison purposes. Logistic regression (LR) classifier was employed. The method was validated by a 10-fold cross-validation. As results, we have observed that the effect of 3 different reference montages on the computed features. The proposed method employed 3 different types of feature selection techniques for comparison purposes as well. The results show that the DFA analysis performed better in LE data compared with the IR and AR data. In addition, during Wilcoxon ranking, the AR performed better than LE and IR. Based on the results, it was concluded that the DFA provided useful information to discriminate the MDD patients and with further validation can be employed in clinics for diagnosis of MDD.

Statistical analysis of textural features for improved classification of oral histopathological images.

PubMed

Muthu Rama Krishnan, M; Shah, Pratik; Chakraborty, Chandan; Ray, Ajoy K

2012-04-01

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback-Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier's performance from 80.69% to 90.75%. Results are here studied and discussed.

Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection

NASA Astrophysics Data System (ADS)

Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.

2015-04-01

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

Practical low-cost visual communication using binary images for deaf sign language.

PubMed

Manoranjan, M D; Robinson, J A

2000-03-01

Deaf sign language transmitted by video requires a temporal resolution of 8 to 10 frames/s for effective communication. Conventional videoconferencing applications, when operated over low bandwidth telephone lines, provide very low temporal resolution of pictures, of the order of less than a frame per second, resulting in jerky movement of objects. This paper presents a practical solution for sign language communication, offering adequate temporal resolution of images using moving binary sketches or cartoons, implemented on standard personal computer hardware with low-cost cameras and communicating over telephone lines. To extract cartoon points an efficient feature extraction algorithm adaptive to the global statistics of the image is proposed. To improve the subjective quality of the binary images, irreversible preprocessing techniques, such as isolated point removal and predictive filtering, are used. A simple, efficient and fast recursive temporal prefiltering scheme, using histograms of successive frames, reduces the additive and multiplicative noise from low-cost cameras. An efficient three-dimensional (3-D) compression scheme codes the binary sketches. Subjective tests performed on the system confirm that it can be used for sign language communication over telephone lines.

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

PubMed

Sun, Wenqing; Zheng, Bin; Qian, Wei

2017-10-01

This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

2016-03-01

Although mammography is the only clinically acceptable imaging modality used in the population-based breast cancer screening, its efficacy is quite controversy. One of the major challenges is how to help radiologists more accurately classify between benign and malignant lesions. The purpose of this study is to investigate a new mammographic mass classification scheme based on a deep learning method. In this study, we used an image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms, which includes 280 malignant and 280 benign mass ROIs, respectively. An eight layer deep learning network was applied, which employs three pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perception (MLP) classifier for feature categorization. In order to improve robustness of selected features, each convolution layer is connected with a max-pooling layer. A number of 20, 10, and 5 feature maps were utilized for the 1st, 2nd and 3rd convolution layer, respectively. The convolution networks are followed by a MLP classifier, which generates a classification score to predict likelihood of a ROI depicting a malignant mass. Among 560 ROIs, 420 ROIs were used as a training dataset and the remaining 140 ROIs were used as a validation dataset. The result shows that the new deep learning based classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.810+/-0.036. This study demonstrated the potential superiority of using a deep learning based classifier to distinguish malignant and benign breast masses without segmenting the lesions and extracting the pre-defined image features.

The Solution to Pollution is Distribution: Design Your Own Chaotic Flow

NASA Astrophysics Data System (ADS)

Tigera, R. G.; Roth, E. J.; Neupauer, R.; Mays, D. C.

2015-12-01

Plume spreading promotes the molecular mixing that drives chemical reactions in porous media in general, and remediation reactions in groundwater aquifers in particular. Theoretical analysis suggests that engineered injection and extraction, a specific sequence of pumping through wells surrounding a contaminant plume, can improve groundwater remediation through chaotic advection. Selection of an engineered injection and extraction scheme is difficult, however, because the engineer is faced with the difficulty of recommending a pumping scheme for a contaminated site without having any previous knowledge of how the scheme will perform. To address this difficulty, this presentation describes a Graphical User Interface (GUI) designed to help engineers develop, test, and observe pumping schemes as described in previous research (Mays, D.C. and Neupauer, R.M., 2012, Plume spreading in groundwater by stretching and folding, Water Resour. Res., 48, W07501, doi:10.1029/2011WR011567). The inputs allow the user to manipulate the model conditions such as number of wells, plume size, and pumping scheme. Plume evolution is modeled, assuming no diffusion or dispersion, using analytical solutions for injection or extraction through individual wells or pairs or wells (i.e., dipoles). Using the GUI, an engineered injection and extraction scheme can be determined that best fits the remediation needs of the contaminated site. By creating multiple injection and extraction schemes, the user can learn about the plume shapes created from different schemes and, ultimately, recommend a pumping scheme based on some experience of fluid flow as shown in the GUI. The pumping schemes developed through this GUI are expected to guide more advanced modeling and laboratory studies that account for the crucial role of dispersion in groundwater remediation.

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

Design of Cancelable Palmprint Templates Based on Look Up Table

NASA Astrophysics Data System (ADS)

Qiu, Jian; Li, Hengjian; Dong, Jiwen

2018-03-01

A novel cancelable palmprint templates generation scheme is proposed in this paper. Firstly, the Gabor filter and chaotic matrix are used to extract palmprint features. It is then arranged into a row vector and divided into equal size blocks. These blocks are converted to corresponding decimals and mapped to look up tables, forming final cancelable palmprint features based on the selected check bits. Finally, collaborative representation based classification with regularized least square is used for classification. Experimental results on the Hong Kong PolyU Palmprint Database verify that the proposed cancelable templates can achieve very high performance and security levels. Meanwhile, it can also satisfy the needs of real-time applications.

Reaction schemes visualized in network form: the syntheses of strychnine as an example.

PubMed

Proudfoot, John R

2013-05-24

Representation of synthesis sequences in a network form provides an effective method for the comparison of multiple reaction schemes and an opportunity to emphasize features such as reaction scale that are often relegated to experimental sections. An example of data formatting that allows construction of network maps in Cytoscape is presented, along with maps that illustrate the comparison of multiple reaction sequences, comparison of scaffold changes within sequences, and consolidation to highlight common key intermediates used across sequences. The 17 different synthetic routes reported for strychnine are used as an example basis set. The reaction maps presented required a significant data extraction and curation, and a standardized tabular format for reporting reaction information, if applied in a consistent way, could allow the automated combination of reaction information across different sources.

Local intensity adaptive image coding

NASA Technical Reports Server (NTRS)

Huck, Friedrich O.

1989-01-01

The objective of preprocessing for machine vision is to extract intrinsic target properties. The most important properties ordinarily are structure and reflectance. Illumination in space, however, is a significant problem as the extreme range of light intensity, stretching from deep shadow to highly reflective surfaces in direct sunlight, impairs the effectiveness of standard approaches to machine vision. To overcome this critical constraint, an image coding scheme is being investigated which combines local intensity adaptivity, image enhancement, and data compression. It is very effective under the highly variant illumination that can exist within a single frame or field of view, and it is very robust to noise at low illuminations. Some of the theory and salient features of the coding scheme are reviewed. Its performance is characterized in a simulated space application, the research and development activities are described.

Learning target masks in infrared linescan imagery

NASA Astrophysics Data System (ADS)

Fechner, Thomas; Rockinger, Oliver; Vogler, Axel; Knappe, Peter

1997-04-01

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

Emotion recognition from EEG using higher order crossings.

PubMed

Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J

2010-03-01

Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion experiences, recurring affective states, time-dependent emotional trends).

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

NASA Technical Reports Server (NTRS)

Solarna, David; Moser, Gabriele; Le Moigne-Stewart, Jacqueline; Serpico, Sebastiano B.

2017-01-01

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images.

PubMed

Abbasian Ardakani, Ali; Gharbali, Akbar; Mohammadi, Afshin

2015-01-01

The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve ( Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.

Convex Hull Aided Registration Method (CHARM).

PubMed

Fan, Jingfan; Yang, Jian; Zhao, Yitian; Ai, Danni; Liu, Yonghuai; Wang, Ge; Wang, Yongtian

2017-09-01

Non-rigid registration finds many applications such as photogrammetry, motion tracking, model retrieval, and object recognition. In this paper we propose a novel convex hull aided registration method (CHARM) to match two point sets subject to a non-rigid transformation. First, two convex hulls are extracted from the source and target respectively. Then, all points of the point sets are projected onto the reference plane through each triangular facet of the hulls. From these projections, invariant features are extracted and matched optimally. The matched feature point pairs are mapped back onto the triangular facets of the convex hulls to remove outliers that are outside any relevant triangular facet. The rigid transformation from the source to the target is robustly estimated by the random sample consensus (RANSAC) scheme through minimizing the distance between the matched feature point pairs. Finally, these feature points are utilized as the control points to achieve non-rigid deformation in the form of thin-plate spline of the entire source point set towards the target one. The experimental results based on both synthetic and real data show that the proposed algorithm outperforms several state-of-the-art ones with respect to sampling, rotational angle, and data noise. In addition, the proposed CHARM algorithm also shows higher computational efficiency compared to these methods.

Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

PubMed Central

Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

2010-01-01

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

PubMed

Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

2010-01-01

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

Region based feature extraction from non-cooperative iris images using triplet half-band filter bank

NASA Astrophysics Data System (ADS)

Barpanda, Soubhagya Sankar; Majhi, Banshidhar; Sa, Pankaj Kumar

2015-09-01

In this paper, we have proposed energy based features using a multi-resolution analysis (MRA) on iris template. The MRA is based on our suggested triplet half-band filter bank (THFB). The THFB derivation process is discussed in detail. The iris template is divided into six equispaced sub-templates and two level decomposition has been made to each sub-template using THFB except second one. The reason for discarding the second template is due to the fact that it mostly contains the noise due to eyelids, eyelashes, and occlusion due to segmentation failure. Subsequently, energy features are derived from the decomposed coefficients of each sub-template. The proposed feature has been experimented on standard databases like CASIAv3, UBIRISv1, and IITD and mostly on iris images which encounter a segmentation failure. Comparative analysis has been done with existing features based on Gabor transform, Fourier transform, and CDF 9/7 filter bank. The proposed scheme shows superior performance with respect to FAR, GAR and AUC.

Multifractal texture estimation for detection and segmentation of brain tumors.

PubMed

Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

2013-11-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors

PubMed Central

Islam, Atiq; Reza, Syed M. S.

2016-01-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available. PMID:23807424

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

PubMed Central

Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon

2017-01-01

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs). PMID:29232908

A new classification scheme of plastic wastes based upon recycling labels

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

Özkan, Kemal, E-mail: kozkan@ogu.edu.tr; Ergin, Semih, E-mail: sergin@ogu.edu.tr; Işık, Şahin, E-mail: sahini@ogu.edu.tr

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize thesemore » materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.« less

Tracking Simulation of Third-Integer Resonant Extraction for Fermilab's Mu2e Experiment

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

Park, Chong Shik; Amundson, James; Michelotti, Leo

2015-02-13

The Mu2e experiment at Fermilab requires acceleration and transport of intense proton beams in order to deliver stable, uniform particle spills to the production target. To meet the experimental requirement, particles will be extracted slowly from the Delivery Ring to the external beamline. Using Synergia2, we have performed multi-particle tracking simulations of third-integer resonant extraction in the Delivery Ring, including space charge effects, physical beamline elements, and apertures. A piecewise linear ramp profile of tune quadrupoles was used to maintain a constant averaged spill rate throughout extraction. To study and minimize beam losses, we implemented and introduced a number ofmore » features, beamline element apertures, and septum plane alignments. Additionally, the RF Knockout (RFKO) technique, which excites particles transversely, is employed for spill regulation. Combined with a feedback system, it assists in fine-tuning spill uniformity. Simulation studies were carried out to optimize the RFKO feedback scheme, which will be helpful in designing the final spill regulation system.« less

Dental panoramic image analysis for enhancement biomarker of mandibular condyle for osteoporosis early detection

NASA Astrophysics Data System (ADS)

Suprijanto; Azhari; Juliastuti, E.; Septyvergy, A.; Setyagar, N. P. P.

2016-03-01

Osteoporosis is a degenerative disease characterized by low Bone Mineral Density (BMD). Currently, a BMD level is determined by Dual Energy X-ray Absorptiometry (DXA) at the lumbar vertebrae and femur. Previous studies reported that dental panoramic radiography image has potential information for early osteoporosis detection. This work reported alternative scheme, that consists of the determination of the Region of Interest (ROI) the condyle mandibular in the image as biomarker and feature extraction from ROI and classification of bone conditions. The minimum value of intensity in the cavity area is used to compensate an offset on the ROI. For feature extraction, the fraction of intensity values in the ROI that represent high bone density and the ROI total area is perfomed. The classification will be evaluated from the ability of each feature and its combinations for the BMD detection in 2 classes (normal and abnormal), with the artificial neural network method. The evaluation system used 105 panoramic image data from menopause women which consist of 36 training data and 69 test data that were divided into 2 classes. The 2 classes of classification obtained 88.0% accuracy rate and 88.0% sensitivity rate.

Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks.

PubMed

Deng, De-Ming; Chang, Cheng-Hung

2015-05-14

Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.

Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at participating nodes. Therefore, the feature-extraction method based on the Haar DWT is presented that employs a maximum-entropy measure to determine significant wavelet coefficients. Features are formed by calculating the energy of coefficients grouped around the competing clusters. A DWT-based feature extraction algorithm used for vehicle classification in WSNs can be enhanced by an added rule for selecting the optimal number of resolution levels to improve the correct classification rate and reduce energy consumption expended in local algorithm computations. Published field trial data for vehicular ground targets, measured with multiple sensor types, are used to evaluate the wavelet-assisted algorithms. Extracted features are used in established target recognition routines, e.g., the Bayesian minimum-error-rate classifier, to compare the effects on the classification performance of the wavelet compression. Simulations of feature sets and recognition routines at different resolution levels in target scenarios indicate the impact on classification rates, while formulas are provided to estimate reduction in resource use due to distributed compression.

Development and selection of Asian-specific humeral implants based on statistical atlas: toward planning minimally invasive surgery.

PubMed

Wu, K; Daruwalla, Z J; Wong, K L; Murphy, D; Ren, H

2015-08-01

The commercial humeral implants based on the Western population are currently not entirely compatible with Asian patients, due to differences in bone size, shape and structure. Surgeons may have to compromise or use different implants that are less conforming, which may cause complications of as well as inconvenience to the implant position. The construction of Asian humerus atlases of different clusters has therefore been proposed to eradicate this problem and to facilitate planning minimally invasive surgical procedures [6,31]. According to the features of the atlases, new implants could be designed specifically for different patients. Furthermore, an automatic implant selection algorithm has been proposed as well in order to reduce the complications caused by implant and bone mismatch. Prior to the design of the implant, data clustering and extraction of the relevant features were carried out on the datasets of each gender. The fuzzy C-means clustering method is explored in this paper. Besides, two new schemes of implant selection procedures, namely the Procrustes analysis-based scheme and the group average distance-based scheme, were proposed to better search for the matching implants for new coming patients from the database. Both these two algorithms have not been used in this area, while they turn out to have excellent performance in implant selection. Additionally, algorithms to calculate the matching scores between various implants and the patient data are proposed in this paper to assist the implant selection procedure. The results obtained have indicated the feasibility of the proposed development and selection scheme. The 16 sets of male data were divided into two clusters with 8 and 8 subjects, respectively, and the 11 female datasets were also divided into two clusters with 5 and 6 subjects, respectively. Based on the features of each cluster, the implants designed by the proposed algorithm fit very well on their reference humeri and the proposed implant selection procedure allows for a scenario of treating a patient with merely a preoperative anatomical model in order to correctly select the implant that has the best fit. Based on the leave-one-out validation, it can be concluded that both the PA-based method and GAD-based method are able to achieve excellent performance when dealing with the problem of implant selection. The accuracy and average execution time for the PA-based method were 100 % and 0.132 s, respectively, while those of the GAD- based method were 100 % and 0.058 s. Therefore, the GAD-based method outperformed the PA-based method in terms of execution speed. The primary contributions of this paper include the proposal of methods for development of Asian-, gender- and cluster-specific implants based on shape features and selection of the best fit implants for future patients according to their features. To the best of our knowledge, this is the first work that proposes implant design and selection for Asian patients automatically based on features extracted from cluster-specific statistical atlases.

Classification and machine recognition of severe weather patterns

NASA Technical Reports Server (NTRS)

Wang, P. P.; Burns, R. C.

1976-01-01

Forecasting and warning of severe weather conditions are treated from the vantage point of pattern recognition by machine. Pictorial patterns and waveform patterns are distinguished. Time series data on sferics are dealt with by considering waveform patterns. A severe storm patterns recognition machine is described, along with schemes for detection via cross-correlation of time series (same channel or different channels). Syntactic and decision-theoretic approaches to feature extraction are discussed. Active and decayed tornados and thunderstorms, lightning discharges, and funnels and their related time series data are studied.

A concept for extraction of habitat features from laser scanning and hypersprectral imaging for evaluation of Natura 2000 sites - the ChangeHabitats2 project approach

NASA Astrophysics Data System (ADS)

Székely, B.; Kania, A.; Pfeifer, N.; Heilmeier, H.; Tamás, J.; Szöllősi, N.; Mücke, W.

2012-04-01

The goal of the ChangeHabitats2 project is the development of cost- and time-efficient habitat assessment strategies by employing effective field work techniques supported by modern airborne remote sensing methods, i.e. hyperspectral imagery and laser scanning (LiDAR). An essential task of the project is the design of a novel field work technique that on the one hand fulfills the reporting requirements of the Flora-Fauna-Habitat (FFH-) directive and on the other hand serves as a reference for the aerial data analysis. Correlations between parameters derived from remotely sensed data and terrestrial field measurements shall be exploited in order to create half- or fully-automated methods for the extraction of relevant Natura2000 habitat parameters. As a result of these efforts a comprehensive conceptual model has been developed for extraction and integration of Natura 2000 relevant geospatial data. This scheme is an attempt to integrate various activities within ChangeHabitats2 project defining pathways of development, as well as encompassing existing data processing chains, theoretical approaches and field work. The conceptual model includes definition of processing levels (similar to those existing in remote sensing), whereas these levels cover the range from the raw data to the extracted habitat feature. For instance, the amount of dead wood (standing or lying on the surface) is an important evaluation criterion for the habitat. The tree trunks lying on the ground surface typically can be extracted from the LiDAR point cloud, and the amount of wood can be estimated accordingly. The final result will be considered as a habitat feature derived from laser scanning data. Furthermore, we are also interested not only in the determination of the specific habitat feature, but also in the detection of its variations (especially in deterioration). In this approach the variation of this important habitat feature is considered to be a differential habitat feature, that can be immediately used in the evaluation of the Natura 2000 sites. The goal of the project is the identification of many potential habitat features that can be extracted or implied from remotely sensed data, and the development of processing chains to provide data that can be used in the everyday field work of ecological site assessment. This is a contribution of ChangeHabitats2 project financed by the European Union within the Industry Academia Partnership Pathways (IAPP), as a part of FP7 Marie Curie Actions.

Automated segmentations of skin, soft-tissue, and skeleton, from torso CT images

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryujiro; Kiryu, Takuji; Hoshi, Hiroaki

2004-05-01

We have been developing a computer-aided diagnosis (CAD) scheme for automatically recognizing human tissue and organ regions from high-resolution torso CT images. We show some initial results for extracting skin, soft-tissue and skeleton regions. 139 patient cases of torso CT images (male 92, female 47; age: 12-88) were used in this study. Each case was imaged with a common protocol (120kV/320mA) and covered the whole torso with isotopic spatial resolution of about 0.63 mm and density resolution of 12 bits. A gray-level thresholding based procedure was applied to separate the human body from background. The density and distance features to body surface were used to determine the skin, and separate soft-tissue from the others. A 3-D region growing based method was used to extract the skeleton. We applied this system to the 139 cases and found that the skin, soft-tissue and skeleton regions were recognized correctly for 93% of the patient cases. The accuracy of segmentation results was acceptable by evaluating the results slice by slice. This scheme will be included in CAD systems for detecting and diagnosing the abnormal lesions in multi-slice torso CT images.

Association between dynamic features of breast DCE-MR imaging and clinical response of neoadjuvant chemotherapy: a preliminary analysis

NASA Astrophysics Data System (ADS)

Huang, Lijuan; Fan, Ming; Li, Lihua; Zhang, Juan; Shao, Guoliang; Zheng, Bin

2016-03-01

Neoadjuvant chemotherapy (NACT) is being used increasingly in the management of patients with breast cancer for systemically reducing the size of primary tumor before surgery in order to improve survival. The clinical response of patients to NACT is correlated with reduced or abolished of their primary tumor, which is important for treatment in the next stage. Recently, the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used for evaluation of the response of patients to NACT. To measure this correlation, we extracted the dynamic features from the DCE- MRI and performed association analysis between these features and the clinical response to NACT. In this study, 59 patients are screened before NATC, of which 47 are complete or partial response, and 12 are no response. We segmented the breast areas depicted on each MR image by a computer-aided diagnosis (CAD) scheme, registered images acquired from the sequential MR image scan series, and calculated eighteen features extracted from DCE-MRI. We performed SVM with the 18 features for classification between patients of response and no response. Furthermore, 6 of the 18 features are selected to refine the classification by using Genetic Algorithm. The accuracy, sensitivity and specificity are 87%, 95.74% and 50%, respectively. The calculated area under a receiver operating characteristic (ROC) curve is 0.79+/-0.04. This study indicates that the features of DCE-MRI of breast cancer are associated with the response of NACT. Therefore, our method could be helpful for evaluation of NACT in treatment of breast cancer.

A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

NASA Astrophysics Data System (ADS)

Li, Yongbo; Li, Guoyan; Yang, Yuantao; Liang, Xihui; Xu, Minqiang

2018-05-01

The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.

Emotion recognition based on physiological changes in music listening.

PubMed

Kim, Jonghwa; André, Elisabeth

2008-12-01

Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\\% and 70\\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.

CNN universal machine as classificaton platform: an art-like clustering algorithm.

PubMed

Bálya, David

2003-12-01

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

Theoretical investigation of carrier transfer by an optical contacting scheme for optoelectronic application

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

Yang, Jianfeng, E-mail: jianfeng.yang@student.unsw.edu.au; Zhang, Zhilong; Chen, Weijian

2016-04-21

As a promising charge carrier transfer scheme, optical coupling could potentially improve the performance of an optoelectronic device for energy harvesting based on well developed nanotechnology. By extracting carriers optically, the functional features of the nano-structured material could be better used by minimizing the concerns about its electrical properties. In this paper, we present a rigorous electromagnetic model to analyze the optical carrier transfer problem. The flow of the energy is analyzed carefully by the photon transfer spectrum, and the photon emitters (electron-hole pairs) are assumed in a thermal equilibrium described by Bose-Einstein distribution. The result shows that an energymore » selective carrier transfer can be optically achieved at the device level by integrating the emitter and receiver into a nano-optical resonator, where both the photon emission and absorption are significantly amplified by a near-field coupling around the resonant frequency. General design and optimization schemes in practice are addressed by examining the influence of the photonic design and an energy dependent emissivity of the emitter, which can be used to develop the optical contacting concept further.« less

Regional shape-based feature space for segmenting biomedical images using neural networks

NASA Astrophysics Data System (ADS)

Sundaramoorthy, Gopal; Hoford, John D.; Hoffman, Eric A.

1993-07-01

In biomedical images, structure of interest, particularly the soft tissue structures, such as the heart, airways, bronchial and arterial trees often have grey-scale and textural characteristics similar to other structures in the image, making it difficult to segment them using only gray- scale and texture information. However, these objects can be visually recognized by their unique shapes and sizes. In this paper we discuss, what we believe to be, a novel, simple scheme for extracting features based on regional shapes. To test the effectiveness of these features for image segmentation (classification), we use an artificial neural network and a statistical cluster analysis technique. The proposed shape-based feature extraction algorithm computes regional shape vectors (RSVs) for all pixels that meet a certain threshold criteria. The distance from each such pixel to a boundary is computed in 8 directions (or in 26 directions for a 3-D image). Together, these 8 (or 26) values represent the pixel's (or voxel's) RSV. All RSVs from an image are used to train a multi-layered perceptron neural network which uses these features to 'learn' a suitable classification strategy. To clearly distinguish the desired object from other objects within an image, several examples from inside and outside the desired object are used for training. Several examples are presented to illustrate the strengths and weaknesses of our algorithm. Both synthetic and actual biomedical images are considered. Future extensions to this algorithm are also discussed.

Multilevel depth and image fusion for human activity detection.

PubMed

Ni, Bingbing; Pei, Yong; Moulin, Pierre; Yan, Shuicheng

2013-10-01

Recognizing complex human activities usually requires the detection and modeling of individual visual features and the interactions between them. Current methods only rely on the visual features extracted from 2-D images, and therefore often lead to unreliable salient visual feature detection and inaccurate modeling of the interaction context between individual features. In this paper, we show that these problems can be addressed by combining data from a conventional camera and a depth sensor (e.g., Microsoft Kinect). We propose a novel complex activity recognition and localization framework that effectively fuses information from both grayscale and depth image channels at multiple levels of the video processing pipeline. In the individual visual feature detection level, depth-based filters are applied to the detected human/object rectangles to remove false detections. In the next level of interaction modeling, 3-D spatial and temporal contexts among human subjects or objects are extracted by integrating information from both grayscale and depth images. Depth information is also utilized to distinguish different types of indoor scenes. Finally, a latent structural model is developed to integrate the information from multiple levels of video processing for an activity detection. Extensive experiments on two activity recognition benchmarks (one with depth information) and a challenging grayscale + depth human activity database that contains complex interactions between human-human, human-object, and human-surroundings demonstrate the effectiveness of the proposed multilevel grayscale + depth fusion scheme. Higher recognition and localization accuracies are obtained relative to the previous methods.

Biologically-inspired data decorrelation for hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Picon, Artzai; Ghita, Ovidiu; Rodriguez-Vaamonde, Sergio; Iriondo, Pedro Ma; Whelan, Paul F.

2011-12-01

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

Automated 3D Phenotype Analysis Using Data Mining

PubMed Central

Plyusnin, Ilya; Evans, Alistair R.; Karme, Aleksis; Gionis, Aristides; Jernvall, Jukka

2008-01-01

The ability to analyze and classify three-dimensional (3D) biological morphology has lagged behind the analysis of other biological data types such as gene sequences. Here, we introduce the techniques of data mining to the study of 3D biological shapes to bring the analyses of phenomes closer to the efficiency of studying genomes. We compiled five training sets of highly variable morphologies of mammalian teeth from the MorphoBrowser database. Samples were labeled either by dietary class or by conventional dental types (e.g. carnassial, selenodont). We automatically extracted a multitude of topological attributes using Geographic Information Systems (GIS)-like procedures that were then used in several combinations of feature selection schemes and probabilistic classification models to build and optimize classifiers for predicting the labels of the training sets. In terms of classification accuracy, computational time and size of the feature sets used, non-repeated best-first search combined with 1-nearest neighbor classifier was the best approach. However, several other classification models combined with the same searching scheme proved practical. The current study represents a first step in the automatic analysis of 3D phenotypes, which will be increasingly valuable with the future increase in 3D morphology and phenomics databases. PMID:18320060

Automatic breast tissue density estimation scheme in digital mammography images

NASA Astrophysics Data System (ADS)

Menechelli, Renan C.; Pacheco, Ana Luisa V.; Schiabel, Homero

2017-03-01

Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes - taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

Automatic Detection of Frontal Face Midline by Chain-coded Merlin-Farber Hough Trasform

NASA Astrophysics Data System (ADS)

Okamoto, Daichi; Ohyama, Wataru; Wakabayashi, Tetsushi; Kimura, Fumitaka

We propose a novel approach for detection of the facial midline (facial symmetry axis) from a frontal face image. The facial midline has several applications, for instance reducing computational cost required for facial feature extraction (FFE) and postoperative assessment for cosmetic or dental surgery. The proposed method detects the facial midline of a frontal face from an edge image as the symmetry axis using the Merlin-Faber Hough transformation. And a new performance improvement scheme for midline detection by MFHT is present. The main concept of the proposed scheme is suppression of redundant vote on the Hough parameter space by introducing chain code representation for the binary edge image. Experimental results on the image dataset containing 2409 images from FERET database indicate that the proposed algorithm can improve the accuracy of midline detection from 89.9% to 95.1 % for face images with different scales and rotation.

A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

NASA Astrophysics Data System (ADS)

Liu, Tao; Im, Jungho; Quackenbush, Lindi J.

2015-12-01

This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features-two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)-were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest.

Observing rotation and deformation of sea ice with synthetic aperture radar

NASA Technical Reports Server (NTRS)

Vesecky, J. F.; Samadani, R.; Daida, J. M.; Smith, M. P.; Bracewell, R. N.

1987-01-01

The ESA's ERS-1 satellite will carry SARs over the polar regions; an important component in the use of these data is an automated scheme for the extraction of sea ice velocity fields from a sequence of SAR images of the same geographical region. The image pyramid area-correlation hierarchical method is noted to be vulnerable to uncertainties for sea ice rotations greater than 10-15 deg between SAR observations. Rotation-invariant methods can successfully track isolated floes in the marginal ice zone. Hu's (1962) invariant moments are also worth considering as a possible basis for rotation-invariant tracking methods. Feature tracking is inherently robust for tracking rotating sea ice, but is limited when features are floe-lead boundaries. A variety of techniques appears neccessary.

Information Extraction for Clinical Data Mining: A Mammography Case Study

PubMed Central

Nassif, Houssam; Woods, Ryan; Burnside, Elizabeth; Ayvaci, Mehmet; Shavlik, Jude; Page, David

2013-01-01

Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) to describe and report their findings. Mammography records are then stored in a well-defined database format (NMD). Lately, researchers have applied data mining and machine learning techniques to these databases. They successfully built breast cancer classifiers that can help in early detection of malignancy. However, the validity of these models depends on the quality of the underlying databases. Unfortunately, most databases suffer from inconsistencies, missing data, inter-observer variability and inappropriate term usage. In addition, many databases are not compliant with the NMD format and/or solely consist of text reports. BI-RADS feature extraction from free text and consistency checks between recorded predictive variables and text reports are crucial to addressing this problem. We describe a general scheme for concept information retrieval from free text given a lexicon, and present a BI-RADS features extraction algorithm for clinical data mining. It consists of a syntax analyzer, a concept finder and a negation detector. The syntax analyzer preprocesses the input into individual sentences. The concept finder uses a semantic grammar based on the BI-RADS lexicon and the experts’ input. It parses sentences detecting BI-RADS concepts. Once a concept is located, a lexical scanner checks for negation. Our method can handle multiple latent concepts within the text, filtering out ultrasound concepts. On our dataset, our algorithm achieves 97.7% precision, 95.5% recall and an F1-score of 0.97. It outperforms manual feature extraction at the 5% statistical significance level. PMID:23765123

Information Extraction for Clinical Data Mining: A Mammography Case Study.

PubMed

Nassif, Houssam; Woods, Ryan; Burnside, Elizabeth; Ayvaci, Mehmet; Shavlik, Jude; Page, David

2009-01-01

Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) to describe and report their findings. Mammography records are then stored in a well-defined database format (NMD). Lately, researchers have applied data mining and machine learning techniques to these databases. They successfully built breast cancer classifiers that can help in early detection of malignancy. However, the validity of these models depends on the quality of the underlying databases. Unfortunately, most databases suffer from inconsistencies, missing data, inter-observer variability and inappropriate term usage. In addition, many databases are not compliant with the NMD format and/or solely consist of text reports. BI-RADS feature extraction from free text and consistency checks between recorded predictive variables and text reports are crucial to addressing this problem. We describe a general scheme for concept information retrieval from free text given a lexicon, and present a BI-RADS features extraction algorithm for clinical data mining. It consists of a syntax analyzer, a concept finder and a negation detector. The syntax analyzer preprocesses the input into individual sentences. The concept finder uses a semantic grammar based on the BI-RADS lexicon and the experts' input. It parses sentences detecting BI-RADS concepts. Once a concept is located, a lexical scanner checks for negation. Our method can handle multiple latent concepts within the text, filtering out ultrasound concepts. On our dataset, our algorithm achieves 97.7% precision, 95.5% recall and an F 1 -score of 0.97. It outperforms manual feature extraction at the 5% statistical significance level.

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

PubMed Central

Zhao, Ming; Lin, Jing; Miao, Yonghao; Xu, Xiaoqiang

2016-01-01

Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox. PMID:27827831

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals.

PubMed

Zhao, Ming; Lin, Jing; Miao, Yonghao; Xu, Xiaoqiang

2016-11-02

Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox.

Pyramid image codes

NASA Technical Reports Server (NTRS)

Watson, Andrew B.

1990-01-01

All vision systems, both human and machine, transform the spatial image into a coded representation. Particular codes may be optimized for efficiency or to extract useful image features. Researchers explored image codes based on primary visual cortex in man and other primates. Understanding these codes will advance the art in image coding, autonomous vision, and computational human factors. In cortex, imagery is coded by features that vary in size, orientation, and position. Researchers have devised a mathematical model of this transformation, called the Hexagonal oriented Orthogonal quadrature Pyramid (HOP). In a pyramid code, features are segregated by size into layers, with fewer features in the layers devoted to large features. Pyramid schemes provide scale invariance, and are useful for coarse-to-fine searching and for progressive transmission of images. The HOP Pyramid is novel in three respects: (1) it uses a hexagonal pixel lattice, (2) it uses oriented features, and (3) it accurately models most of the prominent aspects of primary visual cortex. The transform uses seven basic features (kernels), which may be regarded as three oriented edges, three oriented bars, and one non-oriented blob. Application of these kernels to non-overlapping seven-pixel neighborhoods yields six oriented, high-pass pyramid layers, and one low-pass (blob) layer.

Geometrically robust image watermarking by sector-shaped partitioning of geometric-invariant regions.

PubMed

Tian, Huawei; Zhao, Yao; Ni, Rongrong; Cao, Gang

2009-11-23

In a feature-based geometrically robust watermarking system, it is a challenging task to detect geometric-invariant regions (GIRs) which can survive a broad range of image processing operations. Instead of commonly used Harris detector or Mexican hat wavelet method, a more robust corner detector named multi-scale curvature product (MSCP) is adopted to extract salient features in this paper. Based on such features, disk-like GIRs are found, which consists of three steps. First, robust edge contours are extracted. Then, MSCP is utilized to detect the centers for GIRs. Third, the characteristic scale selection is performed to calculate the radius of each GIR. A novel sector-shaped partitioning method for the GIRs is designed, which can divide a GIR into several sector discs with the help of the most important corner (MIC). The watermark message is then embedded bit by bit in each sector by using Quantization Index Modulation (QIM). The GIRs and the divided sector discs are invariant to geometric transforms, so the watermarking method inherently has high robustness against geometric attacks. Experimental results show that the scheme has a better robustness against various image processing operations including common processing attacks, affine transforms, cropping, and random bending attack (RBA) than the previous approaches.

Local multifractal detrended fluctuation analysis for non-stationary image's texture segmentation

NASA Astrophysics Data System (ADS)

Wang, Fang; Li, Zong-shou; Li, Jin-wei

2014-12-01

Feature extraction plays a great important role in image processing and pattern recognition. As a power tool, multifractal theory is recently employed for this job. However, traditional multifractal methods are proposed to analyze the objects with stationary measure and cannot for non-stationary measure. The works of this paper is twofold. First, the definition of stationary image and 2D image feature detection methods are proposed. Second, a novel feature extraction scheme for non-stationary image is proposed by local multifractal detrended fluctuation analysis (Local MF-DFA), which is based on 2D MF-DFA. A set of new multifractal descriptors, called local generalized Hurst exponent (Lhq) is defined to characterize the local scaling properties of textures. To test the proposed method, both the novel texture descriptor and other two multifractal indicators, namely, local Hölder coefficients based on capacity measure and multifractal dimension Dq based on multifractal differential box-counting (MDBC) method, are compared in segmentation experiments. The first experiment indicates that the segmentation results obtained by the proposed Lhq are better than the MDBC-based Dq slightly and superior to the local Hölder coefficients significantly. The results in the second experiment demonstrate that the Lhq can distinguish the texture images more effectively and provide more robust segmentations than the MDBC-based Dq significantly.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

PubMed

Gatos, Ilias; Tsantis, Stavros; Karamesini, Maria; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Hazle, John D; Kagadis, George C

2017-07-01

To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American Association of Physicists in Medicine.

Scene text recognition in mobile applications by character descriptor and structure configuration.

PubMed

Yi, Chucai; Tian, Yingli

2014-07-01

Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This paper proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods.

Research on structural integration of thermodynamic system for double reheat coal-fired unit with CO2 capture

NASA Astrophysics Data System (ADS)

Wang, Lanjing; Shao, Wenjing; Wang, Zhiyue; Fu, Wenfeng; Zhao, Wensheng

2018-02-01

Taking the MEA chemical absorption carbon capture system with 85% of the carbon capture rate of a 660MW ultra-super critical unit as an example,this paper puts forward a new type of turbine which dedicated to supply steam to carbon capture system. The comparison of the thermal systems of the power plant under different steam supply schemes by using the EBSILON indicated optimal extraction scheme for Steam Extraction System in Carbon Capture System. The results show that the cycle heat efficiency of the unit introduced carbon capture turbine system is higher than that of the usual scheme without it. With the introduction of the carbon capture turbine, the scheme which extracted steam from high pressure cylinder’ s steam input point shows the highest cycle thermal efficiency. Its indexes are superior to other scheme, and more suitable for existing coal-fired power plant integrated post combustion carbon dioxide capture system.

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

PubMed

Chen, Long-Sheng; Lin, Zue-Cheng; Chang, Jing-Rong

2015-11-01

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media.

Comparing the Behavior of Polarimetric SAR Imagery (TerraSAR-X and Radarsat-2) for Automated Sea Ice Classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-08-01

Arctic Sea ice monitoring has attracted increasing attention over the last few decades. Besides the scientific interest in sea ice, the operational aspect of ice charting is becoming more important due to growing navigational possibilities in an increasingly ice free Arctic. For this purpose, satellite borne SAR imagery has become an invaluable tool. In past, mostly single polarimetric datasets were investigated with supervised or unsupervised classification schemes for sea ice investigation. Despite proven sea ice classification achievements on single polarimetric data, a fully automatic, general purpose classifier for single-pol data has not been established due to large variation of sea ice manifestations and incidence angle impact. Recently, through the advent of polarimetric SAR sensors, polarimetric features have moved into the focus of ice classification research. The higher information content four polarimetric channels promises to offer greater insight into sea ice scattering mechanism and overcome some of the shortcomings of single- polarimetric classifiers. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction).

Road Network Extraction from Dsm by Mathematical Morphology and Reasoning

NASA Astrophysics Data System (ADS)

Li, Yan; Wu, Jianliang; Zhu, Lin; Tachibana, Kikuo

2016-06-01

The objective of this research is the automatic extraction of the road network in a scene of the urban area from a high resolution digital surface model (DSM). Automatic road extraction and modeling from remote sensed data has been studied for more than one decade. The methods vary greatly due to the differences of data types, regions, resolutions et al. An advanced automatic road network extraction scheme is proposed to address the issues of tedium steps on segmentation, recognition and grouping. It is on the basis of a geometric road model which describes a multiple-level structure. The 0-dimension element is intersection. The 1-dimension elements are central line and side. The 2-dimension element is plane, which is generated from the 1-dimension elements. The key feature of the presented approach is the cross validation for the three road elements which goes through the entire procedure of their extraction. The advantage of our model and method is that linear elements of the road can be derived directly, without any complex, non-robust connection hypothesis. An example of Japanese scene is presented to display the procedure and the performance of the approach.

Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.

PubMed

Chaddad, Ahmad; Sabri, Siham; Niazi, Tamim; Abdulkarim, Bassam

2018-06-19

We propose a multiscale texture features based on Laplacian-of Gaussian (LoG) filter to predict progression free (PFS) and overall survival (OS) in patients newly diagnosed with glioblastoma (GBM). Experiments use the extracted features derived from 40 patients of GBM with T1-weighted imaging (T1-WI) and Fluid-attenuated inversion recovery (FLAIR) images that were segmented manually into areas of active tumor, necrosis, and edema. Multiscale texture features were extracted locally from each of these areas of interest using a LoG filter and the relation between features to OS and PFS was investigated using univariate (i.e., Spearman's rank correlation coefficient, log-rank test and Kaplan-Meier estimator) and multivariate analyses (i.e., Random Forest classifier). Three and seven features were statistically correlated with PFS and OS, respectively, with absolute correlation values between 0.32 and 0.36 and p < 0.05. Three features derived from active tumor regions only were associated with OS (p < 0.05) with hazard ratios (HR) of 2.9, 3, and 3.24, respectively. Combined features showed an AUC value of 85.37 and 85.54% for predicting the PFS and OS of GBM patients, respectively, using the random forest (RF) classifier. We presented a multiscale texture features to characterize the GBM regions and predict he PFS and OS. The efficiency achievable suggests that this technique can be developed into a GBM MR analysis system suitable for clinical use after a thorough validation involving more patients. Graphical abstract Scheme of the proposed model for characterizing the heterogeneity of GBM regions and predicting the overall survival and progression free survival of GBM patients. (1) Acquisition of pretreatment MRI images; (2) Affine registration of T1-WI image with its corresponding FLAIR images, and GBM subtype (phenotypes) labelling; (3) Extraction of nine texture features from the three texture scales fine, medium, and coarse derived from each of GBM regions; (4) Comparing heterogeneity between GBM regions by ANOVA test; Survival analysis using Univariate (Spearman rank correlation between features and survival (i.e., PFS and OS) based on each of the GBM regions, Kaplan-Meier estimator and log-rank test to predict the PFS and OS of patient groups that grouped based on median of feature), and multivariate (random forest model) for predicting the PFS and OS of patients groups that grouped based on median of PFS and OS.

High-resolution face verification using pore-scale facial features.

PubMed

Li, Dong; Zhou, Huiling; Lam, Kin-Man

2015-08-01

Face recognition methods, which usually represent face images using holistic or local facial features, rely heavily on alignment. Their performances also suffer a severe degradation under variations in expressions or poses, especially when there is one gallery per subject only. With the easy access to high-resolution (HR) face images nowadays, some HR face databases have recently been developed. However, few studies have tackled the use of HR information for face recognition or verification. In this paper, we propose a pose-invariant face-verification method, which is robust to alignment errors, using the HR information based on pore-scale facial features. A new keypoint descriptor, namely, pore-Principal Component Analysis (PCA)-Scale Invariant Feature Transform (PPCASIFT)-adapted from PCA-SIFT-is devised for the extraction of a compact set of distinctive pore-scale facial features. Having matched the pore-scale features of two-face regions, an effective robust-fitting scheme is proposed for the face-verification task. Experiments show that, with one frontal-view gallery only per subject, our proposed method outperforms a number of standard verification methods, and can achieve excellent accuracy even the faces are under large variations in expression and pose.

Land use classification using texture information in ERTS-A MSS imagery

NASA Technical Reports Server (NTRS)

Haralick, R. M. (Principal Investigator); Shanmugam, K. S.; Bosley, R.

1973-01-01

The author has identified the following significant results. Preliminary digital analysis of ERTS-1 MSS imagery reveals that the textural features of the imagery are very useful for land use classification. A procedure for extracting the textural features of ERTS-1 imagery is presented and the results of a land use classification scheme based on the textural features are also presented. The land use classification algorithm using textural features was tested on a 5100 square mile area covered by part of an ERTS-1 MSS band 5 image over the California coastline. The image covering this area was blocked into 648 subimages of size 8.9 square miles each. Based on a color composite of the image set, a total of 7 land use categories were identified. These land use categories are: coastal forest, woodlands, annual grasslands, urban areas, large irrigated fields, small irrigated fields, and water. The automatic classifier was trained to identify the land use categories using only the textural characteristics of the subimages; 75 percent of the subimages were assigned correct identifications. Since texture and spectral features provide completely different kinds of information, a significant increase in identification accuracy will take place when both features are used together.

Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

PubMed

Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong

2016-06-01

Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

A novel quantum steganography scheme for color images

NASA Astrophysics Data System (ADS)

Li, Panchi; Liu, Xiande

In quantum image steganography, embedding capacity and security are two important issues. This paper presents a novel quantum steganography scheme using color images as cover images. First, the secret information is divided into 3-bit segments, and then each 3-bit segment is embedded into the LSB of one color pixel in the cover image according to its own value and using Gray code mapping rules. Extraction is the inverse of embedding. We designed the quantum circuits that implement the embedding and extracting process. The simulation results on a classical computer show that the proposed scheme outperforms several other existing schemes in terms of embedding capacity and security.

Quantum Watermarking Scheme Based on INEQR

NASA Astrophysics Data System (ADS)

Zhou, Ri-Gui; Zhou, Yang; Zhu, Changming; Wei, Lai; Zhang, Xiafen; Ian, Hou

2018-04-01

Quantum watermarking technology protects copyright by embedding invisible quantum signal in quantum multimedia data. In this paper, a watermarking scheme based on INEQR was presented. Firstly, the watermark image is extended to achieve the requirement of embedding carrier image. Secondly, the swap and XOR operation is used on the processed pixels. Since there is only one bit per pixel, XOR operation can achieve the effect of simple encryption. Thirdly, both the watermark image extraction and embedding operations are described, where the key image, swap operation and LSB algorithm are used. When the embedding is made, the binary image key is changed. It means that the watermark has been embedded. Of course, if the watermark image is extracted, the key's state need detected. When key's state is |1>, this extraction operation is carried out. Finally, for validation of the proposed scheme, both the Signal-to-noise ratio (PSNR) and the security of the scheme are analyzed.

An Automatic Detection System of Lung Nodule Based on Multi-Group Patch-Based Deep Learning Network.

PubMed

Jiang, Hongyang; Ma, He; Qian, Wei; Gao, Mengdi; Li, Yan

2017-07-14

High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography (CT) image transformation, the lung nodule segmentation and the feature extraction, to construct a whole CADe system. It is difficult for those schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multi-group patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks (CNN) model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multi-group patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.

Estimating cognitive workload using wavelet entropy-based features during an arithmetic task.

PubMed

Zarjam, Pega; Epps, Julien; Chen, Fang; Lovell, Nigel H

2013-12-01

Electroencephalography (EEG) has shown promise as an indicator of cognitive workload; however, precise workload estimation is an ongoing research challenge. In this investigation, seven levels of workload were induced using an arithmetic task, and the entropy of wavelet coefficients extracted from EEG signals is shown to distinguish all seven levels. For a subject-independent multi-channel classification scheme, the entropy features achieved high accuracy, up to 98% for channels from the frontal lobes, in the delta frequency band. This suggests that a smaller number of EEG channels in only one frequency band can be deployed for an effective EEG-based workload classification system. Together with analysis based on phase locking between channels, these results consistently suggest increased synchronization of neural responses for higher load levels. Copyright © 2013 Elsevier Ltd. All rights reserved.

Subject-specific and pose-oriented facial features for face recognition across poses.

PubMed

Lee, Ping-Han; Hsu, Gee-Sern; Wang, Yun-Wen; Hung, Yi-Ping

2012-10-01

Most face recognition scenarios assume that frontal faces or mug shots are available for enrollment to the database, faces of other poses are collected in the probe set. Given a face from the probe set, one needs to determine whether a match in the database exists. This is under the assumption that in forensic applications, most suspects have their mug shots available in the database, and face recognition aims at recognizing the suspects when their faces of various poses are captured by a surveillance camera. This paper considers a different scenario: given a face with multiple poses available, which may or may not include a mug shot, develop a method to recognize the face with poses different from those captured. That is, given two disjoint sets of poses of a face, one for enrollment and the other for recognition, this paper reports a method best for handling such cases. The proposed method includes feature extraction and classification. For feature extraction, we first cluster the poses of each subject's face in the enrollment set into a few pose classes and then decompose the appearance of the face in each pose class using Embedded Hidden Markov Model, which allows us to define a set of subject-specific and pose-priented (SSPO) facial components for each subject. For classification, an Adaboost weighting scheme is used to fuse the component classifiers with SSPO component features. The proposed method is proven to outperform other approaches, including a component-based classifier with local facial features cropped manually, in an extensive performance evaluation study.

Acoustic signature recognition technique for Human-Object Interactions (HOI) in persistent surveillance systems

NASA Astrophysics Data System (ADS)

Alkilani, Amjad; Shirkhodaie, Amir

2013-05-01

Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

The Interrelations of Features of Questions, Mark Schemes and Examinee Responses and Their Impact upon Marker Agreement

ERIC Educational Resources Information Center

Black, Beth; Suto, Irenka; Bramley, Tom

2011-01-01

In this paper we develop an evidence-based framework for considering many of the factors affecting marker agreement in GCSEs and A levels. A logical analysis of the demands of the marking task suggests a core grouping comprising: (i) question features; (ii) mark scheme features; and (iii) examinee response features. The framework synthesises…

Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network

PubMed Central

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2014-01-01

Visual sensor networks (VSNs) usually generate a low-resolution (LR) frame-sequence due to energy and processing constraints. These LR-frames are not very appropriate for use in certain surveillance applications. It is very important to enhance the resolution of the captured LR-frames using resolution enhancement schemes. In this paper, an effective framework for a super-resolution (SR) scheme is proposed that enhances the resolution of LR key-frames extracted from frame-sequences captured by visual-sensors. In a VSN, a visual processing hub (VPH) collects a huge amount of visual data from camera sensors. In the proposed framework, at the VPH, key-frames are extracted using our recent key-frame extraction technique and are streamed to the base station (BS) after compression. A novel effective SR scheme is applied at BS to produce a high-resolution (HR) output from the received key-frames. The proposed SR scheme uses optimized orthogonal matching pursuit (OOMP) for sparse-representation recovery in SR. OOMP does better in terms of detecting true sparsity than orthogonal matching pursuit (OMP). This property of the OOMP helps produce a HR image which is closer to the original image. The K-SVD dictionary learning procedure is incorporated for dictionary learning. Batch-OMP improves the dictionary learning process by removing the limitation in handling a large set of observed signals. Experimental results validate the effectiveness of the proposed scheme and show its superiority over other state-of-the-art schemes. PMID:24566632

Sparse representations-based super-resolution of key-frames extracted from frames-sequences generated by a visual sensor network.

PubMed

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2014-02-21

Visual sensor networks (VSNs) usually generate a low-resolution (LR) frame-sequence due to energy and processing constraints. These LR-frames are not very appropriate for use in certain surveillance applications. It is very important to enhance the resolution of the captured LR-frames using resolution enhancement schemes. In this paper, an effective framework for a super-resolution (SR) scheme is proposed that enhances the resolution of LR key-frames extracted from frame-sequences captured by visual-sensors. In a VSN, a visual processing hub (VPH) collects a huge amount of visual data from camera sensors. In the proposed framework, at the VPH, key-frames are extracted using our recent key-frame extraction technique and are streamed to the base station (BS) after compression. A novel effective SR scheme is applied at BS to produce a high-resolution (HR) output from the received key-frames. The proposed SR scheme uses optimized orthogonal matching pursuit (OOMP) for sparse-representation recovery in SR. OOMP does better in terms of detecting true sparsity than orthogonal matching pursuit (OMP). This property of the OOMP helps produce a HR image which is closer to the original image. The K-SVD dictionary learning procedure is incorporated for dictionary learning. Batch-OMP improves the dictionary learning process by removing the limitation in handling a large set of observed signals. Experimental results validate the effectiveness of the proposed scheme and show its superiority over other state-of-the-art schemes.

A new classification scheme of plastic wastes based upon recycling labels.

PubMed

Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, Idil

2015-01-01

Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher's Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views.

PubMed

Chaaraoui, Alexandros Andre; Flórez-Revuelta, Francisco

2014-01-01

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.

PubMed

Liu, Xiao; Wang, Xiaoli; Su, Qiang; Zhang, Mo; Zhu, Yanhong; Wang, Qiugen; Wang, Qian

2017-01-01

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.

Breast tumor angiogenesis analysis using 3D power Doppler ultrasound

NASA Astrophysics Data System (ADS)

Chang, Ruey-Feng; Huang, Sheng-Fang; Lee, Yu-Hau; Chen, Dar-Ren; Moon, Woo Kyung

2006-03-01

Angiogenesis is the process that correlates to tumor growth, invasion, and metastasis. Breast cancer angiogenesis has been the most extensively studied and now serves as a paradigm for understanding the biology of angiogenesis and its effects on tumor outcome and patient prognosis. Most studies on characterization of angiogenesis focus on pixel/voxel counts more than morphological analysis. Nevertheless, in cancer, the blood flow is greatly affected by the morphological changes, such as the number of vessels, branching pattern, length, and diameter. This paper presents a computer-aided diagnostic (CAD) system that can quantify vascular morphology using 3-D power Doppler ultrasound (US) on breast tumors. We propose a scheme to extract the morphological information from angiography and to relate them to tumor diagnosis outcome. At first, a 3-D thinning algorithm helps narrow down the vessels into their skeletons. The measurements of vascular morphology significantly rely on the traversing of the vascular trees produced from skeletons. Our study of 3-D assessment of vascular morphological features regards vessel count, length, bifurcation, and diameter of vessels. Investigations into 221 solid breast tumors including 110 benign and 111 malignant cases, the p values using the Student's t-test for all features are less than 0.05 indicating that the proposed features are deemed statistically significant. Our scheme focuses on the vascular architecture without involving the technique of tumor segmentation. The results show that the proposed method is feasible, and have a good agreement with the diagnosis of the pathologists.

Correlative feature analysis on FFDM

PubMed Central

Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

2008-01-01

Identifying the corresponding images of a lesion in different views is an essential step in improving the diagnostic ability of both radiologists and computer-aided diagnosis (CAD) systems. Because of the nonrigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this pilot study, we present a computerized framework that differentiates between corresponding images of the same lesion in different views and noncorresponding images, i.e., images of different lesions. A dual-stage segmentation method, which employs an initial radial gradient index (RGI) based segmentation and an active contour model, is applied to extract mass lesions from the surrounding parenchyma. Then various lesion features are automatically extracted from each of the two views of each lesion to quantify the characteristics of density, size, texture and the neighborhood of the lesion, as well as its distance to the nipple. A two-step scheme is employed to estimate the probability that the two lesion images from different mammographic views are of the same physical lesion. In the first step, a correspondence metric for each pairwise feature is estimated by a Bayesian artificial neural network (BANN). Then, these pairwise correspondence metrics are combined using another BANN to yield an overall probability of correspondence. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing corresponding pairs from noncorresponding pairs. Using a FFDM database with 123 corresponding image pairs and 82 noncorresponding pairs, the distance feature yielded an area under the ROC curve (AUC) of 0.81±0.02 with leave-one-out (by physical lesion) evaluation, and the feature metric subset, which included distance, gradient texture, and ROI-based correlation, yielded an AUC of 0.87±0.02. The improvement by using multiple feature metrics was statistically significant compared to single feature performance. PMID:19175108

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

PubMed

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

2017-12-07

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

Odor Recognition vs. Classification in Artificial Olfaction

NASA Astrophysics Data System (ADS)

Raman, Baranidharan; Hertz, Joshua; Benkstein, Kurt; Semancik, Steve

2011-09-01

Most studies in chemical sensing have focused on the problem of precise identification of chemical species that were exposed during the training phase (the recognition problem). However, generalization of training to predict the chemical composition of untrained gases based on their similarity with analytes in the training set (the classification problem) has received very limited attention. These two analytical tasks pose conflicting constraints on the system. While correct recognition requires detection of molecular features that are unique to an analyte, generalization to untrained chemicals requires detection of features that are common across a desired class of analytes. A simple solution that addresses both issues simultaneously can be obtained from biological olfaction, where the odor class and identity information are decoupled and extracted individually over time. Mimicking this approach, we proposed a hierarchical scheme that allowed initial discrimination between broad chemical classes (e.g. contains oxygen) followed by finer refinements using additional data into sub-classes (e.g. ketones vs. alcohols) and, eventually, specific compositions (e.g. ethanol vs. methanol) [1]. We validated this approach using an array of temperature-controlled chemiresistors. We demonstrated that a small set of training analytes is sufficient to allow generalization to novel chemicals and that the scheme provides robust categorization despite aging. Here, we provide further characterization of this approach.

A Standard-Compliant Virtual Meeting System with Active Video Object Tracking

NASA Astrophysics Data System (ADS)

Lin, Chia-Wen; Chang, Yao-Jen; Wang, Chih-Ming; Chen, Yung-Chang; Sun, Ming-Ting

2002-12-01

This paper presents an H.323 standard compliant virtual video conferencing system. The proposed system not only serves as a multipoint control unit (MCU) for multipoint connection but also provides a gateway function between the H.323 LAN (local-area network) and the H.324 WAN (wide-area network) users. The proposed virtual video conferencing system provides user-friendly object compositing and manipulation features including 2D video object scaling, repositioning, rotation, and dynamic bit-allocation in a 3D virtual environment. A reliable, and accurate scheme based on background image mosaics is proposed for real-time extracting and tracking foreground video objects from the video captured with an active camera. Chroma-key insertion is used to facilitate video objects extraction and manipulation. We have implemented a prototype of the virtual conference system with an integrated graphical user interface to demonstrate the feasibility of the proposed methods.

Personal recognition using hand shape and texture.

PubMed

Kumar, Ajay; Zhang, David

2006-08-01

This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. The proposed combination is of significance since both the palmprint and hand-shape images are proposed to be extracted from the single hand image acquired from a digital camera. Several new hand-shape features that can be used to represent the hand shape and improve the performance are investigated. The new approach for palmprint recognition using discrete cosine transform coefficients, which can be directly obtained from the camera hardware, is demonstrated. None of the prior work on hand-shape or palmprint recognition has given any attention on the critical issue of feature selection. Our experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of proposed features is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), k-NN, SVM, and FFN. Although more work remains to be done, our results to date indicate that the combination of selected hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

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.

FEX: A Knowledge-Based System For Planimetric Feature Extraction

NASA Astrophysics Data System (ADS)

Zelek, John S.

1988-10-01

Topographical planimetric features include natural surfaces (rivers, lakes) and man-made surfaces (roads, railways, bridges). In conventional planimetric feature extraction, a photointerpreter manually interprets and extracts features from imagery on a stereoplotter. Visual planimetric feature extraction is a very labour intensive operation. The advantages of automating feature extraction include: time and labour savings; accuracy improvements; and planimetric data consistency. FEX (Feature EXtraction) combines techniques from image processing, remote sensing and artificial intelligence for automatic feature extraction. The feature extraction process co-ordinates the information and knowledge in a hierarchical data structure. The system simulates the reasoning of a photointerpreter in determining the planimetric features. Present efforts have concentrated on the extraction of road-like features in SPOT imagery. Keywords: Remote Sensing, Artificial Intelligence (AI), SPOT, image understanding, knowledge base, apars.

Integrated sensing and actuation of dielectric elastomer actuator

NASA Astrophysics Data System (ADS)

Ye, Zhihang; Chen, Zheng

2017-04-01

Dielectric elastomer (DE) is a type of soft actuating material, the shape of which can be changed under electrical voltage stimuli. DE materials have great potential in applications involving energy harvesters, micro-manipulators, and adaptive optics. In this paper, a stripe DE actuator with integrated sensing and actuation is designed and fabricated, and characterized through several experiments. Considering the actuator's capacitor-like structure and its deform mechanism, detecting the actuator's displacement through the actuator's circuit feature is a potential approach. A self-sensing scheme that adds a high frequency probing signal into actuation signal is developed. A fast Fourier transform (FFT) algorithm is used to extract the magnitude change of the probing signal, and a non-linear fitting method and artificial neural network (ANN) approach are utilized to reflect the relationship between the probing signal and the actuator's displacement. Experimental results showed this structure has capability of performing self-sensing and actuation, simultaneously. With an enhanced ANN, the self-sensing scheme can achieve 2.5% accuracy.

Spectral identification of minerals using imaging spectrometry data: Evaluating the effects of signal to noise and spectral resolution using the tricorder algorithm

NASA Technical Reports Server (NTRS)

Swayze, Gregg A.; Clark, Roger N.

1995-01-01

The rapid development of sophisticated imaging spectrometers and resulting flood of imaging spectrometry data has prompted a rapid parallel development of spectral-information extraction technology. Even though these extraction techniques have evolved along different lines (band-shape fitting, endmember unmixing, near-infrared analysis, neural-network fitting, and expert systems to name a few), all are limited by the spectrometer's signal to noise (S/N) and spectral resolution in producing useful information. This study grew from a need to quantitatively determine what effects these parameters have on our ability to differentiate between mineral absorption features using a band-shape fitting algorithm. We chose to evaluate the AVIRIS, HYDICE, MIVIS, GERIS, VIMS, NIMS, and ASTER instruments because they collect data over wide S/N and spectral-resolution ranges. The study evaluates the performance of the Tricorder algorithm, in differentiating between mineral spectra in the 0.4-2.5 micrometer spectral region. The strength of the Tricorder algorithm is in its ability to produce an easily understood comparison of band shape that can concentrate on small relevant portions of the spectra, giving it an advantage over most unmixing schemes, and in that it need not spend large amounts of time reoptimizing each time a new mineral component is added to its reference library, as is the case with neural-network schemes. We believe the flexibility of the Tricorder algorithm is unparalleled among spectral-extraction techniques and that the results from this study, although dealing with minerals, will have direct applications to spectral identification in other disciplines.

Secure searching of biomarkers through hybrid homomorphic encryption scheme.

PubMed

Kim, Miran; Song, Yongsoo; Cheon, Jung Hee

2017-07-26

As genome sequencing technology develops rapidly, there has lately been an increasing need to keep genomic data secure even when stored in the cloud and still used for research. We are interested in designing a protocol for the secure outsourcing matching problem on encrypted data. We propose an efficient method to securely search a matching position with the query data and extract some information at the position. After decryption, only a small amount of comparisons with the query information should be performed in plaintext state. We apply this method to find a set of biomarkers in encrypted genomes. The important feature of our method is to encode a genomic database as a single element of polynomial ring. Since our method requires a single homomorphic multiplication of hybrid scheme for query computation, it has the advantage over the previous methods in parameter size, computation complexity, and communication cost. In particular, the extraction procedure not only prevents leakage of database information that has not been queried by user but also reduces the communication cost by half. We evaluate the performance of our method and verify that the computation on large-scale personal data can be securely and practically outsourced to a cloud environment during data analysis. It takes about 3.9 s to search-and-extract the reference and alternate sequences at the queried position in a database of size 4M. Our solution for finding a set of biomarkers in DNA sequences shows the progress of cryptographic techniques in terms of their capability can support real-world genome data analysis in a cloud environment.

A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Rosen, Mark; Madabhushi, Anant

2008-03-01

Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multi-site ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.

Computerized breast cancer analysis system using three stage semi-supervised learning method.

PubMed

Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

2016-10-01

A large number of labeled medical image data is usually a requirement to train a well-performed computer-aided detection (CAD) system. But the process of data labeling is time consuming, and potential ethical and logistical problems may also present complications. As a result, incorporating unlabeled data into CAD system can be a feasible way to combat these obstacles. In this study we developed a three stage semi-supervised learning (SSL) scheme that combines a small amount of labeled data and larger amount of unlabeled data. The scheme was modified on our existing CAD system using the following three stages: data weighing, feature selection, and newly proposed dividing co-training data labeling algorithm. Global density asymmetry features were incorporated to the feature pool to reduce the false positive rate. Area under the curve (AUC) and accuracy were computed using 10 fold cross validation method to evaluate the performance of our CAD system. The image dataset includes mammograms from 400 women who underwent routine screening examinations, and each pair contains either two cranio-caudal (CC) or two mediolateral-oblique (MLO) view mammograms from the right and the left breasts. From these mammograms 512 regions were extracted and used in this study, and among them 90 regions were treated as labeled while the rest were treated as unlabeled. Using our proposed scheme, the highest AUC observed in our research was 0.841, which included the 90 labeled data and all the unlabeled data. It was 7.4% higher than using labeled data only. With the increasing amount of labeled data, AUC difference between using mixed data and using labeled data only reached its peak when the amount of labeled data was around 60. This study demonstrated that our proposed three stage semi-supervised learning can improve the CAD performance by incorporating unlabeled data. Using unlabeled data is promising in computerized cancer research and may have a significant impact for future CAD system applications. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Continuing Search for a Near-Perfect Numerical Flux Scheme. Part 1; [AUSM+

NASA Technical Reports Server (NTRS)

Liou, Meng-Sing

1994-01-01

While enjoying demonstrated improvement in accuracy, efficiency, and robustness over existing schemes, the Advection Upstream Splitting Scheme (AUSM) was found to have some deficiencies in extreme cases. This recent progress towards improving the AUSM while retaining its advantageous features is described. The new scheme, termed AUSM+, features: unification of velocity and Mach number splitting; exact capture of a single stationary shock; and improvement in accuracy. A general construction of the AUSM+ scheme is layed out and then focus is on the analysis of the a scheme and its mathematical properties, heretofore unreported. Monotonicity and positivity are proved, and a CFL-like condition is given for first and second order schemes and for generalized curvilinear co-ordinates. Finally, results of numerical tests on many problems are given to confirm the capability and improvements on a variety of problems including those failed by prominent schemes.

Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images.

PubMed

Sihong Chen; Jing Qin; Xing Ji; Baiying Lei; Tianfu Wang; Dong Ni; Jie-Zhi Cheng

2017-03-01

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.

On-line determination of transient stability status using multilayer perceptron neural network

NASA Astrophysics Data System (ADS)

Frimpong, Emmanuel Asuming; Okyere, Philip Yaw; Asumadu, Johnson

2018-01-01

A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Two samples of frequency deviation values at all generator buses are obtained. At each generator bus, the maximum frequency deviation within the two samples is extracted. A vector is then constructed from the extracted maximum frequency deviations. The Euclidean norm of the constructed vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using data generated from the New England test system. The scheme successfully predicted the stability status of all two hundred and five disturbance test cases.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

Normalization of similarity-based individual brain networks from gray matter MRI and its association with neurodevelopment in infants with intrauterine growth restriction.

PubMed

Batalle, Dafnis; Muñoz-Moreno, Emma; Figueras, Francesc; Bargallo, Nuria; Eixarch, Elisenda; Gratacos, Eduard

2013-12-01

Obtaining individual biomarkers for the prediction of altered neurological outcome is a challenge of modern medicine and neuroscience. Connectomics based on magnetic resonance imaging (MRI) stands as a good candidate to exhaustively extract information from MRI by integrating the information obtained in a few network features that can be used as individual biomarkers of neurological outcome. However, this approach typically requires the use of diffusion and/or functional MRI to extract individual brain networks, which require high acquisition times and present an extreme sensitivity to motion artifacts, critical problems when scanning fetuses and infants. Extraction of individual networks based on morphological similarity from gray matter is a new approach that benefits from the power of graph theory analysis to describe gray matter morphology as a large-scale morphological network from a typical clinical anatomic acquisition such as T1-weighted MRI. In the present paper we propose a methodology to normalize these large-scale morphological networks to a brain network with standardized size based on a parcellation scheme. The proposed methodology was applied to reconstruct individual brain networks of 63 one-year-old infants, 41 infants with intrauterine growth restriction (IUGR) and 22 controls, showing altered network features in the IUGR group, and their association with neurodevelopmental outcome at two years of age by means of ordinal regression analysis of the network features obtained with Bayley Scale for Infant and Toddler Development, third edition. Although it must be more widely assessed, this methodology stands as a good candidate for the development of biomarkers for altered neurodevelopment in the pediatric population. © 2013 Elsevier Inc. All rights reserved.

Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics.

PubMed

San Segundo, Eugenia; Tsanas, Athanasios; Gómez-Vilda, Pedro

2017-01-01

There is a growing consensus that hybrid approaches are necessary for successful speaker characterization in Forensic Speaker Comparison (FSC); hence this study explores the forensic potential of voice features combining source and filter characteristics. The former relate to the action of the vocal folds while the latter reflect the geometry of the speaker's vocal tract. This set of features have been extracted from pause fillers, which are long enough for robust feature estimation while spontaneous enough to be extracted from voice samples in real forensic casework. Speaker similarity was measured using standardized Euclidean Distances (ED) between pairs of speakers: 54 different-speaker (DS) comparisons, 54 same-speaker (SS) comparisons and 12 comparisons between monozygotic twins (MZ). Results revealed that the differences between DS and SS comparisons were significant in both high quality and telephone-filtered recordings, with no false rejections and limited false acceptances; this finding suggests that this set of voice features is highly speaker-dependent and therefore forensically useful. Mean ED for MZ pairs lies between the average ED for SS comparisons and DS comparisons, as expected according to the literature on twin voices. Specific cases of MZ speakers with very high ED (i.e. strong dissimilarity) are discussed in the context of sociophonetic and twin studies. A preliminary simplification of the Vocal Profile Analysis (VPA) Scheme is proposed, which enables the quantification of voice quality features in the perceptual assessment of speaker similarity, and allows for the calculation of perceptual-acoustic correlations. The adequacy of z-score normalization for this study is also discussed, as well as the relevance of heat maps for detecting the so-called phantoms in recent approaches to the biometric menagerie. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

PubMed

Hayat, Maqsood; Khan, Asifullah

2011-02-21

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor. Copyright © 2010 Elsevier Ltd. All rights reserved.

A computerized scheme for lung nodule detection in multiprojection chest radiography

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

Guo Wei; Li Qiang; Boyce, Sarah J.

2012-04-15

Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme. Methods: Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification ofmore » initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes. Results: At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules. Conclusions: This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.« less

Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images

PubMed Central

Sparks, Rachel; Madabhushi, Anant

2016-01-01

Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01. PMID:27264985

Natural texture retrieval based on perceptual similarity measurement

NASA Astrophysics Data System (ADS)

Gao, Ying; Dong, Junyu; Lou, Jianwen; Qi, Lin; Liu, Jun

2018-04-01

A typical texture retrieval system performs feature comparison and might not be able to make human-like judgments of image similarity. Meanwhile, it is commonly known that perceptual texture similarity is difficult to be described by traditional image features. In this paper, we propose a new texture retrieval scheme based on texture perceptual similarity. The key of the proposed scheme is that prediction of perceptual similarity is performed by learning a non-linear mapping from image features space to perceptual texture space by using Random Forest. We test the method on natural texture dataset and apply it on a new wallpapers dataset. Experimental results demonstrate that the proposed texture retrieval scheme with perceptual similarity improves the retrieval performance over traditional image features.

Limitations and requirements of content-based multimedia authentication systems

NASA Astrophysics Data System (ADS)

Wu, Chai W.

2001-08-01

Recently, a number of authentication schemes have been proposed for multimedia data such as images and sound data. They include both label based systems and semifragile watermarks. The main requirement for such authentication systems is that minor modifications such as lossy compression which do not alter the content of the data preserve the authenticity of the data, whereas modifications which do modify the content render the data not authentic. These schemes can be classified into two main classes depending on the model of image authentication they are based on. One of the purposes of this paper is to look at some of the advantages and disadvantages of these image authentication schemes and their relationship with fundamental limitations of the underlying model of image authentication. In particular, we study feature-based algorithms which generate an authentication tag based on some inherent features in the image such as the location of edges. The main disadvantage of most proposed feature-based algorithms is that similar images generate similar features, and therefore it is possible for a forger to generate dissimilar images that have the same features. On the other hand, the class of hash-based algorithms utilizes a cryptographic hash function or a digital signature scheme to reduce the data and generate an authentication tag. It inherits the security of digital signatures to thwart forgery attacks. The main disadvantage of hash-based algorithms is that the image needs to be modified in order to be made authenticatable. The amount of modification is on the order of the noise the image can tolerate before it is rendered inauthentic. The other purpose of this paper is to propose a multimedia authentication scheme which combines some of the best features of both classes of algorithms. The proposed scheme utilizes cryptographic hash functions and digital signature schemes and the data does not need to be modified in order to be made authenticatable. Several applications including the authentication of images on CD-ROM and handwritten documents will be discussed.

Compressive-sampling-based positioning in wireless body area networks.

PubMed

Banitalebi-Dehkordi, Mehdi; Abouei, Jamshid; Plataniotis, Konstantinos N

2014-01-01

Recent achievements in wireless technologies have opened up enormous opportunities for the implementation of ubiquitous health care systems in providing rich contextual information and warning mechanisms against abnormal conditions. This helps with the automatic and remote monitoring/tracking of patients in hospitals and facilitates and with the supervision of fragile, elderly people in their own domestic environment through automatic systems to handle the remote drug delivery. This paper presents a new modeling and analysis framework for the multipatient positioning in a wireless body area network (WBAN) which exploits the spatial sparsity of patients and a sparse fast Fourier transform (FFT)-based feature extraction mechanism for monitoring of patients and for reporting the movement tracking to a central database server containing patient vital information. The main goal of this paper is to achieve a high degree of accuracy and resolution in the patient localization with less computational complexity in the implementation using the compressive sensing theory. We represent the patients' positions as a sparse vector obtained by the discrete segmentation of the patient movement space in a circular grid. To estimate this vector, a compressive-sampling-based two-level FFT (CS-2FFT) feature vector is synthesized for each received signal from the biosensors embedded on the patient's body at each grid point. This feature extraction process benefits in the combination of both short-time and long-time properties of the received signals. The robustness of the proposed CS-2FFT-based algorithm in terms of the average positioning error is numerically evaluated using the realistic parameters in the IEEE 802.15.6-WBAN standard in the presence of additive white Gaussian noise. Due to the circular grid pattern and the CS-2FFT feature extraction method, the proposed scheme represents a significant reduction in the computational complexity, while improving the level of the resolution and the localization accuracy when compared to some classical CS-based positioning algorithms.

Developing a disease outbreak event corpus.

PubMed

Conway, Mike; Kawazoe, Ai; Chanlekha, Hutchatai; Collier, Nigel

2010-09-28

In recent years, there has been a growth in work on the use of information extraction technologies for tracking disease outbreaks from online news texts, yet publicly available evaluation standards (and associated resources) for this new area of research have been noticeably lacking. This study seeks to create a "gold standard" data set against which to test how accurately disease outbreak information extraction systems can identify the semantics of disease outbreak events. Additionally, we hope that the provision of an annotation scheme (and associated corpus) to the community will encourage open evaluation in this new and growing application area. We developed an annotation scheme for identifying infectious disease outbreak events in news texts. An event--in the context of our annotation scheme--consists minimally of geographical (eg, country and province) and disease name information. However, the scheme also allows for the rich encoding of other domain salient concepts (eg, international travel, species, and food contamination). The work resulted in a 200-document corpus of event-annotated disease outbreak reports that can be used to evaluate the accuracy of event detection algorithms (in this case, for the BioCaster biosurveillance online news information extraction system). In the 200 documents, 394 distinct events were identified (mean 1.97 events per document, range 0-25 events per document). We also provide a download script and graphical user interface (GUI)-based event browsing software to facilitate corpus exploration. In summary, we present an annotation scheme and corpus that can be used in the evaluation of disease outbreak event extraction algorithms. The annotation scheme and corpus were designed both with the particular evaluation requirements of the BioCaster system in mind as well as the wider need for further evaluation resources in this growing research area.

Active Thermal Extraction and Temperature Sensing of Near-field Thermal Radiation

DOE PAGES

Ding, D.; Kim, T.; Minnich, A. J.

2016-09-06

Recently, we proposed an active thermal extraction (ATX) scheme that enables thermally populated surface phonon polaritons to escape into the far-field. The concept is based on a fluorescence upconversion process that also occurs in laser cooling of solids (LCS). Here, we present a generalized analysis of our scheme using the theoretical framework for LCS. We show that both LCS and ATX can be described with the same mathematical formalism by replacing the electron-phonon coupling parameter in LCS with the electron-photon coupling parameter in ATX. Using this framework, we compare the ideal efficiency and power extracted for the two schemes andmore » examine the parasitic loss mechanisms. As a result, this work advances the application of ATX to manipulate near-field thermal radiation for applications such as temperature sensing and active radiative cooling.« less

Combined processing of lead concentrates

NASA Astrophysics Data System (ADS)

Kubasov, V. L.; Paretskii, V. M.; Sidorin, G. N.; Travkin, V. F.

2013-06-01

A combined scheme of processing of lead concentrates with the production of pure metallic lead and the important components containing in these concentrates is considered. This scheme includes sulfating roasting of the lead concentrates and two-stage leaching of the formed cinder with the formation of a sulfate solution and lead sulfate. When transformed into a carbonate form, lead sulfate is used for the production of pure metallic lead. Silver, indium, copper, cadmium, nickel, cobalt, and other important components are separately extracted from a solution. At the last stage, zinc is extracted by either extraction followed by electrolytic extraction of a metal or the return of the forming solution of sulfuric acid to cinder leaching.

An improved approach for the segmentation of starch granules in microscopic images

PubMed Central

2010-01-01

Background Starches are the main storage polysaccharides in plants and are distributed widely throughout plants including seeds, roots, tubers, leaves, stems and so on. Currently, microscopic observation is one of the most important ways to investigate and analyze the structure of starches. The position, shape, and size of the starch granules are the main measurements for quantitative analysis. In order to obtain these measurements, segmentation of starch granules from the background is very important. However, automatic segmentation of starch granules is still a challenging task because of the limitation of imaging condition and the complex scenarios of overlapping granules. Results We propose a novel method to segment starch granules in microscopic images. In the proposed method, we first separate starch granules from background using automatic thresholding and then roughly segment the image using watershed algorithm. In order to reduce the oversegmentation in watershed algorithm, we use the roundness of each segment, and analyze the gradient vector field to find the critical points so as to identify oversegments. After oversegments are found, we extract the features, such as the position and intensity of the oversegments, and use fuzzy c-means clustering to merge the oversegments to the objects with similar features. Experimental results demonstrate that the proposed method can alleviate oversegmentation of watershed segmentation algorithm successfully. Conclusions We present a new scheme for starch granules segmentation. The proposed scheme aims to alleviate the oversegmentation in watershed algorithm. We use the shape information and critical points of gradient vector flow (GVF) of starch granules to identify oversegments, and use fuzzy c-mean clustering based on prior knowledge to merge these oversegments to the objects. Experimental results on twenty microscopic starch images demonstrate the effectiveness of the proposed scheme. PMID:21047380

Efficient authentication scheme based on near-ring root extraction problem

NASA Astrophysics Data System (ADS)

Muthukumaran, V.; Ezhilmaran, D.

2017-11-01

An authentication protocolis the type of computer communication protocol or cryptography protocol specifically designed for transfer of authentication data between two entities. We have planned a two new entity authentication scheme on the basis of root extraction problem near-ring in this article. We suggest that this problem is suitably difficult to serve as a cryptographic assumption over the platform of near-ring N. The security issues also discussed.

A construction scheme of web page comment information extraction system based on frequent subtree mining

NASA Astrophysics Data System (ADS)

Zhang, Xiaowen; Chen, Bingfeng

2017-08-01

Based on the frequent sub-tree mining algorithm, this paper proposes a construction scheme of web page comment information extraction system based on frequent subtree mining, referred to as FSM system. The entire system architecture and the various modules to do a brief introduction, and then the core of the system to do a detailed description, and finally give the system prototype.

A computerized scheme of SARS detection in early stage based on chest image of digital radiograph

NASA Astrophysics Data System (ADS)

Zheng, Zhong; Lan, Rihui; Lv, Guozheng

2004-05-01

A computerized scheme for early severe acute respiratory syndrome(SARS) lesion detection in digital chest radiographs is presented in this paper. The total scheme consists of two main parts: the first part is to determine suspect lesions by the theory of locally orderless images(LOI) and their spatial features; the second part is to select real lesions among these suspect ones by their frequent features. The method we used in the second part is firstly developed by Katsuragawa et al with necessary modification. Preliminary results indicate that these features are good criterions to tell early SARS lesions apart from other normal lung structures.

Multi-stage robust scheme for citrus identification from high resolution airborne images

NASA Astrophysics Data System (ADS)

Amorós-López, Julia; Izquierdo Verdiguier, Emma; Gómez-Chova, Luis; Muñoz-Marí, Jordi; Zoilo Rodríguez-Barreiro, Jorge; Camps-Valls, Gustavo; Calpe-Maravilla, Javier

2008-10-01

Identification of land cover types is one of the most critical activities in remote sensing. Nowadays, managing land resources by using remote sensing techniques is becoming a common procedure to speed up the process while reducing costs. However, data analysis procedures should satisfy the accuracy figures demanded by institutions and governments for further administrative actions. This paper presents a methodological scheme to update the citrus Geographical Information Systems (GIS) of the Comunidad Valenciana autonomous region, Spain). The proposed approach introduces a multi-stage automatic scheme to reduce visual photointerpretation and ground validation tasks. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution (VHR) images (0.5m) acquired in the visible and near infrared. Next, several automatic classifiers (decision trees, multilayer perceptron, and support vector machines) are trained and combined to improve the final accuracy of the results. The proposed strategy fulfills the high accuracy demanded by policy makers by means of combining automatic classification methods with visual photointerpretation available resources. A level of confidence based on the agreement between classifiers allows us an effective management by fixing the quantity of parcels to be reviewed. The proposed methodology can be applied to similar problems and applications.

Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition.

PubMed

Ding, Changxing; Choi, Jonghyun; Tao, Dacheng; Davis, Larry S

2016-03-01

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

Combining Digital Watermarking and Fingerprinting Techniques to Identify Copyrights for Color Images

PubMed Central

Hsieh, Shang-Lin; Chen, Chun-Che; Shen, Wen-Shan

2014-01-01

This paper presents a copyright identification scheme for color images that takes advantage of the complementary nature of watermarking and fingerprinting. It utilizes an authentication logo and the extracted features of the host image to generate a fingerprint, which is then stored in a database and also embedded in the host image to produce a watermarked image. When a dispute over the copyright of a suspect image occurs, the image is first processed by watermarking. If the watermark can be retrieved from the suspect image, the copyright can then be confirmed; otherwise, the watermark then serves as the fingerprint and is processed by fingerprinting. If a match in the fingerprint database is found, then the suspect image will be considered a duplicated one. Because the proposed scheme utilizes both watermarking and fingerprinting, it is more robust than those that only adopt watermarking, and it can also obtain the preliminary result more quickly than those that only utilize fingerprinting. The experimental results show that when the watermarked image suffers slight attacks, watermarking alone is enough to identify the copyright. The results also show that when the watermarked image suffers heavy attacks that render watermarking incompetent, fingerprinting can successfully identify the copyright, hence demonstrating the effectiveness of the proposed scheme. PMID:25114966

A numerical resolution study of high order essentially non-oscillatory schemes applied to incompressible flow

NASA Technical Reports Server (NTRS)

Weinan, E.; Shu, Chi-Wang

1994-01-01

High order essentially non-oscillatory (ENO) schemes, originally designed for compressible flow and in general for hyperbolic conservation laws, are applied to incompressible Euler and Navier-Stokes equations with periodic boundary conditions. The projection to divergence-free velocity fields is achieved by fourth-order central differences through fast Fourier transforms (FFT) and a mild high-order filtering. The objective of this work is to assess the resolution of ENO schemes for large scale features of the flow when a coarse grid is used and small scale features of the flow, such as shears and roll-ups, are not fully resolved. It is found that high-order ENO schemes remain stable under such situations and quantities related to large scale features, such as the total circulation around the roll-up region, are adequately resolved.

A numerical resolution study of high order essentially non-oscillatory schemes applied to incompressible flow

NASA Technical Reports Server (NTRS)

Weinan, E.; Shu, Chi-Wang

1992-01-01

High order essentially non-oscillatory (ENO) schemes, originally designed for compressible flow and in general for hyperbolic conservation laws, are applied to incompressible Euler and Navier-Stokes equations with periodic boundary conditions. The projection to divergence-free velocity fields is achieved by fourth order central differences through Fast Fourier Transforms (FFT) and a mild high-order filtering. The objective of this work is to assess the resolution of ENO schemes for large scale features of the flow when a coarse grid is used and small scale features of the flow, such as shears and roll-ups, are not fully resolved. It is found that high-order ENO schemes remain stable under such situations and quantities related to large-scale features, such as the total circulation around the roll-up region, are adequately resolved.

Treelets Binary Feature Retrieval for Fast Keypoint Recognition.

PubMed

Zhu, Jianke; Wu, Chenxia; Chen, Chun; Cai, Deng

2015-10-01

Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches, we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. To effectively extract the binary features from each patch surrounding the keypoint, we make use of treelets transform that can group the highly correlated data together and reduce the noise through the local analysis. Treelets is a multiresolution analysis tool, which provides an orthogonal basis to reflect the geometry of the noise-free data. To facilitate the real-world applications, we have proposed two novel approaches. One is the convolutional treelets that capture the image patch information locally and globally while reducing the computational cost. The other is the higher-order treelets that reflect the relationship between the rows and columns within image patch. An efficient sub-signature-based locality sensitive hashing scheme is employed for fast approximate nearest neighbor search in patch retrieval. Experimental evaluations on both synthetic data and the real-world Oxford dataset have shown that our proposed treelets binary feature retrieval methods outperform the state-of-the-art feature descriptors and classification-based approaches.

Analysis of high-speed digital phonoscopy pediatric images

NASA Astrophysics Data System (ADS)

Unnikrishnan, Harikrishnan; Donohue, Kevin D.; Patel, Rita R.

2012-02-01

The quantitative characterization of vocal fold (VF) motion can greatly enhance the diagnosis and treatment of speech pathologies. The recent availability of high-speed systems has created new opportunities to understand VF dynamics. This paper presents quantitative methods for analyzing VF dynamics with high-speed digital phonoscopy, with a focus on expected VF changes during childhood. A robust method for automatic VF edge tracking during phonation is introduced and evaluated against 4 expert human observers. Results from 100 test frames show a subpixel difference between the VF edges selected by algorithm and expert observers. Waveforms created from the VF edge displacement are used to created motion features with limited sensitivity to variations of camera resolution on the imaging plane. New features are introduced based on acceleration ratios of critical points over each phonation cycle, which have the potential for studying issues related to impact stress. A novel denoising and hybrid interpolation/extrapolation scheme is also introduced to reduce the impact of quantization errors and large sampling intervals relative to the phonation cycle. Features extracted from groups of 4 adults and 5 children show large differences for features related to asymmetry between the right and left fold and consistent differences for impact acceleration ratio.

Evaluation of the effectiveness of color attributes for video indexing

NASA Astrophysics Data System (ADS)

Chupeau, Bertrand; Forest, Ronan

2001-10-01

Color features are reviewed and their effectiveness assessed in the application framework of key-frame clustering for abstracting unconstrained video. Existing color spaces and associated quantization schemes are first studied. Description of global color distribution by means of histograms is then detailed. In our work, 12 combinations of color space and quantization were selected, together with 12 histogram metrics. Their respective effectiveness with respect to picture similarity measurement was evaluated through a query-by-example scenario. For that purpose, a set of still-picture databases was built by extracting key frames from several video clips, including news, documentaries, sports and cartoons. Classical retrieval performance evaluation criteria were adapted to the specificity of our testing methodology.

Evaluation of the effectiveness of color attributes for video indexing

NASA Astrophysics Data System (ADS)

Chupeau, Bertrand; Forest, Ronan

2001-01-01

Color features are reviewed and their effectiveness assessed in the application framework of key-frame clustering for abstracting unconstrained video. Existing color spaces and associated quantization schemes are first studied. Description of global color distribution by means of histograms is then detailed. In our work, twelve combinations of color space and quantization were selected, together with twelve histogram metrics. Their respective effectiveness with respect to picture similarity measurement was evaluated through a query-be-example scenario. For that purpose, a set of still-picture databases was built by extracting key-frames from several video clips, including news, documentaries, sports and cartoons. Classical retrieval performance evaluation criteria were adapted to the specificity of our testing methodology.

Evaluation of the effectiveness of color attributes for video indexing

NASA Astrophysics Data System (ADS)

Chupeau, Bertrand; Forest, Ronan

2000-12-01

Color features are reviewed and their effectiveness assessed in the application framework of key-frame clustering for abstracting unconstrained video. Existing color spaces and associated quantization schemes are first studied. Description of global color distribution by means of histograms is then detailed. In our work, twelve combinations of color space and quantization were selected, together with twelve histogram metrics. Their respective effectiveness with respect to picture similarity measurement was evaluated through a query-be-example scenario. For that purpose, a set of still-picture databases was built by extracting key-frames from several video clips, including news, documentaries, sports and cartoons. Classical retrieval performance evaluation criteria were adapted to the specificity of our testing methodology.

Molecular orbital imaging via above-threshold ionization with circularly polarized pulses.

PubMed

Zhu, Xiaosong; Zhang, Qingbin; Hong, Weiyi; Lu, Peixiang; Xu, Zhizhan

2011-07-18

Above-threshold ionization (ATI) for aligned or orientated linear molecules by circularly polarized laser pulsed is investigated. It is found that the all-round structural information of the molecular orbital is extracted with only one shot by the circularly polarized probe pulse rather than with multi-shot detections in a linearly polarized case. The obtained photoelectron momentum spectrum directly depicts the symmetry and electron distribution of the occupied molecular orbital, which results from the strong sensitivity of the ionization probability to these structural features. Our investigation indicates that the circularly polarized probe scheme would present a simple method to study the angle-dependent ionization and image the occupied electronic orbital.

Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk

NASA Astrophysics Data System (ADS)

Li, Yane; Fan, Ming; Cheng, Hu; Zhang, Peng; Zheng, Bin; Li, Lihua

2018-01-01

This study aims to develop and test a new imaging marker-based short-term breast cancer risk prediction model. An age-matched dataset of 566 screening mammography cases was used. All ‘prior’ images acquired in the two screening series were negative, while in the ‘current’ screening images, 283 cases were positive for cancer and 283 cases remained negative. For each case, two bilateral cranio-caudal view mammograms acquired from the ‘prior’ negative screenings were selected and processed by a computer-aided image processing scheme, which segmented the entire breast area into nine strip-based local regions, extracted the element regions using difference of Gaussian filters, and computed both global- and local-based bilateral asymmetrical image features. An initial feature pool included 190 features related to the spatial distribution and structural similarity of grayscale values, as well as of the magnitude and phase responses of multidirectional Gabor filters. Next, a short-term breast cancer risk prediction model based on a generalized linear model was built using an embedded stepwise regression analysis method to select features and a leave-one-case-out cross-validation method to predict the likelihood of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) values significantly increased from 0.5863  ±  0.0237 to 0.6870  ±  0.0220 when the model trained by the image features extracted from the global regions and by the features extracted from both the global and the matched local regions (p  =  0.0001). The odds ratio values monotonically increased from 1.00-8.11 with a significantly increasing trend in slope (p  =  0.0028) as the model-generated risk score increased. In addition, the AUC values were 0.6555  ±  0.0437, 0.6958  ±  0.0290, and 0.7054  ±  0.0529 for the three age groups of 37-49, 50-65, and 66-87 years old, respectively. AUC values of 0.6529  ±  0.1100, 0.6820  ±  0.0353, 0.6836  ±  0.0302 and 0.8043  ±  0.1067 were yielded for the four mammography density sub-groups (BIRADS from 1-4), respectively. This study demonstrated that bilateral asymmetry features extracted from local regions combined with the global region in bilateral negative mammograms could be used as a new imaging marker to assist in the prediction of short-term breast cancer risk.

Conversion and Extraction of Insoluble Organic Materials in Meteorites

NASA Technical Reports Server (NTRS)

Locke, Darren R.; Burton, Aaron S.; Niles, Paul B.

2016-01-01

We endeavor to develop and implement methods in our laboratory to convert and extract insoluble organic materials (IOM) from low car-bon bearing meteorites (such as ordinary chondrites) and Precambrian terrestrial rocks for the purpose of determining IOM structure and prebiotic chemistries preserved in these types of samples. The general scheme of converting and extracting IOM in samples is summarized in Figure 1. First, powdered samples are solvent extracted in a micro-Soxhlet apparatus multiple times using solvents ranging from non-polar to polar (hexane - non-polar, dichloromethane - non-polar to polar, methanol - polar protic, and acetonitrile - polar aprotic). Second, solid residue from solvent extractions is processed using strong acids, hydrochloric and hydrofluoric, to dissolve minerals and isolate IOM. Third, the isolated IOM is subjected to both thermal (pyrolysis) and chemical (oxidation) degradation to release compounds from the macromolecular material. Finally, products from oxidation and pyrolysis are analyzed by gas chromatography - mass spectrometry (GCMS). We are working toward an integrated method and analysis scheme that will allow us to determine prebiotic chemistries in ordinary chondrites and Precambrian terrestrial rocks. Powerful techniques that we are including are stepwise, flash, and gradual pyrolysis and ruthenium tetroxide oxidation. More details of the integrated scheme will be presented.

Efficient iris recognition by characterizing key local variations.

PubMed

Ma, Li; Tan, Tieniu; Wang, Yunhong; Zhang, Dexin

2004-06-01

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

A Comparative Study of Different EEG Reference Choices for Diagnosing Unipolar Depression.

PubMed

Mumtaz, Wajid; Malik, Aamir Saeed

2018-06-02

The choice of an electroencephalogram (EEG) reference has fundamental importance and could be critical during clinical decision-making because an impure EEG reference could falsify the clinical measurements and subsequent inferences. In this research, the suitability of three EEG references was compared while classifying depressed and healthy brains using a machine-learning (ML)-based validation method. In this research, the EEG data of 30 unipolar depressed subjects and 30 age-matched healthy controls were recorded. The EEG data were analyzed in three different EEG references, the link-ear reference (LE), average reference (AR), and reference electrode standardization technique (REST). The EEG-based functional connectivity (FC) was computed. Also, the graph-based measures, such as the distances between nodes, minimum spanning tree, and maximum flow between the nodes for each channel pair, were calculated. An ML scheme provided a mechanism to compare the performances of the extracted features that involved a general framework such as the feature extraction (graph-based theoretic measures), feature selection, classification, and validation. For comparison purposes, the performance metrics such as the classification accuracies, sensitivities, specificities, and F scores were computed. When comparing the three references, the diagnostic accuracy showed better performances during the REST, while the LE and AR showed less discrimination between the two groups. Based on the results, it can be concluded that the choice of appropriate reference is critical during the clinical scenario. The REST reference is recommended for future applications of EEG-based diagnosis of mental illnesses.

Laser ion source activities at Brookhaven National Laboratory

DOE PAGES

Kanesue, Takeshi; Okamura, Masahiro

2015-07-31

In Brookhaven National Laboratory (BNL), we have been developing laser ion sources for diverse accelerators. Tabletop Nd:YAG lasers with up to several Joules of energy are mainly used to create ablation plasmas for stable operations. The obtained charge states depend on laser power density and target species. Two types of ion extraction schemes, Direct Plasma Injection Scheme (DPIS) and conventional static extraction, are used depending on application. We optimized and select a suitable laser irradiation condition and a beam extraction scheme to meet the requirement of the following accelerator system. We have demonstrated to accelerate more than 5 x 10more » 10 of C 6+ ions using the DPIS. We successfully commissioned low charge ion beam provider to the user facilities in BNL. As a result, to achieve higher current, higher charge state and lower emittance, further studies will continue.« less

Mining and beneficiation of lunar ores

NASA Technical Reports Server (NTRS)

Bunch, T. E.; Williams, R. J.; Mckay, D. S.; Giles, D.

1979-01-01

The beneficiation of lunar plagioclase and ilmenite ores to feedstock grade permits a rapid growth of the space manufacturing economy by maximizing the production rate of metals and oxygen. A beneficiation scheme based on electrostatic and magnetic separation is preferred over conventional schemes, but such a scheme cannot be completely modeled because beneficiation processes are empirical and because some properties of lunar minerals have not been measured. To meet anticipated shipping and processing needs, the peak lunar mining rate will exceed 1000 tons/hr by the fifth year of operation. Such capabilities will be best obtained by automated mining vehicles and conveyor systems rather than trucks. It may be possible to extract about 40 kg of volatiles (60 percent H2O) by thermally processing the less than 20 micron ilmenite concentrate extracted from 130 tons of ilmenite ore. A thermodynamic analysis of an extraction process is presented.

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2015-03-01

The morphological analysis of bone marrow smears is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually with the use of bright field microscope. This is a time consuming, partly subjective and tedious process. Furthermore, repeated examinations of a slide yield intra- and inter-observer variances. For this reason an automation of morphological bone marrow analysis is pursued. This analysis comprises several steps: image acquisition and smear detection, cell localization and segmentation, feature extraction and cell classification. The automated classification of bone marrow cells is depending on the automated cell segmentation and the choice of adequate features extracted from different parts of the cell. In this work we focus on the evaluation of support vector machines (SVMs) and random forests (RFs) for the differentiation of bone marrow cells in 16 different classes, including immature and abnormal cell classes. Data sets of different segmentation quality are used to test the two approaches. Automated solutions for the morphological analysis for bone marrow smears could use such a classifier to pre-classify bone marrow cells and thereby shortening the examination duration.

Automated analysis of food-borne pathogens using a novel microbial cell culture, sensing and classification system.

PubMed

Xiang, Kun; Li, Yinglei; Ford, William; Land, Walker; Schaffer, J David; Congdon, Robert; Zhang, Jing; Sadik, Omowunmi

2016-02-21

We hereby report the design and implementation of an Autonomous Microbial Cell Culture and Classification (AMC(3)) system for rapid detection of food pathogens. Traditional food testing methods require multistep procedures and long incubation period, and are thus prone to human error. AMC(3) introduces a "one click approach" to the detection and classification of pathogenic bacteria. Once the cultured materials are prepared, all operations are automatic. AMC(3) is an integrated sensor array platform in a microbial fuel cell system composed of a multi-potentiostat, an automated data collection system (Python program, Yocto Maxi-coupler electromechanical relay module) and a powerful classification program. The classification scheme consists of Probabilistic Neural Network (PNN), Support Vector Machines (SVM) and General Regression Neural Network (GRNN) oracle-based system. Differential Pulse Voltammetry (DPV) is performed on standard samples or unknown samples. Then, using preset feature extractions and quality control, accepted data are analyzed by the intelligent classification system. In a typical use, thirty-two extracted features were analyzed to correctly classify the following pathogens: Escherichia coli ATCC#25922, Escherichia coli ATCC#11775, and Staphylococcus epidermidis ATCC#12228. 85.4% accuracy range was recorded for unknown samples, and within a shorter time period than the industry standard of 24 hours.

Automatic orientation and 3D modelling from markerless rock art imagery

NASA Astrophysics Data System (ADS)

Lerma, J. L.; Navarro, S.; Cabrelles, M.; Seguí, A. E.; Hernández, D.

2013-02-01

This paper investigates the use of two detectors and descriptors on image pyramids for automatic image orientation and generation of 3D models. The detectors and descriptors replace manual measurements and are used to detect, extract and match features across multiple imagery. The Scale-Invariant Feature Transform (SIFT) and the Speeded Up Robust Features (SURF) will be assessed based on speed, number of features, matched features, and precision in image and object space depending on the adopted hierarchical matching scheme. The influence of applying in addition Area Based Matching (ABM) with normalised cross-correlation (NCC) and least squares matching (LSM) is also investigated. The pipeline makes use of photogrammetric and computer vision algorithms aiming minimum interaction and maximum accuracy from a calibrated camera. Both the exterior orientation parameters and the 3D coordinates in object space are sequentially estimated combining relative orientation, single space resection and bundle adjustment. The fully automatic image-based pipeline presented herein to automate the image orientation step of a sequence of terrestrial markerless imagery is compared with manual bundle block adjustment and terrestrial laser scanning (TLS) which serves as ground truth. The benefits of applying ABM after FBM will be assessed both in image and object space for the 3D modelling of a complex rock art shelter.

Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach.

PubMed

Cai, Hongmin; Peng, Yanxia; Ou, Caiwen; Chen, Minsheng; Li, Li

2014-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported. The study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination. Seven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively. Multi-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.

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

Harmon, S; Jeraj, R; Galavis, P

Purpose: Sensitivity of PET-derived texture features to reconstruction methods has been reported for features extracted from axial planes; however, studies often utilize three dimensional techniques. This work aims to quantify the impact of multi-plane (3D) vs. single-plane (2D) feature extraction on radiomics-based analysis, including sensitivity to reconstruction parameters and potential loss of spatial information. Methods: Twenty-three patients with solid tumors underwent [{sup 18}F]FDG PET/CT scans under identical protocols. PET data were reconstructed using five sets of reconstruction parameters. Tumors were segmented using an automatic, in-house algorithm robust to reconstruction variations. 50 texture features were extracted using two Methods: 2D patchesmore » along axial planes and 3D patches. For each method, sensitivity of features to reconstruction parameters was calculated as percent difference relative to the average value across reconstructions. Correlations between feature values were compared when using 2D and 3D extraction. Results: 21/50 features showed significantly different sensitivity to reconstruction parameters when extracted in 2D vs 3D (wilcoxon α<0.05), assessed by overall range of variation, Rangevar(%). Eleven showed greater sensitivity to reconstruction in 2D extraction, primarily first-order and co-occurrence features (average Rangevar increase 83%). The remaining ten showed higher variation in 3D extraction (average Range{sub var}increase 27%), mainly co-occurence and greylevel run-length features. Correlation of feature value extracted in 2D and feature value extracted in 3D was poor (R<0.5) in 12/50 features, including eight co-occurrence features. Feature-to-feature correlations in 2D were marginally higher than 3D, ∣R∣>0.8 in 16% and 13% of all feature combinations, respectively. Larger sensitivity to reconstruction parameters were seen for inter-feature correlation in 2D(σ=6%) than 3D (σ<1%) extraction. Conclusion: Sensitivity and correlation of various texture features were shown to significantly differ between 2D and 3D extraction. Additionally, inter-feature correlations were more sensitive to reconstruction variation using single-plane extraction. This work highlights a need for standardized feature extraction/selection techniques in radiomics.« less

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

A parameterized logarithmic image processing method with Laplacian of Gaussian filtering for lung nodule enhancement in chest radiographs.

PubMed

Chen, Sheng; Yao, Liping; Chen, Bao

2016-11-01

The enhancement of lung nodules in chest radiographs (CXRs) plays an important role in the manual as well as computer-aided detection (CADe) lung cancer. In this paper, we proposed a parameterized logarithmic image processing (PLIP) method combined with the Laplacian of a Gaussian (LoG) filter to enhance lung nodules in CXRs. We first applied several LoG filters with varying parameters to an original CXR to enhance the nodule-like structures as well as the edges in the image. We then applied the PLIP model, which can enhance lung nodule images with high contrast and was beneficial in extracting effective features for nodule detection in the CADe scheme. Our method combined the advantages of both the PLIP algorithm and the LoG algorithm, which can enhance lung nodules in chest radiographs with high contrast. To test our nodule enhancement method, we tested a CADe scheme, with a relatively high performance in nodule detection, using a publically available database containing 140 nodules in 140 CXRs enhanced through our nodule enhancement method. The CADe scheme attained a sensitivity of 81 and 70 % with an average of 5.0 frame rate (FP) and 2.0 FP, respectively, in a leave-one-out cross-validation test. By contrast, the CADe scheme based on the original image recorded a sensitivity of 77 and 63 % at 5.0 FP and 2.0 FP, respectively. We introduced the measurement of enhancement by entropy evaluation to objectively assess our method. Experimental results show that the proposed method obtains an effective enhancement of lung nodules in CXRs for both radiologists and CADe schemes.

Computerized scheme for detection of diffuse lung diseases on CR chest images

NASA Astrophysics Data System (ADS)

Pereira, Roberto R., Jr.; Shiraishi, Junji; Li, Feng; Li, Qiang; Doi, Kunio

2008-03-01

We have developed a new computer-aided diagnostic (CAD) scheme for detection of diffuse lung disease in computed radiographic (CR) chest images. One hundred ninety-four chest images (56 normals and 138 abnormals with diffuse lung diseases) were used. The 138 abnormal cases were classified into three levels of severity (34 mild, 60 moderate, and 44 severe) by an experienced chest radiologist with use of five different patterns, i.e., reticular, reticulonodular, nodular, air-space opacity, and emphysema. In our computerized scheme, the first moment of the power spectrum, the root-mean-square variation, and the average pixel value were determined for each region of interest (ROI), which was selected automatically in the lung fields. The average pixel value and its dependence on the location of the ROI were employed for identifying abnormal patterns due to air-space opacity or emphysema. A rule-based method was used for determining three levels of abnormality for each ROI (0: normal, 1: mild, 2: moderate, and 3: severe). The distinction between normal lungs and abnormal lungs with diffuse lung disease was determined based on the fractional number of abnormal ROIs by taking into account the severity of abnormalities. Preliminary results indicated that the area under the ROC curve was 0.889 for the 44 severe cases, 0.825 for the 104 severe and moderate cases, and 0.794 for all cases. We have identified a number of problems and reasons causing false positives on normal cases, and also false negatives on abnormal cases. In addition, we have discussed potential approaches for improvement of our CAD scheme. In conclusion, the CAD scheme for detection of diffuse lung diseases based on texture features extracted from CR chest images has the potential to assist radiologists in their interpretation of diffuse lung diseases.

Automatic identification of epileptic seizures from EEG signals using linear programming boosting.

PubMed

Hassan, Ahnaf Rashik; Subasi, Abdulhamit

2016-11-01

Computerized epileptic seizure detection is essential for expediting epilepsy diagnosis and research and for assisting medical professionals. Moreover, the implementation of an epilepsy monitoring device that has low power and is portable requires a reliable and successful seizure detection scheme. In this work, the problem of automated epilepsy seizure detection using singe-channel EEG signals has been addressed. At first, segments of EEG signals are decomposed using a newly proposed signal processing scheme, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Six spectral moments are extracted from the CEEMDAN mode functions and train and test matrices are formed afterward. These matrices are fed into the classifier to identify epileptic seizures from EEG signal segments. In this work, we implement an ensemble learning based machine learning algorithm, namely linear programming boosting (LPBoost) to perform classification. The efficacy of spectral features in the CEEMDAN domain is validated by graphical and statistical analyses. The performance of CEEMDAN is compared to those of its predecessors to further inspect its suitability. The effectiveness and the appropriateness of LPBoost are demonstrated as opposed to the commonly used classification models. Resubstitution and 10 fold cross-validation error analyses confirm the superior algorithm performance of the proposed scheme. The algorithmic performance of our epilepsy seizure identification scheme is also evaluated against state-of-the-art works in the literature. Experimental outcomes manifest that the proposed seizure detection scheme performs better than the existing works in terms of accuracy, sensitivity, specificity, and Cohen's Kappa coefficient. It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for device implementation, eliminate the onus of clinicians for analyzing a large bulk of data manually, and expedite epilepsy diagnosis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Vertical Feature Mask Feature Classification Flag Extraction

Atmospheric Science Data Center

2013-03-28

  Vertical Feature Mask Feature Classification Flag Extraction This routine demonstrates extraction of the ... in a CALIPSO Lidar Level 2 Vertical Feature Mask feature classification flag value. It is written in Interactive Data Language (IDL) ...

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genomic Definition of Hypervirulent and Multidrug-Resistant Klebsiella pneumoniae Clonal Groups

PubMed Central

Bialek-Davenet, Suzanne; Criscuolo, Alexis; Ailloud, Florent; Passet, Virginie; Jones, Louis; Delannoy-Vieillard, Anne-Sophie; Garin, Benoit; Le Hello, Simon; Arlet, Guillaume; Nicolas-Chanoine, Marie-Hélène; Decré, Dominique

2014-01-01

Multidrug-resistant and highly virulent Klebsiella pneumoniae isolates are emerging, but the clonal groups (CGs) corresponding to these high-risk strains have remained imprecisely defined. We aimed to identify K. pneumoniae CGs on the basis of genome-wide sequence variation and to provide a simple bioinformatics tool to extract virulence and resistance gene data from genomic data. We sequenced 48 K. pneumoniae isolates, mostly of serotypes K1 and K2, and compared the genomes with 119 publicly available genomes. A total of 694 highly conserved genes were included in a core-genome multilocus sequence typing scheme, and cluster analysis of the data enabled precise definition of globally distributed hypervirulent and multidrug-resistant CGs. In addition, we created a freely accessible database, BIGSdb-Kp, to enable rapid extraction of medically and epidemiologically relevant information from genomic sequences of K. pneumoniae. Although drug-resistant and virulent K. pneumoniae populations were largely nonoverlapping, isolates with combined virulence and resistance features were detected. PMID:25341126

MPEG-7 based video annotation and browsing

NASA Astrophysics Data System (ADS)

Hoeynck, Michael; Auweiler, Thorsten; Wellhausen, Jens

2003-11-01

The huge amount of multimedia data produced worldwide requires annotation in order to enable universal content access and to provide content-based search-and-retrieval functionalities. Since manual video annotation can be time consuming, automatic annotation systems are required. We review recent approaches to content-based indexing and annotation of videos for different kind of sports and describe our approach to automatic annotation of equestrian sports videos. We especially concentrate on MPEG-7 based feature extraction and content description, where we apply different visual descriptors for cut detection. Further, we extract the temporal positions of single obstacles on the course by analyzing MPEG-7 edge information. Having determined single shot positions as well as the visual highlights, the information is jointly stored with meta-textual information in an MPEG-7 description scheme. Based on this information, we generate content summaries which can be utilized in a user-interface in order to provide content-based access to the video stream, but further for media browsing on a streaming server.

Distributed Scene Analysis For Autonomous Road Vehicle Guidance

NASA Astrophysics Data System (ADS)

Mysliwetz, Birger D.; Dickmanns, E. D.

1987-01-01

An efficient distributed processing scheme has been developed for visual road boundary tracking by 'VaMoRs', a testbed vehicle for autonomous mobility and computer vision. Ongoing work described here is directed to improving the robustness of the road boundary detection process in the presence of shadows, ill-defined edges and other disturbing real world effects. The system structure and the techniques applied for real-time scene analysis are presented along with experimental results. All subfunctions of road boundary detection for vehicle guidance, such as edge extraction, feature aggregation and camera pointing control, are executed in parallel by an onboard multiprocessor system. On the image processing level local oriented edge extraction is performed in multiple 'windows', tighly controlled from a hierarchically higher, modelbased level. The interpretation process involving a geometric road model and the observer's relative position to the road boundaries is capable of coping with ambiguity in measurement data. By using only selected measurements to update the model parameters even high noise levels can be dealt with and misleading edges be rejected.

A 400-kWe high-efficiency steam turbine for industrial cogeneration

NASA Technical Reports Server (NTRS)

Leibowitz, H. M.

1982-01-01

An advanced state-of-the-art steam turbine-generator developed to serve as the power conversion subsystem for the Department of Energy's Sandia National Laboratories' Solar Total-Energy Project (STEP) is described. The turbine-generator, which is designed to provide 400-kW of net electrical power, represents the largest turbine-generator built specifically for commercial solar-powered cogeneration. The controls for the turbine-generator incorporate a multiple, partial-arc entry to provide efficient off-design performance, as well as an extraction control scheme to permit extraction flow regulation while maintaining 110-spsig pressure. Normal turbine operation is achieved while synchronized to a local utility and in a stand-alone mode. In both cases, the turbine-generator features automatic load control as well as remote start-up and shutdown capability. Tests totaling 200 hours were conducted to confirm the integrity of the turbine's mechanical structure and control function. Performance tests resulted in a measured inlet throttle flow of 8,450 pounds per hour, which was near design conditions.

Product information representation for feature conversion and implementation of group technology automated coding

NASA Astrophysics Data System (ADS)

Medland, A. J.; Zhu, Guowang; Gao, Jian; Sun, Jian

1996-03-01

Feature conversion, also called feature transformation and feature mapping, is defined as the process of converting features from one view of an object to another view of the object. In a relatively simple implementation, for each application the design features are automatically converted into features specific for that application. All modifications have to be made via the design features. This is the approach that has attracted most attention until now. In the ideal situation, however, conversions directly from application views to the design view, and to other applications views, are also possible. In this paper, some difficulties faced in feature conversion are discussed. A new representation scheme of feature-based parts models has been proposed for the purpose of one-way feature conversion. The parts models consist of five different levels of abstraction, extending from an assembly level and its attributes, single parts and their attributes, single features and their attributes, one containing the geometric reference element and finally one for detailed geometry. One implementation of feature conversion for rotational components within GT (Group Technology) has already been undertaken using an automated coding procedure operating on a design-feature database. This database has been generated by a feature-based design system, and the GT coding scheme used in this paper is a specific scheme created for a textile machine manufacturing plant. Such feature conversion techniques presented here are only in their early stages of development and further research is underway.

Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data

NASA Astrophysics Data System (ADS)

Zhu, Zhe; Harowicz, Michael; Zhang, Jun; Saha, Ashirbani; Grimm, Lars J.; Hwang, Shelley; Mazurowski, Maciej A.

2018-02-01

Approximately 25% of patients with ductal carcinoma in situ (DCIS) diagnosed from core needle biopsy are subsequently upstaged to invasive cancer at surgical excision. Identifying patients with occult invasive disease is important as it changes treatment and precludes enrollment in active surveillance for DCIS. In this study, we investigated upstaging of DCIS to invasive disease using deep features. While deep neural networks require large amounts of training data, the available data to predict DCIS upstaging is sparse and thus directly training a neural network is unlikely to be successful. In this work, a pre-trained neural network is used as a feature extractor and a support vector machine (SVM) is trained on the extracted features. We used the dynamic contrast-enhanced (DCE) MRIs of patients at our institution from January 1, 2000, through March 23, 2014 who underwent MRI following a diagnosis of DCIS. Among the 131 DCIS patients, there were 35 patients who were upstaged to invasive cancer. Area under the ROC curve within the 10-fold cross-validation scheme was used for validation of our predictive model. The use of deep features was able to achieve an AUC of 0.68 (95% CI: 0.56-0.78) to predict occult invasive disease. This preliminary work demonstrates the promise of deep features to predict surgical upstaging following a diagnosis of DCIS.

A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification.

PubMed

Tan, Hua; Bao, Jiguang; Zhou, Xiaobo

2012-11-15

It becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression ('driver' mutation) from those that do not ('passenger' mutation). However, the existing methods have multifaceted shortcomings, in the sense that they either adopt incomplete feature space or depend on protein structural databases which are usually far from integrated. In this article, we investigated multiple aspects of a missense mutation and identified a novel feature space that well distinguishes cancer-associated driver mutations from passenger ones. An index (DX score) was proposed to evaluate the discriminating capability of each feature, and a subset of these features which ranks top was selected to build the SVM classifier. Cross-validation showed that the classifier trained on our selected features significantly outperforms the existing ones both in precision and robustness. We applied our method to several datasets of missense mutations culled from published database and literature and obtained more reasonable results than previous studies. The software is available online at http://www.methodisthealth.com/software and https://sites.google.com/site/drivermutationidentification/. xzhou@tmhs.org. Supplementary data are available at Bioinformatics online.

Analyzing tree-shape anatomical structures using topological descriptors of branching and ensemble of classifiers.

PubMed

Skoura, Angeliki; Bakic, Predrag R; Megalooikonomou, Vasilis

2013-01-01

The analysis of anatomical tree-shape structures visualized in medical images provides insight into the relationship between tree topology and pathology of the corresponding organs. In this paper, we propose three methods to extract descriptive features of the branching topology; the asymmetry index, the encoding of branching patterns using a node labeling scheme and an extension of the Sholl analysis. Based on these descriptors, we present classification schemes for tree topologies with respect to the underlying pathology. Moreover, we present a classifier ensemble approach which combines the predictions of the individual classifiers to optimize the classification accuracy. We applied the proposed methodology to a dataset of x-ray galactograms, medical images which visualize the breast ductal tree, in order to recognize images with radiological findings regarding breast cancer. The experimental results demonstrate the effectiveness of the proposed framework compared to state-of-the-art techniques suggesting that the proposed descriptors provide more valuable information regarding the topological patterns of ductal trees and indicating the potential of facilitating early breast cancer diagnosis.

Analyzing tree-shape anatomical structures using topological descriptors of branching and ensemble of classifiers

PubMed Central

Skoura, Angeliki; Bakic, Predrag R.; Megalooikonomou, Vasilis

2014-01-01

The analysis of anatomical tree-shape structures visualized in medical images provides insight into the relationship between tree topology and pathology of the corresponding organs. In this paper, we propose three methods to extract descriptive features of the branching topology; the asymmetry index, the encoding of branching patterns using a node labeling scheme and an extension of the Sholl analysis. Based on these descriptors, we present classification schemes for tree topologies with respect to the underlying pathology. Moreover, we present a classifier ensemble approach which combines the predictions of the individual classifiers to optimize the classification accuracy. We applied the proposed methodology to a dataset of x-ray galactograms, medical images which visualize the breast ductal tree, in order to recognize images with radiological findings regarding breast cancer. The experimental results demonstrate the effectiveness of the proposed framework compared to state-of-the-art techniques suggesting that the proposed descriptors provide more valuable information regarding the topological patterns of ductal trees and indicating the potential of facilitating early breast cancer diagnosis. PMID:25414850

Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens.

PubMed

Frantzidis, Christos A; Gilou, Sotiria; Billis, Antonis; Karagianni, Maria; Bratsas, Charalampos D; Bamidis, Panagiotis

2016-03-01

Recent neuroscientific studies focused on the identification of pathological neurophysiological patterns (emotions, geriatric depression, memory impairment and sleep disturbances) through computerised clinical decision-support systems. Almost all these research attempts employed either resting-state condition (e.g. eyes-closed) or event-related potentials extracted during a cognitive task known to be affected by the disease under consideration. This Letter reviews existing data mining techniques and aims to enhance their robustness by proposing a holistic decision framework dealing with comorbidities and early symptoms' identification, while it could be applied in realistic occasions. Multivariate features are elicited and fused in order to be compared with average activities characteristic of each neuropathology group. A proposed model of the specific cognitive function which may be based on previous findings (a priori information) and/or validated by current experimental data should be then formed. So, the proposed scheme facilitates the early identification and prevention of neurodegenerative phenomena. Neurophysiological semantic annotation is hypothesised to enhance the importance of the proposed framework in facilitating the personalised healthcare of the information society and medical informatics research community.

Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

PubMed Central

Novellino, A.; D'Angelo, P.; Cozzi, L.; Chiappalone, M.; Sanguineti, V.; Martinoia, S.

2007-01-01

One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses. PMID:18350128

Iris recognition based on key image feature extraction.

PubMed

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Pattern recognition applied to seismic signals of Llaima volcano (Chile): An evaluation of station-dependent classifiers

NASA Astrophysics Data System (ADS)

Curilem, Millaray; Huenupan, Fernando; Beltrán, Daniel; San Martin, Cesar; Fuentealba, Gustavo; Franco, Luis; Cardona, Carlos; Acuña, Gonzalo; Chacón, Max; Khan, M. Salman; Becerra Yoma, Nestor

2016-04-01

Automatic pattern recognition applied to seismic signals from volcanoes may assist seismic monitoring by reducing the workload of analysts, allowing them to focus on more challenging activities, such as producing reports, implementing models, and understanding volcanic behaviour. In a previous work, we proposed a structure for automatic classification of seismic events in Llaima volcano, one of the most active volcanoes in the Southern Andes, located in the Araucanía Region of Chile. A database of events taken from three monitoring stations on the volcano was used to create a classification structure, independent of which station provided the signal. The database included three types of volcanic events: tremor, long period, and volcano-tectonic and a contrast group which contains other types of seismic signals. In the present work, we maintain the same classification scheme, but we consider separately the stations information in order to assess whether the complementary information provided by different stations improves the performance of the classifier in recognising seismic patterns. This paper proposes two strategies for combining the information from the stations: i) combining the features extracted from the signals from each station and ii) combining the classifiers of each station. In the first case, the features extracted from the signals from each station are combined forming the input for a single classification structure. In the second, a decision stage combines the results of the classifiers for each station to give a unique output. The results confirm that the station-dependent strategies that combine the features and the classifiers from several stations improves the classification performance, and that the combination of the features provides the best performance. The results show an average improvement of 9% in the classification accuracy when compared with the station-independent method.

Experience improves feature extraction in Drosophila.

PubMed

Peng, Yueqing; Xi, Wang; Zhang, Wei; Zhang, Ke; Guo, Aike

2007-05-09

Previous exposure to a pattern in the visual scene can enhance subsequent recognition of that pattern in many species from honeybees to humans. However, whether previous experience with a visual feature of an object, such as color or shape, can also facilitate later recognition of that particular feature from multiple visual features is largely unknown. Visual feature extraction is the ability to select the key component from multiple visual features. Using a visual flight simulator, we designed a novel protocol for visual feature extraction to investigate the effects of previous experience on visual reinforcement learning in Drosophila. We found that, after conditioning with a visual feature of objects among combinatorial shape-color features, wild-type flies exhibited poor ability to extract the correct visual feature. However, the ability for visual feature extraction was greatly enhanced in flies trained previously with that visual feature alone. Moreover, we demonstrated that flies might possess the ability to extract the abstract category of "shape" but not a particular shape. Finally, this experience-dependent feature extraction is absent in flies with defective MBs, one of the central brain structures in Drosophila. Our results indicate that previous experience can enhance visual feature extraction in Drosophila and that MBs are required for this experience-dependent visual cognition.

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

Isolation of 1E4 IgM Anti-Fasciola hepatica Rediae Monoclonal Antibody from Ascites: Comparison of Two Purification Protocols.

PubMed

Alba, Annia; Marcet, Ricardo; Otero, Oscar; Hernández, Hilda M; Figueredo, Mabel; Sarracent, Jorge

2016-02-01

Purification of immunoglobulin M (IgM) antibodies could be challenging, and is often characterized by the optimization of the purification protocol to best suit the particular features of the molecule. Here, two different schemes were compared to purify, from ascites, the 1E4 IgM monoclonal antibody (mAb) previously raised against the stage of redia of the trematode Fasciola hepatica. This immunoglobulin is used as capture antibody in an immunoenzymatic assay to detect parasite ongoing infection in its intermediate hosts. The first purification protocol of the 1E4 mAb involved two chromatographic steps: an affinity chromatography on a Concanavalin A matrix followed by size exclusion chromatography. An immunoaffinity chromatography was selected as the second protocol for one-step purification of the antibody using the crude extract of adult parasites coupled to a commercial matrix. Immunoreactivity of the fractions during purification schemes was assessed by indirect immunoenzymatic assays against the crude extract of F. hepatica rediae, while purity was estimated by protein electrophoresis. Losses on the recovery of the antibody isolated by the first purification protocol occurred due to protein precipitation during the concentration of the sample and to low resolution of the size exclusion molecular chromatography step regarding this particular immunoglobulin. The immunoaffinity chromatography using F. hepatica antigens as ligands proved to be the most suitable protocol yielding a pure and immunoreactive antibody. The purification protocols used are discussed regarding efficiency and difficulties.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

A blur-invariant local feature for motion blurred image matching

NASA Astrophysics Data System (ADS)

Tong, Qiang; Aoki, Terumasa

2017-07-01

Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

The Australian Pharmaceutical Benefits Scheme data collection: a practical guide for researchers.

PubMed

Mellish, Leigh; Karanges, Emily A; Litchfield, Melisa J; Schaffer, Andrea L; Blanch, Bianca; Daniels, Benjamin J; Segrave, Alicia; Pearson, Sallie-Anne

2015-11-02

The Pharmaceutical Benefits Scheme (PBS) is Australia's national drug subsidy program. This paper provides a practical guide to researchers using PBS data to examine prescribed medicine use. Excerpts of the PBS data collection are available in a variety of formats. We describe the core components of four publicly available extracts (the Australian Statistics on Medicines, PBS statistics online, section 85 extract, under co-payment extract). We also detail common analytical challenges and key issues regarding the interpretation of utilisation using the PBS collection and its various extracts. Research using routinely collected data is increasing internationally. PBS data are a valuable resource for Australian pharmacoepidemiological and pharmaceutical policy research. A detailed knowledge of the PBS, the nuances of data capture, and the extracts available for research purposes are necessary to ensure robust methodology, interpretation, and translation of study findings into policy and practice.

Automatic extraction of plots from geo-registered UAS imagery of crop fields with complex planting schemes

NASA Astrophysics Data System (ADS)

Hearst, Anthony A.

Complex planting schemes are common in experimental crop fields and can make it difficult to extract plots of interest from high-resolution imagery of the fields gathered by Unmanned Aircraft Systems (UAS). This prevents UAS imagery from being applied in High-Throughput Precision Phenotyping and other areas of agricultural research. If the imagery is accurately geo-registered, then it may be possible to extract plots from the imagery based on their map coordinates. To test this approach, a UAS was used to acquire visual imagery of 5 ha of soybean fields containing 6.0 m2 plots in a complex planting scheme. Sixteen artificial targets were setup in the fields before flights and different spatial configurations of 0 to 6 targets were used as Ground Control Points (GCPs) for geo-registration, resulting in a total of 175 geo-registered image mosaics with a broad range of geo-registration accuracies. Geo-registration accuracy was quantified based on the horizontal Root Mean Squared Error (RMSE) of targets used as checkpoints. Twenty test plots were extracted from the geo-registered imagery. Plot extraction accuracy was quantified based on the percentage of the desired plot area that was extracted. It was found that using 4 GCPs along the perimeter of the field minimized the horizontal RMSE and enabled a plot extraction accuracy of at least 70%, with a mean plot extraction accuracy of 92%. Future work will focus on further enhancing the plot extraction accuracy through additional image processing techniques so that it becomes sufficiently accurate for all practical purposes in agricultural research and potentially other areas of research.

Feature extraction for document text using Latent Dirichlet Allocation

NASA Astrophysics Data System (ADS)

Prihatini, P. M.; Suryawan, I. K.; Mandia, IN

2018-01-01

Feature extraction is one of stages in the information retrieval system that used to extract the unique feature values of a text document. The process of feature extraction can be done by several methods, one of which is Latent Dirichlet Allocation. However, researches related to text feature extraction using Latent Dirichlet Allocation method are rarely found for Indonesian text. Therefore, through this research, a text feature extraction will be implemented for Indonesian text. The research method consists of data acquisition, text pre-processing, initialization, topic sampling and evaluation. The evaluation is done by comparing Precision, Recall and F-Measure value between Latent Dirichlet Allocation and Term Frequency Inverse Document Frequency KMeans which commonly used for feature extraction. The evaluation results show that Precision, Recall and F-Measure value of Latent Dirichlet Allocation method is higher than Term Frequency Inverse Document Frequency KMeans method. This shows that Latent Dirichlet Allocation method is able to extract features and cluster Indonesian text better than Term Frequency Inverse Document Frequency KMeans method.

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

NASA Astrophysics Data System (ADS)

Novelli, Antonio; Aguilar, Manuel A.; Nemmaoui, Abderrahim; Aguilar, Fernando J.; Tarantino, Eufemia

2016-10-01

This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using Object Based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 data comparisons were also extended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific spectral contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overall accuracies respectively, evaluated over the whole study area.

Recognizing characters of ancient manuscripts

NASA Astrophysics Data System (ADS)

Diem, Markus; Sablatnig, Robert

2010-02-01

Considering printed Latin text, the main issues of Optical Character Recognition (OCR) systems are solved. However, for degraded handwritten document images, basic preprocessing steps such as binarization, gain poor results with state-of-the-art methods. In this paper ancient Slavonic manuscripts from the 11th century are investigated. In order to minimize the consequences of false character segmentation, a binarization-free approach based on local descriptors is proposed. Additionally local information allows the recognition of partially visible or washed out characters. The proposed algorithm consists of two steps: character classification and character localization. Initially Scale Invariant Feature Transform (SIFT) features are extracted which are subsequently classified using Support Vector Machines (SVM). Afterwards, the interest points are clustered according to their spatial information. Thereby, characters are localized and finally recognized based on a weighted voting scheme of pre-classified local descriptors. Preliminary results show that the proposed system can handle highly degraded manuscript images with background clutter (e.g. stains, tears) and faded out characters.

Bi-model processing for early detection of breast tumor in CAD system

NASA Astrophysics Data System (ADS)

Mughal, Bushra; Sharif, Muhammad; Muhammad, Nazeer

2017-06-01

Early screening of skeptical masses in mammograms may reduce mortality rate among women. This rate can be further reduced upon developing the computer-aided diagnosis system with decrease in false assumptions in medical informatics. This method highlights the early tumor detection in digitized mammograms. For improving the performance of this system, a novel bi-model processing algorithm is introduced. It divides the region of interest into two parts, the first one is called pre-segmented region (breast parenchyma) and other is the post-segmented region (suspicious region). This system follows the scheme of the preprocessing technique of contrast enhancement that can be utilized to segment and extract the desired feature of the given mammogram. In the next phase, a hybrid feature block is presented to show the effective performance of computer-aided diagnosis. In order to assess the effectiveness of the proposed method, a database provided by the society of mammographic images is tested. Our experimental outcomes on this database exhibit the usefulness and robustness of the proposed method.

Exposomics research using suspect screening and non ...

EPA Pesticide Factsheets

High-resolution mass spectrometry (HRMS) is used for suspect screening (SSA) and non-targeted analysis (NTA) in an attempt to characterize xenobiotic chemicals in various samples broadly and efficiently. These important techniques aid characterization of the exposome, the totality of human exposures, and provide critical information on thousands of chemicals in commerce for which exposure data are lacking. The Environmental Protection Agency (EPA) SSA and NTA capabilities consist of analytical instrumentation [liquid chromatography (LC) with time of flight (TOF) and quadrupole-TOF (Q-TOF) HRMS], workflows (feature extraction, formula generation, structure prediction, spectral matching, chemical confirmation), and tools (databases; models for predicting retention time, functional use, media occurrence, and media concentration; and schemes for ranking features and chemicals). Suspect screening (SSA) and non-targeted analysis (NTA) are used to characterize xenobiotic chemicals in various samples and aid characterization of the exposome, the totality of human exposures, and provide critical information on thousands of chemicals in commerce for which exposure data are lacking.

Waveform Optimization for Target Estimation by Cognitive Radar with Multiple Antennas.

PubMed

Yao, Yu; Zhao, Junhui; Wu, Lenan

2018-05-29

A new scheme based on Kalman filtering to optimize the waveforms of an adaptive multi-antenna radar system for target impulse response (TIR) estimation is presented. This work aims to improve the performance of TIR estimation by making use of the temporal correlation between successive received signals, and minimize the mean square error (MSE) of TIR estimation. The waveform design approach is based upon constant learning from the target feature at the receiver. Under the multiple antennas scenario, a dynamic feedback loop control system is established to real-time monitor the change in the target features extracted form received signals. The transmitter adapts its transmitted waveform to suit the time-invariant environment. Finally, the simulation results show that, as compared with the waveform design method based on the MAP criterion, the proposed waveform design algorithm is able to improve the performance of TIR estimation for extended targets with multiple iterations, and has a relatively lower level of complexity.

Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

PubMed

Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

2016-01-01

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

NASA Astrophysics Data System (ADS)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Efficient feature extraction from wide-area motion imagery by MapReduce in Hadoop

NASA Astrophysics Data System (ADS)

Cheng, Erkang; Ma, Liya; Blaisse, Adam; Blasch, Erik; Sheaff, Carolyn; Chen, Genshe; Wu, Jie; Ling, Haibin

2014-06-01

Wide-Area Motion Imagery (WAMI) feature extraction is important for applications such as target tracking, traffic management and accident discovery. With the increasing amount of WAMI collections and feature extraction from the data, a scalable framework is needed to handle the large amount of information. Cloud computing is one of the approaches recently applied in large scale or big data. In this paper, MapReduce in Hadoop is investigated for large scale feature extraction tasks for WAMI. Specifically, a large dataset of WAMI images is divided into several splits. Each split has a small subset of WAMI images. The feature extractions of WAMI images in each split are distributed to slave nodes in the Hadoop system. Feature extraction of each image is performed individually in the assigned slave node. Finally, the feature extraction results are sent to the Hadoop File System (HDFS) to aggregate the feature information over the collected imagery. Experiments of feature extraction with and without MapReduce are conducted to illustrate the effectiveness of our proposed Cloud-Enabled WAMI Exploitation (CAWE) approach.

The Sivers effect and the Single Spin Asymmetry A_N in p(transv. pol.) p --> h X processes

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

Anselmino, Mauro; Boglione, Mariaelena; D'Alesio, Umberto

2013-09-01

The single spin asymmetry A_N, for large P_T single inclusive particle production in p(transv. pol.) p collisions, is considered within a generalised parton model and a transverse momentum dependent factorisation scheme. The focus is on the Sivers effect and the study of its potential contribution to A_N, based on a careful analysis of the Sivers functions extracted from azimuthal asymmetries in semi-inclusive deep inelastic scattering processes. It is found that such Sivers functions could explain most features of the A_N data, including some recent STAR results which show the persistence of a non zero A_N up to surprisingly large P_Tmore » values.« less

Tracking prominent points in image sequences

NASA Astrophysics Data System (ADS)

Hahn, Michael

1994-03-01

Measuring image motion and inferring scene geometry and camera motion are main aspects of image sequence analysis. The determination of image motion and the structure-from-motion problem are tasks that can be addressed independently or in cooperative processes. In this paper we focus on tracking prominent points. High stability, reliability, and accuracy are criteria for the extraction of prominent points. This implies that tracking should work quite well with those features; unfortunately, the reality looks quite different. In the experimental investigations we processed a long sequence of 128 images. This mono sequence is taken in an outdoor environment at the experimental field of Mercedes Benz in Rastatt. Different tracking schemes are explored and the results with respect to stability and quality are reported.

Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs)

PubMed Central

Dimitriadis, Stavros I.; Salis, Christos; Tarnanas, Ioannis; Linden, David E.

2017-01-01

The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or “nodes,” and quantifying the strength of the connections between nodes, or “edges,” as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the “true” connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects. PMID:28491032

Digital holographic-based cancellable biometric for personal authentication

NASA Astrophysics Data System (ADS)

Verma, Gaurav; Sinha, Aloka

2016-05-01

In this paper, we propose a new digital holographic-based cancellable biometric scheme for personal authentication and verification. The realization of cancellable biometric is presented by using an optoelectronic experimental approach, in which an optically recorded hologram of the fingerprint of a person is numerically reconstructed. Each reconstructed feature has its own perspective, which is utilized to generate user-specific fingerprint features by using a feature-extraction process. New representations of the user-specific fingerprint features can be obtained from the same hologram, by changing the reconstruction distance (d) by an amount Δd between the recording plane and the reconstruction plane. This parameter is the key to make the cancellable user-specific fingerprint features using a digital holographic technique, which allows us to choose different reconstruction distances when reissuing the user-specific fingerprint features in the event of compromise. We have shown theoretically that each user-specific fingerprint feature has a unique identity with a high discrimination ability, and the chances of a match between them are minimal. In this aspect, a recognition system has also been demonstrated using the fingerprint biometric of the enrolled person at a particular reconstruction distance. For the performance evaluation of a fingerprint recognition system—the false acceptance ratio, the false rejection ratio and the equal error rate are calculated using correlation. The obtained results show good discrimination ability between the genuine and the impostor populations with the highest recognition rate of 98.23%.

Introducing the MIT Regional Climate Model (MRCM)

NASA Astrophysics Data System (ADS)

Eltahir, Elfatih A. B.; Winter, Jonathn M.; Marcella, Marc P.; Gianotti, Rebecca L.; Im, Eun-Soon

2013-04-01

During the last decade researchers at MIT have worked on improving the skill of Regional Climate Model version 3 (RegCM3) in simulating climate over different regions through the incorporation of new physical schemes or modification of original schemes. The MIT Regional Climate Model (MRCM) features several modifications over RegCM3 including coupling of Integrated Biosphere Simulator (IBIS), a new surface albedo assignment method, a new convective cloud and rainfall auto-conversion scheme, and a modified boundary layer height and cloud scheme. Here, we introduce the MRCM and briefly describe the major model modifications relative to RegCM3 and their impact on the model performance. The most significant difference relative to the RegCM3 original configuration is coupling the Integrated Biosphere Simulator (IBIS) land-surface scheme (Winter et al., 2009). Based on the simulations using IBIS over the North America, the Maritime Continent, Southwest Asia and West Africa, we demonstrate that the use of IBIS as the land surface scheme results in better representation of surface energy and water budgets in comparison to BATS. Furthermore, the addition of a new irrigation scheme to IBIS makes it possible to investigate the effects of irrigation over any region. Also a new surface albedo assignment method used together with IBIS brings further improvement in simulations of surface radiation (Marcella and Eltahir, 2013). Another important feature of the MRCM is the introduction of a new convective cloud and rainfall auto-conversion scheme (Gianotti and Eltahir, 2013). This modification brings more physical realism into an important component of the model, and succeeds in simulating convective-radiative feedback improving model performance across several radiation fields and rainfall characteristics. Other features of MRCM such as the modified boundary layer height and cloud scheme, and the improvements in the dust emission and transport representations will be discussed.

Text analysis of MEDLINE for discovering functional relationships among genes: evaluation of keyword extraction weighting schemes.

PubMed

Liu, Ying; Navathe, Shamkant B; Pivoshenko, Alex; Dasigi, Venu G; Dingledine, Ray; Ciliax, Brian J

2006-01-01

One of the key challenges of microarray studies is to derive biological insights from the gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the functional links among genes. However, the quality of the keyword lists significantly affects the clustering results. We compared two keyword weighting schemes: normalised z-score and term frequency-inverse document frequency (TFIDF). Two gene sets were tested to evaluate the effectiveness of the weighting schemes for keyword extraction for gene clustering. Using established measures of cluster quality, the results produced from TFIDF-weighted keywords outperformed those produced from normalised z-score weighted keywords. The optimised algorithms should be useful for partitioning genes from microarray lists into functionally discrete clusters.

Computerized Detection of Lung Nodules by Means of “Virtual Dual-Energy” Radiography

PubMed Central

Chen, Sheng; Suzuki, Kenji

2014-01-01

Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of “virtual dual-energy” (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated “extremely subtle” or “very subtle” by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) per image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially. PMID:23193306

Computerized detection of lung nodules by means of "virtual dual-energy" radiography.

PubMed

Chen, Sheng; Suzuki, Kenji

2013-02-01

Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of "virtual dual-energy" (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated "extremely subtle" or "very subtle" by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) per image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially.

A framework for feature extraction from hospital medical data with applications in risk prediction.

PubMed

Tran, Truyen; Luo, Wei; Phung, Dinh; Gupta, Sunil; Rana, Santu; Kennedy, Richard Lee; Larkins, Ann; Venkatesh, Svetha

2014-12-30

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Comparative analysis of feature extraction methods in satellite imagery

NASA Astrophysics Data System (ADS)

Karim, Shahid; Zhang, Ye; Asif, Muhammad Rizwan; Ali, Saad

2017-10-01

Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.

Specific features of two diffraction schemes for a widely divergent X-ray beam

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

Avetyan, K. T.; Levonyan, L. V.; Semerjian, H. S.

2015-03-15

We investigated the specific features of two diffraction schemes for a widely divergent X-ray beam that use a circular diaphragm 30–50 μm in diameter as a point source of characteristic radiation. In one of the schemes, the diaphragm was set in front of the crystal (the diaphragm-crystal (d-c) scheme); in the other, it was installed behind the crystal (the crystal-diaphragm (c-d) scheme). It was established that the diffraction image in the c-d scheme is a topographic map of the investigated crystal area. In the d-c scheme at L = 2l (l and L are the distances between the crystal andmore » the diaphragm and between the photographic plate and the diaphragm, respectively), the branches of hyperbolas formed in this family of planes (hkl) by the characteristic K{sub α} and K{sub β} radiations, including higher order reflections, converge into one straight line. It is experimentally demonstrated that this convergence is very sensitive to structural inhomogeneities in the crystal under study.« less

Efficient reversible data hiding in encrypted image with public key cryptosystem

NASA Astrophysics Data System (ADS)

Xiang, Shijun; Luo, Xinrong

2017-12-01

This paper proposes a new reversible data hiding scheme for encrypted images by using homomorphic and probabilistic properties of Paillier cryptosystem. The proposed method can embed additional data directly into encrypted image without any preprocessing operations on original image. By selecting two pixels as a group for encryption, data hider can retrieve the absolute differences of groups of two pixels by employing a modular multiplicative inverse method. Additional data can be embedded into encrypted image by shifting histogram of the absolute differences by using the homomorphic property in encrypted domain. On the receiver side, legal user can extract the marked histogram in encrypted domain in the same way as data hiding procedure. Then, the hidden data can be extracted from the marked histogram and the encrypted version of original image can be restored by using inverse histogram shifting operations. Besides, the marked absolute differences can be computed after decryption for extraction of additional data and restoration of original image. Compared with previous state-of-the-art works, the proposed scheme can effectively avoid preprocessing operations before encryption and can efficiently embed and extract data in encrypted domain. The experiments on the standard image files also certify the effectiveness of the proposed scheme.

Mapping axonal density and average diameter using non-monotonic time-dependent gradient-echo MRI

NASA Astrophysics Data System (ADS)

Nunes, Daniel; Cruz, Tomás L.; Jespersen, Sune N.; Shemesh, Noam

2017-04-01

White Matter (WM) microstructures, such as axonal density and average diameter, are crucial to the normal function of the Central Nervous System (CNS) as they are closely related with axonal conduction velocities. Conversely, disruptions of these microstructural features may result in severe neurological deficits, suggesting that their noninvasive mapping could be an important step towards diagnosing and following pathophysiology. Whereas diffusion based MRI methods have been proposed to map these features, they typically entail the application of powerful gradients, which are rarely available in the clinic, or extremely long acquisition schemes to extract information from parameter-intensive models. In this study, we suggest that simple and time-efficient multi-gradient-echo (MGE) MRI can be used to extract the axon density from susceptibility-driven non-monotonic decay in the time-dependent signal. We show, both theoretically and with simulations, that a non-monotonic signal decay will occur for multi-compartmental microstructures - such as axons and extra-axonal spaces, which were here used as a simple model for the microstructure - and that, for axons parallel to the main magnetic field, the axonal density can be extracted. We then experimentally demonstrate in ex-vivo rat spinal cords that its different tracts - characterized by different microstructures - can be clearly contrasted using the MGE-derived maps. When the quantitative results are compared against ground-truth histology, they reflect the axonal fraction (though with a bias, as evident from Bland-Altman analysis). As well, the extra-axonal fraction can be estimated. The results suggest that our model is oversimplified, yet at the same time evidencing a potential and usefulness of the approach to map underlying microstructures using a simple and time-efficient MRI sequence. We further show that a simple general-linear-model can predict the average axonal diameters from the four model parameters, and map these average axonal diameters in the spinal cords. While clearly further modelling and theoretical developments are necessary, we conclude that salient WM microstructural features can be extracted from simple, SNR-efficient multi-gradient echo MRI, and that this paves the way towards easier estimation of WM microstructure in vivo.

Mapping axonal density and average diameter using non-monotonic time-dependent gradient-echo MRI.

PubMed

Nunes, Daniel; Cruz, Tomás L; Jespersen, Sune N; Shemesh, Noam

2017-04-01

White Matter (WM) microstructures, such as axonal density and average diameter, are crucial to the normal function of the Central Nervous System (CNS) as they are closely related with axonal conduction velocities. Conversely, disruptions of these microstructural features may result in severe neurological deficits, suggesting that their noninvasive mapping could be an important step towards diagnosing and following pathophysiology. Whereas diffusion based MRI methods have been proposed to map these features, they typically entail the application of powerful gradients, which are rarely available in the clinic, or extremely long acquisition schemes to extract information from parameter-intensive models. In this study, we suggest that simple and time-efficient multi-gradient-echo (MGE) MRI can be used to extract the axon density from susceptibility-driven non-monotonic decay in the time-dependent signal. We show, both theoretically and with simulations, that a non-monotonic signal decay will occur for multi-compartmental microstructures - such as axons and extra-axonal spaces, which were here used as a simple model for the microstructure - and that, for axons parallel to the main magnetic field, the axonal density can be extracted. We then experimentally demonstrate in ex-vivo rat spinal cords that its different tracts - characterized by different microstructures - can be clearly contrasted using the MGE-derived maps. When the quantitative results are compared against ground-truth histology, they reflect the axonal fraction (though with a bias, as evident from Bland-Altman analysis). As well, the extra-axonal fraction can be estimated. The results suggest that our model is oversimplified, yet at the same time evidencing a potential and usefulness of the approach to map underlying microstructures using a simple and time-efficient MRI sequence. We further show that a simple general-linear-model can predict the average axonal diameters from the four model parameters, and map these average axonal diameters in the spinal cords. While clearly further modelling and theoretical developments are necessary, we conclude that salient WM microstructural features can be extracted from simple, SNR-efficient multi-gradient echo MRI, and that this paves the way towards easier estimation of WM microstructure in vivo. Copyright © 2017 Elsevier Inc. All rights reserved.

An Improved Biometrics-Based Remote User Authentication Scheme with User Anonymity

PubMed Central

Kumari, Saru

2013-01-01

The authors review the biometrics-based user authentication scheme proposed by An in 2012. The authors show that there exist loopholes in the scheme which are detrimental for its security. Therefore the authors propose an improved scheme eradicating the flaws of An's scheme. Then a detailed security analysis of the proposed scheme is presented followed by its efficiency comparison. The proposed scheme not only withstands security problems found in An's scheme but also provides some extra features with mere addition of only two hash operations. The proposed scheme allows user to freely change his password and also provides user anonymity with untraceability. PMID:24350272

An improved biometrics-based remote user authentication scheme with user anonymity.

PubMed

Khan, Muhammad Khurram; Kumari, Saru

2013-01-01

The authors review the biometrics-based user authentication scheme proposed by An in 2012. The authors show that there exist loopholes in the scheme which are detrimental for its security. Therefore the authors propose an improved scheme eradicating the flaws of An's scheme. Then a detailed security analysis of the proposed scheme is presented followed by its efficiency comparison. The proposed scheme not only withstands security problems found in An's scheme but also provides some extra features with mere addition of only two hash operations. The proposed scheme allows user to freely change his password and also provides user anonymity with untraceability.

Securing SIFT: Privacy-preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.

PubMed

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Qin, Zhan; Ren, Kui

2016-05-13

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, addition of noise and change in 3D viewpoint and illumination.

SecSIFT: Privacy-preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.

PubMed

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Qin, Zhan; Ren, Kui

2016-05-13

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, addition of noise and change in 3D viewpoint and illumination.

Deep transfer learning for automatic target classification: MWIR to LWIR

NASA Astrophysics Data System (ADS)

Ding, Zhengming; Nasrabadi, Nasser; Fu, Yun

2016-05-01

Publisher's Note: This paper, originally published on 5/12/2016, was replaced with a corrected/revised version on 5/18/2016. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. When dealing with sparse or no labeled data in the target domain, transfer learning shows its appealing performance by borrowing the supervised knowledge from external domains. Recently deep structure learning has been exploited in transfer learning due to its attractive power in extracting effective knowledge through multi-layer strategy, so that deep transfer learning is promising to address the cross-domain mismatch. In general, cross-domain disparity can be resulted from the difference between source and target distributions or different modalities, e.g., Midwave IR (MWIR) and Longwave IR (LWIR). In this paper, we propose a Weighted Deep Transfer Learning framework for automatic target classification through a task-driven fashion. Specifically, deep features and classifier parameters are obtained simultaneously for optimal classification performance. In this way, the proposed deep structures can extract more effective features with the guidance of the classifier performance; on the other hand, the classifier performance is further improved since it is optimized on more discriminative features. Furthermore, we build a weighted scheme to couple source and target output by assigning pseudo labels to target data, therefore we can transfer knowledge from source (i.e., MWIR) to target (i.e., LWIR). Experimental results on real databases demonstrate the superiority of the proposed algorithm by comparing with others.

Automated classification of mandibular cortical bone on dental panoramic radiographs for early detection of osteoporosis

NASA Astrophysics Data System (ADS)

Horiba, Kazuki; Muramatsu, Chisako; Hayashi, Tatsuro; Fukui, Tatsumasa; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

2015-03-01

Findings on dental panoramic radiographs (DPRs) have shown that mandibular cortical index (MCI) based on the morphology of mandibular inferior cortex was significantly correlated with osteoporosis. MCI on DPRs can be categorized into one of three groups and has the high potential for identifying patients with osteoporosis. However, most DPRs are used only for diagnosing dental conditions by dentists in their routine clinical work. Moreover, MCI is not generally quantified but assessed subjectively. In this study, we investigated a computer-aided diagnosis (CAD) system that automatically classifies mandibular cortical bone for detection of osteoporotic patients at early stage. First, an inferior border of mandibular bone was detected by use of an active contour method. Second, regions of interest including the cortical bone are extracted and analyzed for its thickness and roughness. Finally, support vector machine (SVM) differentiate cases into three MCI categories by features including the thickness and roughness. Ninety eight DPRs were used to evaluate our proposed scheme. The number of cases classified to Class I, II, and III by a dental radiologist are 56, 25 and 17 cases, respectively. Experimental result based on the leave-one-out cross-validation evaluation showed that the sensitivities for the classes I, II, and III were 94.6%, 57.7% and 94.1%, respectively. Distribution of the groups in the feature space indicates a possibility of MCI quantification by the proposed method. Therefore, our scheme has a potential in identifying osteoporotic patients at an early stage.

Principal visual word discovery for automatic license plate detection.

PubMed

Zhou, Wengang; Li, Houqiang; Lu, Yijuan; Tian, Qi

2012-09-01

License plates detection is widely considered a solved problem, with many systems already in operation. However, the existing algorithms or systems work well only under some controlled conditions. There are still many challenges for license plate detection in an open environment, such as various observation angles, background clutter, scale changes, multiple plates, uneven illumination, and so on. In this paper, we propose a novel scheme to automatically locate license plates by principal visual word (PVW), discovery and local feature matching. Observing that characters in different license plates are duplicates of each other, we bring in the idea of using the bag-of-words (BoW) model popularly applied in partial-duplicate image search. Unlike the classic BoW model, for each plate character, we automatically discover the PVW characterized with geometric context. Given a new image, the license plates are extracted by matching local features with PVW. Besides license plate detection, our approach can also be extended to the detection of logos and trademarks. Due to the invariance virtue of scale-invariant feature transform feature, our method can adaptively deal with various changes in the license plates, such as rotation, scaling, illumination, etc. Promising results of the proposed approach are demonstrated with an experimental study in license plate detection.

Automatic extraction of planetary image features

NASA Technical Reports Server (NTRS)

LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

2013-01-01

A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.

PubMed

Khushaba, Rami N; Takruri, Maen; Miro, Jaime Valls; Kodagoda, Sarath

2014-07-01

Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

PubMed

Whitney, Jon; Corredor, German; Janowczyk, Andrew; Ganesan, Shridar; Doyle, Scott; Tomaszewski, John; Feldman, Michael; Gilmore, Hannah; Madabhushi, Anant

2018-05-30

Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Neural mechanisms of selective attention in the somatosensory system.

PubMed

Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

2016-09-01

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

A novel feature extraction scheme with ensemble coding for protein-protein interaction prediction.

PubMed

Du, Xiuquan; Cheng, Jiaxing; Zheng, Tingting; Duan, Zheng; Qian, Fulan

2014-07-18

Protein-protein interactions (PPIs) play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in functional genomics. Although PPI data have been increasing because of the development of high-throughput technologies and computational methods, many problems are still far from being solved. In this study, a novel predictor was designed by using the Random Forest (RF) algorithm with the ensemble coding (EC) method. To reduce computational time, a feature selection method (DX) was adopted to rank the features and search the optimal feature combination. The DXEC method integrates many features and physicochemical/biochemical properties to predict PPIs. On the Gold Yeast dataset, the DXEC method achieves 67.2% overall precision, 80.74% recall, and 70.67% accuracy. On the Silver Yeast dataset, the DXEC method achieves 76.93% precision, 77.98% recall, and 77.27% accuracy. On the human dataset, the prediction accuracy reaches 80% for the DXEC-RF method. We extended the experiment to a bigger and more realistic dataset that maintains 50% recall on the Yeast All dataset and 80% recall on the Human All dataset. These results show that the DXEC method is suitable for performing PPI prediction. The prediction service of the DXEC-RF classifier is available at http://ailab.ahu.edu.cn:8087/ DXECPPI/index.jsp.

Neural mechanisms of selective attention in the somatosensory system

PubMed Central

Hysaj, Kristjana; Niebur, Ernst

2016-01-01

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. PMID:27334956

Image analysis and machine learning in digital pathology: Challenges and opportunities.

PubMed

Madabhushi, Anant; Lee, George

2016-10-01

With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology. Copyright © 2016 Elsevier B.V. All rights reserved.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Visual Semantic Based 3D Video Retrieval System Using HDFS.

PubMed

Kumar, C Ranjith; Suguna, S

2016-08-01

This paper brings out a neoteric frame of reference for visual semantic based 3d video search and retrieval applications. Newfangled 3D retrieval application spotlight on shape analysis like object matching, classification and retrieval not only sticking up entirely with video retrieval. In this ambit, we delve into 3D-CBVR (Content Based Video Retrieval) concept for the first time. For this purpose, we intent to hitch on BOVW and Mapreduce in 3D framework. Instead of conventional shape based local descriptors, we tried to coalesce shape, color and texture for feature extraction. For this purpose, we have used combination of geometric & topological features for shape and 3D co-occurrence matrix for color and texture. After thriving extraction of local descriptors, TB-PCT (Threshold Based- Predictive Clustering Tree) algorithm is used to generate visual codebook and histogram is produced. Further, matching is performed using soft weighting scheme with L 2 distance function. As a final step, retrieved results are ranked according to the Index value and acknowledged to the user as a feedback .In order to handle prodigious amount of data and Efficacious retrieval, we have incorporated HDFS in our Intellection. Using 3D video dataset, we future the performance of our proposed system which can pan out that the proposed work gives meticulous result and also reduce the time intricacy.

Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification

PubMed Central

White, Daniel J.; William, Peter E.; Hoffman, Michael W.; Balkir, Sina

2013-01-01

A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fouriér Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of only the features needed by the back-end, in contrast to the FFT, where all coefficients must be calculated simultaneously. We also show that the FFT coefficients can be easily calculated from the AHT results by a simple back-substitution. The scheme is tailored for low-power, parallel analog implementation in an integrated circuit (IC). Two different applications are tested with an ideal front-end model and compared to existing studies with the same data sets. Results from the military vehicle classification and identification of machine-bearing fault applications shows that the front-end suits a wide range of harmonic signal sources. Analog-related errors are modeled to evaluate the feasibility of and to set design parameters for an IC implementation to maintain good system-level performance. Design of a preliminary transistor-level integrator circuit in a 0.13 μm complementary metal-oxide-silicon (CMOS) integrated circuit process showed the ability to use online self-calibration to reduce fabrication errors to a sufficiently low level. Estimated power dissipation is about three orders of magnitude less than similar vehicle classification systems that use commercially available FFT spectral extraction. PMID:23892765

Shape based segmentation of MRIs of the bones in the knee using phase and intensity information

NASA Astrophysics Data System (ADS)

Fripp, Jurgen; Bourgeat, Pierrick; Crozier, Stuart; Ourselin, Sébastien

2007-03-01

The segmentation of the bones from MR images is useful for performing subsequent segmentation and quantitative measurements of cartilage tissue. In this paper, we present a shape based segmentation scheme for the bones that uses texture features derived from the phase and intensity information in the complex MR image. The phase can provide additional information about the tissue interfaces, but due to the phase unwrapping problem, this information is usually discarded. By using a Gabor filter bank on the complex MR image, texture features (including phase) can be extracted without requiring phase unwrapping. These texture features are then analyzed using a support vector machine classifier to obtain probability tissue matches. The segmentation of the bone is fully automatic and performed using a 3D active shape model based approach driven using gradient and texture information. The 3D active shape model is automatically initialized using a robust affine registration. The approach is validated using a database of 18 FLASH MR images that are manually segmented, with an average segmentation overlap (Dice similarity coefficient) of 0.92 compared to 0.9 obtained using the classifier only.

Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad

2015-01-01

Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

Robust Optical Recognition of Cursive Pashto Script Using Scale, Rotation and Location Invariant Approach

PubMed Central

Ahmad, Riaz; Naz, Saeeda; Afzal, Muhammad Zeshan; Amin, Sayed Hassan; Breuel, Thomas

2015-01-01

The presence of a large number of unique shapes called ligatures in cursive languages, along with variations due to scaling, orientation and location provides one of the most challenging pattern recognition problems. Recognition of the large number of ligatures is often a complicated task in oriental languages such as Pashto, Urdu, Persian and Arabic. Research on cursive script recognition often ignores the fact that scaling, orientation, location and font variations are common in printed cursive text. Therefore, these variations are not included in image databases and in experimental evaluations. This research uncovers challenges faced by Arabic cursive script recognition in a holistic framework by considering Pashto as a test case, because Pashto language has larger alphabet set than Arabic, Persian and Urdu. A database containing 8000 images of 1000 unique ligatures having scaling, orientation and location variations is introduced. In this article, a feature space based on scale invariant feature transform (SIFT) along with a segmentation framework has been proposed for overcoming the above mentioned challenges. The experimental results show a significantly improved performance of proposed scheme over traditional feature extraction techniques such as principal component analysis (PCA). PMID:26368566

Extracting the Bosonic Spectra of Pb Using Superconducting-Tip STS and Comparing it with the Cuprates

NASA Astrophysics Data System (ADS)

Niestemski, F. C.; Johnston, S.; Contryman, A. W.; Camp, C. D.; Devereaux, T. P.; Manoharan, H. C.

2012-02-01

In high-temperature superconductors the meaning of the common feature labeled ``peak-dip-hump'' is still a point of great debate. In terms of scanning tunneling spectroscopy (STS) this refers to the shape of satellite features that occur outside the coherence peaks in the dI/dV spectra. There are many conflicting interpretations and labeling schemes for this feature in both the hole- and electron-doped cuprates. The path to resolving this confusion is to study a well-understood BCS superconductor to better observe the way that the STM measures bosonic information. Utilizing the ultra-low electronic noise of our home-built low-temperature STM, and utilizing a superconducting tip for increased spectral resolution, we recreate the original McMillan and Rowell S-I-S junctionootnotetextW. L. McMillan and J. M. Rowell Phys. Rev. Lett., 14, 108-112 (1965) with the STM equivalent (S-Vacuum-S). This method provides very high energy resolution for both the filled and empty electronic states in both the superconducting and normal state. We compare this data to first-principle Eliashberg calculations and relate this data to ``peak-dip-hump'' in the high Tc case.

ECG Identification System Using Neural Network with Global and Local Features

ERIC Educational Resources Information Center

Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

2016-01-01

This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

Cluster analysis based on dimensional information with applications to feature selection and classification

NASA Technical Reports Server (NTRS)

Eigen, D. J.; Fromm, F. R.; Northouse, R. A.

1974-01-01

A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.

A Quantum Multi-proxy Blind Signature Scheme Based on Genuine Four-Qubit Entangled State

NASA Astrophysics Data System (ADS)

Tian, Juan-Hong; Zhang, Jian-Zhong; Li, Yan-Ping

2016-02-01

In this paper, we propose a multi-proxy blind signature scheme based on controlled teleportation. Genuine four-qubit entangled state functions as quantum channel. The scheme uses the physical characteristics of quantum mechanics to implement delegation, signature and verification. The security analysis shows the scheme satisfies the security features of multi-proxy signature, unforgeability, undeniability, blindness and unconditional security.

Impact of different parameterization schemes on simulation of mesoscale convective system over south-east India

NASA Astrophysics Data System (ADS)

Madhulatha, A.; Rajeevan, M.

2018-02-01

Main objective of the present paper is to examine the role of various parameterization schemes in simulating the evolution of mesoscale convective system (MCS) occurred over south-east India. Using the Weather Research and Forecasting (WRF) model, numerical experiments are conducted by considering various planetary boundary layer, microphysics, and cumulus parameterization schemes. Performances of different schemes are evaluated by examining boundary layer, reflectivity, and precipitation features of MCS using ground-based and satellite observations. Among various physical parameterization schemes, Mellor-Yamada-Janjic (MYJ) boundary layer scheme is able to produce deep boundary layer height by simulating warm temperatures necessary for storm initiation; Thompson (THM) microphysics scheme is capable to simulate the reflectivity by reasonable distribution of different hydrometeors during various stages of system; Betts-Miller-Janjic (BMJ) cumulus scheme is able to capture the precipitation by proper representation of convective instability associated with MCS. Present analysis suggests that MYJ, a local turbulent kinetic energy boundary layer scheme, which accounts strong vertical mixing; THM, a six-class hybrid moment microphysics scheme, which considers number concentration along with mixing ratio of rain hydrometeors; and BMJ, a closure cumulus scheme, which adjusts thermodynamic profiles based on climatological profiles might have contributed for better performance of respective model simulations. Numerical simulation carried out using the above combination of schemes is able to capture storm initiation, propagation, surface variations, thermodynamic structure, and precipitation features reasonably well. This study clearly demonstrates that the simulation of MCS characteristics is highly sensitive to the choice of parameterization schemes.

Design of fire detection equipment based on ultraviolet detection technology

NASA Astrophysics Data System (ADS)

Liu, Zhenji; Liu, Jin; Chu, Sheng; Ping, Chao; Yuan, Xiaobing

2015-03-01

Utilized the feature of wide bandgap semiconductor of MgZnO, researched and developed a kind of Mid-Ultraviolet-Band(MUV) ultraviolet detector which has passed the simulation experiment in the sun circumstance. Based on the ultraviolet detector, it gives out a design scheme of gun-shot detection device, which is composed of twelve ultraviolet detectors, signal amplifier, processor, annunciator , azimuth indicator and the bracket. Through Analysing the feature of solar blind, ultraviolet responsivity, fire feature of gunshots and detection distance, the feasibility of this design scheme is proved.

Compact millijoule diode-seeded two-stage fiber master oscillator power amplifier using a multipass and forward pumping scheme.

PubMed

Lai, Po-Yen; Chang, Chun-Lin; Huang, Sheng-Lung; Chen, Shih-Hung

2018-05-01

The multipass scheme for a diode-seeded fiber master oscillator power amplifier with a nanojoule-to-millijoule output energy level at a repetition rate of <100  kHz is numerically analyzed for comparison to an experimental benchmark. For a 6/125 single-mode preamplifier with a small input energy (<1  nJ), there is a significant improvement in the output energy from 0.7% to 80% and 95% of the maximum extractable energy using the double-pass and four-pass schemes, respectively. For a 30/250 large-mode-area power amplifier using the double-pass and forward pumping scheme, the required input energy is decreased from 100 μJ to 18 μJ for millijoule energy extraction with accompanying Stokes waves of less than 10% of the total energy. The system based on the full master oscillator power amplifier configuration with an output energy exceeding millijoule level can be optimally simplified to two stages for commercialization.

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

2018-03-01

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p < 0.05) and odds ratio was 4.60 with a 95% confidence interval of [3.16, 6.70]. Study demonstrated that this new LPP-based feature regeneration approach enabled to produce an optimal feature vector and yield improved performance in assisting to predict risk of women having breast cancer detected in the next subsequent mammography screening.

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

NASA Astrophysics Data System (ADS)

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-08-01

The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793  ±  0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features.

PubMed

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

2016-09-15

Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme. In this work, we decompose sleep-EEG signal segments using tunable-Q factor wavelet transform (TQWT). Various spectral features are then computed from TQWT sub-bands. The performance of spectral features in the TQWT domain has been determined by intuitive and graphical analyses, statistical validation, and Fisher criteria. Random forest is used to perform classification. Optimal choices and the effects of TQWT and random forest parameters have been determined and expounded. Experimental outcomes manifest the efficacy of our feature generation scheme in terms of p-values of ANOVA analysis and Fisher criteria. The proposed scheme yields 90.38%, 91.50%, 92.11%, 94.80%, 97.50% for 6-stage to 2-stage classification of sleep states on the benchmark Sleep-EDF data-set. In addition, its performance on DREAMS Subjects Data-set is also promising. The performance of the proposed method is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient. Additionally, the proposed scheme gives high detection accuracy for sleep stages non-REM 1 and REM. Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously. The proposed scheme will alleviate the burden of the physicians, speed-up sleep disorder diagnosis, and expedite sleep research. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated detection of videotaped neonatal seizures of epileptic origin.

PubMed

Karayiannis, Nicolaos B; Xiong, Yaohua; Tao, Guozhi; Frost, James D; Wise, Merrill S; Hrachovy, Richard A; Mizrahi, Eli M

2006-06-01

This study aimed at the development of a seizure-detection system by training neural networks with quantitative motion information extracted from short video segments of neonatal seizures of the myoclonic and focal clonic types and random infant movements. The motion of the infants' body parts was quantified by temporal motion-strength signals extracted from video segments by motion-segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The motion of the infants' body parts also was quantified by temporal motion-trajectory signals extracted from video recordings by robust motion trackers based on block-motion models. These motion trackers were developed to adjust autonomously to illumination and contrast changes that may occur during the video-frame sequence. Video segments were represented by quantitative features obtained by analyzing motion-strength and motion-trajectory signals in both the time and frequency domains. Seizure recognition was performed by conventional feed-forward neural networks, quantum neural networks, and cosine radial basis function neural networks, which were trained to detect neonatal seizures of the myoclonic and focal clonic types and to distinguish them from random infant movements. The computational tools and procedures developed for automated seizure detection were evaluated on a set of 240 video segments of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). Regardless of the decision scheme used for interpreting the responses of the trained neural networks, all the neural network models exhibited sensitivity and specificity>90%. For one of the decision schemes proposed for interpreting the responses of the trained neural networks, the majority of the trained neural-network models exhibited sensitivity>90% and specificity>95%. In particular, cosine radial basis function neural networks achieved the performance targets of this phase of the project (i.e., sensitivity>95% and specificity>95%). The best among the motion segmentation and tracking methods developed in this study produced quantitative features that constitute a reliable basis for detecting neonatal seizures. The performance targets of this phase of the project were achieved by combining the quantitative features obtained by analyzing motion-strength signals with those produced by analyzing motion-trajectory signals. The computational procedures and tools developed in this study to perform off-line analysis of short video segments will be used in the next phase of this project, which involves the integration of these procedures and tools into a system that can process and analyze long video recordings of infants monitored for seizures in real time.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

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

Wang Xiaojia; Mao Qirong; Zhan Yongzhao

There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions.more » The experiments show that this method can improve the recognition rate and the time of feature extraction.« less

Highly Stable Wideband Microwave Extraction by Synchronizing Widely Tunable Optoelectronic Oscillator with Optical Frequency Comb

PubMed Central

Hou, D.; Xie, X. P.; Zhang, Y. L.; Wu, J. T.; Chen, Z. Y.; Zhao, J. Y.

2013-01-01

Optical frequency combs (OFCs), based on mode-locked lasers (MLLs), have attracted considerable attention in many fields over recent years. Among the applications of OFCs, one of the most challenging works is the extraction of a highly stable microwave with low phase noise. Many synchronisation schemes have been exploited to synchronise an electronic oscillator with the pulse train from a MLL, helping to extract an ultra-stable microwave. Here, we demonstrate novel wideband microwave extraction from a stable OFC by synchronising a single widely tunable optoelectronic oscillator (OEO) with an OFC at different harmonic frequencies, using an optical phase detection technique. The tunable range of the proposed microwave extraction extends from 2 GHz to 4 GHz, and in a long-term synchronisation experiment over 12 hours, the proposed synchronisation scheme provided a rms timing drift of 18 fs and frequency instabilities at 1.2 × 10−15/1 s and 2.2 × 10−18/10000 s. PMID:24336459

Highly Stable Wideband Microwave Extraction by Synchronizing Widely Tunable Optoelectronic Oscillator with Optical Frequency Comb

NASA Astrophysics Data System (ADS)

Hou, D.; Xie, X. P.; Zhang, Y. L.; Wu, J. T.; Chen, Z. Y.; Zhao, J. Y.

2013-12-01

Optical frequency combs (OFCs), based on mode-locked lasers (MLLs), have attracted considerable attention in many fields over recent years. Among the applications of OFCs, one of the most challenging works is the extraction of a highly stable microwave with low phase noise. Many synchronisation schemes have been exploited to synchronise an electronic oscillator with the pulse train from a MLL, helping to extract an ultra-stable microwave. Here, we demonstrate novel wideband microwave extraction from a stable OFC by synchronising a single widely tunable optoelectronic oscillator (OEO) with an OFC at different harmonic frequencies, using an optical phase detection technique. The tunable range of the proposed microwave extraction extends from 2 GHz to 4 GHz, and in a long-term synchronisation experiment over 12 hours, the proposed synchronisation scheme provided a rms timing drift of 18 fs and frequency instabilities at 1.2 × 10-15/1 s and 2.2 × 10-18/10000 s.

Hydrological challenges to groundwater trading: Lessons from south-west Western Australia

NASA Astrophysics Data System (ADS)

Skurray, James H.; Roberts, E. J.; Pannell, David J.

2012-01-01

SummaryPerth, Western Australia (pop. 1.6 m) derives 60% of its public water supply from the Gnangara groundwater system (GGS). Horticulture, domestic self-supply, and municipal parks are other major consumers of GGS groundwater. The system supports important wetlands and groundwater-dependent ecosystems. Underlying approximately 2200 km 2 of the Swan Coastal Plain, the GGS comprises several aquifer levels with partial interconnectivity. Supplies of GGS groundwater are under unprecedented stress, due to reduced recharge and increases in extraction. Stored reserves in the superficial aquifer fell by 700 GL between 1979 and 2008. Over a similar period, annual extraction for public supply increased by more than 350% from the system overall. Some management areas are over-allocated by as much as 69%. One potential policy response is a trading scheme for groundwater use. There has been only limited trading between GGS irrigators. Design and implementation of a robust groundwater trading scheme faces hydrological and/or hydro-economic challenges, among others. Groundwater trading involves transfers of the right to extract water. The resulting potential for spatial (and temporal) redistribution of the impacts of extraction requires management. Impacts at the respective selling and buying locations may differ in scale and nature. Negative externalities from groundwater trading may be uncertain as well as not monetarily compensable. An ideal groundwater trading scheme would ensure that marginal costs from trades do not exceed marginal benefits, incorporating future effects and impacts on third-parties. If this condition could be met, all transactions would result in constant or improved overall welfare. This paper examines issues that could reduce public welfare if groundwater trading is not subject to well-designed governance arrangements that are appropriate to meeting the above condition. It also outlines some opportunities to address key risks within the design of a groundwater trading scheme. We present a number of challenges, focusing on those with hydrological bases and/or information requirements. These include the appropriate hydrological definition of the boundaries of a trading area, the establishment and defining of sustainable yield and consumptive pool, and the estimation of effects of extractions on ecosystems and human users. We suggest several possible design tools. A combination of sustainable extraction limits, trading rules, management areas, and/or exchange rates may enable a trading scheme to address the above goals.

A new approach to develop computer-aided detection schemes of digital mammograms

NASA Astrophysics Data System (ADS)

Tan, Maxine; Qian, Wei; Pu, Jiantao; Liu, Hong; Zheng, Bin

2015-06-01

The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman’s age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779   ±   0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.

Image segmentation-based robust feature extraction for color image watermarking

NASA Astrophysics Data System (ADS)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

NASA Astrophysics Data System (ADS)

Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

2015-07-01

Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

LDFT-based watermarking resilient to local desynchronization attacks.

PubMed

Tian, Huawei; Zhao, Yao; Ni, Rongrong; Qin, Lunming; Li, Xuelong

2013-12-01

Up to now, a watermarking scheme that is robust against desynchronization attacks (DAs) is still a grand challenge. Most image watermarking resynchronization schemes in literature can survive individual global DAs (e.g., rotation, scaling, translation, and other affine transforms), but few are resilient to challenging cropping and local DAs. The main reason is that robust features for watermark synchronization are only globally invariable rather than locally invariable. In this paper, we present a blind image watermarking resynchronization scheme against local transform attacks. First, we propose a new feature transform named local daisy feature transform (LDFT), which is not only globally but also locally invariable. Then, the binary space partitioning (BSP) tree is used to partition the geometrically invariant LDFT space. In the BSP tree, the location of each pixel is fixed under global transform, local transform, and cropping. Lastly, the watermarking sequence is embedded bit by bit into each leaf node of the BSP tree by using the logarithmic quantization index modulation watermarking embedding method. Simulation results show that the proposed watermarking scheme can survive numerous kinds of distortions, including common image-processing attacks, local and global DAs, and noninvertible cropping.

An overview of structure-activity relationship studies of curcumin analogs as antioxidant and anti-inflammatory agents.

PubMed

Arshad, Laiba; Haque, Md Areeful; Abbas Bukhari, Syed Nasir; Jantan, Ibrahim

2017-04-01

Curcumin, extracted mainly from Curcuma longa rhizomes, has been reported to possess potent anti-inflammatory and anti-oxidant activities. Although safe at higher doses and exhibiting multiple biological activities, curcumin still has the problem of poor bioavailability which has been an attractive area of research over the last few years. A number of efforts have been made by modifying structural features of curcumin. This review highlights the structurally modified and more stable newly synthesized curcumin analogs that have been screened against antioxidant and anti-inflammatory activities. Also the structure-activity relationship to gain insight into future guidelines for scheming new compounds has been discussed, and further these analogs being more stable may serve as promising agents for use in different pathological conditions.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

[Research on Control System of an Exoskeleton Upper-limb Rehabilitation Robot].

PubMed

Wang, Lulu; Hu, Xin; Hu, Jie; Fang, Youfang; He, Rongrong; Yu, Hongliu

2016-12-01

In order to help the patients with upper-limb disfunction go on rehabilitation training,this paper proposed an upper-limb exoskeleton rehabilitation robot with four degrees of freedom(DOF),and realized two control schemes,i.e.,voice control and electromyography control.The hardware and software design of the voice control system was completed based on RSC-4128 chips,which realized the speech recognition technology of a specific person.Besides,this study adapted self-made surface eletromyogram(sEMG)signal extraction electrodes to collect sEMG signals and realized pattern recognition by conducting sEMG signals processing,extracting time domain features and fixed threshold algorithm.In addition,the pulse-width modulation(PWM)algorithm was used to realize the speed adjustment of the system.Voice control and electromyography control experiments were then carried out,and the results showed that the mean recognition rate of the voice control and electromyography control reached 93.1%and 90.9%,respectively.The results proved the feasibility of the control system.This study is expected to lay a theoretical foundation for the further improvement of the control system of the upper-limb rehabilitation robot.

Introducing a new aspect in marble quarry rehabilitation in Greece

NASA Astrophysics Data System (ADS)

Kaliampakos, D. C.; Mavrikos, A. A.

2006-06-01

For many years the marble extraction sector of the Greek mining industry has been in conflict with the public, especially in terms of rehabilitation of marble quarry sites. One of the main reasons for that is that the marble extraction sector has been unable to adjust to the existing legislative guidelines for the rehabilitation, such as extensive backfilling and re-vegetation. In the majority of cases these methods fail due to erosion of the backfill soil and adverse climatic conditions. As a result the number of abandoned marble quarry sites is continuously increasing. The present paper suggests a different approach regarding the rehabilitation of marble quarries. More specifically, the paper questions the applicability, the effectiveness, and the social usefulness of the above-mentioned guidelines and suggests the establishment of new land-uses, which are based on an in-depth analysis of the area’s special features, by taking full advantage of its potentials. What is more, the rehabilitation scheme proposes that the new land-uses and the quarrying activity may co-exist and operate simultaneously for a long period of time.

Analysis of hyperspectral fluorescence images for poultry skin tumor inspection

NASA Astrophysics Data System (ADS)

Kong, Seong G.; Chen, Yud-Ren; Kim, Intaek; Kim, Moon S.

2004-02-01

We present a hyperspectral fluorescence imaging system with a fuzzy inference scheme for detecting skin tumors on poultry carcasses. Hyperspectral images reveal spatial and spectral information useful for finding pathological lesions or contaminants on agricultural products. Skin tumors are not obvious because the visual signature appears as a shape distortion rather than a discoloration. Fluorescence imaging allows the visualization of poultry skin tumors more easily than reflectance. The hyperspectral image samples obtained for this poultry tumor inspection contain 65 spectral bands of fluorescence in the visible region of the spectrum at wavelengths ranging from 425 to 711 nm. The large amount of hyperspectral image data is compressed by use of a discrete wavelet transform in the spatial domain. Principal-component analysis provides an effective compressed representation of the spectral signal of each pixel in the spectral domain. A small number of significant features are extracted from two major spectral peaks of relative fluorescence intensity that have been identified as meaningful spectral bands for detecting tumors. A fuzzy inference scheme that uses a small number of fuzzy rules and Gaussian membership functions successfully detects skin tumors on poultry carcasses. Spatial-filtering techniques are used to significantly reduce false positives.

Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning.

PubMed

Ye, Jiaxing; Kobayashi, Takumi; Iwata, Masaya; Tsuda, Hiroshi; Murakawa, Masahiro

2018-03-09

Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load.

Robustness-Based Simplification of 2D Steady and Unsteady Vector Fields.

PubMed

Skraba, Primoz; Bei Wang; Guoning Chen; Rosen, Paul

2015-08-01

Vector field simplification aims to reduce the complexity of the flow by removing features in order of their relevance and importance, to reveal prominent behavior and obtain a compact representation for interpretation. Most existing simplification techniques based on the topological skeleton successively remove pairs of critical points connected by separatrices, using distance or area-based relevance measures. These methods rely on the stable extraction of the topological skeleton, which can be difficult due to instability in numerical integration, especially when processing highly rotational flows. In this paper, we propose a novel simplification scheme derived from the recently introduced topological notion of robustness which enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them. This leads to a hierarchical simplification scheme that encodes flow magnitude in its perturbation metric. Our novel simplification algorithm is based on degree theory and has minimal boundary restrictions. Finally, we provide an implementation under the piecewise-linear setting and apply it to both synthetic and real-world datasets. We show local and complete hierarchical simplifications for steady as well as unsteady vector fields.

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

Evaluating suitability of Pol-SAR (TerraSAR-X, Radarsat-2) for automated sea ice classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-05-01

Satellite borne SAR imagery has become an invaluable tool in the field of sea ice monitoring. Previously, single polarimetric imagery were employed in supervised and unsupervised classification schemes for sea ice investigation, which was preceded by image processing techniques such as segmentation and textural features. Recently, through the advent of polarimetric SAR sensors, investigation of polarimetric features in sea ice has attracted increased attention. While dual-polarimetric data has already been investigated in a number of works, full-polarimetric data has so far not been a major scientific focus. To explore the possibilities of full-polarimetric data and compare the differences in C- and X-bands, we endeavor to analyze in detail an array of datasets, simultaneously acquired, in C-band (RADARSAT-2) and X-band (TerraSAR-X) over ice infested areas. First, we propose an array of polarimetric features (Pauli and lexicographic based). Ancillary data from national ice services, SMOS data and expert judgement were utilized to identify the governing ice regimes. Based on these observations, we then extracted mentioned features. The subsequent supervised classification approach was based on an Artificial Neural Network (ANN). To gain quantitative insight into the quality of the features themselves (and reduce a possible impact of the Hughes phenomenon), we employed mutual information to unearth the relevance and redundancy of features. The results of this information theoretic analysis guided a pruning process regarding the optimal subset of features. In the last step we compared the classified results of all sensors and images, stated respective accuracies and discussed output discrepancies in the cases of simultaneous acquisitions.

Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

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.

Secure image retrieval with multiple keys

NASA Astrophysics Data System (ADS)

Liang, Haihua; Zhang, Xinpeng; Wei, Qiuhan; Cheng, Hang

2018-03-01

This article proposes a secure image retrieval scheme under a multiuser scenario. In this scheme, the owner first encrypts and uploads images and their corresponding features to the cloud; then, the user submits the encrypted feature of the query image to the cloud; next, the cloud compares the encrypted features and returns encrypted images with similar content to the user. To find the nearest neighbor in the encrypted features, an encryption with multiple keys is proposed, in which the query feature of each user is encrypted by his/her own key. To improve the key security and space utilization, global optimization and Gaussian distribution are, respectively, employed to generate multiple keys. The experiments show that the proposed encryption can provide effective and secure image retrieval for each user and ensure confidentiality of the query feature of each user.

Massive parallel 3D PIC simulation of negative ion extraction

NASA Astrophysics Data System (ADS)

Revel, Adrien; Mochalskyy, Serhiy; Montellano, Ivar Mauricio; Wünderlich, Dirk; Fantz, Ursel; Minea, Tiberiu

2017-09-01

The 3D PIC-MCC code ONIX is dedicated to modeling Negative hydrogen/deuterium Ion (NI) extraction and co-extraction of electrons from radio-frequency driven, low pressure plasma sources. It provides valuable insight on the complex phenomena involved in the extraction process. In previous calculations, a mesh size larger than the Debye length was used, implying numerical electron heating. Important steps have been achieved in terms of computation performance and parallelization efficiency allowing successful massive parallel calculations (4096 cores), imperative to resolve the Debye length. In addition, the numerical algorithms have been improved in terms of grid treatment, i.e., the electric field near the complex geometry boundaries (plasma grid) is calculated more accurately. The revised model preserves the full 3D treatment, but can take advantage of a highly refined mesh. ONIX was used to investigate the role of the mesh size, the re-injection scheme for lost particles (extracted or wall absorbed), and the electron thermalization process on the calculated extracted current and plasma characteristics. It is demonstrated that all numerical schemes give the same NI current distribution for extracted ions. Concerning the electrons, the pair-injection technique is found well-adapted to simulate the sheath in front of the plasma grid.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

Solute concentration at a well in non-Gaussian aquifers under constant and time-varying pumping schedule

NASA Astrophysics Data System (ADS)

Libera, Arianna; de Barros, Felipe P. J.; Riva, Monica; Guadagnini, Alberto

2017-10-01

Our study is keyed to the analysis of the interplay between engineering factors (i.e., transient pumping rates versus less realistic but commonly analyzed uniform extraction rates) and the heterogeneous structure of the aquifer (as expressed by the probability distribution characterizing transmissivity) on contaminant transport. We explore the joint influence of diverse (a) groundwater pumping schedules (constant and variable in time) and (b) representations of the stochastic heterogeneous transmissivity (T) field on temporal histories of solute concentrations observed at an extraction well. The stochastic nature of T is rendered by modeling its natural logarithm, Y = ln T, through a typical Gaussian representation and the recently introduced Generalized sub-Gaussian (GSG) model. The latter has the unique property to embed scale-dependent non-Gaussian features of the main statistics of Y and its (spatial) increments, which have been documented in a variety of studies. We rely on numerical Monte Carlo simulations and compute the temporal evolution at the well of low order moments of the solute concentration (C), as well as statistics of the peak concentration (Cp), identified as the environmental performance metric of interest in this study. We show that the pumping schedule strongly affects the pattern of the temporal evolution of the first two statistical moments of C, regardless the nature (Gaussian or non-Gaussian) of the underlying Y field, whereas the latter quantitatively influences their magnitude. Our results show that uncertainty associated with C and Cp estimates is larger when operating under a transient extraction scheme than under the action of a uniform withdrawal schedule. The probability density function (PDF) of Cp displays a long positive tail in the presence of time-varying pumping schedule. All these aspects are magnified in the presence of non-Gaussian Y fields. Additionally, the PDF of Cp displays a bimodal shape for all types of pumping schemes analyzed, independent of the type of heterogeneity considered.

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

Low complexity feature extraction for classification of harmonic signals

NASA Astrophysics Data System (ADS)

William, Peter E.

In this dissertation, feature extraction algorithms have been developed for extraction of characteristic features from harmonic signals. The common theme for all developed algorithms is the simplicity in generating a significant set of features directly from the time domain harmonic signal. The features are a time domain representation of the composite, yet sparse, harmonic signature in the spectral domain. The algorithms are adequate for low-power unattended sensors which perform sensing, feature extraction, and classification in a standalone scenario. The first algorithm generates the characteristic features using only the duration between successive zero-crossing intervals. The second algorithm estimates the harmonics' amplitudes of the harmonic structure employing a simplified least squares method without the need to estimate the true harmonic parameters of the source signal. The third algorithm, resulting from a collaborative effort with Daniel White at the DSP Lab, University of Nebraska-Lincoln, presents an analog front end approach that utilizes a multichannel analog projection and integration to extract the sparse spectral features from the analog time domain signal. Classification is performed using a multilayer feedforward neural network. Evaluation of the proposed feature extraction algorithms for classification through the processing of several acoustic and vibration data sets (including military vehicles and rotating electric machines) with comparison to spectral features shows that, for harmonic signals, time domain features are simpler to extract and provide equivalent or improved reliability over the spectral features in both the detection probabilities and false alarm rate.

Quantitative 3-D Imaging, Segmentation and Feature Extraction of the Respiratory System in Small Mammals for Computational Biophysics Simulations

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

Trease, Lynn L.; Trease, Harold E.; Fowler, John

2007-03-15

One of the critical steps toward performing computational biology simulations, using mesh based integration methods, is in using topologically faithful geometry derived from experimental digital image data as the basis for generating the computational meshes. Digital image data representations contain both the topology of the geometric features and experimental field data distributions. The geometric features that need to be captured from the digital image data are three-dimensional, therefore the process and tools we have developed work with volumetric image data represented as data-cubes. This allows us to take advantage of 2D curvature information during the segmentation and feature extraction process.more » The process is basically: 1) segmenting to isolate and enhance the contrast of the features that we wish to extract and reconstruct, 2) extracting the geometry of the features in an isosurfacing technique, and 3) building the computational mesh using the extracted feature geometry. “Quantitative” image reconstruction and feature extraction is done for the purpose of generating computational meshes, not just for producing graphics "screen" quality images. For example, the surface geometry that we extract must represent a closed water-tight surface.« less

Exploring a new quantitative image marker to assess benefit of chemotherapy to ovarian cancer patients

NASA Astrophysics Data System (ADS)

Mirniaharikandehei, Seyedehnafiseh; Patil, Omkar; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

2017-03-01

Accurately assessing the potential benefit of chemotherapy to cancer patients is an important prerequisite to developing precision medicine in cancer treatment. The previous study has shown that total psoas area (TPA) measured on preoperative cross-section CT image might be a good image marker to predict long-term outcome of pancreatic cancer patients after surgery. However, accurate and automated segmentation of TPA from the CT image is difficult due to the fuzzy boundary or connection of TPA to other muscle areas. In this study, we developed a new interactive computer-aided detection (ICAD) scheme aiming to segment TPA from the abdominal CT images more accurately and assess the feasibility of using this new quantitative image marker to predict the benefit of ovarian cancer patients receiving Bevacizumab-based chemotherapy. ICAD scheme was applied to identify a CT image slice of interest, which is located at the level of L3 (vertebral spines). The cross-sections of the right and left TPA are segmented using a set of adaptively adjusted boundary conditions. TPA is then quantitatively measured. In addition, recent studies have investigated that muscle radiation attenuation which reflects fat deposition in the tissue might be a good image feature for predicting the survival rate of cancer patients. The scheme and TPA measurement task were applied to a large national clinical trial database involving 1,247 ovarian cancer patients. By comparing with manual segmentation results, we found that ICAD scheme could yield higher accuracy and consistency for this task. Using a new ICAD scheme can provide clinical researchers a useful tool to more efficiently and accurately extract TPA as well as muscle radiation attenuation as new image makers, and allow them to investigate the discriminatory power of it to predict progression-free survival and/or overall survival of the cancer patients before and after taking chemotherapy.

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

Optical asymmetric watermarking using modified wavelet fusion and diffractive imaging

NASA Astrophysics Data System (ADS)

Mehra, Isha; Nishchal, Naveen K.

2015-05-01

In most of the existing image encryption algorithms the generated keys are in the form of a noise like distribution with a uniform distributed histogram. However, the noise like distribution is an apparent sign indicating the presence of the keys. If the keys are to be transferred through some communication channels, then this may lead to a security problem. This is because; the noise like features may easily catch people's attention and bring more attacks. To address this problem it is required to transfer the keys to some other meaningful images to disguise the attackers. The watermarking schemes are complementary to image encryption schemes. In most of the iterative encryption schemes, support constraints play an important role of the keys in order to decrypt the meaningful data. In this article, we have transferred the support constraints which are generated by axial translation of CCD camera using amplitude-, and phase- truncation approach, into different meaningful images. This has been done by developing modified fusion technique in wavelet transform domain. The second issue is, in case, the meaningful images are caught by the attacker then how to solve the copyright protection. To resolve this issue, watermark detection plays a crucial role. For this purpose, it is necessary to recover the original image using the retrieved watermarks/support constraints. To address this issue, four asymmetric keys have been generated corresponding to each watermarked image to retrieve the watermarks. For decryption, an iterative phase retrieval algorithm is applied to extract the plain-texts from corresponding retrieved watermarks.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients

NASA Astrophysics Data System (ADS)

Sadatnezhad, Khadijeh; Boostani, Reza; Ghanizadeh, Ahmad

2010-12-01

There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

MindEdit: A P300-based text editor for mobile devices.

PubMed

Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

2017-01-01

Practical application of Brain-Computer Interfaces (BCIs) requires that the whole BCI system be portable. The mobility of BCI systems involves two aspects: making the electroencephalography (EEG) recording devices portable, and developing software applications with low computational complexity to be able to run on low computational-power devices such as tablets and smartphones. This paper addresses the development of MindEdit; a P300-based text editor for Android-based devices. Given the limited resources of mobile devices and their limited computational power, a novel ensemble classifier is utilized that uses Principal Component Analysis (PCA) features to identify P300 evoked potentials from EEG recordings. PCA computations in the proposed method are channel-based as opposed to concatenating all channels as in traditional feature extraction methods; thus, this method has less computational complexity compared to traditional P300 detection methods. The performance of the method is demonstrated on data recorded from MindEdit on an Android tablet using the Emotiv wireless neuroheadset. Results demonstrate the capability of the introduced PCA ensemble classifier to classify P300 data with maximum average accuracy of 78.37±16.09% for cross-validation data and 77.5±19.69% for online test data using only 10 trials per symbol and a 33-character training dataset. Our analysis indicates that the introduced method outperforms traditional feature extraction methods. For a faster operation of MindEdit, a variable number of trials scheme is introduced that resulted in an online average accuracy of 64.17±19.6% and a maximum bitrate of 6.25bit/min. These results demonstrate the efficacy of using the developed BCI application with mobile devices. Copyright © 2016 Elsevier Ltd. All rights reserved.

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients.

PubMed

Sadatnezhad, Khadijeh; Boostani, Reza; Ghanizadeh, Ahmad

2010-12-01

There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

Audio feature extraction using probability distribution function

NASA Astrophysics Data System (ADS)

Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.

2015-05-01

Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.

Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

NASA Astrophysics Data System (ADS)

Musa Abbagoni, Baba; Yeung, Hoi

2016-08-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas-liquid flow regimes objectively with the gas-liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the success of a clamp-on ultrasound sensor for flow regime classification that would be possible in industry practice. It is considerably more promising than other techniques as it uses a non-invasive and non-radioactive sensor.

Automated detection of videotaped neonatal seizures based on motion segmentation methods.

PubMed

Karayiannis, Nicolaos B; Tao, Guozhi; Frost, James D; Wise, Merrill S; Hrachovy, Richard A; Mizrahi, Eli M

2006-07-01

This study was aimed at the development of a seizure detection system by training neural networks using quantitative motion information extracted by motion segmentation methods from short video recordings of infants monitored for seizures. The motion of the infants' body parts was quantified by temporal motion strength signals extracted from video recordings by motion segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by direct thresholding, by clustering of the pixel velocities, and by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The computational tools and procedures developed for automated seizure detection were tested and evaluated on 240 short video segments selected and labeled by physicians from a set of video recordings of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). The experimental study described in this paper provided the basis for selecting the most effective strategy for training neural networks to detect neonatal seizures as well as the decision scheme used for interpreting the responses of the trained neural networks. Depending on the decision scheme used for interpreting the responses of the trained neural networks, the best neural networks exhibited sensitivity above 90% or specificity above 90%. The best among the motion segmentation methods developed in this study produced quantitative features that constitute a reliable basis for detecting myoclonic and focal clonic neonatal seizures. The performance targets of this phase of the project may be achieved by combining the quantitative features described in this paper with those obtained by analyzing motion trajectory signals produced by motion tracking methods. A video system based upon automated analysis potentially offers a number of advantages. Infants who are at risk for seizures could be monitored continuously using relatively inexpensive and non-invasive video techniques that supplement direct observation by nursery personnel. This would represent a major advance in seizure surveillance and offers the possibility for earlier identification of potential neurological problems and subsequent intervention.

Effects of Cumulus Parameterization on the U.S. Summer Precipitation Prediction by the Regional Climate-Weather Research and Forecasting Model (CWRF)

NASA Astrophysics Data System (ADS)

Qiao, F.; Liang, X.

2011-12-01

Accurate prediction of U.S. summer precipitation, including its geographic distribution, the occurrence frequency and intensity, and diurnal cycle, has been a long-standing problem for most climate and weather models. This study employs the Climate-Weather Research and Forecasting model (CWRF) to investigate the effects of cumulus parameterization on prediction of these key precipitation features during the summers of 1993 and 2008 when severe floods occurred over the U.S. Midwest. Among the 12 widely-used cumulus schemes incorporated in the CWRF, the Ensemble Cumulus Parameterization modified from G3 (ECP) scheme and the Zhang-McFarland cumulus scheme modified by Liang (ZML) well reproduce the geographic distributions of observed 1993 and 2008 floods, albeit both slightly underestimating the maximum amount. However, the ZML scheme greatly overestimates the rainfall amount over the North American Monsoon region and Southeast U.S. while the ECP scheme has a better performance over the entire U.S. Compared to global general circulations models that tend to produce too frequent rainy events at reduced intensity, the CWRF better captures both frequency and intensity of extreme events (heavy rainfall and dry bells). However, most existing cumulus schemes in the CWRF are likely to convert atmospheric moisture into rainfall too fast, leading to less rainy days and stronger heavy rainfall events. A few cumulus schemes can depict the diurnal characteristics in certain but not all the regions over the U.S. For example, the Grell scheme shows its superiority in reproducing the eastward diurnal phase transition and the nocturnal peaks over the Great Plains, whereas the other schemes all fail in capturing this feature. By investigating the critical trigger function(s) that enable these cumulus schemes to capture the observed features, it provides opportunity to better understand the underlying mechanisms that drive the diurnal variation, and thus significantly improves the U.S. summer rainfall diurnal cycle prediction. These will be discussed. For an oral presentation at AGU Fall Meeting 2011 A15: Cloud, Convection, Precipitation, and Radiation: Observations and Modeling, San Francisco, California, USA, 5-9 December 2011.

Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal

PubMed Central

Ahn, Jong-Hyo; Kwak, Dae-Ho; Koh, Bong-Hwan

2014-01-01

This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault. PMID:25196008

Automated Coronal Loop Identification Using Digital Image Processing Techniques

NASA Technical Reports Server (NTRS)

Lee, Jong K.; Gary, G. Allen; Newman, Timothy S.

2003-01-01

The results of a master thesis project on a study of computer algorithms for automatic identification of optical-thin, 3-dimensional solar coronal loop centers from extreme ultraviolet and X-ray 2-dimensional images will be presented. These center splines are proxies of associated magnetic field lines. The project is pattern recognition problems in which there are no unique shapes or edges and in which photon and detector noise heavily influence the images. The study explores extraction techniques using: (1) linear feature recognition of local patterns (related to the inertia-tensor concept), (2) parametric space via the Hough transform, and (3) topological adaptive contours (snakes) that constrains curvature and continuity as possible candidates for digital loop detection schemes. We have developed synthesized images for the coronal loops to test the various loop identification algorithms. Since the topology of these solar features is dominated by the magnetic field structure, a first-order magnetic field approximation using multiple dipoles provides a priori information in the identification process. Results from both synthesized and solar images will be presented.

Digital relief generation from 3D models

NASA Astrophysics Data System (ADS)

Wang, Meili; Sun, Yu; Zhang, Hongming; Qian, Kun; Chang, Jian; He, Dongjian

2016-09-01

It is difficult to extend image-based relief generation to high-relief generation, as the images contain insufficient height information. To generate reliefs from three-dimensional (3D) models, it is necessary to extract the height fields from the model, but this can only generate bas-reliefs. To overcome this problem, an efficient method is proposed to generate bas-reliefs and high-reliefs directly from 3D meshes. To produce relief features that are visually appropriate, the 3D meshes are first scaled. 3D unsharp masking is used to enhance the visual features in the 3D mesh, and average smoothing and Laplacian smoothing are implemented to achieve better smoothing results. A nonlinear variable scaling scheme is then employed to generate the final bas-reliefs and high-reliefs. Using the proposed method, relief models can be generated from arbitrary viewing positions with different gestures and combinations of multiple 3D models. The generated relief models can be printed by 3D printers. The proposed method provides a means of generating both high-reliefs and bas-reliefs in an efficient and effective way under the appropriate scaling factors.

Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition.

PubMed

Al-Qazzaz, Noor Kamal; Ali, Sawal; Ahmad, Siti Anom; Escudero, Javier

2017-07-01

The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI.

Extraction and representation of common feature from uncertain facial expressions with cloud model.

PubMed

Wang, Shuliang; Chi, Hehua; Yuan, Hanning; Geng, Jing

2017-12-01

Human facial expressions are key ingredient to convert an individual's innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

PyEEG: an open source Python module for EEG/MEG feature extraction.

PubMed

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

PubMed Central

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. PMID:21512582

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

NASA Astrophysics Data System (ADS)

Chirra, Prathyush; Leo, Patrick; Yim, Michael; Bloch, B. Nicolas; Rastinehad, Ardeshir R.; Purysko, Andrei; Rosen, Mark; Madabhushi, Anant; Viswanath, Satish

2018-02-01

The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination

NASA Astrophysics Data System (ADS)

Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael

2017-05-01

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

Density-Gradient Mediated Band Extraction of Leukocytes from Whole Blood Using Centrifugo-Pneumatic Siphon Valving on Centrifugal Microfluidic Discs

PubMed Central

Kearney, Sinéad M.; Kilcawley, Niamh A.; Early, Philip L.; Glynn, Macdara T.; Ducrée, Jens

2016-01-01

Here we present retrieval of Peripheral Blood Mononuclear Cells by density-gradient medium based centrifugation for subsequent analysis of the leukocytes on an integrated microfluidic “Lab-on-a-Disc” cartridge. Isolation of white blood cells constitutes a critical sample preparation step for many bioassays. Centrifugo-pneumatic siphon valves are particularly suited for blood processing as they function without need of surface treatment and are ‘low-pass’, i.e., holding at high centrifugation speeds and opening upon reduction of the spin rate. Both ‘hydrostatically’ and ‘hydrodynamically’ triggered centrifugo-pneumatic siphon valving schemes are presented. Firstly, the geometry of the pneumatic chamber of hydrostatically primed centrifugo-pneumatic siphon valves is optimised to enable smooth and uniform layering of blood on top of the density-gradient medium; this feature proves to be key for efficient Peripheral Blood Mononuclear Cell extraction. A theoretical analysis of hydrostatically primed valves is also presented which determines the optimum priming pressure for the individual valves. Next, ‘dual siphon’ configurations for both hydrostatically and hydrodynamically primed centrifugo-pneumatic siphon valves are introduced; here plasma and Peripheral Blood Mononuclear Cells are extracted through a distinct siphon valve. This work represents a first step towards enabling on disc multi-parameter analysis. Finally, the efficiency of Peripheral Blood Mononuclear Cells extraction in these structures is characterised using a simplified design. A microfluidic mechanism, which we termed phase switching, is identified which affects the efficiency of Peripheral Blood Mononuclear Cell extraction. PMID:27167376

Cryptanalysis and Improvement of a Biometric-Based Multi-Server Authentication and Key Agreement Scheme.

PubMed

Wang, Chengqi; Zhang, Xiao; Zheng, Zhiming

2016-01-01

With the security requirements of networks, biometrics authenticated schemes which are applied in the multi-server environment come to be more crucial and widely deployed. In this paper, we propose a novel biometric-based multi-server authentication and key agreement scheme which is based on the cryptanalysis of Mishra et al.'s scheme. The informal and formal security analysis of our scheme are given, which demonstrate that our scheme satisfies the desirable security requirements. The presented scheme provides a variety of significant functionalities, in which some features are not considered in the most of existing authentication schemes, such as, user revocation or re-registration and biometric information protection. Compared with several related schemes, our scheme has more secure properties and lower computation cost. It is obviously more appropriate for practical applications in the remote distributed networks.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

PubMed Central

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

PubMed

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Development of a fully automatic scheme for detection of masses in whole breast ultrasound images.

PubMed

Ikedo, Yuji; Fukuoka, Daisuke; Hara, Takeshi; Fujita, Hiroshi; Takada, Etsuo; Endo, Tokiko; Morita, Takako

2007-11-01

Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.

Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening.

PubMed

Cao, Lu; Graauw, Marjo de; Yan, Kuan; Winkel, Leah; Verbeek, Fons J

2016-05-03

Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast cancer progression is associated with a defect in EGFR endocytosis. In order to find related Ribonucleic acid (RNA) regulators in this process, high-throughput imaging with fluorescent markers is used to visualize the complex EGFR endocytosis process. Subsequently a dedicated automatic image and data analysis system is developed and applied to extract the phenotype measurement and distinguish different developmental episodes from a huge amount of images acquired through high-throughput imaging. For the image analysis, a phenotype measurement quantifies the important image information into distinct features or measurements. Therefore, the manner in which prominent measurements are chosen to represent the dynamics of the EGFR process becomes a crucial step for the identification of the phenotype. In the subsequent data analysis, classification is used to categorize each observation by making use of all prominent measurements obtained from image analysis. Therefore, a better construction for a classification strategy will support to raise the performance level in our image and data analysis system. In this paper, we illustrate an integrated analysis method for EGFR signalling through image analysis of microscopy images. Sophisticated wavelet-based texture measurements are used to obtain a good description of the characteristic stages in the EGFR signalling. A hierarchical classification strategy is designed to improve the recognition of phenotypic episodes of EGFR during endocytosis. Different strategies for normalization, feature selection and classification are evaluated. The results of performance assessment clearly demonstrate that our hierarchical classification scheme combined with a selected set of features provides a notable improvement in the temporal analysis of EGFR endocytosis. Moreover, it is shown that the addition of the wavelet-based texture features contributes to this improvement. Our workflow can be applied to drug discovery to analyze defected EGFR endocytosis processes.

Automatic differentiation of melanoma and clark nevus skin lesions

NASA Astrophysics Data System (ADS)

LeAnder, R. W.; Kasture, A.; Pandey, A.; Umbaugh, S. E.

2007-03-01

Skin cancer is the most common form of cancer in the United States. Although melanoma accounts for just 11% of all types of skin cancer, it is responsible for most of the deaths, claiming more than 7910 lives annually. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of pattern recognition techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as for contributing information about the essential optical characteristics that identify them. Purpose: This study sought to find the most effective features to extract from melanoma, melanoma in situ and Clark nevus lesions, and to find the most effective pattern-classification criteria and algorithms for differentiating those lesions, using the Computer Vision and Image Processing Tools (CVIPtools) software package. Methods: Due to changes in ambient lighting during the photographic process, color differences between images can occur. These differences were minimized by capturing dermoscopic images instead of photographic images. Differences in skin color between patients were minimized via image color normalization, by converting original color images to relative-color images. Relative-color images also helped minimize changes in color that occur due to changes in the photographic and digitization processes. Tumors in the relative-color images were segmented and morphologically filtered. Filtered, relative-color, tumor features were then extracted and various pattern-classification schemes were applied. Results: Experimentation resulted in four useful pattern classification methods, the best of which was an overall classification rate of 100% for melanoma and melanoma in situ (grouped) and 60% for Clark nevus. Conclusion: Melanoma and melanoma in situ have feature parameters and feature values that are similar enough to be considered one class of tumor that significantly differs from Clark nevus. Consequently, grouping melanoma and melanoma in situ together achieves the best results in classifying and automatically differentiating melanoma from Clark nevus lesions.

Adaptive angular-velocity Vold-Kalman filter order tracking - Theoretical basis, numerical implementation and parameter investigation

NASA Astrophysics Data System (ADS)

Pan, M.-Ch.; Chu, W.-Ch.; Le, Duc-Do

2016-12-01

The paper presents an alternative Vold-Kalman filter order tracking (VKF_OT) method, i.e. adaptive angular-velocity VKF_OT technique, to extract and characterize order components in an adaptive manner for the condition monitoring and fault diagnosis of rotary machinery. The order/spectral waveforms to be tracked can be recursively solved by using Kalman filter based on the one-step state prediction. The paper comprises theoretical derivation of computation scheme, numerical implementation, and parameter investigation. Comparisons of the adaptive VKF_OT scheme with two other ones are performed through processing synthetic signals of designated order components. Processing parameters such as the weighting factor and the correlation matrix of process noise, and data conditions like the sampling frequency, which influence tracking behavior, are explored. The merits such as adaptive processing nature and computation efficiency brought by the proposed scheme are addressed although the computation was performed in off-line conditions. The proposed scheme can simultaneously extract multiple spectral components, and effectively decouple close and crossing orders associated with multi-axial reference rotating speeds.

A random forest model based classification scheme for neonatal amplitude-integrated EEG.

PubMed

Chen, Weiting; Wang, Yu; Cao, Guitao; Chen, Guoqiang; Gu, Qiufang

2014-01-01

Modern medical advances have greatly increased the survival rate of infants, while they remain in the higher risk group for neurological problems later in life. For the infants with encephalopathy or seizures, identification of the extent of brain injury is clinically challenging. Continuous amplitude-integrated electroencephalography (aEEG) monitoring offers a possibility to directly monitor the brain functional state of the newborns over hours, and has seen an increasing application in neonatal intensive care units (NICUs). This paper presents a novel combined feature set of aEEG and applies random forest (RF) method to classify aEEG tracings. To that end, a series of experiments were conducted on 282 aEEG tracing cases (209 normal and 73 abnormal ones). Basic features, statistic features and segmentation features were extracted from both the tracing as a whole and the segmented recordings, and then form a combined feature set. All the features were sent to a classifier afterwards. The significance of feature, the data segmentation, the optimization of RF parameters, and the problem of imbalanced datasets were examined through experiments. Experiments were also done to evaluate the performance of RF on aEEG signal classifying, compared with several other widely used classifiers including SVM-Linear, SVM-RBF, ANN, Decision Tree (DT), Logistic Regression(LR), ML, and LDA. The combined feature set can better characterize aEEG signals, compared with basic features, statistic features and segmentation features respectively. With the combined feature set, the proposed RF-based aEEG classification system achieved a correct rate of 92.52% and a high F1-score of 95.26%. Among all of the seven classifiers examined in our work, the RF method got the highest correct rate, sensitivity, specificity, and F1-score, which means that RF outperforms all of the other classifiers considered here. The results show that the proposed RF-based aEEG classification system with the combined feature set is efficient and helpful to better detect the brain disorders in newborns.

An efficient indexing scheme for binary feature based biometric database

NASA Astrophysics Data System (ADS)

Gupta, P.; Sana, A.; Mehrotra, H.; Hwang, C. Jinshong

2007-04-01

The paper proposes an efficient indexing scheme for binary feature template using B+ tree. In this scheme the input image is decomposed into approximation, vertical, horizontal and diagonal coefficients using the discrete wavelet transform. The binarized approximation coefficient at second level is divided into four quadrants of equal size and Hamming distance (HD) for each quadrant with respect to sample template of all ones is measured. This HD value of each quadrant is used to generate upper and lower range values which are inserted into B+ tree. The nodes of tree at first level contain the lower and upper range values generated from HD of first quadrant. Similarly, lower and upper range values for the three quadrants are stored in the second, third and fourth level respectively. Finally leaf node contains the set of identifiers. At the time of identification, the test image is used to generate HD for four quadrants. Then the B+ tree is traversed based on the value of HD at every node and terminates to leaf nodes with set of identifiers. The feature vector for each identifier is retrieved from the particular bin of secondary memory and matched with test feature template to get top matches. The proposed scheme is implemented on ear biometric database collected at IIT Kanpur. The system is giving an overall accuracy of 95.8% at penetration rate of 34%.

Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations

DTIC Science & Technology

2008-07-01

classification of the targets of interest. The MMI features extracted in this manner have two properties that provide a sound justification for...are generalizations of well- known feature extraction methods such as Principal Components Analysis (PCA) and Independent Component Analysis (ICA...augment (without degrading performance) a large class of generic fusion processes. Ontologies Classifications Feature extraction Feature analysis

Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

NASA Astrophysics Data System (ADS)

Shi, Wenzhong; Deng, Susu; Xu, Wenbing

2018-02-01

For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (< 5 years) landslides and approximately 35% of historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should be filtered using a filtering strategy based on supplementary information provided by expert knowledge or other data sources.

Surface reconstruction and deformation monitoring of stratospheric airship based on laser scanning technology

NASA Astrophysics Data System (ADS)

Guo, Kai; Xie, Yongjie; Ye, Hu; Zhang, Song; Li, Yunfei

2018-04-01

Due to the uncertainty of stratospheric airship's shape and the security problem caused by the uncertainty, surface reconstruction and surface deformation monitoring of airship was conducted based on laser scanning technology and a √3-subdivision scheme based on Shepard interpolation was developed. Then, comparison was conducted between our subdivision scheme and the original √3-subdivision scheme. The result shows our subdivision scheme could reduce the shrinkage of surface and the number of narrow triangles. In addition, our subdivision scheme could keep the sharp features. So, surface reconstruction and surface deformation monitoring of airship could be conducted precisely by our subdivision scheme.

A comparative study of advanced shock-capturing schemes applied to Burgers' equation

NASA Technical Reports Server (NTRS)

Yang, H. Q.; Przekwas, A. J.

1992-01-01

A systematic evaluation is conducted of all extant numerical schemes for nonlinear scalar transport problems, and several advanced shock-capturing schemes are used to solve the nonlinear Burgers' equation in order to characterize their ability to resolve the sharp discontinuity, expansion zone, and propagation and collision features of shocks. For discontinuous functions, the Warming-Beam scheme generates preshock wiggles, while the Lax-Wendroff scheme generates postshock ones. Such limiters as the MUSCL or the superbee are more compressive than minimod or monotonic limiters. The performance of such TVD schemes as the upwind, the symmetric, and the Roe-Sweby, resemble each other.

Global High Resolution Crustal Magnetic Field Mapping at the Surface of the Moon from Lunar Prospector and SELENE/Kaguya Satellites

NASA Astrophysics Data System (ADS)

Ravat, D.; Purucker, M.; Olsen, N.; Finlay, C.

2017-12-01

We derive new models of the lunar crustal magnetic field at the lunar surface with data from Lunar Prospector (LP) and SELENE/Kaguya (K) satellite using a global set of 35820 1° equal area monopoles (O'Brien and Parker, 1994; Olsen et al., 2017). The resulting fields have similar features to surface fields obtained by Tsunakawa et al. (2015) using 230 subset regions and the primary differences are due to our stringent data selection (see below). The use of monopoles allows closer spacing than dipoles with lesser amount of regularization and moderate cluster computer resources. We use the scheme of iteratively reweighted least-squares inversion to compute the initial model. Then the amplitudes of these monopoles are determined by minimizing the misfit to the components together with the global average of |Br| at the ellipsoid surface (i.e. applying a L1 model regularization of Br). In a final step we transform the point-source representation to a spherical harmonic expansion. We extract high quality data segments using a processing scheme based on internal/external dipole field removal, low order polynomial removal, and a new processing scheme called Joint Equivalent Source Cross-validation. In the cross-validation procedure we analyze the fit of modeled components to data in 10° latitudinal segments from an inversion of triplets of nearby passes to a single set of dipoles along the passes. We evaluate the fit using four criteria in each segment: correlation coefficient, amplitude ratio, RMS of the misfit, and standard deviation of field values themselves. We fine-tune the criteria to the choice we would have made in visually retaining pass segments and this yields a global dataset of more than 2.87 million (x 3 components) points at altitudes <60 km. The selected Lunar Prospector and Kaguya magnetic data independently show similar features and statistics for altitudes, observed and modeled components, and their misfit (number of observation locations: LP 1.8 million and K 1.07 million x 3 components). We use these data to make a regional assessment of key magnetic features on the Moon (including impacts and swirls), the depth of magnetization of regional sources, and source parameters of isolated anomalies.

Improved integral images compression based on multi-view extraction

NASA Astrophysics Data System (ADS)

Dricot, Antoine; Jung, Joel; Cagnazzo, Marco; Pesquet, Béatrice; Dufaux, Frédéric

2016-09-01

Integral imaging is a technology based on plenoptic photography that captures and samples the light-field of a scene through a micro-lens array. It provides views of the scene from several angles and therefore is foreseen as a key technology for future immersive video applications. However, integral images have a large resolution and a structure based on micro-images which is challenging to encode. A compression scheme for integral images based on view extraction has previously been proposed, with average BD-rate gains of 15.7% (up to 31.3%) reported over HEVC when using one single extracted view. As the efficiency of the scheme depends on a tradeoff between the bitrate required to encode the view and the quality of the image reconstructed from the view, it is proposed to increase the number of extracted views. Several configurations are tested with different positions and different number of extracted views. Compression efficiency is increased with average BD-rate gains of 22.2% (up to 31.1%) reported over the HEVC anchor, with a realistic runtime increase.

Should learners reason one step at a time? A randomised trial of two diagnostic scheme designs.

PubMed

Blissett, Sarah; Morrison, Deric; McCarty, David; Sibbald, Matthew

2017-04-01

Making a diagnosis can be difficult for learners as they must integrate multiple clinical variables. Diagnostic schemes can help learners with this complex task. A diagnostic scheme is an algorithm that organises possible diagnoses by assigning signs or symptoms (e.g. systolic murmur) to groups of similar diagnoses (e.g. aortic stenosis and aortic sclerosis) and provides distinguishing features to help discriminate between similar diagnoses (e.g. carotid pulse). The current literature does not identify whether scheme layouts should guide learners to reason one step at a time in a terminally branching scheme or weigh multiple variables simultaneously in a hybrid scheme. We compared diagnostic accuracy, perceptual errors and cognitive load using two scheme layouts for cardiac auscultation. Focused on the task of identifying murmurs on Harvey, a cardiopulmonary simulator, 86 internal medicine residents used two scheme layouts. The terminally branching scheme organised the information into single variable decisions. The hybrid scheme combined single variable decisions with a chart integrating multiple distinguishing features. Using a crossover design, participants completed one set of murmurs (diastolic or systolic) with either the terminally branching or the hybrid scheme. The second set of murmurs was completed with the other scheme. A repeated measures manova was performed to compare diagnostic accuracy, perceptual errors and cognitive load between the scheme layouts. There was a main effect of the scheme layout (Wilks' λ = 0.841, F 3,80 = 5.1, p = 0.003). Use of a terminally branching scheme was associated with increased diagnostic accuracy (65 versus 53%, p = 0.02), fewer perceptual errors (0.61 versus 0.98 errors, p = 0.001) and lower cognitive load (3.1 versus 3.5/7, p = 0.023). The terminally branching scheme was associated with improved diagnostic accuracy, fewer perceptual errors and lower cognitive load, suggesting that terminally branching schemes are effective for improving diagnostic accuracy. These findings can inform the design of schemes and other clinical decision aids. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

A Nested Modeling Scheme for High-resolution Simulation of the Aquitard Compaction in a Regional Groundwater Extraction Field

NASA Astrophysics Data System (ADS)

Aichi, M.; Tokunaga, T.

2006-12-01

In the fields that experienced both significant drawdown/land subsidence and the recovery of groundwater potential, temporal change of the effective stress in the clayey layers is not simple. Conducting consolidation tests of core samples is a straightforward approach to know the pre-consolidation stress. However, especially in the urban area, the cost of boring and the limitation of sites for boring make it difficult to carry out enough number of tests. Numerical simulation to reproduce stress history can contribute to selecting boring sites and to complement the results of the laboratory tests. To trace the effective stress profile in the clayey layers by numerical simulation, discretization in the clayey layers should be fine. At the same time, the size of the modeled domain should be large enough to calculate the effect of regional groundwater extraction. Here, we developed a new scheme to reduce memory consumption based on domain decomposition technique. A finite element model of coupled groundwater flow and land subsidence is used for the local model, and a finite difference groundwater flow model is used for the regional model. The local model is discretized to fine mesh in the clayey layers to reproduce the temporal change of pore pressure in the layers while the regional model is discretized to relatively coarse mesh to reproduce the effect of the regional groundwater extraction on the groundwater flow. We have tested this scheme by comparing the results obtained from this scheme with those from the finely gridded model for the entire calculation domain. The difference between the results of these models was small enough and our new scheme can be used for the practical problem.

Semantic Shot Classification in Sports Video

NASA Astrophysics Data System (ADS)

Duan, Ling-Yu; Xu, Min; Tian, Qi

2003-01-01

In this paper, we present a unified framework for semantic shot classification in sports videos. Unlike previous approaches, which focus on clustering by aggregating shots with similar low-level features, the proposed scheme makes use of domain knowledge of a specific sport to perform a top-down video shot classification, including identification of video shot classes for each sport, and supervised learning and classification of the given sports video with low-level and middle-level features extracted from the sports video. It is observed that for each sport we can predefine a small number of semantic shot classes, about 5~10, which covers 90~95% of sports broadcasting video. With the supervised learning method, we can map the low-level features to middle-level semantic video shot attributes such as dominant object motion (a player), camera motion patterns, and court shape, etc. On the basis of the appropriate fusion of those middle-level shot classes, we classify video shots into the predefined video shot classes, each of which has a clear semantic meaning. The proposed method has been tested over 4 types of sports videos: tennis, basketball, volleyball and soccer. Good classification accuracy of 85~95% has been achieved. With correctly classified sports video shots, further structural and temporal analysis, such as event detection, video skimming, table of content, etc, will be greatly facilitated.

Multi-modal highlight generation for sports videos using an information-theoretic excitability measure

NASA Astrophysics Data System (ADS)

Hasan, Taufiq; Bořil, Hynek; Sangwan, Abhijeet; L Hansen, John H.

2013-12-01

The ability to detect and organize `hot spots' representing areas of excitement within video streams is a challenging research problem when techniques rely exclusively on video content. A generic method for sports video highlight selection is presented in this study which leverages both video/image structure as well as audio/speech properties. Processing begins where the video is partitioned into small segments and several multi-modal features are extracted from each segment. Excitability is computed based on the likelihood of the segmental features residing in certain regions of their joint probability density function space which are considered both exciting and rare. The proposed measure is used to rank order the partitioned segments to compress the overall video sequence and produce a contiguous set of highlights. Experiments are performed on baseball videos based on signal processing advancements for excitement assessment in the commentators' speech, audio energy, slow motion replay, scene cut density, and motion activity as features. Detailed analysis on correlation between user excitability and various speech production parameters is conducted and an effective scheme is designed to estimate the excitement level of commentator's speech from the sports videos. Subjective evaluation of excitability and ranking of video segments demonstrate a higher correlation with the proposed measure compared to well-established techniques indicating the effectiveness of the overall approach.

Advanced Steel Microstructural Classification by Deep Learning Methods.

PubMed

Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

2018-02-01

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Biometric feature embedding using robust steganography technique

NASA Astrophysics Data System (ADS)

Rashid, Rasber D.; Sellahewa, Harin; Jassim, Sabah A.

2013-05-01

This paper is concerned with robust steganographic techniques to hide and communicate biometric data in mobile media objects like images, over open networks. More specifically, the aim is to embed binarised features extracted using discrete wavelet transforms and local binary patterns of face images as a secret message in an image. The need for such techniques can arise in law enforcement, forensics, counter terrorism, internet/mobile banking and border control. What differentiates this problem from normal information hiding techniques is the added requirement that there should be minimal effect on face recognition accuracy. We propose an LSB-Witness embedding technique in which the secret message is already present in the LSB plane but instead of changing the cover image LSB values, the second LSB plane will be changed to stand as a witness/informer to the receiver during message recovery. Although this approach may affect the stego quality, it is eliminating the weakness of traditional LSB schemes that is exploited by steganalysis techniques for LSB, such as PoV and RS steganalysis, to detect the existence of secrete message. Experimental results show that the proposed method is robust against PoV and RS attacks compared to other variants of LSB. We also discussed variants of this approach and determine capacity requirements for embedding face biometric feature vectors while maintain accuracy of face recognition.

Topological Principles of Control in Dynamical Networks

NASA Astrophysics Data System (ADS)

Kim, Jason; Pasqualetti, Fabio; Bassett, Danielle

Networked biological systems, such as the brain, feature complex patterns of interactions. To predict and correct the dynamic behavior of such systems, it is imperative to understand how the underlying topological structure affects and limits the function of the system. Here, we use network control theory to extract topological features that favor or prevent network controllability, and to understand the network-wide effect of external stimuli on large-scale brain systems. Specifically, we treat each brain region as a dynamic entity with real-valued state, and model the time evolution of all interconnected regions using linear, time-invariant dynamics. We propose a simplified feed-forward scheme where the effect of upstream regions (drivers) on the connected downstream regions (non-drivers) is characterized in closed-form. Leveraging this characterization of the simplified model, we derive topological features that predict the controllability properties of non-simplified networks. We show analytically and numerically that these predictors are accurate across a large range of parameters. Among other contributions, our analysis shows that heterogeneity in the network weights facilitate controllability, and allows us to implement targeted interventions that profoundly improve controllability. By assuming an underlying dynamical mechanism, we are able to understand the complex topology of networked biological systems in a functionally meaningful way.

Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier.

PubMed

Kumar, Mukesh; Rath, Nitish Kumar; Rath, Santanu Kumar

2016-04-01

Microarray-based gene expression profiling has emerged as an efficient technique for classification, prognosis, diagnosis, and treatment of cancer. Frequent changes in the behavior of this disease generates an enormous volume of data. Microarray data satisfies both the veracity and velocity properties of big data, as it keeps changing with time. Therefore, the analysis of microarray datasets in a small amount of time is essential. They often contain a large amount of expression, but only a fraction of it comprises genes that are significantly expressed. The precise identification of genes of interest that are responsible for causing cancer are imperative in microarray data analysis. Most existing schemes employ a two-phase process such as feature selection/extraction followed by classification. In this paper, various statistical methods (tests) based on MapReduce are proposed for selecting relevant features. After feature selection, a MapReduce-based K-nearest neighbor (mrKNN) classifier is also employed to classify microarray data. These algorithms are successfully implemented in a Hadoop framework. A comparative analysis is done on these MapReduce-based models using microarray datasets of various dimensions. From the obtained results, it is observed that these models consume much less execution time than conventional models in processing big data. Copyright © 2016 Elsevier Inc. All rights reserved.

Max-AUC Feature Selection in Computer-Aided Detection of Polyps in CT Colonography

PubMed Central

Xu, Jian-Wu; Suzuki, Kenji

2014-01-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level. PMID:24608058

Max-AUC feature selection in computer-aided detection of polyps in CT colonography.

PubMed

Xu, Jian-Wu; Suzuki, Kenji

2014-03-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.

Bit-Oriented Quantum Public-Key Cryptosystem Based on Bell States

NASA Astrophysics Data System (ADS)

Wu, WanQing; Cai, QingYu; Zhang, HuanGuo; Liang, XiaoYan

2018-02-01

Quantum public key encryption system provides information confidentiality using quantum mechanics. This paper presents a quantum public key cryptosystem (Q P K C) based on the Bell states. By H o l e v o's theorem, the presented scheme provides the security of the secret key using one-wayness during the QPKC. While the QPKC scheme is information theoretic security under chosen plaintext attack (C P A). Finally some important features of presented QPKC scheme can be compared with other QPKC scheme.

Bit-Oriented Quantum Public-Key Cryptosystem Based on Bell States

NASA Astrophysics Data System (ADS)

Wu, WanQing; Cai, QingYu; Zhang, HuanGuo; Liang, XiaoYan

2018-06-01

Quantum public key encryption system provides information confidentiality using quantum mechanics. This paper presents a quantum public key cryptosystem ( Q P K C) based on the Bell states. By H o l e v o' s theorem, the presented scheme provides the security of the secret key using one-wayness during the QPKC. While the QPKC scheme is information theoretic security under chosen plaintext attack ( C P A). Finally some important features of presented QPKC scheme can be compared with other QPKC scheme.

Glioma survival prediction with the combined analysis of in vivo 11C-MET-PET, ex vivo and patient features by supervised machine learning.

PubMed

Papp, Laszlo; Poetsch, Nina; Grahovac, Marko; Schmidbauer, Victor; Woehrer, Adelheid; Preusser, Matthias; Mitterhauser, Markus; Kiesel, Barbara; Wadsak, Wolfgang; Beyer, Thomas; Hacker, Marcus; Traub-Weidinger, Tatjana

2017-11-24

Gliomas are the most common types of tumors in the brain. While the definite diagnosis is routinely made ex vivo by histopathologic and molecular examination, diagnostic work-up of patients with suspected glioma is mainly done by using magnetic resonance imaging (MRI). Nevertheless, L-S-methyl- 11 C-methionine ( 11 C-MET) Positron Emission Tomography (PET) holds a great potential in characterization of gliomas. The aim of this study was to establish machine learning (ML) driven survival models for glioma built on 11 C-MET-PET, ex vivo and patient characteristics. Methods: 70 patients with a treatment naïve glioma, who had a positive 11 C-MET-PET and histopathology-derived ex vivo feature extraction, such as World Health Organization (WHO) 2007 tumor grade, histology and isocitrate dehydrogenase (IDH1-R132H) mutation status were included. The 11 C-MET-positive primary tumors were delineated semi-automatically on PET images followed by the feature extraction of tumor-to-background ratio based general and higher-order textural features by applying five different binning approaches. In vivo and ex vivo features, as well as patient characteristics (age, weight, height, body-mass-index, Karnofsky-score) were merged to characterize the tumors. Machine learning approaches were utilized to identify relevant in vivo, ex vivo and patient features and their relative weights for 36 months survival prediction. The resulting feature weights were used to establish three predictive models per binning configuration based on a combination of: in vivo/ex vivo and clinical patient information (M36IEP), in vivo and patient-only information (M36IP), and in vivo only (M36I). In addition a binning-independent ex vivo and patient-only (M36EP) model was created. The established models were validated in a Monte Carlo (MC) cross-validation scheme. Results: Most prominent ML-selected and -weighted features were patient and ex vivo based followed by in vivo features. The highest area under the curve (AUC) values of our models as revealed by the MC cross-validation were: 0.9 (M36IEP), 0.87 (M36EP), 0.77 (M36IP) and 0.72 (M36I). Conclusion: Survival prediction of glioma patients based on amino acid PET using computer-supported predictive models based on in vivo, ex vivo and patient features is highly accurate. Copyright © 2017 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

A Low-Storage-Consumption XML Labeling Method for Efficient Structural Information Extraction

NASA Astrophysics Data System (ADS)

Liang, Wenxin; Takahashi, Akihiro; Yokota, Haruo

Recently, labeling methods to extract and reconstruct the structural information of XML data, which are important for many applications such as XPath query and keyword search, are becoming more attractive. To achieve efficient structural information extraction, in this paper we propose C-DO-VLEI code, a novel update-friendly bit-vector encoding scheme, based on register-length bit operations combining with the properties of Dewey Order numbers, which cannot be implemented in other relevant existing schemes such as ORDPATH. Meanwhile, the proposed method also achieves lower storage consumption because it does not require either prefix schema or any reserved codes for node insertion. We performed experiments to evaluate and compare the performance and storage consumption of the proposed method with those of the ORDPATH method. Experimental results show that the execution times for extracting depth information and parent node labels using the C-DO-VLEI code are about 25% and 15% less, respectively, and the average label size using the C-DO-VLEI code is about 24% smaller, comparing with ORDPATH.

A semi-blind logo watermarking scheme for color images by comparison and modification of DFT coefficients

NASA Astrophysics Data System (ADS)

Kusyk, Janusz; Eskicioglu, Ahmet M.

2005-10-01

Digital watermarking is considered to be a major technology for the protection of multimedia data. Some of the important applications are broadcast monitoring, copyright protection, and access control. In this paper, we present a semi-blind watermarking scheme for embedding a logo in color images using the DFT domain. After computing the DFT of the luminance layer of the cover image, the magnitudes of DFT coefficients are compared, and modified. A given watermark is embedded in three frequency bands: Low, middle, and high. Our experiments show that the watermarks extracted from the lower frequencies have the best visual quality for low pass filtering, adding Gaussian noise, JPEG compression, resizing, rotation, and scaling, and the watermarks extracted from the higher frequencies have the best visual quality for cropping, intensity adjustment, histogram equalization, and gamma correction. Extractions from the fragmented and translated image are identical to extractions from the unattacked watermarked image. The collusion and rewatermarking attacks do not provide the hacker with useful tools.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

Single-trial laser-evoked potentials feature extraction for prediction of pain perception.

PubMed

Huang, Gan; Xiao, Ping; Hu, Li; Hung, Yeung Sam; Zhang, Zhiguo

2013-01-01

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

A zero-liquid-discharge scheme for vanadium extraction process by electrodialysis-based technology.

PubMed

Wang, Meng; Xing, Hong-Bo; Jia, Yu-Xiang; Ren, Qing-Chun

2015-12-30

The sharp increase of demand for vanadium makes the treatment of the wastewater generated from its extraction process become an urgent problem. In this study, a hybrid process coupling the electrodialysis with the cooling crystallization is put forward for upgrading the conventional vanadium extraction process to zero discharge. Accordingly, the objective of this work lies in evaluating the feasibility of the proposed scheme on the basis of a systematic study on the influences of membrane types and operating parameters on the electrodialysis performance. The results indicate that the relative importance of osmosis and electro-osmosis to overall water transport is closely related to the applied current density. The increase in the applied current density and the decrease in the mole ratio of water and salt flux will contribute to the concentration degree. Moreover, it is worth noting that a relatively large concentration ratio can result in the remarkable decrease of current efficiency and increase of energy consumption. In general, the reclamation scheme can easily achieve the recovered water with relatively low salt content and the highly concentrated Na2SO4 solution (e.g., 300 g/L) for producing high-purity sodium sulphate crystals. Copyright © 2015. Published by Elsevier B.V.

Detection of triglycerides using immobilized enzymes in food and biological samples

NASA Astrophysics Data System (ADS)

Raichur, Ashish; Lesi, Abiodun; Pedersen, Henrik

1996-04-01

A scheme for the determination of total triglyceride (fat) content in biomedical and food samples is being developed. The primary emphasis is to minimize the reagents used, simplify sample preparation and develop a robust system that would facilitate on-line monitoring. The new detection scheme developed thus far involves extracting triglycerides into an organic solvent (cyclohexane) and performing partial least squares (PLS) analysis on the NIR (1100 - 2500 nm) absorbance spectra of the solution. A training set using 132 spectra of known triglyceride mixtures was complied. Eight PLS calibrations were generated and were used to predict the total fat extracted from commercial samples such as mayonnaise, butter, corn oil and coconut oil. The results typically gave a correlation coefficient (r) of 0.99 or better. Predictions were typically within 90% and better at higher concentrations. Experiments were also performed using an immobilized lipase reactor to hydrolyze the fat extracted into the organic solvent. Performing PLS analysis on the difference spectra of the substrate and product could enhance specificity. This is being verified experimentally. Further work with biomedical samples is to be performed. This scheme may be developed into a feasible detection method for triglycerides in the biomedical and food industries.

An end to end secure CBIR over encrypted medical database.

PubMed

Bellafqira, Reda; Coatrieux, Gouenou; Bouslimi, Dalel; Quellec, Gwenole

2016-08-01

In this paper, we propose a new secure content based image retrieval (SCBIR) system adapted to the cloud framework. This solution allows a physician to retrieve images of similar content within an outsourced and encrypted image database, without decrypting them. Contrarily to actual CBIR approaches in the encrypted domain, the originality of the proposed scheme stands on the fact that the features extracted from the encrypted images are themselves encrypted. This is achieved by means of homomorphic encryption and two non-colluding servers, we however both consider as honest but curious. In that way an end to end secure CBIR process is ensured. Experimental results carried out on a diabetic retinopathy database encrypted with the Paillier cryptosystem indicate that our SCBIR achieves retrieval performance as good as if images were processed in their non-encrypted form.

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks.

PubMed

Yu, Lequan; Chen, Hao; Dou, Qi; Qin, Jing; Heng, Pheng-Ann

2017-04-01

Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams, respectively. This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.

The discrete one-sided Lipschitz condition for convex scalar conservation laws

NASA Technical Reports Server (NTRS)

Brenier, Yann; Osher, Stanley

1986-01-01

Physical solutions to convex scalar conservation laws satisfy a one-sided Lipschitz condition (OSLC) that enforces both the entropy condition and their variation boundedness. Consistency with this condition is therefore desirable for a numerical scheme and was proved for both the Godunov and the Lax-Friedrichs scheme--also, in a weakened version, for the Roe scheme, all of them being only first order accurate. A new, fully second order scheme is introduced here, which is consistent with the OSLC. The modified equation is considered and shows interesting features. Another second order scheme is then considered and numerical results are discussed.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.

PubMed

Dimitriadis, S I; Liparas, Dimitris; Tsolaki, Magda N

2018-05-15

In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. Based on preprocessed MRI images from the organizers of a neuroimaging challenge, 3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD. Copyright © 2017 Elsevier B.V. All rights reserved.

Cryptanalysis and Improvement of a Biometric-Based Multi-Server Authentication and Key Agreement Scheme

PubMed Central

Wang, Chengqi; Zhang, Xiao; Zheng, Zhiming

2016-01-01

With the security requirements of networks, biometrics authenticated schemes which are applied in the multi-server environment come to be more crucial and widely deployed. In this paper, we propose a novel biometric-based multi-server authentication and key agreement scheme which is based on the cryptanalysis of Mishra et al.’s scheme. The informal and formal security analysis of our scheme are given, which demonstrate that our scheme satisfies the desirable security requirements. The presented scheme provides a variety of significant functionalities, in which some features are not considered in the most of existing authentication schemes, such as, user revocation or re-registration and biometric information protection. Compared with several related schemes, our scheme has more secure properties and lower computation cost. It is obviously more appropriate for practical applications in the remote distributed networks. PMID:26866606

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Protection of Health Imagery by Region Based Lossless Reversible Watermarking Scheme

PubMed Central

Priya, R. Lakshmi; Sadasivam, V.

2015-01-01

Providing authentication and integrity in medical images is a problem and this work proposes a new blind fragile region based lossless reversible watermarking technique to improve trustworthiness of medical images. The proposed technique embeds the watermark using a reversible least significant bit embedding scheme. The scheme combines hashing, compression, and digital signature techniques to create a content dependent watermark making use of compressed region of interest (ROI) for recovery of ROI as reported in literature. The experiments were carried out to prove the performance of the scheme and its assessment reveals that ROI is extracted in an intact manner and PSNR values obtained lead to realization that the presented scheme offers greater protection for health imageries. PMID:26649328

Heat Mining or Replenishable Geothermal Energy? A Project for Advanced-Level Physics Students

ERIC Educational Resources Information Center

Dugdale, Pam

2014-01-01

There is growing interest in the use of low enthalpy geothermal (LEG) energy schemes, whereby heated water is extracted from sandstone aquifers for civic heating projects. While prevalent in countries with volcanic activity, a recently proposed scheme for Manchester offered the perfect opportunity to engage students in the viability of this form…

Finger vein recognition based on the hyperinformation feature

NASA Astrophysics Data System (ADS)

Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Yang, Lu

2014-01-01

The finger vein is a promising biometric pattern for personal identification due to its advantages over other existing biometrics. In finger vein recognition, feature extraction is a critical step, and many feature extraction methods have been proposed to extract the gray, texture, or shape of the finger vein. We treat them as low-level features and present a high-level feature extraction framework. Under this framework, base attribute is first defined to represent the characteristics of a certain subcategory of a subject. Then, for an image, the correlation coefficient is used for constructing the high-level feature, which reflects the correlation between this image and all base attributes. Since the high-level feature can reveal characteristics of more subcategories and contain more discriminative information, we call it hyperinformation feature (HIF). Compared with low-level features, which only represent the characteristics of one subcategory, HIF is more powerful and robust. In order to demonstrate the potential of the proposed framework, we provide a case study to extract HIF. We conduct comprehensive experiments to show the generality of the proposed framework and the efficiency of HIF on our databases, respectively. Experimental results show that HIF significantly outperforms the low-level features.

Finance schemes for funding private orthodontic treatment.

PubMed

Perks, S

1997-02-01

Over the last ten years there has been a steady increase in the volume of private dental treatment and numerous finance schemes have been developed to help both patients and dentists. Private orthodontic treatment is increasing and the purpose of this article is to summarise the main features of the schemes currently available to fund private orthodontic treatment and to provide a source of reference.

Adaptive Numerical Dissipative Control in High Order Schemes for Multi-D Non-Ideal MHD

NASA Technical Reports Server (NTRS)

Yee, H. C.; Sjoegreen, B.

2004-01-01

The goal is to extend our adaptive numerical dissipation control in high order filter schemes and our new divergence-free methods for ideal MHD to non-ideal MHD that include viscosity and resistivity. The key idea consists of automatic detection of different flow features as distinct sensors to signal the appropriate type and amount of numerical dissipation/filter where needed and leave the rest of the region free of numerical dissipation contamination. These scheme-independent detectors are capable of distinguishing shocks/shears, flame sheets, turbulent fluctuations and spurious high-frequency oscillations. The detection algorithm is based on an artificial compression method (ACM) (for shocks/shears), and redundant multi-resolution wavelets (WAV) (for the above types of flow feature). These filter approaches also provide a natural and efficient way for the minimization of Div(B) numerical error. The filter scheme consists of spatially sixth order or higher non-dissipative spatial difference operators as the base scheme for the inviscid flux derivatives. If necessary, a small amount of high order linear dissipation is used to remove spurious high frequency oscillations. For example, an eighth-order centered linear dissipation (AD8) might be included in conjunction with a spatially sixth-order base scheme. The inviscid difference operator is applied twice for the viscous flux derivatives. After the completion of a full time step of the base scheme step, the solution is adaptively filtered by the product of a 'flow detector' and the 'nonlinear dissipative portion' of a high-resolution shock-capturing scheme. In addition, the scheme independent wavelet flow detector can be used in conjunction with spatially compact, spectral or spectral element type of base schemes. The ACM and wavelet filter schemes using the dissipative portion of a second-order shock-capturing scheme with sixth-order spatial central base scheme for both the inviscid and viscous MHD flux derivatives and a fourth-order Runge-Kutta method are denoted.

Implicit schemes and parallel computing in unstructured grid CFD

NASA Technical Reports Server (NTRS)

Venkatakrishnam, V.

1995-01-01

The development of implicit schemes for obtaining steady state solutions to the Euler and Navier-Stokes equations on unstructured grids is outlined. Applications are presented that compare the convergence characteristics of various implicit methods. Next, the development of explicit and implicit schemes to compute unsteady flows on unstructured grids is discussed. Next, the issues involved in parallelizing finite volume schemes on unstructured meshes in an MIMD (multiple instruction/multiple data stream) fashion are outlined. Techniques for partitioning unstructured grids among processors and for extracting parallelism in explicit and implicit solvers are discussed. Finally, some dynamic load balancing ideas, which are useful in adaptive transient computations, are presented.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

PubMed Central

Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

2015-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. PMID:25987366

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

PubMed

Guo, Jian; Qian, Kun; Zhang, Gongxuan; Xu, Huijie; Schuller, Björn

2017-12-01

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

PubMed

Fesharaki, Nooshin Jafari; Pourghassem, Hossein

2013-07-01

Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

Acousto-Optic Technology for Topographic Feature Extraction and Image Analysis.

DTIC Science & Technology

1981-03-01

This report contains all findings of the acousto - optic technology study for feature extraction conducted by Deft Laboratories Inc. for the U.S. Army...topographic feature extraction and image analysis using acousto - optic (A-O) technology. A conclusion of this study was that A-O devices are potentially

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using computer-extracted mammographic features to predict occult invasive disease in patients with biopsy proven DCIS. We proposed a computer-vision algorithm based approach to extract mammographic features from magnification views of full field digital mammography (FFDM) for patients with DCIS. After an expert breast radiologist provided a region of interest (ROI) mask for the DCIS lesion, the proposed approach is able to segment individual microcalcifications (MCs), detect the boundary of the MC cluster (MCC), and extract 113 mammographic features from MCs and MCC within the ROI. In this study, we extracted mammographic features from 99 patients with DCIS (74 pure DCIS; 25 DCIS plus invasive disease). The predictive power of the mammographic features was demonstrated through binary classifications between pure DCIS and DCIS with invasive disease using linear discriminant analysis (LDA). Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features. The generalization performance was assessed using Leave-One-Out Cross-Validation and Receiver Operating Characteristic (ROC) curve analysis. Using the computer-extracted mammographic features, the proposed model was able to distinguish DCIS with invasive disease from pure DCIS, with an average classification performance of AUC = 0.61 +/- 0.05. Overall, the proposed computer-extracted mammographic features are promising for predicting occult invasive disease in DCIS.

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yinggang; Zhang, Libao; Yu, Xianchuan

2015-01-01

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

Design and implementation of adaptive PI control schemes for web tension control in roll-to-roll (R2R) manufacturing.

PubMed

Raul, Pramod R; Pagilla, Prabhakar R

2015-05-01

In this paper, two adaptive Proportional-Integral (PI) control schemes are designed and discussed for control of web tension in Roll-to-Roll (R2R) manufacturing systems. R2R systems are used to transport continuous materials (called webs) on rollers from the unwind roll to the rewind roll. Maintaining web tension at the desired value is critical to many R2R processes such as printing, coating, lamination, etc. Existing fixed gain PI tension control schemes currently used in industrial practice require extensive tuning and do not provide the desired performance for changing operating conditions and material properties. The first adaptive PI scheme utilizes the model reference approach where the controller gains are estimated based on matching of the actual closed-loop tension control systems with an appropriately chosen reference model. The second adaptive PI scheme utilizes the indirect adaptive control approach together with relay feedback technique to automatically initialize the adaptive PI gains. These adaptive tension control schemes can be implemented on any R2R manufacturing system. The key features of the two adaptive schemes is that their designs are simple for practicing engineers, easy to implement in real-time, and automate the tuning process. Extensive experiments are conducted on a large experimental R2R machine which mimics many features of an industrial R2R machine. These experiments include trials with two different polymer webs and a variety of operating conditions. Implementation guidelines are provided for both adaptive schemes. Experimental results comparing the two adaptive schemes and a fixed gain PI tension control scheme used in industrial practice are provided and discussed. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Embedded feature ranking for ensemble MLP classifiers.

PubMed

Windeatt, Terry; Duangsoithong, Rakkrit; Smith, Raymond

2011-06-01

A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.

Relevance popularity: A term event model based feature selection scheme for text classification.

PubMed

Feng, Guozhong; An, Baiguo; Yang, Fengqin; Wang, Han; Zhang, Libiao

2017-01-01

Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.

Engagement Assessment Using EEG Signals

NASA Technical Reports Server (NTRS)

Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

2012-01-01

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

The optional selection of micro-motion feature based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Bo; Ren, Hongmei; Xiao, Zhi-he; Sheng, Jing

2017-11-01

Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

DETECTION OF ARSENOSUGARS FROM KELP EXTRACTS VIA IC-ELECTROSPRAY IONIZATION-MS-MS AND IC MEMBRANE HYDRIDE GENERATION ICP-MS

EPA Science Inventory

The selectivity and the ability to obtain structural information from detection schemes used in arsenic speciation research are growing analytical requirements driven by the growing number of arsenicalS extracted from natural products and the need to minimize misidentification in...

An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.

PubMed

Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui

2017-08-17

It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.

Improved system integration for integrated gasification combined cycle (IGCC) systems.

PubMed

Frey, H Christopher; Zhu, Yunhua

2006-03-01

Integrated gasification combined cycle (IGCC) systems are a promising technology for power generation. They include an air separation unit (ASU), a gasification system, and a gas turbine combined cycle power block, and feature competitive efficiency and lower emissions compared to conventional power generation technology. IGCC systems are not yet in widespread commercial use and opportunities remain to improve system feasibility via improved process integration. A process simulation model was developed for IGCC systems with alternative types of ASU and gas turbine integration. The model is applied to evaluate integration schemes involving nitrogen injection, air extraction, and combinations of both, as well as different ASU pressure levels. The optimal nitrogen injection only case in combination with an elevated pressure ASU had the highest efficiency and power output and approximately the lowest emissions per unit output of all cases considered, and thus is a recommended design option. The optimal combination of air extraction coupled with nitrogen injection had slightly worse efficiency, power output, and emissions than the optimal nitrogen injection only case. Air extraction alone typically produced lower efficiency, lower power output, and higher emissions than all other cases. The recommended nitrogen injection only case is estimated to provide annualized cost savings compared to a nonintegrated design. Process simulation modeling is shown to be a useful tool for evaluation and screening of technology options.

A judicious multiple hypothesis tracker with interacting feature extraction

NASA Astrophysics Data System (ADS)

McAnanama, James G.; Kirubarajan, T.

2009-05-01

The multiple hypotheses tracker (mht) is recognized as an optimal tracking method due to the enumeration of all possible measurement-to-track associations, which does not involve any approximation in its original formulation. However, its practical implementation is limited by the NP-hard nature of this enumeration. As a result, a number of maintenance techniques such as pruning and merging have been proposed to bound the computational complexity. It is possible to improve the performance of a tracker, mht or not, using feature information (e.g., signal strength, size, type) in addition to kinematic data. However, in most tracking systems, the extraction of features from the raw sensor data is typically independent of the subsequent association and filtering stages. In this paper, a new approach, called the Judicious Multi Hypotheses Tracker (jmht), whereby there is an interaction between feature extraction and the mht, is presented. The measure of the quality of feature extraction is input into measurement-to-track association while the prediction step feeds back the parameters to be used in the next round of feature extraction. The motivation for this forward and backward interaction between feature extraction and tracking is to improve the performance in both steps. This approach allows for a more rational partitioning of the feature space and removes unlikely features from the assignment problem. Simulation results demonstrate the benefits of the proposed approach.

Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

NASA Astrophysics Data System (ADS)

He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Chen, Ying

2014-05-01

A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face-iris-fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single-scalar score via a trained, supported, vector regression model for a final decision. A large-scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for face-iris-fingerprint multimodal biometric systems.