EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

PubMed

Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

2017-10-01

The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Dimension-based attention in visual short-term memory.

PubMed

Pilling, Michael; Barrett, Doug J K

2016-07-01

We investigated how dimension-based attention influences visual short-term memory (VSTM). This was done through examining the effects of cueing a feature dimension in two perceptual comparison tasks (change detection and sameness detection). In both tasks, a memory array and a test array consisting of a number of colored shapes were presented successively, interleaved by a blank interstimulus interval (ISI). In Experiment 1 (change detection), the critical event was a feature change in one item across the memory and test arrays. In Experiment 2 (sameness detection), the critical event was the absence of a feature change in one item across the two arrays. Auditory cues indicated the feature dimension (color or shape) of the critical event with 80 % validity; the cues were presented either prior to the memory array, during the ISI, or simultaneously with the test array. In Experiment 1, the cue validity influenced sensitivity only when the cue was given at the earliest position; in Experiment 2, the cue validity influenced sensitivity at all three cue positions. We attributed the greater effectiveness of top-down guidance by cues in the sameness detection task to the more active nature of the comparison process required to detect sameness events (Hyun, Woodman, Vogel, Hollingworth, & Luck, Journal of Experimental Psychology: Human Perception and Performance, 35; 1140-1160, 2009).

Statistical performance of image cytometry for DNA, lipids, cytokeratin, & CD45 in a model system for circulation tumor cell detection.

PubMed

Futia, Gregory L; Schlaepfer, Isabel R; Qamar, Lubna; Behbakht, Kian; Gibson, Emily A

2017-07-01

Detection of circulating tumor cells (CTCs) in a blood sample is limited by the sensitivity and specificity of the biomarker panel used to identify CTCs over other blood cells. In this work, we present Bayesian theory that shows how test sensitivity and specificity set the rarity of cell that a test can detect. We perform our calculation of sensitivity and specificity on our image cytometry biomarker panel by testing on pure disease positive (D + ) populations (MCF7 cells) and pure disease negative populations (D - ) (leukocytes). In this system, we performed multi-channel confocal fluorescence microscopy to image biomarkers of DNA, lipids, CD45, and Cytokeratin. Using custom software, we segmented our confocal images into regions of interest consisting of individual cells and computed the image metrics of total signal, second spatial moment, spatial frequency second moment, and the product of the spatial-spatial frequency moments. We present our analysis of these 16 features. The best performing of the 16 features produced an average separation of three standard deviations between D + and D - and an average detectable rarity of ∼1 in 200. We performed multivariable regression and feature selection to combine multiple features for increased performance and showed an average separation of seven standard deviations between the D + and D - populations making our average detectable rarity of ∼1 in 480. Histograms and receiver operating characteristics (ROC) curves for these features and regressions are presented. We conclude that simple regression analysis holds promise to further improve the separation of rare cells in cytometry applications. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Role of multidetector computed tomography in evaluating incidentally detected breast lesions.

PubMed

Moschetta, Marco; Scardapane, Arnaldo; Lorusso, Valentina; Rella, Leonarda; Telegrafo, Michele; Serio, Gabriella; Angelelli, Giuseppe; Ianora, Amato Antonio Stabile

2015-01-01

Computed tomography (CT) does not represent the primary method for the evaluation of breast lesions; however, it can detect breast abnormalities, even when performed for other reasons related to thoracic structures. The aim of this study is to evaluate the potential benefits of 320-row multidetector CT (MDCT) in evaluating and differentiating incidentally detected breast lesions by using vessel probe and 3D analysis software with net enhancement value. Sixty-two breast lesions in 46 patients who underwent 320-row chest CT examination were retrospectively evaluated. CT scans were assessed searching for the presence, location, number, morphological features, and density of breast nodules. Net enhancement was calculated by subtracting precontrast density from the density obtained by postcontrast values. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of CT were calculated for morphological features and net enhancement. Thirty of 62 lesions were found to be malignant at histological examination and 32 were found to be benign. When morphological features were considered, the sensitivity, specificity, accuracy, PPV, and NPV of CT were 87%, 100%, 88%, 100%, and 50%, respectively. Based on net enhancement, CT reached a sensitivity, specificity, accuracy, PPV, and NPV of 100%, 94%, 97%, 94%, and 100%, respectively. MDCT allows to recognize and characterize breast lesions based on morphological features. Net enhancement can be proposed as an additional accurate feature of CT.

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

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.

Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients andmore » compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm{sup 3} in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.« less

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.

Automatic detection of ECG cable interchange by analyzing both morphology and interlead relations.

PubMed

Han, Chengzong; Gregg, Richard E; Feild, Dirk Q; Babaeizadeh, Saeed

2014-01-01

ECG cable interchange can generate erroneous diagnoses. For algorithms detecting ECG cable interchange, high specificity is required to maintain a low total false positive rate because the prevalence of interchange is low. In this study, we propose and evaluate an improved algorithm for automatic detection and classification of ECG cable interchange. The algorithm was developed by using both ECG morphology information and redundancy information. ECG morphology features included QRS-T and P-wave amplitude, frontal axis and clockwise vector loop rotation. The redundancy features were derived based on the EASI™ lead system transformation. The classification was implemented using linear support vector machine. The development database came from multiple sources including both normal subjects and cardiac patients. An independent database was used to test the algorithm performance. Common cable interchanges were simulated by swapping either limb cables or precordial cables. For the whole validation database, the overall sensitivity and specificity for detecting precordial cable interchange were 56.5% and 99.9%, and the sensitivity and specificity for detecting limb cable interchange (excluding left arm-left leg interchange) were 93.8% and 99.9%. Defining precordial cable interchange or limb cable interchange as a single positive event, the total false positive rate was 0.7%. When the algorithm was designed for higher sensitivity, the sensitivity for detecting precordial cable interchange increased to 74.6% and the total false positive rate increased to 2.7%, while the sensitivity for detecting limb cable interchange was maintained at 93.8%. The low total false positive rate was maintained at 0.6% for the more abnormal subset of the validation database including only hypertrophy and infarction patients. The proposed algorithm can detect and classify ECG cable interchanges with high specificity and low total false positive rate, at the cost of decreased sensitivity for certain precordial cable interchanges. The algorithm could also be configured for higher sensitivity for different applications where a lower specificity can be tolerated. Copyright © 2014 Elsevier Inc. All rights reserved.

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

NASA Astrophysics Data System (ADS)

Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

2012-12-01

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

PubMed

van der Ster, Björn J P; Bennis, Frank C; Delhaas, Tammo; Westerhof, Berend E; Stok, Wim J; van Lieshout, Johannes J

2017-01-01

Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock. Method: In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of -50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included : volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. Model performance was tested by quantifying the predictions with three methods : sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates. Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73-0.98 and class 2: 0.56-0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived pulse height. The highest combination for class 2 was found for the model with the volumetric features (0.72 and 0.91). Conclusion: The most sensitive models for the detection of advanced CBV reduction comprised data that describe features from volumetric parameters and from cerebral blood flow velocity hemodynamics. In a validated model of hemorrhage in humans these parameters provide the best indication of the progression of central hypovolemia.

Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

2018-05-01

Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

Detecting paralinguistic events in audio stream using context in features and probabilistic decisions☆

PubMed Central

Gupta, Rahul; Audhkhasi, Kartik; Lee, Sungbok; Narayanan, Shrikanth

2017-01-01

Non-verbal communication involves encoding, transmission and decoding of non-lexical cues and is realized using vocal (e.g. prosody) or visual (e.g. gaze, body language) channels during conversation. These cues perform the function of maintaining conversational flow, expressing emotions, and marking personality and interpersonal attitude. In particular, non-verbal cues in speech such as paralanguage and non-verbal vocal events (e.g. laughters, sighs, cries) are used to nuance meaning and convey emotions, mood and attitude. For instance, laughters are associated with affective expressions while fillers (e.g. um, ah, um) are used to hold floor during a conversation. In this paper we present an automatic non-verbal vocal events detection system focusing on the detect of laughter and fillers. We extend our system presented during Interspeech 2013 Social Signals Sub-challenge (that was the winning entry in the challenge) for frame-wise event detection and test several schemes for incorporating local context during detection. Specifically, we incorporate context at two separate levels in our system: (i) the raw frame-wise features and, (ii) the output decisions. Furthermore, our system processes the output probabilities based on a few heuristic rules in order to reduce erroneous frame-based predictions. Our overall system achieves an Area Under the Receiver Operating Characteristics curve of 95.3% for detecting laughters and 90.4% for fillers on the test set drawn from the data specifications of the Interspeech 2013 Social Signals Sub-challenge. We perform further analysis to understand the interrelation between the features and obtained results. Specifically, we conduct a feature sensitivity analysis and correlate it with each feature's stand alone performance. The observations suggest that the trained system is more sensitive to a feature carrying higher discriminability with implications towards a better system design. PMID:28713197

Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method

NASA Astrophysics Data System (ADS)

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

2016-03-01

Current commercialized CAD schemes have high false-positive (FP) detection rates and also have high correlations in positive lesion detection with radiologists. Thus, we recently investigated a new approach to improve the efficacy of applying CAD to assist radiologists in reading and interpreting screening mammograms. Namely, we developed a new global feature based CAD approach/scheme that can cue the warning sign on the cases with high risk of being positive. In this study, we investigate the possibility of fusing global feature or case-based scores with the local or lesion-based CAD scores using an adaptive cueing method. We hypothesize that the information from the global feature extraction (features extracted from the whole breast regions) are different from and can provide supplementary information to the locally-extracted features (computed from the segmented lesion regions only). On a large and diverse full-field digital mammography (FFDM) testing dataset with 785 cases (347 negative and 438 cancer cases with masses only), we ran our lesion-based and case-based CAD schemes "as is" on the whole dataset. To assess the supplementary information provided by the global features, we used an adaptive cueing method to adaptively adjust the original CAD-generated detection scores (Sorg) of a detected suspicious mass region based on the computed case-based score (Scase) of the case associated with this detected region. Using the adaptive cueing method, better sensitivity results were obtained at lower FP rates (<= 1 FP per image). Namely, increases of sensitivities (in the FROC curves) of up to 6.7% and 8.2% were obtained for the ROI and Case-based results, respectively.

Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.

PubMed

Tripathy, R K; Dandapat, S

2016-06-01

The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.

Apnea Detection Method for Cheyne-Stokes Respiration Analysis on Newborn

NASA Astrophysics Data System (ADS)

Niimi, Taiga; Itoh, Yushi; Natori, Michiya; Aoki, Yoshimitsu

2013-04-01

Cheyne-Stokes respiration is especially prevalent in preterm newborns, but its severity may not be recognized. It is characterized by apnea and cyclical weakening and strengthening of the breathing. We developed a method for detecting apnea and this abnormal respiration and for estimating its malignancy. Apnea was detected based on a "difference" feature (calculated from wavelet coefficients) and a modified maximum displacement feature (related to the respiratory waveform shape). The waveform is calculated from vertical motion of the thoracic and abdominal region during respiration using a vision sensor. Our proposed detection method effectively detects apnea (sensitivity 88.4%, specificity 99.7%).

Computer-aided detection of bladder mass within non-contrast-enhanced region of CT Urography (CTU)

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon; Zhou, Chuan

2016-03-01

We are developing a computer-aided detection system for bladder cancer in CT urography (CTU). We have previously developed methods for detection of bladder masses within the contrast-enhanced region of the bladder. In this study, we investigated methods for detection of bladder masses within the non-contrast enhanced region. The bladder was first segmented using a newly developed deep-learning convolutional neural network in combination with level sets. The non-contrast-enhanced region was separated from the contrast-enhanced region with a maximum-intensityprojection- based method. The non-contrast region was smoothed and a gray level threshold was employed to segment the bladder wall and potential masses. The bladder wall was transformed into a straightened thickness profile, which was analyzed to identify lesion candidates as a prescreening step. The lesion candidates were segmented using our autoinitialized cascaded level set (AI-CALS) segmentation method, and 27 morphological features were extracted for each candidate. Stepwise feature selection with simplex optimization and leave-one-case-out resampling were used for training and validation of a false positive (FP) classifier. In each leave-one-case-out cycle, features were selected from the training cases and a linear discriminant analysis (LDA) classifier was designed to merge the selected features into a single score for classification of the left-out test case. A data set of 33 cases with 42 biopsy-proven lesions in the noncontrast enhanced region was collected. During prescreening, the system obtained 83.3% sensitivity at an average of 2.4 FPs/case. After feature extraction and FP reduction by LDA, the system achieved 81.0% sensitivity at 2.0 FPs/case, and 73.8% sensitivity at 1.5 FPs/case.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors.

PubMed

Xi, Xugang; Tang, Minyan; Miran, Seyed M; Luo, Zhizeng

2017-05-27

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

PubMed Central

Xi, Xugang; Tang, Minyan; Miran, Seyed M.; Luo, Zhizeng

2017-01-01

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection. PMID:28555016

High sensitivity detection of NO2 employing cavity ringdown spectroscopy and an external cavity continuously tunable quantum cascade laser.

PubMed

Rao, Gottipaty N; Karpf, Andreas

2010-09-10

A trace gas sensor for the detection of nitrogen dioxide based on cavity ringdown spectroscopy (CRDS) and a continuous wave external cavity tunable quantum cascade laser operating at room temperature has been designed, and its features and performance characteristics are reported. By measuring the ringdown times of the cavity at different concentrations of NO(2), we report a sensitivity of 1.2 ppb for the detection of NO(2) in Zero Air.

Rapid Processing of a Global Feature in the ON Visual Pathways of Behaving Monkeys.

PubMed

Huang, Jun; Yang, Yan; Zhou, Ke; Zhao, Xudong; Zhou, Quan; Zhu, Hong; Yang, Yingshan; Zhang, Chunming; Zhou, Yifeng; Zhou, Wu

2017-01-01

Visual objects are recognized by their features. Whereas, some features are based on simple components (i.e., local features, such as orientation of line segments), some features are based on the whole object (i.e., global features, such as an object having a hole in it). Over the past five decades, behavioral, physiological, anatomical, and computational studies have established a general model of vision, which starts from extracting local features in the lower visual pathways followed by a feature integration process that extracts global features in the higher visual pathways. This local-to-global model is successful in providing a unified account for a vast sets of perception experiments, but it fails to account for a set of experiments showing human visual systems' superior sensitivity to global features. Understanding the neural mechanisms underlying the "global-first" process will offer critical insights into new models of vision. The goal of the present study was to establish a non-human primate model of rapid processing of global features for elucidating the neural mechanisms underlying differential processing of global and local features. Monkeys were trained to make a saccade to a target in the black background, which was different from the distractors (white circle) in color (e.g., red circle target), local features (e.g., white square target), a global feature (e.g., white ring with a hole target) or their combinations (e.g., red square target). Contrary to the predictions of the prevailing local-to-global model, we found that (1) detecting a distinction or a change in the global feature was faster than detecting a distinction or a change in color or local features; (2) detecting a distinction in color was facilitated by a distinction in the global feature, but not in the local features; and (3) detecting the hole was interfered by the local features of the hole (e.g., white ring with a squared hole). These results suggest that monkey ON visual systems have a subsystem that is more sensitive to distinctions in the global feature than local features. They also provide the behavioral constraints for identifying the underlying neural substrates.

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.

Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines.

PubMed

Peng, Fei; Wu, Han; Jia, Xin-Hong; Rao, Yun-Jiang; Wang, Zi-Nan; Peng, Zheng-Pu

2014-06-02

An ultra-long phase-sensitive optical time domain reflectometry (Φ-OTDR) that can achieve high-sensitivity intrusion detection over 131.5km fiber with high spatial resolution of 8m is presented, which is the longest Φ-OTDR reported to date, to the best of our knowledge. It is found that the combination of distributed Raman amplification with heterodyne detection can extend the sensing distance and enhances the sensitivity substantially, leading to the realization of ultra-long Φ-OTDR with high sensitivity and spatial resolution. Furthermore, the feasibility of applying such an ultra-long Φ-OTDR to pipeline security monitoring is demonstrated and the features of intrusion signal can be extracted with improved SNR by using the wavelet detrending/denoising method proposed.

MOF-Bacteriophage Biosensor for Highly Sensitive and Specific Detection of Staphylococcus aureus.

PubMed

Bhardwaj, Neha; Bhardwaj, Sanjeev K; Mehta, Jyotsana; Kim, Ki-Hyun; Deep, Akash

2017-10-04

To produce a sensitive and specific biosensor for Staphylococcus aureus, bacteriophages have been interfaced with a water-dispersible and environmentally stable metal-organic framework (MOF), NH 2 -MIL-53(Fe). The conjugation of the MOF with bacteriophages has been achieved through the use of glutaraldehyde as cross-linker. Highly sensitive detection of S. aureus in both synthetic and real samples was realized by the proposed MOF-bacteriophage biosensor based on the photoluminescence quenching phenomena: limit of detection (31 CFU/mL) and range of detection (40 to 4 × 10 8 CFU/mL). This is the first report exploiting the use of an MOF-bacteriophage complex for the biosensing of S. aureus. The results of our study highlight that the proposed biosensor is more sensitive than most of the previous methods while exhibiting some advanced features like specificity, regenerability, extended range of linear detection, and stability for long-term storage (even at room temperature).

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

PubMed Central

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

2017-01-01

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

Automated feature extraction in color retinal images by a model based approach.

PubMed

Li, Huiqi; Chutatape, Opas

2004-02-01

Color retinal photography is an important tool to detect the evidence of various eye diseases. Novel methods to extract the main features in color retinal images have been developed in this paper. Principal component analysis is employed to locate optic disk; A modified active shape model is proposed in the shape detection of optic disk; A fundus coordinate system is established to provide a better description of the features in the retinal images; An approach to detect exudates by the combined region growing and edge detection is proposed. The success rates of disk localization, disk boundary detection, and fovea localization are 99%, 94%, and 100%, respectively. The sensitivity and specificity of exudate detection are 100% and 71%, correspondingly. The success of the proposed algorithms can be attributed to the utilization of the model-based methods. The detection and analysis could be applied to automatic mass screening and diagnosis of the retinal diseases.

Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia

NASA Astrophysics Data System (ADS)

Javanmardi, Sara; McDonald, David W.; Caruana, Rich; Forouzan, Sholeh; Lopes, Cristina V.

A challenge facing user generated content systems is vandalism, i.e. edits that damage content quality. The high visibility and easy access to social networks makes them popular targets for vandals. Detecting and removing vandalism is critical for these user generated content systems. Because vandalism can take many forms, there are many different kinds of features that are potentially useful for detecting it. The complex nature of vandalism, and the large number of potential features, make vandalism detection difficult and time consuming for human editors. Machine learning techniques hold promise for developing accurate, tunable, and maintainable models that can be incorporated into vandalism detection tools. We describe a method for training classifiers for vandalism detection that yields classifiers that are more accurate on the PAN 2010 corpus than others previously developed. Because of the high turnaround in social network systems, it is important for vandalism detection tools to run in real-time. To this aim, we use feature selection to find the minimal set of features consistent with high accuracy. In addition, because some features are more costly to compute than others, we use cost-sensitive feature selection to reduce the total computational cost of executing our models. In addition to the features previously used for spam detection, we introduce new features based on user action histories. The user history features contribute significantly to classifier performance. The approach we use is general and can easily be applied to other user generated content systems.

The Objective Identification and Quantification of Interstitial Lung Abnormalities in Smokers.

PubMed

Ash, Samuel Y; Harmouche, Rola; Ross, James C; Diaz, Alejandro A; Hunninghake, Gary M; Putman, Rachel K; Onieva, Jorge; Martinez, Fernando J; Choi, Augustine M; Lynch, David A; Hatabu, Hiroto; Rosas, Ivan O; Estepar, Raul San Jose; Washko, George R

2017-08-01

Previous investigation suggests that visually detected interstitial changes in the lung parenchyma of smokers are highly clinically relevant and predict outcomes, including death. Visual subjective analysis to detect these changes is time-consuming, insensitive to subtle changes, and requires training to enhance reproducibility. Objective detection of such changes could provide a method of disease identification without these limitations. The goal of this study was to develop and test a fully automated image processing tool to objectively identify radiographic features associated with interstitial abnormalities in the computed tomography scans of a large cohort of smokers. An automated tool that uses local histogram analysis combined with distance from the pleural surface was used to detect radiographic features consistent with interstitial lung abnormalities in computed tomography scans from 2257 individuals from the Genetic Epidemiology of COPD study, a longitudinal observational study of smokers. The sensitivity and specificity of this tool was determined based on its ability to detect the visually identified presence of these abnormalities. The tool had a sensitivity of 87.8% and a specificity of 57.5% for the detection of interstitial lung abnormalities, with a c-statistic of 0.82, and was 100% sensitive and 56.7% specific for the detection of the visual subtype of interstitial abnormalities called fibrotic parenchymal abnormalities, with a c-statistic of 0.89. In smokers, a fully automated image processing tool is able to identify those individuals who have interstitial lung abnormalities with moderate sensitivity and specificity. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

A ROC-based feature selection method for computer-aided detection and diagnosis

NASA Astrophysics Data System (ADS)

Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

2014-03-01

Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

HIGHLY SENSITIVE ASSAY FOR ANTICHOLINESTERASE COMPOUNDS USING 96 WELL PLATE FORMAT

EPA Science Inventory

The rapid and sensitive detection of organophosphate insecticides using a 96 well plate format is reported. Several features of this assay make it attractive for development as a laboratory-based or field screening assay. Acetylcholinesterase (AChE) was stabilized in a gelati...

Fabrication of a novel transparent SERS substrate comprised of Ag-nanoparticle arrays and its application in rapid detection of ractopamine on meat

USDA-ARS?s Scientific Manuscript database

Surface-enhanced Raman spectroscopy (SERS) is an emerging analytical tool that boasts the feature of high detection sensitivity and molecular fingerprint specificity attracting increased attention and showing promise in applications including detecting residues of veterinary drugs. In practice, spec...

Finite element model updating and damage detection for bridges using vibration measurement.

DOT National Transportation Integrated Search

2013-12-01

In this report, the results of a study on developing a damage detection methodology based on Statistical Pattern Recognition are : presented. This methodology uses a new damage sensitive feature developed in this study that relies entirely on modal :...

Automated Detection of Actinic Keratoses in Clinical Photographs

PubMed Central

Hames, Samuel C.; Sinnya, Sudipta; Tan, Jean-Marie; Morze, Conrad; Sahebian, Azadeh; Soyer, H. Peter; Prow, Tarl W.

2015-01-01

Background Clinical diagnosis of actinic keratosis is known to have intra- and inter-observer variability, and there is currently no non-invasive and objective measure to diagnose these lesions. Objective The aim of this pilot study was to determine if automatically detecting and circumscribing actinic keratoses in clinical photographs is feasible. Methods Photographs of the face and dorsal forearms were acquired in 20 volunteers from two groups: the first with at least on actinic keratosis present on the face and each arm, the second with no actinic keratoses. The photographs were automatically analysed using colour space transforms and morphological features to detect erythema. The automated output was compared with a senior consultant dermatologist’s assessment of the photographs, including the intra-observer variability. Performance was assessed by the correlation between total lesions detected by automated method and dermatologist, and whether the individual lesions detected were in the same location as the dermatologist identified lesions. Additionally, the ability to limit false positives was assessed by automatic assessment of the photographs from the no actinic keratosis group in comparison to the high actinic keratosis group. Results The correlation between the automatic and dermatologist counts was 0.62 on the face and 0.51 on the arms, compared to the dermatologist’s intra-observer variation of 0.83 and 0.93 for the same. Sensitivity of automatic detection was 39.5% on the face, 53.1% on the arms. Positive predictive values were 13.9% on the face and 39.8% on the arms. Significantly more lesions (p<0.0001) were detected in the high actinic keratosis group compared to the no actinic keratosis group. Conclusions The proposed method was inferior to assessment by the dermatologist in terms of sensitivity and positive predictive value. However, this pilot study used only a single simple feature and was still able to achieve sensitivity of detection of 53.1% on the arms.This suggests that image analysis is a feasible avenue of investigation for overcoming variability in clinical assessment. Future studies should focus on more sophisticated features to improve sensitivity for actinic keratoses without erythema and limit false positives associated with the anatomical structures on the face. PMID:25615930

Detection of breast cancer in automated 3D breast ultrasound

NASA Astrophysics Data System (ADS)

Tan, Tao; Platel, Bram; Mus, Roel; Karssemeijer, Nico

2012-03-01

Automated 3D breast ultrasound (ABUS) is a novel imaging modality, in which motorized scans of the breasts are made with a wide transducer through a membrane under modest compression. The technology has gained high interest and may become widely used in screening of dense breasts, where sensitivity of mammography is poor. ABUS has a high sensitivity for detecting solid breast lesions. However, reading ABUS images is time consuming, and subtle abnormalities may be missed. Therefore, we are developing a computer aided detection (CAD) system to help reduce reading time and errors. In the multi-stage system we propose, segmentations of the breast and nipple are performed, providing landmarks for the detection algorithm. Subsequently, voxel features characterizing coronal spiculation patterns, blobness, contrast, and locations with respect to landmarks are extracted. Using an ensemble of classifiers, a likelihood map indicating potential malignancies is computed. Local maxima in the likelihood map are determined using a local maxima detector and form a set of candidate lesions in each view. These candidates are further processed in a second detection stage, which includes region segmentation, feature extraction and a final classification. Region segmentation is performed using a 3D spiral-scanning dynamic programming method. Region features include descriptors of shape, acoustic behavior and texture. Performance was determined using a 78-patient dataset with 93 images, including 50 malignant lesions. We used 10-fold cross-validation. Using FROC analysis we found that the system obtains a lesion sensitivity of 60% and 70% at 2 and 4 false positives per image respectively.

Detection of gastritis by single- and double-contrast radiography

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

Thoeni, R.F.; Goldberg, H.I.; Ominsky, S.

1983-09-01

Sixty-eight patients with various types of gastritis, 23 patients with normal stomachs, and four patients with other gastric diseases were examined in a prospective study to assess the sensitivity and specificity of single-contrast (SC) and double-contrast (DC) upper gastrointestinal examinations in the evaluation of gastritis. All patients underwent endoscopy with biopsy followed first by DC and then by SC radiography. The respective sensitivities of SC and DC radiography were 58% and 72% for all examinations and 59% and 77% for adequate examinations only. The respective specificities were 59% and 55% based on all examinations. Useful radiographic features included polypoid defectsmore » and erosions detected by both methods, abnormal folds and flattened margins detected by the SC technique, and narrowed lumen and crenulated margins detected by the DC technique. In 93% of all cases, the correct diagnosis was based on two or more of these radiographic features. According to this study, the radiographic sensitivity in the detection of gastritis is reliable only in cases of moderate-to-severe disease and only when based on findings of the DC examination. Neither SC nor DC radiography should be used as the primary screening method for patients with suspected gastritis, and the radiographic diagnosis should be restricted to the terms ''erosive'' or ''nonerosive gastritis.''« less

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

2017-03-01

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Detection of obstacles on runway using Ego-Motion compensation and tracking of significant features

NASA Technical Reports Server (NTRS)

Kasturi, Rangachar (Principal Investigator); Camps, Octavia (Principal Investigator); Gandhi, Tarak; Devadiga, Sadashiva

1996-01-01

This report describes a method for obstacle detection on a runway for autonomous navigation and landing of an aircraft. Detection is done in the presence of extraneous features such as tiremarks. Suitable features are extracted from the image and warping using approximately known camera and plane parameters is performed in order to compensate ego-motion as far as possible. Residual disparity after warping is estimated using an optical flow algorithm. Features are tracked from frame to frame so as to obtain more reliable estimates of their motion. Corrections are made to motion parameters with the residual disparities using a robust method, and features having large residual disparities are signaled as obstacles. Sensitivity analysis of the procedure is also studied. Nelson's optical flow constraint is proposed to separate moving obstacles from stationary ones. A Bayesian framework is used at every stage so that the confidence in the estimates can be determined.

Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

PubMed Central

2012-01-01

Background Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection. Methods For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features. Results We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes. Conclusions We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters to be supplied in advance. In the case of the SVM classifier, one has to select a single parameter. PMID:22703641

Correlation between radiographic findings of osteoarthritis and arthroscopic findings of articular cartilage degeneration within the patellofemoral joint.

PubMed

Kijowski, Richard; Blankenbaker, Donna; Stanton, Paul; Fine, Jason; De Smet, Arthur

2006-12-01

To correlate radiographic findings of osteoarthritis on axial knee radiographs with arthroscopic findings of articular cartilage degeneration within the patellofemoral joint in patients with chronic knee pain. The study group consisted of 104 patients with osteoarthritis of the patellofemoral joint and 30 patients of similar age with no osteoarthritis of the patellofemoral joint. All patients in the study group had an axial radiograph of the knee performed prior to arthroscopic knee surgery. At the time of arthroscopy, each articular surface of the patellofemoral joint was graded using the Noyes classification system. Two radiologists retrospectively reviewed the knee radiographs to determine the presence of marginal osteophytes, joint-space narrowing, subchondral sclerosis, and subchondral cysts. The sensitivity and specificity of the various radiographic features of osteoarthritis for the detection of articular cartilage degeneration within the patellofemoral joint were determined. The sensitivity of marginal osteophytes, joint-space narrowing, subchondral sclerosis, and subchondral cysts for the detection of articular cartilage degeneration within the patellofemoral joint was 73%, 37%, 4%, and 0% respectively. The specificity of marginal osteophytes, joint-space narrowing, subchondral sclerosis, and subchondral cysts for the detection of articular cartilage degeneration within the patellofemoral joint was 67%, 90%, 100%, and 100% respectively. Marginal osteophytes were the most sensitive radiographic feature for the detection of articular cartilage degeneration within the patellofemoral joint. Joint-space narrowing, subchondral sclerosis, and subchondral cysts were insensitive radiographic features of osteoarthritis, and rarely occurred in the absence of associated osteophyte formation.

Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes.

PubMed

Jensen, Morten Hasselstrøm; Christensen, Toke Folke; Tarnow, Lise; Seto, Edmund; Dencker Johansen, Mette; Hejlesen, Ole Kristian

2013-07-01

Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection. Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by sample-based sensitivity and specificity and event-based sensitivity and number of false-positives. The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively. This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.

Quantitative image cytometry measurements of lipids, DNA, CD45 and cytokeratin for circulating tumor cell identification in a model system

NASA Astrophysics Data System (ADS)

Futia, Gregory L.; Qamar, Lubna; Behbakht, Kian; Gibson, Emily A.

2016-04-01

Circulating tumor cell (CTC) identification has applications in both early detection and monitoring of solid cancers. The rarity of CTCs, expected at ~1-50 CTCs per million nucleated blood cells (WBCs), requires identifying methods based on biomarkers with high sensitivity and specificity for accurate identification. Discovery of biomarkers with ever higher sensitivity and specificity to CTCs is always desirable to potentially find more CTCs in cancer patients thus increasing their clinical utility. Here, we investigate quantitative image cytometry measurements of lipids with the biomarker panel of DNA, Cytokeratin (CK), and CD45 commonly used to identify CTCs. We engineered a device for labeling suspended cell samples with fluorescent antibodies and dyes. We used it to prepare samples for 4 channel confocal laser scanning microscopy. The total data acquired at high resolution from one sample is ~ 1.3 GB. We developed software to perform the automated segmentation of these images into regions of interest (ROIs) containing individual cells. We quantified image features of total signal, spatial second moment, spatial frequency second moment, and their product for each ROI. We performed measurements on pure WBCs, cancer cell line MCF7 and mixed samples. Multivariable regressions and feature selection were used to determine combination features that are more sensitive and specific than any individual feature separately. We also demonstrate that computation of spatial characteristics provides higher sensitivity and specificity than intensity alone. Statistical models allowed quantification of the required sensitivity and specificity for detecting small levels of CTCs in a human blood sample.

Diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach.

PubMed

Somasundaram, K; Rajendran, P Alli

2015-01-01

Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time.

Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach

PubMed Central

Somasundaram, K.; Alli Rajendran, P.

2015-01-01

Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time. PMID:25945362

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

Computerized scheme for vertebra detection in CT scout image

NASA Astrophysics Data System (ADS)

Guo, Wei; Chen, Qiang; Zhou, Hanxun; Zhang, Guodong; Cong, Lin; Li, Qiang

2016-03-01

Our purposes are to develop a vertebra detection scheme for automated scan planning, which would assist radiological technologists in their routine work for the imaging of vertebrae. Because the orientations of vertebrae were various, and the Haar-like features were only employed to represent the subject on the vertical, horizontal, or diagonal directions, we rotated the CT scout image seven times to make the vertebrae roughly horizontal in least one of the rotated images. Then, we employed Adaboost learning algorithm to construct a strong classifier for the vertebra detection by use of Haar-like features, and combined the detection results with the overlapping region according to the number of times they were detected. Finally, most of the false positives were removed by use of the contextual relationship between them. The detection scheme was evaluated on a database with 76 CT scout image. Our detection scheme reported 1.65 false positives per image at a sensitivity of 94.3% for initial detection of vertebral candidates, and then the performance of detection was improved to 0.95 false positives per image at a sensitivity of 98.6% for the further steps of false positive reduction. The proposed scheme achieved a high performance for the detection of vertebrae with different orientations.

An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions.

PubMed

Shu, Ting; Zhang, Bob; Yan Tang, Yuan

2017-04-01

Researchers have recently discovered that Diabetes Mellitus can be detected through non-invasive computerized method. However, the focus has been on facial block color features. In this paper, we extensively study the effects of texture features extracted from facial specific regions at detecting Diabetes Mellitus using eight texture extractors. The eight methods are from four texture feature families: (1) statistical texture feature family: Image Gray-scale Histogram, Gray-level Co-occurance Matrix, and Local Binary Pattern, (2) structural texture feature family: Voronoi Tessellation, (3) signal processing based texture feature family: Gaussian, Steerable, and Gabor filters, and (4) model based texture feature family: Markov Random Field. In order to determine the most appropriate extractor with optimal parameter(s), various parameter(s) of each extractor are experimented. For each extractor, the same dataset (284 Diabetes Mellitus and 231 Healthy samples), classifiers (k-Nearest Neighbors and Support Vector Machines), and validation method (10-fold cross validation) are used. According to the experiments, the first and third families achieved a better outcome at detecting Diabetes Mellitus than the other two. The best texture feature extractor for Diabetes Mellitus detection is the Image Gray-scale Histogram with bin number=256, obtaining an accuracy of 99.02%, a sensitivity of 99.64%, and a specificity of 98.26% by using SVM. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.

PubMed

Hussain, Lal; Ahmed, Adeel; Saeed, Sharjil; Rathore, Saima; Awan, Imtiaz Ahmed; Shah, Saeed Arif; Majid, Abdul; Idris, Adnan; Awan, Anees Ahmed

2018-02-06

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

Multiscale quantification of tissue spiculation and distortion for detection of architectural distortion and spiculated mass in mammography

NASA Astrophysics Data System (ADS)

Lao, Zhiqiang; Zheng, Xin

2011-03-01

This paper proposes a multiscale method to quantify tissue spiculation and distortion in mammography CAD systems that aims at improving the sensitivity in detecting architectural distortion and spiculated mass. This approach addresses the difficulty of predetermining the neighborhood size for feature extraction in characterizing lesions demonstrating spiculated mass/architectural distortion that may appear in different sizes. The quantification is based on the recognition of tissue spiculation and distortion pattern using multiscale first-order phase portrait model in texture orientation field generated by Gabor filter bank. A feature map is generated based on the multiscale quantification for each mammogram and two features are then extracted from the feature map. These two features will be combined with other mass features to provide enhanced discriminate ability in detecting lesions demonstrating spiculated mass and architectural distortion. The efficiency and efficacy of the proposed method are demonstrated with results obtained by applying the method to over 500 cancer cases and over 1000 normal cases.

Computerized detection of unruptured aneurysms in MRA images: reduction of false positives using anatomical location features

NASA Astrophysics Data System (ADS)

Uchiyama, Yoshikazu; Gao, Xin; Hara, Takeshi; Fujita, Hiroshi; Ando, Hiromichi; Yamakawa, Hiroyasu; Asano, Takahiko; Kato, Hiroki; Iwama, Toru; Kanematsu, Masayuki; Hoshi, Hiroaki

2008-03-01

The detection of unruptured aneurysms is a major subject in magnetic resonance angiography (MRA). However, their accurate detection is often difficult because of the overlapping between the aneurysm and the adjacent vessels on maximum intensity projection images. The purpose of this study is to develop a computerized method for the detection of unruptured aneurysms in order to assist radiologists in image interpretation. The vessel regions were first segmented using gray-level thresholding and a region growing technique. The gradient concentration (GC) filter was then employed for the enhancement of the aneurysms. The initial candidates were identified in the GC image using a gray-level threshold. For the elimination of false positives (FPs), we determined shape features and an anatomical location feature. Finally, rule-based schemes and quadratic discriminant analysis were employed along with these features for distinguishing between the aneurysms and the FPs. The sensitivity for the detection of unruptured aneurysms was 90.0% with 1.52 FPs per patient. Our computerized scheme can be useful in assisting the radiologists in the detection of unruptured aneurysms in MRA images.

Detection of Perlger-Huet anomaly based on augmented fast marching method and speeded up robust features.

PubMed

Sun, Minglei; Yang, Shaobao; Jiang, Jinling; Wang, Qiwei

2015-01-01

Pelger-Huet anomaly (PHA) and Pseudo Pelger-Huet anomaly (PPHA) are neutrophil with abnormal morphology. They have the bilobed or unilobed nucleus and excessive clumping chromatin. Currently, detection of this kind of cell mainly depends on the manual microscopic examination by a clinician, thus, the quality of detection is limited by the efficiency and a certain subjective consciousness of the clinician. In this paper, a detection method for PHA and PPHA is proposed based on karyomorphism and chromatin distribution features. Firstly, the skeleton of the nucleus is extracted using an augmented Fast Marching Method (AFMM) and width distribution is obtained through distance transform. Then, caryoplastin in the nucleus is extracted based on Speeded Up Robust Features (SURF) and a K-nearest-neighbor (KNN) classifier is constructed to analyze the features. Experiment shows that the sensitivity and specificity of this method achieved 87.5% and 83.33%, which means that the detection accuracy of PHA is acceptable. Meanwhile, the detection method should be helpful to the automatic morphological classification of blood cells.

Detection of Tampering Inconsistencies on Mobile Photos

NASA Astrophysics Data System (ADS)

Cao, Hong; Kot, Alex C.

Fast proliferation of mobile cameras and the deteriorating trust on digital images have created needs in determining the integrity of photos captured by mobile devices. As tampering often creates some inconsistencies, we propose in this paper a novel framework to statistically detect the image tampering inconsistency using accurately detected demosaicing weights features. By first cropping four non-overlapping blocks, each from one of the four quadrants in the mobile photo, we extract a set of demosaicing weights features from each block based on a partial derivative correlation model. Through regularizing the eigenspectrum of the within-photo covariance matrix and performing eigenfeature transformation, we further derive a compact set of eigen demosaicing weights features, which are sensitive to image signal mixing from different photo sources. A metric is then proposed to quantify the inconsistency based on the eigen weights features among the blocks cropped from different regions of the mobile photo. Through comparison, we show our eigen weights features perform better than the eigen features extracted from several other conventional sets of statistical forensics features in detecting the presence of tampering. Experimentally, our method shows a good confidence in tampering detection especially when one of the four cropped blocks is from a different camera model or brand with different demosaicing process.

Automated detection of retinal whitening in malarial retinopathy

NASA Astrophysics Data System (ADS)

Joshi, V.; Agurto, C.; Barriga, S.; Nemeth, S.; Soliz, P.; MacCormick, I.; Taylor, T.; Lewallen, S.; Harding, S.

2016-03-01

Cerebral malaria (CM) is a severe neurological complication associated with malarial infection. Malaria affects approximately 200 million people worldwide, and claims 600,000 lives annually, 75% of whom are African children under five years of age. Because most of these mortalities are caused by the high incidence of CM misdiagnosis, there is a need for an accurate diagnostic to confirm the presence of CM. The retinal lesions associated with malarial retinopathy (MR) such as retinal whitening, vessel discoloration, and hemorrhages, are highly specific to CM, and their detection can improve the accuracy of CM diagnosis. This paper will focus on development of an automated method for the detection of retinal whitening which is a unique sign of MR that manifests due to retinal ischemia resulting from CM. We propose to detect the whitening region in retinal color images based on multiple color and textural features. First, we preprocess the image using color and textural features of the CMYK and CIE-XYZ color spaces to minimize camera reflex. Next, we utilize color features of the HSL, CMYK, and CIE-XYZ channels, along with the structural features of difference of Gaussians. A watershed segmentation algorithm is used to assign each image region a probability of being inside the whitening, based on extracted features. The algorithm was applied to a dataset of 54 images (40 with whitening and 14 controls) that resulted in an image-based (binary) classification with an AUC of 0.80. This provides 88% sensitivity at a specificity of 65%. For a clinical application that requires a high specificity setting, the algorithm can be tuned to a specificity of 89% at a sensitivity of 82%. This is the first published method for retinal whitening detection and combining it with the detection methods for vessel discoloration and hemorrhages can further improve the detection accuracy for malarial retinopathy.

Detection of artifacts from high energy bursts in neonatal EEG.

PubMed

Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well. © 2013 Elsevier Ltd. All rights reserved.

Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent.

PubMed

Canuto, Holly C; McLachlan, Charles; Kettunen, Mikko I; Velic, Marko; Krishnan, Anant S; Neves, Andre' A; de Backer, Maaike; Hu, D-E; Hobson, Michael P; Brindle, Kevin M

2009-05-01

A targeted Gd(3+)-based contrast agent has been developed that detects tumor cell death by binding to the phosphatidylserine (PS) exposed on the plasma membrane of dying cells. Although this agent has been used to detect tumor cell death in vivo, the differences in signal intensity between treated and untreated tumors was relatively small. As cell death is often spatially heterogeneous within tumors, we investigated whether an image analysis technique that parameterizes heterogeneity could be used to increase the sensitivity of detection of this targeted contrast agent. Two-dimensional (2D) Minkowski functionals (MFs) provided an automated and reliable method for parameterization of image heterogeneity, which does not require prior assumptions about the number of regions or features in the image, and were shown to increase the sensitivity of detection of the contrast agent as compared to simple signal intensity analysis. (c) 2009 Wiley-Liss, Inc.

Classification of SD-OCT volumes for DME detection: an anomaly detection approach

NASA Astrophysics Data System (ADS)

Sankar, S.; Sidibé, D.; Cheung, Y.; Wong, T. Y.; Lamoureux, E.; Milea, D.; Meriaudeau, F.

2016-03-01

Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binary Pattern (LBP) features are considered. The dimensionality of the extracted features is reduced using PCA. As the last stage, a GMM is fitted with features from normal volumes. During testing, features extracted from the test volume are evaluated with the fitted model for anomaly and classification is made based on the number of B-scans detected as outliers. The proposed method is tested on two OCT datasets achieving a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, experiments show that the proposed method achieves better classification performances than other recently published works.

GridMass: a fast two-dimensional feature detection method for LC/MS.

PubMed

Treviño, Victor; Yañez-Garza, Irma-Luz; Rodriguez-López, Carlos E; Urrea-López, Rafael; Garza-Rodriguez, Maria-Lourdes; Barrera-Saldaña, Hugo-Alberto; Tamez-Peña, José G; Winkler, Robert; Díaz de-la-Garza, Rocío-Isabel

2015-01-01

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community. Copyright © 2015 John Wiley & Sons, Ltd.

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.

PCA feature extraction for change detection in multidimensional unlabeled data.

PubMed

Kuncheva, Ludmila I; Faithfull, William J

2014-01-01

When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

NASA Astrophysics Data System (ADS)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

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.

Functionalized gold nanoparticle supported sensory mechanisms applied in detection of chemical and biological threat agents: a review.

PubMed

Upadhyayula, Venkata K K

2012-02-17

There is a great necessity for development of novel sensory concepts supportive of smart sensing capabilities in defense and homeland security applications for detection of chemical and biological threat agents. A smart sensor is a detection device that can exhibit important features such as speed, sensitivity, selectivity, portability, and more importantly, simplicity in identifying a target analyte. Emerging nanomaterial based sensors, particularly those developed by utilizing functionalized gold nanoparticles (GNPs) as a sensing component potentially offer many desirable features needed for threat agent detection. The sensitiveness of physical properties expressed by GNPs, e.g. color, surface plasmon resonance, electrical conductivity and binding affinity are significantly enhanced when they are subjected to functionalization with an appropriate metal, organic or biomolecular functional groups. This sensitive nature of functionalized GNPs can be potentially exploited in the design of threat agent detection devices with smart sensing capabilities. In the presence of a target analyte (i.e., a chemical or biological threat agent) a change proportional to concentration of the analyte is observed, which can be measured either by colorimetric, fluorimetric, electrochemical or spectroscopic means. This article provides a review of how functionally modified gold colloids are applied in the detection of a broad range of threat agents, including radioactive substances, explosive compounds, chemical warfare agents, biotoxins, and biothreat pathogens through any of the four sensory means mentioned previously. Copyright © 2011 Elsevier B.V. All rights reserved.

Implicit Binding of Facial Features During Change Blindness

PubMed Central

Lyyra, Pessi; Mäkelä, Hanna; Hietanen, Jari K.; Astikainen, Piia

2014-01-01

Change blindness refers to the inability to detect visual changes if introduced together with an eye-movement, blink, flash of light, or with distracting stimuli. Evidence of implicit detection of changed visual features during change blindness has been reported in a number of studies using both behavioral and neurophysiological measurements. However, it is not known whether implicit detection occurs only at the level of single features or whether complex organizations of features can be implicitly detected as well. We tested this in adult humans using intact and scrambled versions of schematic faces as stimuli in a change blindness paradigm while recording event-related potentials (ERPs). An enlargement of the face-sensitive N170 ERP component was observed at the right temporal electrode site to changes from scrambled to intact faces, even if the participants were not consciously able to report such changes (change blindness). Similarly, the disintegration of an intact face to scrambled features resulted in attenuated N170 responses during change blindness. Other ERP deflections were modulated by changes, but unlike the N170 component, they were indifferent to the direction of the change. The bidirectional modulation of the N170 component during change blindness suggests that implicit change detection can also occur at the level of complex features in the case of facial stimuli. PMID:24498165

Implicit binding of facial features during change blindness.

PubMed

Lyyra, Pessi; Mäkelä, Hanna; Hietanen, Jari K; Astikainen, Piia

2014-01-01

Change blindness refers to the inability to detect visual changes if introduced together with an eye-movement, blink, flash of light, or with distracting stimuli. Evidence of implicit detection of changed visual features during change blindness has been reported in a number of studies using both behavioral and neurophysiological measurements. However, it is not known whether implicit detection occurs only at the level of single features or whether complex organizations of features can be implicitly detected as well. We tested this in adult humans using intact and scrambled versions of schematic faces as stimuli in a change blindness paradigm while recording event-related potentials (ERPs). An enlargement of the face-sensitive N170 ERP component was observed at the right temporal electrode site to changes from scrambled to intact faces, even if the participants were not consciously able to report such changes (change blindness). Similarly, the disintegration of an intact face to scrambled features resulted in attenuated N170 responses during change blindness. Other ERP deflections were modulated by changes, but unlike the N170 component, they were indifferent to the direction of the change. The bidirectional modulation of the N170 component during change blindness suggests that implicit change detection can also occur at the level of complex features in the case of facial stimuli.

Simultaneous determination of indoor ammonia pollution and its biological metabolite in the human body with a recyclable nanocrystalline lanthanide-functionalized MOF

NASA Astrophysics Data System (ADS)

Hao, Ji-Na; Yan, Bing

2016-01-01

A Eu3+ post-functionalized metal-organic framework of nanosized Ga(OH)bpydc(Eu3+@Ga(OH)bpydc, 1a) with intense luminescence is synthesized and characterized. Luminescence measurements reveal that 1a can detect ammonia gas selectively and sensitively among various indoor air pollutants. 1a can simultaneously determine a biological ammonia metabolite (urinary urea) in the human body, which is a rare example of a luminescent sensor that can monitor pollutants in the environment and also detect their biological markers. Furthermore, 1a exhibits appealing features including high selectivity and sensitivity, fast response, simple and quick regeneration, and excellent recyclability.A Eu3+ post-functionalized metal-organic framework of nanosized Ga(OH)bpydc(Eu3+@Ga(OH)bpydc, 1a) with intense luminescence is synthesized and characterized. Luminescence measurements reveal that 1a can detect ammonia gas selectively and sensitively among various indoor air pollutants. 1a can simultaneously determine a biological ammonia metabolite (urinary urea) in the human body, which is a rare example of a luminescent sensor that can monitor pollutants in the environment and also detect their biological markers. Furthermore, 1a exhibits appealing features including high selectivity and sensitivity, fast response, simple and quick regeneration, and excellent recyclability. Electronic supplementary information (ESI) available: Experimental section; XPS spectra; N2 adsorption-desorption isotherms; ICP data; SEM image; PXRD patterns and other luminescence data. See DOI: 10.1039/c5nr06066d

Computerized detection of breast cancer using resonance-frequency-based electrical impedance spectroscopy

NASA Astrophysics Data System (ADS)

Gao, Wei; Fan, Ming; Zhao, Weijie; Zheng, Bin; Li, Lihua

2017-03-01

This study developed and tested a multi-probe resonance-frequency-based electrical impedance spectroscopy (REIS) system aimed at detection of breast cancer. The REIS system consists of specially designed mechanical supporting device that can be easily lifted to fit women of different height, a seven probe sensor cup, and a computer providing software for system control and management. The sensor cup includes one central probe for direct contact with the nipple, and other six probes uniformly distributed at a distance of 35mm away from the center probe to enable contact with breast skin surface. It takes about 18 seconds for this system to complete a data acquisition process. We utilized this system for examination of breast cancer, collecting a dataset of 289 cases including biopsy verified 74 malignant and 215 benign tumors. After that, 23 REIS based features, including seven frequency, fifteen magnitude features were extracted, and an age feature. To reduce redundancy we selected 6 features using the evolutionary algorithm for classification. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. A multivariable logistic regression method was performed for detection of the tumors. The results of our study showed for the 23 REIS features AUC and ACC, Sensitivity and Specificity of 0.796, 0.727, 0.731 and 0.726, respectively. The AUC and ACC, Sensitivity and Specificity for the 6 REIS features of 0.840, 0.80, 0.703 and 0.833, respectively, and AUC of 0.662 and 0.619 for the frequency and magnitude based REIS features, respectively. The performance of the classifiers using all the 6 features was significantly better than solely using magnitude features (p=3.29e-08) and frequency features (5.61e-07). Smote algorithm was used to expand small samples to balance the dataset, the AUC after data balance of 0.846 increased than the original data classification performance. The results indicated that the REIS system is a promising tool for detection of breast cancer and may be acceptable for clinical implementation.

Optical filter for highlighting spectral features part I: design and development of the filter for discrimination of human skin with and without an application of cosmetic foundation.

PubMed

Nishino, Ken; Nakamura, Mutsuko; Matsumoto, Masayuki; Tanno, Osamu; Nakauchi, Shigeki

2011-03-28

Light reflected from an object's surface contains much information about its physical and chemical properties. Changes in the physical properties of an object are barely detectable in spectra. Conventional trichromatic systems, on the other hand, cannot detect most spectral features because spectral information is compressively represented as trichromatic signals forming a three-dimensional subspace. We propose a method for designing a filter that optically modulates a camera's spectral sensitivity to find an alternative subspace highlighting an object's spectral features more effectively than the original trichromatic space. We designed and developed a filter that detects cosmetic foundations on human face. Results confirmed that the filter can visualize and nondestructively inspect the foundation distribution.

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video.

PubMed

Ghosh, Tonmoy; Fattah, Shaikh Anowarul; Wahid, Khan A

2018-01-01

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

High-performance computer aided detection system for polyp detection in CT colonography with fluid and fecal tagging

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Wang, Shijun; Kabadi, Suraj; Summers, Ronald M.

2009-02-01

CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. Computer-aided detection (CAD) of polyps has improved consistency and sensitivity of virtual colonoscopy interpretation and reduced interpretation burden. A CAD system typically consists of four stages: (1) image preprocessing including colon segmentation; (2) initial detection generation; (3) feature selection; and (4) detection classification. In our experience, three existing problems limit the performance of our current CAD system. First, highdensity orally administered contrast agents in fecal-tagging CTC have scatter effects on neighboring tissues. The scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This pseudo-enhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially when polyps are submerged in the contrast agents. Second, general kernel approach for surface curvature computation in the second stage of our CAD system could yield erroneous results for thin structures such as small (6-9 mm) polyps and for touching structures such as polyps that lie on haustral folds. Those erroneous curvatures will reduce the sensitivity of polyp detection. The third problem is that more than 150 features are selected from each polyp candidate in the third stage of our CAD system. These high dimensional features make it difficult to learn a good decision boundary for detection classification and reduce the accuracy of predictions. Therefore, an improved CAD system for polyp detection in CTC data is proposed by introducing three new techniques. First, a scale-based scatter correction algorithm is applied to reduce pseudo-enhancement effects in the image pre-processing stage. Second, a cubic spline interpolation method is utilized to accurately estimate curvatures for initial detection generation. Third, a new dimensionality reduction classifier, diffusion map and local linear embedding (DMLLE), is developed for classification and false positives (FP) reduction. Performance of the improved CAD system is evaluated and compared with our existing CAD system (without applying those techniques) using CT scans of 1186 patients. These scans are divided into a training set and a test set. The sensitivity of the improved CAD system increased 18% on training data at a rate of 5 FPs per patient and 15% on test data at a rate of 5 FPs per patient. Our results indicated that the improved CAD system achieved significantly better performance on medium-sized colonic adenomas with higher sensitivity and lower FP rate in CTC.

Greater perceptual sensitivity to happy facial expression.

PubMed

Maher, Stephen; Ekstrom, Tor; Chen, Yue

2014-01-01

Perception of subtle facial expressions is essential for social functioning; yet it is unclear if human perceptual sensitivities differ in detecting varying types of facial emotions. Evidence diverges as to whether salient negative versus positive emotions (such as sadness versus happiness) are preferentially processed. Here, we measured perceptual thresholds for the detection of four types of emotion in faces--happiness, fear, anger, and sadness--using psychophysical methods. We also evaluated the association of the perceptual performances with facial morphological changes between neutral and respective emotion types. Human observers were highly sensitive to happiness compared with the other emotional expressions. Further, this heightened perceptual sensitivity to happy expressions can be attributed largely to the emotion-induced morphological change of a particular facial feature (end-lip raise).

Three-Year-Old Children Detect Social Exclusion in Third-Party Interactions

ERIC Educational Resources Information Center

Hwang, Hyesung G.; Marrus, Natasha; Irvin, Kelsey; Markson, Lori

2017-01-01

Humans are motivated to connect with others and are sensitive to social exclusion--intentionally leaving out others. This ability to detect social exclusion is suggested to be evolutionarily adaptive, biologically hardwired, and an important feature of social-cognitive development. Yet it is unclear when children start to independently detect…

Ability of primary auditory cortical neurons to detect amplitude modulation with rate and temporal codes: neurometric analysis

PubMed Central

Johnson, Jeffrey S.; Yin, Pingbo; O'Connor, Kevin N.

2012-01-01

Amplitude modulation (AM) is a common feature of natural sounds, and its detection is biologically important. Even though most sounds are not fully modulated, the majority of physiological studies have focused on fully modulated (100% modulation depth) sounds. We presented AM noise at a range of modulation depths to awake macaque monkeys while recording from neurons in primary auditory cortex (A1). The ability of neurons to detect partial AM with rate and temporal codes was assessed with signal detection methods. On average, single-cell synchrony was as or more sensitive than spike count in modulation detection. Cells are less sensitive to modulation depth if tested away from their best modulation frequency, particularly for temporal measures. Mean neural modulation detection thresholds in A1 are not as sensitive as behavioral thresholds, but with phase locking the most sensitive neurons are more sensitive, suggesting that for temporal measures the lower-envelope principle cannot account for thresholds. Three methods of preanalysis pooling of spike trains (multiunit, similar to convergence from a cortical column; within cell, similar to convergence of cells with matched response properties; across cell, similar to indiscriminate convergence of cells) all result in an increase in neural sensitivity to modulation depth for both temporal and rate codes. For the across-cell method, pooling of a few dozen cells can result in detection thresholds that approximate those of the behaving animal. With synchrony measures, indiscriminate pooling results in sensitive detection of modulation frequencies between 20 and 60 Hz, suggesting that differences in AM response phase are minor in A1. PMID:22422997

Non-volatile analysis in fruits by laser resonant ionization spectrometry: application to resveratrol (3,5,4'-trihydroxystilbene) in grapes

NASA Astrophysics Data System (ADS)

Montero, C.; Orea, J. M.; Soledad Muñoz, M.; Lobo, R. F. M.; González Ureña, A.

A laser desorption (LD) coupled with resonance-enhanced multiphoton ionisation (REMPI) and time-of-flight mass spectrometry (TOFMS) technique for non-volatile trace analysis compounds is presented. Essential features are: (a) an enhanced desorption yield due to the mixing of metal powder with the analyte in the sample preparation, (b) a high resolution, great sensitivity and low detection limit due to laser resonant ionisation and mass spectrometry detection. Application to resveratrol content in grapes demonstrated the capability of the analytical method with a sensitivity of 0.2 pg per single laser shot and a detection limit of 5 ppb.

Encoding properties of haltere neurons enable motion feature detection in a biological gyroscope

PubMed Central

Fox, Jessica L.; Fairhall, Adrienne L.; Daniel, Thomas L.

2010-01-01

The halteres of dipteran insects are essential sensory organs for flight control. They are believed to detect Coriolis and other inertial forces associated with body rotation during flight. Flies use this information for rapid flight control. We show that the primary afferent neurons of the haltere’s mechanoreceptors respond selectively with high temporal precision to multiple stimulus features. Although we are able to identify many stimulus features contributing to the response using principal component analysis, predictive models using only two features, common across the cell population, capture most of the cells’ encoding activity. However, different sensitivity to these two features permits each cell to respond to sinusoidal stimuli with a different preferred phase. This feature similarity, combined with diverse phase encoding, allows the haltere to transmit information at a high rate about numerous inertial forces, including Coriolis forces. PMID:20133721

Peptide Functionalized Gold Nanorods for the Sensitive Detection of a Cardiac Biomarker Using Plasmonic Paper Devices.

PubMed

Tadepalli, Sirimuvva; Kuang, Zhifeng; Jiang, Qisheng; Liu, Keng-Ku; Fisher, Marilee A; Morrissey, Jeremiah J; Kharasch, Evan D; Slocik, Joseph M; Naik, Rajesh R; Singamaneni, Srikanth

2015-11-10

The sensitivity of localized surface plasmon resonance (LSPR) of metal nanostructures to adsorbates lends itself to a powerful class of label-free biosensors. Optical properties of plasmonic nanostructures are dependent on the geometrical features and the local dielectric environment. The exponential decay of the sensitivity from the surface of the plasmonic nanotransducer calls for the careful consideration in its design with particular attention to the size of the recognition and analyte layers. In this study, we demonstrate that short peptides as biorecognition elements (BRE) compared to larger antibodies as target capture agents offer several advantages. Using a bioplasmonic paper device (BPD), we demonstrate the selective and sensitive detection of the cardiac biomarker troponin I (cTnI). The smaller sized peptide provides higher sensitivity and a lower detection limit using a BPD. Furthermore, the excellent shelf-life and thermal stability of peptide-based LSPR sensors, which precludes the need for special storage conditions, makes it ideal for use in resource-limited settings.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

2017-02-01

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Improved scintimammography using a high-resolution camera mounted on an upright mammography gantry

NASA Astrophysics Data System (ADS)

Itti, Emmanuel; Patt, Bradley E.; Diggles, Linda E.; MacDonald, Lawrence; Iwanczyk, Jan S.; Mishkin, Fred S.; Khalkhali, Iraj

2003-01-01

99mTc-sestamibi scintimammography (SMM) is a useful adjunct to conventional X-ray mammography (XMM) for the assessment of breast cancer. An increasing number of studies has emphasized fair sensitivity values for the detection of tumors >1 cm, compared to XMM, particularly in situations where high glandular breast densities make mammographic interpretation difficult. In addition, SMM has demonstrated high specificity for cancer, compared to various functional and anatomic imaging modalities. However, large field-of-view (FOV) gamma cameras are difficult to position close to the breasts, which decreases spatial resolution and subsequently, the sensitivity of detection for tumors <1 cm. New dedicated detectors featuring small FOV and increased spatial resolution have recently been developed. In this setting, improvement in tumor detection sensitivity, particularly with regard to small cancers is expected. At Division of Nuclear Medicine, Harbor-UCLA Medical Center, we have performed over 2000 SMM within the last 9 years. We have recently used a dedicated breast camera (LumaGEM™) featuring a 12.8×12.8 cm 2 FOV and an array of 2×2×6 mm 3 discrete crystals coupled to a photon-sensitive photomultiplier tube readout. This camera is mounted on a mammography gantry allowing upright imaging, medial positioning and use of breast compression. Preliminary data indicates significant enhancement of spatial resolution by comparison with standard imaging in the first 10 patients. Larger series will be needed to conclude on sensitivity/specificity issues.

Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution Mass Spectrometry.

PubMed

Woldegebriel, Michael; Derks, Eduard

2017-01-17

In this work, a novel probabilistic untargeted feature detection algorithm for liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) using artificial neural network (ANN) is presented. The feature detection process is approached as a pattern recognition problem, and thus, ANN was utilized as an efficient feature recognition tool. Unlike most existing feature detection algorithms, with this approach, any suspected chromatographic profile (i.e., shape of a peak) can easily be incorporated by training the network, avoiding the need to perform computationally expensive regression methods with specific mathematical models. In addition, with this method, we have shown that the high-resolution raw data can be fully utilized without applying any arbitrary thresholds or data reduction, therefore improving the sensitivity of the method for compound identification purposes. Furthermore, opposed to existing deterministic (binary) approaches, this method rather estimates the probability of a feature being present/absent at a given point of interest, thus giving chance for all data points to be propagated down the data analysis pipeline, weighed with their probability. The algorithm was tested with data sets generated from spiked samples in forensic and food safety context and has shown promising results by detecting features for all compounds in a computationally reasonable time.

Prostate cancer detection: Fusion of cytological and textural features.

PubMed

Nguyen, Kien; Jain, Anil K; Sabata, Bikash

2011-01-01

A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification.

Prostate cancer detection: Fusion of cytological and textural features

PubMed Central

Nguyen, Kien; Jain, Anil K.; Sabata, Bikash

2011-01-01

A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification. PMID:22811959

Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning: A Dynamic Solution to a Dynamic Problem.

PubMed

Blum, Emily S; Porras, Antonio R; Biggs, Elijah; Tabrizi, Pooneh R; Sussman, Rachael D; Sprague, Bruce M; Shalaby-Rana, Eglal; Majd, Massoud; Pohl, Hans G; Linguraru, Marius George

2017-10-21

We sought to define features that describe the dynamic information in diuresis renograms for the early detection of clinically significant hydronephrosis caused by ureteropelvic junction obstruction. We studied the diuresis renogram of 55 patients with a mean ± SD age of 75 ± 66 days who had congenital hydronephrosis at initial presentation. Five patients had bilaterally affected kidneys for a total of 60 diuresis renograms. Surgery was performed on 35 kidneys. We extracted 45 features based on curve shape and wavelet analysis from the drainage curves recorded after furosemide administration. The optimal features were selected as the combination that maximized the ROC AUC obtained from a linear support vector machine classifier trained to classify patients as with or without obstruction. Using these optimal features we performed leave 1 out cross validation to estimate the accuracy, sensitivity and specificity of our framework. Results were compared to those obtained using post-diuresis drainage half-time and the percent of clearance after 30 minutes. Our framework had 93% accuracy, including 91% sensitivity and 96% specificity, to predict surgical cases. This was a significant improvement over the same accuracy of 82%, including 71% sensitivity and 96% specificity obtained from half-time and 30-minute clearance using the optimal thresholds of 24.57 minutes and 55.77%, respectively. Our machine learning framework significantly improved the diagnostic accuracy of clinically significant hydronephrosis compared to half-time and 30-minute clearance. This aids in the clinical decision making process by offering a tool for earlier detection of severe cases and it has the potential to reduce the number of diuresis renograms required for diagnosis. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding

NASA Astrophysics Data System (ADS)

Zhang, Zhifen; Chen, Huabin; Xu, Yanling; Zhong, Jiyong; Lv, Na; Chen, Shanben

2015-08-01

Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding.

Diagnostic performance of 3D standing CT imaging for detection of knee osteoarthritis features.

PubMed

Segal, Neil A; Nevitt, Michael C; Lynch, John A; Niu, Jingbo; Torner, James C; Guermazi, Ali

2015-07-01

To determine the diagnostic performance of standing computerized tomography (SCT) of the knee for osteophytes and subchondral cysts compared with fixed-flexion radiography, using MRI as the reference standard. Twenty participants were recruited from the Multicenter Osteoarthritis Study. Participants' knees were imaged with SCT while standing in a knee-positioning frame, and with postero-anterior fixed-flexion radiography and 1T MRI. Medial and lateral marginal osteophytes and subchondral cysts were scored on bilateral radiographs and coronal SCT images using the OARSI grading system and on coronal MRI using Whole Organ MRI Scoring. Imaging modalities were read separately with images in random order. Sensitivity, specificity and accuracy for the detection of lesions were calculated and differences between modalities were tested using McNemar's test. Participants' mean age was 66.8 years, body mass index was 29.6 kg/m(2) and 50% were women. Of the 160 surfaces (medial and lateral femur and tibia for 40 knees), MRI revealed 84 osteophytes and 10 subchondral cysts. In comparison with osteophytes and subchondral cysts detected by MRI, SCT was significantly more sensitive (93 and 100%; p < 0.004) and accurate (95 and 99%; p < 0.001 for osteophytes) than plain radiographs (sensitivity 60 and 10% and accuracy 79 and 94%, respectively). For osteophytes, differences in sensitivity and accuracy were greatest at the medial femur (p = 0.002). In comparison with MRI, SCT imaging was more sensitive and accurate for detection of osteophytes and subchondral cysts than conventional fixed-flexion radiography. Additional study is warranted to assess diagnostic performance of SCT measures of joint space width, progression of OA features and the patellofemoral joint.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Quantification of confocal fluorescence microscopy for the detection of cervical intraepithelial neoplasia.

PubMed

Sheikhzadeh, Fahime; Ward, Rabab K; Carraro, Anita; Chen, Zhao Yang; van Niekerk, Dirk; Miller, Dianne; Ehlen, Tom; MacAulay, Calum E; Follen, Michele; Lane, Pierre M; Guillaud, Martial

2015-10-24

Cervical cancer remains a major health problem, especially in developing countries. Colposcopic examination is used to detect high-grade lesions in patients with a history of abnormal pap smears. New technologies are needed to improve the sensitivity and specificity of this technique. We propose to test the potential of fluorescence confocal microscopy to identify high-grade lesions. We examined the quantification of ex vivo confocal fluorescence microscopy to differentiate among normal cervical tissue, low-grade Cervical Intraepithelial Neoplasia (CIN), and high-grade CIN. We sought to (1) quantify nuclear morphology and tissue architecture features by analyzing images of cervical biopsies; and (2) determine the accuracy of high-grade CIN detection via confocal microscopy relative to the accuracy of detection by colposcopic impression. Forty-six biopsies obtained from colposcopically normal and abnormal cervical sites were evaluated. Confocal images were acquired at different depths from the epithelial surface and histological images were analyzed using in-house software. The features calculated from the confocal images compared well with those features obtained from the histological images and histopathological reviews of the specimens (obtained by a gynecologic pathologist). The correlations between two of these features (the nuclear-cytoplasmic ratio and the average of three nearest Delaunay-neighbors distance) and the grade of dysplasia were higher than that of colposcopic impression. The sensitivity of detecting high-grade dysplasia by analysing images collected at the surface of the epithelium, and at 15 and 30 μm below the epithelial surface were respectively 100, 100, and 92 %. Quantitative analysis of confocal fluorescence images showed its capacity for discriminating high-grade CIN lesions vs. low-grade CIN lesions and normal tissues, at different depth of imaging. This approach could be used to help clinicians identify high-grade CIN in clinical settings.

Automatic detection of confusion in elderly users of a web-based health instruction video.

PubMed

Postma-Nilsenová, Marie; Postma, Eric; Tates, Kiek

2015-06-01

Because of cognitive limitations and lower health literacy, many elderly patients have difficulty understanding verbal medical instructions. Automatic detection of facial movements provides a nonintrusive basis for building technological tools supporting confusion detection in healthcare delivery applications on the Internet. Twenty-four elderly participants (70-90 years old) were recorded while watching Web-based health instruction videos involving easy and complex medical terminology. Relevant fragments of the participants' facial expressions were rated by 40 medical students for perceived level of confusion and analyzed with automatic software for facial movement recognition. A computer classification of the automatically detected facial features performed more accurately and with a higher sensitivity than the human observers (automatic detection and classification, 64% accuracy, 0.64 sensitivity; human observers, 41% accuracy, 0.43 sensitivity). A drill-down analysis of cues to confusion indicated the importance of the eye and eyebrow region. Confusion caused by misunderstanding of medical terminology is signaled by facial cues that can be automatically detected with currently available facial expression detection technology. The findings are relevant for the development of Web-based services for healthcare consumers.

The effect of feature selection methods on computer-aided detection of masses in mammograms

NASA Astrophysics Data System (ADS)

Hupse, Rianne; Karssemeijer, Nico

2010-05-01

In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

Advancing the speed, sensitivity and accuracy of biomolecular detection using multi-length-scale engineering

PubMed Central

Kelley, Shana O.; Mirkin, Chad A.; Walt, David R.; Ismagilov, Rustem F.; Toner, Mehmet; Sargent, Edward H.

2015-01-01

Rapid progress in identifying disease biomarkers has increased the importance of creating high-performance detection technologies. Over the last decade, the design of many detection platforms has focused on either the nano or micro length scale. Here, we review recent strategies that combine nano- and microscale materials and devices to produce large improvements in detection sensitivity, speed and accuracy, allowing previously undetectable biomarkers to be identified in clinical samples. Microsensors that incorporate nanoscale features can now rapidly detect disease-related nucleic acids expressed in patient samples. New microdevices that separate large clinical samples into nanocompartments allow precise quantitation of analytes, and microfluidic systems that utilize nanoscale binding events can detect rare cancer cells in the bloodstream more accurately than before. These advances will lead to faster and more reliable clinical diagnostic devices. PMID:25466541

Advancing the speed, sensitivity and accuracy of biomolecular detection using multi-length-scale engineering

NASA Astrophysics Data System (ADS)

Kelley, Shana O.; Mirkin, Chad A.; Walt, David R.; Ismagilov, Rustem F.; Toner, Mehmet; Sargent, Edward H.

2014-12-01

Rapid progress in identifying disease biomarkers has increased the importance of creating high-performance detection technologies. Over the last decade, the design of many detection platforms has focused on either the nano or micro length scale. Here, we review recent strategies that combine nano- and microscale materials and devices to produce large improvements in detection sensitivity, speed and accuracy, allowing previously undetectable biomarkers to be identified in clinical samples. Microsensors that incorporate nanoscale features can now rapidly detect disease-related nucleic acids expressed in patient samples. New microdevices that separate large clinical samples into nanocompartments allow precise quantitation of analytes, and microfluidic systems that utilize nanoscale binding events can detect rare cancer cells in the bloodstream more accurately than before. These advances will lead to faster and more reliable clinical diagnostic devices.

Epileptic seizure onset detection based on EEG and ECG data fusion.

PubMed

Qaraqe, Marwa; Ismail, Muhammad; Serpedin, Erchin; Zulfi, Haneef

2016-05-01

This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case. Copyright © 2016 Elsevier Inc. All rights reserved.

Automatic detection of obstructive sleep apnea using speech signals.

PubMed

Goldshtein, Evgenia; Tarasiuk, Ariel; Zigel, Yaniv

2011-05-01

Obstructive sleep apnea (OSA) is a common disorder associated with anatomical abnormalities of the upper airways that affects 5% of the population. Acoustic parameters may be influenced by the vocal tract structure and soft tissue properties. We hypothesize that speech signal properties of OSA patients will be different than those of control subjects not having OSA. Using speech signal processing techniques, we explored acoustic speech features of 93 subjects who were recorded using a text-dependent speech protocol and a digital audio recorder immediately prior to polysomnography study. Following analysis of the study, subjects were divided into OSA (n=67) and non-OSA (n=26) groups. A Gaussian mixture model-based system was developed to model and classify between the groups; discriminative features such as vocal tract length and linear prediction coefficients were selected using feature selection technique. Specificity and sensitivity of 83% and 79% were achieved for the male OSA and 86% and 84% for the female OSA patients, respectively. We conclude that acoustic features from speech signals during wakefulness can detect OSA patients with good specificity and sensitivity. Such a system can be used as a basis for future development of a tool for OSA screening. © 2011 IEEE

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

Automatic seizure detection in SEEG using high frequency activities in wavelet domain.

PubMed

Ayoubian, L; Lacoma, H; Gotman, J

2013-03-01

Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80-500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

Automatic seizure detection in SEEG using high frequency activities in wavelet domain

PubMed Central

Ayoubian, L.; Lacoma, H.; Gotman, J.

2015-01-01

Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80–500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures. PMID:22647836

Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis.

PubMed

Castaldo, R; Xu, W; Melillo, P; Pecchia, L; Santamaria, L; James, C

2016-08-01

Mental stress may cause cognitive dysfunctions, cardiovascular disorders and depression. Mental stress detection via short-term Heart Rate Variability (HRV) analysis has been widely explored in the last years, while ultra-short term (less than 5 minutes) HRV has been not. This study aims to detect mental stress using linear and non-linear HRV features extracted from 3 minutes ECG excerpts recorded from 42 university students, during oral examination (stress) and at rest after a vacation. HRV features were then extracted and analyzed according to the literature using validated software tools. Statistical and data mining analysis were then performed on the extracted HRV features. The best performing machine learning method was the C4.5 tree algorithm, which discriminated between stress and rest with sensitivity, specificity and accuracy rate of 78%, 80% and 79% respectively.

Sensitivity of the Modified Children's Yale-Brown Obsessive Compulsive Scale to Detect Change: Results from Two Multi-Site Trials

ERIC Educational Resources Information Center

Scahill, Lawrence; Sukhodolsky, Denis G.; Anderberg, Emily; Dimitropoulos, Anastasia; Dziura, James; Aman, Michael G.; McCracken, James; Tierney, Elaine; Hallett, Victoria; Katz, Karol; Vitiello, Benedetto; McDougle, Christopher

2016-01-01

Repetitive behavior is a core feature of autism spectrum disorder. We used 8-week data from two federally funded, multi-site, randomized trials with risperidone conducted by the Research Units on Pediatric Psychopharmacology Autism Network to evaluate the sensitivity of the Children's Yale-Brown Obsessive Compulsive Scale modified for autism…

Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

PubMed

Emam, Mahmoud; Han, Qi; Zhang, Hongli

2018-01-01

In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods. © 2017 American Academy of Forensic Sciences.

Automated retrieval of cloud and aerosol properties from the ARM Raman lidar, part 1: feature detection

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

Thorsen, Tyler J.; Fu, Qiang; Newsom, Rob K.

A Feature detection and EXtinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement (ARM) program’s Raman lidar (RL) has been developed. Presented here is part 1 of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitro-gen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio— to identify features using range-dependent detection thresholds. FEX is designed to be context-sensitive with thresholds determined for each profile by calculating the expectedmore » clear-sky signal and noise. The use of multiple quantities pro-vides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically-thin features containing non-spherical particles such as cirrus clouds. Improve-ments over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia site. While we focus on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.« less

Mapping feature-sensitivity and attentional modulation in human auditory cortex with functional magnetic resonance imaging

PubMed Central

Paltoglou, Aspasia E; Sumner, Christian J; Hall, Deborah A

2011-01-01

Feature-specific enhancement refers to the process by which selectively attending to a particular stimulus feature specifically increases the response in the same region of the brain that codes that stimulus property. Whereas there are many demonstrations of this mechanism in the visual system, the evidence is less clear in the auditory system. The present functional magnetic resonance imaging (fMRI) study examined this process for two complex sound features, namely frequency modulation (FM) and spatial motion. The experimental design enabled us to investigate whether selectively attending to FM and spatial motion enhanced activity in those auditory cortical areas that were sensitive to the two features. To control for attentional effort, the difficulty of the target-detection tasks was matched as closely as possible within listeners. Locations of FM-related and motion-related activation were broadly compatible with previous research. The results also confirmed a general enhancement across the auditory cortex when either feature was being attended to, as compared with passive listening. The feature-specific effects of selective attention revealed the novel finding of enhancement for the nonspatial (FM) feature, but not for the spatial (motion) feature. However, attention to spatial features also recruited several areas outside the auditory cortex. Further analyses led us to conclude that feature-specific effects of selective attention are not statistically robust, and appear to be sensitive to the choice of fMRI experimental design and localizer contrast. PMID:21447093

Psychophysical and perceptual performance in a simulated-scotoma model of human eye injury

NASA Astrophysics Data System (ADS)

Brandeis, R.; Egoz, I.; Peri, D.; Sapiens, N.; Turetz, J.

2008-02-01

Macular scotomas, affecting visual functioning, characterize many eye and neurological diseases like AMD, diabetes mellitus, multiple sclerosis, and macular hole. In this work, foveal visual field defects were modeled, and their effects were evaluated on spatial contrast sensitivity and a task of stimulus detection and aiming. The modeled occluding scotomas, of different size, were superimposed on the stimuli presented on the computer display, and were stabilized on the retina using a mono Purkinje Eye-Tracker. Spatial contrast sensitivity was evaluated using square-wave grating stimuli, whose contrast thresholds were measured using the method of constant stimuli with "catch trials". The detection task consisted of a triple conjunctive visual search display of: size (in visual angle), contrast and background (simple, low-level features vs. complex, high-level features). Search/aiming accuracy as well as R.T. measures used for performance evaluation. Artificially generated scotomas suppressed spatial contrast sensitivity in a size dependent manner, similar to previous studies. Deprivation effect was dependent on spatial frequency, consistent with retinal inhomogeneity models. Stimulus detection time was slowed in complex background search situation more than in simple background. Detection speed was dependent on scotoma size and size of stimulus. In contrast, visually guided aiming was more sensitive to scotoma effect in simple background search situation than in complex background. Both stimulus aiming R.T. and accuracy (precision targeting) were impaired, as a function of scotoma size and size of stimulus. The data can be explained by models distinguishing between saliency-based, parallel and serial search processes, guiding visual attention, which are supported by underlying retinal as well as neural mechanisms.

Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.

PubMed

Das, D K; Maiti, A K; Chakraborty, C

2015-03-01

In this paper, we propose a comprehensive image characterization cum classification framework for malaria-infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three-image segmentation algorithms (namely, rule-based, Chan-Vese-based and marker-controlled watershed methods), marker-controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F-statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features' subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria-infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F-statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria-infected stage classification. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT.

PubMed

Lopez-Martin, Manuel; Carro, Belen; Sanchez-Esguevillas, Antonio; Lloret, Jaime

2017-08-26

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

PubMed Central

Carro, Belen; Sanchez-Esguevillas, Antonio

2017-01-01

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. PMID:28846608

Quantitative evaluation of in vivo vital-dye fluorescence endoscopic imaging for the detection of Barrett’s-associated neoplasia

PubMed Central

Thekkek, Nadhi; Lee, Michelle H.; Polydorides, Alexandros D.; Rosen, Daniel G.; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-01-01

Abstract. Current imaging tools are associated with inconsistent sensitivity and specificity for detection of Barrett’s-associated neoplasia. Optical imaging has shown promise in improving the classification of neoplasia in vivo. The goal of this pilot study was to evaluate whether in vivo vital dye fluorescence imaging (VFI) has the potential to improve the accuracy of early-detection of Barrett’s-associated neoplasia. In vivo endoscopic VFI images were collected from 65 sites in 14 patients with confirmed Barrett’s esophagus (BE), dysplasia, or esophageal adenocarcinoma using a modular video endoscope and a high-resolution microendoscope (HRME). Qualitative image features were compared to histology; VFI and HRME images show changes in glandular structure associated with neoplastic progression. Quantitative image features in VFI images were identified for objective image classification of metaplasia and neoplasia, and a diagnostic algorithm was developed using leave-one-out cross validation. Three image features extracted from VFI images were used to classify tissue as neoplastic or not with a sensitivity of 87.8% and a specificity of 77.6% (AUC=0.878). A multimodal approach incorporating VFI and HRME imaging can delineate epithelial changes present in Barrett’s-associated neoplasia. Quantitative analysis of VFI images may provide a means for objective interpretation of BE during surveillance. PMID:25950645

Quantitative evaluation of in vivo vital-dye fluorescence endoscopic imaging for the detection of Barrett's-associated neoplasia.

PubMed

Thekkek, Nadhi; Lee, Michelle H; Polydorides, Alexandros D; Rosen, Daniel G; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-05-01

Current imaging tools are associated with inconsistent sensitivity and specificity for detection of Barrett's-associated neoplasia. Optical imaging has shown promise in improving the classification of neoplasia in vivo. The goal of this pilot study was to evaluate whether in vivo vital dye fluorescence imaging (VFI) has the potential to improve the accuracy of early-detection of Barrett's-associated neoplasia. In vivo endoscopic VFI images were collected from 65 sites in 14 patients with confirmed Barrett's esophagus (BE), dysplasia, oresophageal adenocarcinoma using a modular video endoscope and a high-resolution microendoscope(HRME). Qualitative image features were compared to histology; VFI and HRME images show changes in glandular structure associated with neoplastic progression. Quantitative image features in VFI images were identified for objective image classification of metaplasia and neoplasia, and a diagnostic algorithm was developed using leave-one-out cross validation. Three image features extracted from VFI images were used to classify tissue as neoplastic or not with a sensitivity of 87.8% and a specificity of 77.6% (AUC = 0.878). A multimodal approach incorporating VFI and HRME imaging can delineate epithelial changes present in Barrett's-associated neoplasia. Quantitative analysis of VFI images may provide a means for objective interpretation of BE during surveillance.

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

A nano ultra-performance liquid chromatography-high resolution mass spectrometry approach for global metabolomic profiling and case study on drug-resistant multiple myeloma.

PubMed

Jones, Drew R; Wu, Zhiping; Chauhan, Dharminder; Anderson, Kenneth C; Peng, Junmin

2014-04-01

Global metabolomics relies on highly reproducible and sensitive detection of a wide range of metabolites in biological samples. Here we report the optimization of metabolome analysis by nanoflow ultraperformance liquid chromatography coupled to high-resolution orbitrap mass spectrometry. Reliable peak features were extracted from the LC-MS runs based on mandatory detection in duplicates and additional noise filtering according to blank injections. The run-to-run variation in peak area showed a median of 14%, and the false discovery rate during a mock comparison was evaluated. To maximize the number of peak features identified, we systematically characterized the effect of sample loading amount, gradient length, and MS resolution. The number of features initially rose and later reached a plateau as a function of sample amount, fitting a hyperbolic curve. Longer gradients improved unique feature detection in part by time-resolving isobaric species. Increasing the MS resolution up to 120000 also aided in the differentiation of near isobaric metabolites, but higher MS resolution reduced the data acquisition rate and conferred no benefits, as predicted from a theoretical simulation of possible metabolites. Moreover, a biphasic LC gradient allowed even distribution of peak features across the elution, yielding markedly more peak features than the linear gradient. Using this robust nUPLC-HRMS platform, we were able to consistently analyze ~6500 metabolite features in a single 60 min gradient from 2 mg of yeast, equivalent to ~50 million cells. We applied this optimized method in a case study of drug (bortezomib) resistant and drug-sensitive multiple myeloma cells. Overall, 18% of metabolite features were matched to KEGG identifiers, enabling pathway enrichment analysis. Principal component analysis and heat map data correctly clustered isogenic phenotypes, highlighting the potential for hundreds of small molecule biomarkers of cancer drug resistance.

Visual acuity of the honey bee retina and the limits for feature detection.

PubMed

Rigosi, Elisa; Wiederman, Steven D; O'Carroll, David C

2017-04-06

Visual abilities of the honey bee have been studied for more than 100 years, recently revealing unexpectedly sophisticated cognitive skills rivalling those of vertebrates. However, the physiological limits of the honey bee eye have been largely unaddressed and only studied in an unnatural, dark state. Using a bright display and intracellular recordings, we here systematically investigated the angular sensitivity across the light adapted eye of honey bee foragers. Angular sensitivity is a measure of photoreceptor receptive field size and thus small values indicate higher visual acuity. Our recordings reveal a fronto-ventral acute zone in which angular sensitivity falls below 1.9°, some 30% smaller than previously reported. By measuring receptor noise and responses to moving dark objects, we also obtained direct measures of the smallest features detectable by the retina. In the frontal eye, single photoreceptors respond to objects as small as 0.6° × 0.6°, with >99% reliability. This indicates that honey bee foragers possess significantly better resolution than previously reported or estimated behaviourally, and commonly assumed in modelling of bee acuity.

Computer-assisted diagnosis of melanoma.

PubMed

Fuller, Collin; Cellura, A Paul; Hibler, Brian P; Burris, Katy

2016-03-01

The computer-assisted diagnosis of melanoma is an exciting area of research where imaging techniques are combined with diagnostic algorithms in an attempt to improve detection and outcomes for patients with skin lesions suspicious for malignancy. Once an image has been acquired, it undergoes a processing pathway which includes preprocessing, enhancement, segmentation, feature extraction, feature selection, change detection, and ultimately classification. Practicality for everyday clinical use remains a vital question. A successful model must obtain results that are on par or outperform experienced dermatologists, keep costs at a minimum, be user-friendly, and be time efficient with high sensitivity and specificity. ©2015 Frontline Medical Communications.

Two-view information fusion for improvement of computer-aided detection (CAD) of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Wei, Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Ge, Jun; Zhang, Yiheng

2006-03-01

We are developing a two-view information fusion method to improve the performance of our CAD system for mass detection. Mass candidates on each mammogram were first detected with our single-view CAD system. Potential object pairs on the two-view mammograms were then identified by using the distance between the object and the nipple. Morphological features, Hessian feature, correlation coefficients between the two paired objects and texture features were used as input to train a similarity classifier that estimated a similarity scores for each pair. Finally, a linear discriminant analysis (LDA) classifier was used to fuse the score from the single-view CAD system and the similarity score. A data set of 475 patients containing 972 mammograms with 475 biopsy-proven masses was used to train and test the CAD system. All cases contained the CC view and the MLO or LM view. We randomly divided the data set into two independent sets of 243 cases and 232 cases. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained from averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 85% and 80% on the test set, the single-view CAD system achieved an FP rate of 2.0, 1.5, and 1.2 FPs/image, respectively. With the two-view fusion system, the FP rates were reduced to 1.7, 1.3, and 1.0 FPs/image, respectively, at the corresponding sensitivities. The improvement was found to be statistically significant (p<0.05) by the AFROC method. Our results indicate that the two-view fusion scheme can improve the performance of mass detection on mammograms.

Non-contact feature detection using ultrasonic Lamb waves

DOEpatents

Sinha, Dipen N [Los Alamos, NM

2011-06-28

Apparatus and method for non-contact ultrasonic detection of features on or within the walls of hollow pipes are described. An air-coupled, high-power ultrasonic transducer for generating guided waves in the pipe wall, and a high-sensitivity, air-coupled transducer for detecting these waves, are disposed at a distance apart and at chosen angle with respect to the surface of the pipe, either inside of or outside of the pipe. Measurements may be made in reflection or transmission modes depending on the relative position of the transducers and the pipe. Data are taken by sweeping the frequency of the incident ultrasonic waves, using a tracking narrow-band filter to reduce detected noise, and transforming the frequency domain data into the time domain using fast Fourier transformation, if required.

Photonic Crystal Enhanced Fluorescence for Early Breast Cancer Biomarker Detection

PubMed Central

Cunningham, Brian T.; Zangar, Richard C.

2013-01-01

Photonic crystal surfaces offer a compelling platform for improving the sensitivity of surface-based fluorescent assays used in disease diagnostics. Through the complementary processes of photonic crystal enhanced excitation and enhanced extraction, a periodic dielectric-based nanostructured surface can simultaneously increase the electric field intensity experienced by surface-bound fluorophores and increase the collection efficiency of emitted fluorescent photons. Through the ability to inexpensively fabricate photonic crystal surfaces over substantial surface areas, they are amenable to single-use applications in biological sensing, such as disease biomarker detection in serum. In this review, we will describe the motivation for implementing high-sensitivity, multiplexed biomarker detection in the context of breast cancer diagnosis. We will summarize recent efforts to improve the detection limits of such assays though the use of photonic crystal surfaces. Reduction of detection limits is driven by low autofluorescent substrates for photonic crystal fabrication, and detection instruments that take advantage of their unique features. PMID:22736539

Multi-test cervical cancer diagnosis with missing data estimation

NASA Astrophysics Data System (ADS)

Xu, Tao; Huang, Xiaolei; Kim, Edward; Long, L. Rodney; Antani, Sameer

2015-03-01

Cervical cancer is a leading most common type of cancer for women worldwide. Existing screening programs for cervical cancer suffer from low sensitivity. Using images of the cervix (cervigrams) as an aid in detecting pre-cancerous changes to the cervix has good potential to improve sensitivity and help reduce the number of cervical cancer cases. In this paper, we present a method that utilizes multi-modality information extracted from multiple tests of a patient's visit to classify the patient visit to be either low-risk or high-risk. Our algorithm integrates image features and text features to make a diagnosis. We also present two strategies to estimate the missing values in text features: Image Classifier Supervised Mean Imputation (ICSMI) and Image Classifier Supervised Linear Interpolation (ICSLI). We evaluate our method on a large medical dataset and compare it with several alternative approaches. The results show that the proposed method with ICSLI strategy achieves the best result of 83.03% specificity and 76.36% sensitivity. When higher specificity is desired, our method can achieve 90% specificity with 62.12% sensitivity.

Sensitivity to Spacing Changes in Faces and Nonface Objects in Preschool-Aged Children and Adults

ERIC Educational Resources Information Center

Cassia, Viola Macchi; Turati, Chiara; Schwarzer, Gudrun

2011-01-01

Sensitivity to variations in the spacing of features in faces and a class of nonface objects (i.e., frontal images of cars) was tested in 3- and 4-year-old children and adults using a delayed or simultaneous two-alternative forced choice matching-to-sample task. In the adults, detection of spacing information was robust against exemplar…

Immuno-PCR: Achievements and Perspectives.

PubMed

Ryazantsev, D Y; Voronina, D V; Zavriev, S K

2016-12-01

The immuno-PCR (iPCR) method combines advantages of enzyme-linked immunosorbent assay and polymerase chain reaction, which is used in iPCR as a method of "visualization" of antigen-antibody interaction. The use of iPCR provides classical PCR sensitivity to objects traditionally detected by ELISA. This method could be very sensitive and allow for detection of quantities of femtograms/ml order. However, iPCR is still not widely used. The aim of this review is to highlight the special features of the iPCR method and to show the main aspects of its development and application in recent years.

New Target for Cosmic Axion Searches.

PubMed

Baumann, Daniel; Green, Daniel; Wallisch, Benjamin

2016-10-21

Future cosmic microwave background experiments have the potential to probe the density of relativistic species at the subpercent level. This sensitivity allows light thermal relics to be detected up to arbitrarily high decoupling temperatures. Conversely, the absence of a detection would require extra light species never to have been in equilibrium with the Standard Model. In this Letter, we exploit this feature to demonstrate the sensitivity of future cosmological observations to the couplings of axions to photons, gluons, and charged fermions. In many cases, the constraints achievable from cosmology will surpass existing bounds from laboratory experiments and astrophysical observations by orders of magnitude.

Detection of white matter lesions in cerebral small vessel disease

NASA Astrophysics Data System (ADS)

Riad, Medhat M.; Platel, Bram; de Leeuw, Frank-Erik; Karssemeijer, Nico

2013-02-01

White matter lesions (WML) are diffuse white matter abnormalities commonly found in older subjects and are important indicators of stroke, multiple sclerosis, dementia and other disorders. We present an automated WML detection method and evaluate it on a dataset of small vessel disease (SVD) patients. In early SVD, small WMLs are expected to be of importance for the prediction of disease progression. Commonly used WML segmentation methods tend to ignore small WMLs and are mostly validated on the basis of total lesion load or a Dice coefficient for all detected WMLs. Therefore, in this paper, we present a method that is designed to detect individual lesions, large or small, and we validate the detection performance of our system with FROC (free-response ROC) analysis. For the automated detection, we use supervised classification making use of multimodal voxel based features from different magnetic resonance imaging (MRI) sequences, including intensities, tissue probabilities, voxel locations and distances, neighborhood textures and others. After preprocessing, including co-registration, brain extraction, bias correction, intensity normalization, and nonlinear registration, ventricle segmentation is performed and features are calculated for each brain voxel. A gentle-boost classifier is trained using these features from 50 manually annotated subjects to give each voxel a probability of being a lesion voxel. We perform ROC analysis to illustrate the benefits of using additional features to the commonly used voxel intensities; significantly increasing the area under the curve (Az) from 0.81 to 0.96 (p<0.05). We perform the FROC analysis by testing our classifier on 50 previously unseen subjects and compare the results with manual annotations performed by two experts. Using the first annotator results as our reference, the second annotator performs at a sensitivity of 0.90 with an average of 41 false positives per subject while our automated method reached the same level of sensitivity at approximately 180 false positives per subject.

Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform.

PubMed

Tripathy, Rajesh K; Zamora-Mendez, Alejandro; de la O Serna, José A; Paternina, Mario R Arrieta; Arrieta, Juan G; Naik, Ganesh R

2018-01-01

Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.

Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

PubMed Central

Tripathy, Rajesh K.; Zamora-Mendez, Alejandro; de la O Serna, José A.; Paternina, Mario R. Arrieta; Arrieta, Juan G.; Naik, Ganesh R.

2018-01-01

Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.

Non-detection of a Helium Exosphere for the Hot Jupiter WASP-12b

NASA Astrophysics Data System (ADS)

Kreidberg, Laura; Oklopčić, Antonija

2018-06-01

An exosphere was recently detected around the exoplanet WASP-107b, a low-density, warm Neptune, based on an absorption feature from metastable helium (which has a vacuum wavelength of 10833 \\AA). Inspired by the WASP-107b detection, we reanalyzed archival HST observations of another evaporating exoplanet, WASP-12b, to search for signs of helium in its exosphere. We find no significant increase in transit depth at 10833 \\AA. We compare this result to theoretical predictions from a 1D model, and find that the expected helium feature amplitude is small, in agreement with the observed non-detection. We discuss possible explanations for why the helium feature is weaker for WASP-12b than WASP-107b, and conclude that the amplitude of the signal is highly sensitive to the stellar spectrum and the geometry of the evaporating gas cloud. These considerations should be taken into account in the design of future searches for helium exospheres.

A fully battery-powered inexpensive spectrophotometric system for high-sensitivity point-of-care analysis on a microfluidic chip

PubMed Central

Dou, Maowei; Lopez, Juan; Rios, Misael; Garcia, Oscar; Xiao, Chuan; Eastman, Michael

2016-01-01

A cost-effective battery-powered spectrophotometric system (BASS) was developed for quantitative point-of-care (POC) analysis on a microfluidic chip. By using methylene blue as a model analyte, we first compared the performance of the BASS with a commercial spectrophotometric system, and further applied the BASS for loop-mediated isothermal amplification (LAMP) detection and subsequent quantitative nucleic acid analysis which exhibited a comparable limit of detection to that of Nanodrop. Compared to the commercial spectrophotometric system, our spectrophotometric system is lower-cost, consumes less reagents, and has a higher detection sensitivity. Most importantly, it does not rely on external power supplies. All these features make our spectrophotometric system highly suitable for a variety of POC analyses, such as field detection. PMID:27143408

Nuclear magnetic resonance detection and spectroscopy of single proteins using quantum logic

NASA Astrophysics Data System (ADS)

Lovchinsky, I.; Sushkov, A. O.; Urbach, E.; de Leon, N. P.; Choi, S.; De Greve, K.; Evans, R.; Gertner, R.; Bersin, E.; Müller, C.; McGuinness, L.; Jelezko, F.; Walsworth, R. L.; Park, H.; Lukin, M. D.

2016-02-01

Nuclear magnetic resonance spectroscopy is a powerful tool for the structural analysis of organic compounds and biomolecules but typically requires macroscopic sample quantities. We use a sensor, which consists of two quantum bits corresponding to an electronic spin and an ancillary nuclear spin, to demonstrate room temperature magnetic resonance detection and spectroscopy of multiple nuclear species within individual ubiquitin proteins attached to the diamond surface. Using quantum logic to improve readout fidelity and a surface-treatment technique to extend the spin coherence time of shallow nitrogen-vacancy centers, we demonstrate magnetic field sensitivity sufficient to detect individual proton spins within 1 second of integration. This gain in sensitivity enables high-confidence detection of individual proteins and allows us to observe spectral features that reveal information about their chemical composition.

Differential diagnosis of neurodegenerative diseases using structural MRI data

PubMed Central

Koikkalainen, Juha; Rhodius-Meester, Hanneke; Tolonen, Antti; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W.; Tong, Tong; Guerrero, Ricardo; Schuh, Andreas; Ledig, Christian; Rueckert, Daniel; Soininen, Hilkka; Remes, Anne M.; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; van der Flier, Wiesje; Lötjönen, Jyrki

2016-01-01

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. PMID:27104138

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video

PubMed Central

Ghosh, Tonmoy; Wahid, Khan A.

2018-01-01

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data. PMID:29468094

Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

PubMed

Kashyap, Kanchan L; Bajpai, Manish K; Khanna, Pritee; Giakos, George

2018-01-01

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function. Copyright © 2017 John Wiley & Sons, Ltd.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

ECG based Myocardial Infarction detection using Hybrid Firefly Algorithm.

PubMed

Kora, Padmavathi

2017-12-01

Myocardial Infarction (MI) is one of the most frequent diseases, and can also cause demise, disability and monetary loss in patients who suffer from cardiovascular disorder. Diagnostic methods of this ailment by physicians are typically invasive, even though they do not fulfill the required detection accuracy. Recent feature extraction methods, for example, Auto Regressive (AR) modelling; Magnitude Squared Coherence (MSC); Wavelet Coherence (WTC) using Physionet database, yielded a collection of huge feature set. A large number of these features may be inconsequential containing some excess and non-discriminative components that present excess burden in computation and loss of execution performance. So Hybrid Firefly and Particle Swarm Optimization (FFPSO) is directly used to optimise the raw ECG signal instead of extracting features using the above feature extraction techniques. Provided results in this paper show that, for the detection of MI class, the FFPSO algorithm with ANN gives 99.3% accuracy, sensitivity of 99.97%, and specificity of 98.7% on MIT-BIH database by including NSR database also. The proposed approach has shown that methods that are based on the feature optimization of the ECG signals are the perfect to diagnosis the condition of the heart patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

NASA Astrophysics Data System (ADS)

Nishikawa, Robert M.; Giger, Maryellen L.; Doi, Kunio; Vyborny, Carl J.; Schmidt, Robert A.; Metz, Charles E.; Wu, Chris Y.; Yin, Fang-Fang; Jiang, Yulei; Huo, Zhimin; Lu, Ping; Zhang, Wei; Ema, Takahiro; Bick, Ulrich; Papaioannou, John; Nagel, Rufus H.

1993-07-01

We are developing an 'intelligent' workstation to assist radiologists in diagnosing breast cancer from mammograms. The hardware for the workstation will consist of a film digitizer, a high speed computer, a large volume storage device, a film printer, and 4 high resolution CRT monitors. The software for the workstation is a comprehensive package of automated detection and classification schemes. Two rule-based detection schemes have been developed, one for breast masses and the other for clustered microcalcifications. The sensitivity of both schemes is 85% with a false-positive rate of approximately 3.0 and 1.5 false detections per image, for the mass and cluster detection schemes, respectively. Computerized classification is performed by an artificial neural network (ANN). The ANN has a sensitivity of 100% with a specificity of 60%. Currently, the ANN, which is a three-layer, feed-forward network, requires as input ratings of 14 different radiographic features of the mammogram that were determined subjectively by a radiologist. We are in the process of developing automated techniques to objectively determine these 14 features. The workstation will be placed in the clinical reading area of the radiology department in the near future, where controlled clinical tests will be performed to measure its efficacy.

Auditory sensitivity of seals and sea lions in complex listening scenarios.

PubMed

Cunningham, Kane A; Southall, Brandon L; Reichmuth, Colleen

2014-12-01

Standard audiometric data, such as audiograms and critical ratios, are often used to inform marine mammal noise-exposure criteria. However, these measurements are obtained using simple, artificial stimuli-i.e., pure tones and flat-spectrum noise-while natural sounds typically have more complex structure. In this study, detection thresholds for complex signals were measured in (I) quiet and (II) masked conditions for one California sea lion (Zalophus californianus) and one harbor seal (Phoca vitulina). In Experiment I, detection thresholds in quiet conditions were obtained for complex signals designed to isolate three common features of natural sounds: Frequency modulation, amplitude modulation, and harmonic structure. In Experiment II, detection thresholds were obtained for the same complex signals embedded in two types of masking noise: Synthetic flat-spectrum noise and recorded shipping noise. To evaluate how accurately standard hearing data predict detection of complex sounds, the results of Experiments I and II were compared to predictions based on subject audiograms and critical ratios combined with a basic hearing model. Both subjects exhibited greater-than-predicted sensitivity to harmonic signals in quiet and masked conditions, as well as to frequency-modulated signals in masked conditions. These differences indicate that the complex features of naturally occurring sounds enhance detectability relative to simple stimuli.

Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics

DOEpatents

Ellingson, William A.; Brada, Mark P.

1995-01-01

A laser is used in a non-destructive manner to detect surface and near-subsurface defects in dense ceramics and particularly in ceramic bodies with complex shapes such as ceramic bearings, turbine blades, races, and the like. The laser's wavelength is selected based upon the composition of the ceramic sample and the laser can be directed on the sample while the sample is static or in dynamic rotate or translate motion. Light is scattered off surface and subsurface defects using a preselected polarization. The change in polarization angle is used to select the depth and characteristics of surface/subsurface defects. The scattered light is detected by an optical train consisting of a charge coupled device (CCD), or vidicon, television camera which, in turn, is coupled to a video monitor and a computer for digitizing the image. An analyzing polarizer in the optical train allows scattered light at a given polarization angle to be observed for enhancing sensitivity to either surface or near-subsurface defects. Application of digital image processing allows subtraction of digitized images in near real-time providing enhanced sensitivity to subsurface defects. Storing known "feature masks" of identified defects in the computer and comparing the detected scatter pattern (Fourier images) with the stored feature masks allows for automatic classification of detected defects.

Dispersion and shape engineered plasmonic nanosensors

NASA Astrophysics Data System (ADS)

Jeong, Hyeon-Ho; Mark, Andrew G.; Alarcón-Correa, Mariana; Kim, Insook; Oswald, Peter; Lee, Tung-Chun; Fischer, Peer

2016-04-01

Biosensors based on the localized surface plasmon resonance (LSPR) of individual metallic nanoparticles promise to deliver modular, low-cost sensing with high-detection thresholds. However, they continue to suffer from relatively low sensitivity and figures of merit (FOMs). Herein we introduce the idea of sensitivity enhancement of LSPR sensors through engineering of the material dispersion function. Employing dispersion and shape engineering of chiral nanoparticles leads to remarkable refractive index sensitivities (1,091 nm RIU-1 at λ=921 nm) and FOMs (>2,800 RIU-1). A key feature is that the polarization-dependent extinction of the nanoparticles is now characterized by rich spectral features, including bipolar peaks and nulls, suitable for tracking refractive index changes. This sensing modality offers strong optical contrast even in the presence of highly absorbing media, an important consideration for use in complex biological media with limited transmission. The technique is sensitive to surface-specific binding events which we demonstrate through biotin-avidin surface coupling.

a Performance Comparison of Feature Detectors for Planetary Rover Mapping and Localization

NASA Astrophysics Data System (ADS)

Wan, W.; Peng, M.; Xing, Y.; Wang, Y.; Liu, Z.; Di, K.; Teng, B.; Mao, X.; Zhao, Q.; Xin, X.; Jia, M.

2017-07-01

Feature detection and matching are key techniques in computer vision and robotics, and have been successfully implemented in many fields. So far there is no performance comparison of feature detectors and matching methods for planetary mapping and rover localization using rover stereo images. In this research, we present a comprehensive evaluation and comparison of six feature detectors, including Moravec, Förstner, Harris, FAST, SIFT and SURF, aiming for optimal implementation of feature-based matching in planetary surface environment. To facilitate quantitative analysis, a series of evaluation criteria, including distribution evenness of matched points, coverage of detected points, and feature matching accuracy, are developed in the research. In order to perform exhaustive evaluation, stereo images, simulated under different baseline, pitch angle, and interval of adjacent rover locations, are taken as experimental data source. The comparison results show that SIFT offers the best overall performance, especially it is less sensitive to changes of image taken at adjacent locations.

SHERLOC on Mars 2020

NASA Astrophysics Data System (ADS)

Beegle, L. W.; Bhartia, R.; DeFlores, L. P.; Abbey, W.; Asher, S. A.; Burton, A. S.; Fries, M.; Conrad, P. G.; Clegg, S. M.; Wiens, R. C.; Edgett, K. S.; Ehlmann, B. L.; Nealson, K. H.; Minitti, M. E.; Popp, J.; Langenhorst, F.; Sobron, P.; Steele, A.; Williford, K. H.; Yingst, R. A.

2017-12-01

The Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC) investigation is part of the Mars 2020 integrated payload. SHERLOC enables non-contact, spatially resolved, and highly sensitivity detection and characterization of organics and minerals in the Martian surface and near subsurface. SHERLOC is an arm-mounted, Deep UV (DUV) resonance Raman and fluorescence spectrometer utilizing a 248.6-nm DUV laser. Deep UV induced native fluorescence is very sensitive to condensed carbon and aromatic organics, enabling detection at or below 10-6 w/w (1 ppm) at <100 µm spatial scales. SHERLOC's deep UV resonance Raman enables detection and classification of aromatic and aliphatic organics with sensitivities of 10-2 to below 10-4 w/w. In addition to organics, the deep UV Raman enables detection and classification of minerals relevant to aqueous chemistry with grain sizes below 20 µm. SHERLOC will be able to map the distribution of organic material with respect to visible features and minerals that are identifiable with the Raman spectrometer. These maps will enable analysis of the distribution of organics with minerals.

A neural network approach to lung nodule segmentation

NASA Astrophysics Data System (ADS)

Hu, Yaoxiu; Menon, Prahlad G.

2016-03-01

Computed tomography (CT) imaging is a sensitive and specific lung cancer screening tool for the high-risk population and shown to be promising for detection of lung cancer. This study proposes an automatic methodology for detecting and segmenting lung nodules from CT images. The proposed methods begin with thorax segmentation, lung extraction and reconstruction of the original shape of the parenchyma using morphology operations. Next, a multi-scale hessian-based vesselness filter is applied to extract lung vasculature in lung. The lung vasculature mask is subtracted from the lung region segmentation mask to extract 3D regions representing candidate pulmonary nodules. Finally, the remaining structures are classified as nodules through shape and intensity features which are together used to train an artificial neural network. Up to 75% sensitivity and 98% specificity was achieved for detection of lung nodules in our testing dataset, with an overall accuracy of 97.62%+/-0.72% using 11 selected features as input to the neural network classifier, based on 4-fold cross-validation studies. Receiver operator characteristics for identifying nodules revealed an area under curve of 0.9476.

Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone.

PubMed

Jiang, Wen Jun; Wittek, Peter; Zhao, Li; Gao, Shi Chao

2014-01-01

Photoplethysmogram (PPG) signals acquired by smartphone cameras are weaker than those acquired by dedicated pulse oximeters. Furthermore, the signals have lower sampling rates, have notches in the waveform and are more severely affected by baseline drift, leading to specific morphological characteristics. This paper introduces a new feature, the inverted triangular area, to address these specific characteristics. The new feature enables real-time adaptive waveform detection using an algorithm of linear time complexity. It can also recognize notches in the waveform and it is inherently robust to baseline drift. An implementation of the algorithm on Android is available for free download. We collected data from 24 volunteers and compared our algorithm in peak detection with two competing algorithms designed for PPG signals, Incremental-Merge Segmentation (IMS) and Adaptive Thresholding (ADT). A sensitivity of 98.0% and a positive predictive value of 98.8% were obtained, which were 7.7% higher than the IMS algorithm in sensitivity, and 8.3% higher than the ADT algorithm in positive predictive value. The experimental results confirmed the applicability of the proposed method.

Combining heterogeneous features for colonic polyp detection in CTC based on semi-definite programming

NASA Astrophysics Data System (ADS)

Wang, Shijun; Yao, Jianhua; Petrick, Nicholas A.; Summers, Ronald M.

2009-02-01

Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible combination for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features, called histogram of curvature features, are rotation, translation and scale invariant and can be treated as complementing our existing feature set. Then in order to make full use of the traditional features (defined as group A) and the new features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to identify an optimized classification kernel based on the combined set of features. We did leave-one-patient-out test on a CTC dataset which contained scans from 50 patients (with 90 6-9mm polyp detections). Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per patient rate of 7, the sensitivity on 6-9mm polyps using the combined features improved from 0.78 (Group A) and 0.73 (Group B) to 0.82 (p<=0.01).

Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

PubMed Central

Rezaee, Kh.; Azizi, E.; Haddadnia, J.

2016-01-01

Background Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method In this algorithm, L-sec epochs of signals are displayed in form of a third-order tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA) on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN) is used to classify the selected features. Results The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion Today, the lack of an automated system to detect or predict the seizure onset is strongly felt. PMID:27672628

Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimationa

PubMed Central

Liu, Jiamin; Kabadi, Suraj; Van Uitert, Robert; Petrick, Nicholas; Deriche, Rachid; Summers, Ronald M.

2011-01-01

Purpose: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation’s effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. Methods: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Results: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. Conclusions: The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC. PMID:21859029

Automatic detection of retina disease: robustness to image quality and localization of anatomy structure.

PubMed

Karnowski, T P; Aykac, D; Giancardo, L; Li, Y; Nichols, T; Tobin, K W; Chaum, E

2011-01-01

The automated detection of diabetic retinopathy and other eye diseases in images of the retina has great promise as a low-cost method for broad-based screening. Many systems in the literature which perform automated detection include a quality estimation step and physiological feature detection, including the vascular tree and the optic nerve / macula location. In this work, we study the robustness of an automated disease detection method with respect to the accuracy of the optic nerve location and the quality of the images obtained as judged by a quality estimation algorithm. The detection algorithm features microaneurysm and exudate detection followed by feature extraction on the detected population to describe the overall retina image. Labeled images of retinas ground-truthed to disease states are used to train a supervised learning algorithm to identify the disease state of the retina image and exam set. Under the restrictions of high confidence optic nerve detections and good quality imagery, the system achieves a sensitivity and specificity of 94.8% and 78.7% with area-under-curve of 95.3%. Analysis of the effect of constraining quality and the distinction between mild non-proliferative diabetic retinopathy, normal retina images, and more severe disease states is included.

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

Hentschke, Clemens M., E-mail: clemens.hentschke@gmail.com; Tönnies, Klaus D.; Beuing, Oliver

Purpose: The early detection of cerebral aneurysms plays a major role in preventing subarachnoid hemorrhage. The authors present a system to automatically detect cerebral aneurysms in multimodal 3D angiographic data sets. The authors’ system is parametrizable for contrast-enhanced magnetic resonance angiography (CE-MRA), time-of-flight magnetic resonance angiography (TOF-MRA), and computed tomography angiography (CTA). Methods: Initial volumes of interest are found by applying a multiscale sphere-enhancing filter. Several features are combined in a linear discriminant function (LDF) to distinguish between true aneurysms and false positives. The features include shape information, spatial information, and probability information. The LDF can either be parametrized bymore » domain experts or automatically by training. Vessel segmentation is avoided as it could heavily influence the detection algorithm. Results: The authors tested their method with 151 clinical angiographic data sets containing 112 aneurysms. The authors reach a sensitivity of 95% with CE-MRA data sets at an average false positive rate per data set (FP{sub DS}) of 8.2. For TOF-MRA, we achieve 95% sensitivity at 11.3 FP{sub DS}. For CTA, we reach a sensitivity of 95% at 22.8 FP{sub DS}. For all modalities, the expert parametrization led to similar or better results than the trained parametrization eliminating the need for training. 93% of aneurysms that were smaller than 5 mm were found. The authors also showed that their algorithm is capable of detecting aneurysms that were previously overlooked by radiologists. Conclusions: The authors present an automatic system to detect cerebral aneurysms in multimodal angiographic data sets. The system proved as a suitable computer-aided detection tool to help radiologists find cerebral aneurysms.« less

Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics.

PubMed

Mahajan, Ruhi; Viangteeravat, Teeradache; Akbilgic, Oguz

2017-12-01

A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals. PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals. An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools. Copyright © 2017 Elsevier B.V. All rights reserved.

An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases.

PubMed

Sengur, Abdulkadir

2008-03-01

In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.

Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning.

PubMed

Mjahad, A; Rosado-Muñoz, A; Bataller-Mompeán, M; Francés-Víllora, J V; Guerrero-Martínez, J F

2017-04-01

To safely select the proper therapy for Ventricullar Fibrillation (VF) is essential to distinct it correctly from Ventricular Tachycardia (VT) and other rhythms. Provided that the required therapy would not be the same, an erroneous detection might lead to serious injuries to the patient or even cause Ventricular Fibrillation (VF). The main novelty of this paper is the use of time-frequency (t-f) representation images as the direct input to the classifier. We hypothesize that this method allow to improve classification results as it allows to eliminate the typical feature selection and extraction stage, and its corresponding loss of information. The standard AHA and MIT-BIH databases were used for evaluation and comparison with other authors. Previous to t-f Pseudo Wigner-Ville (PWV) calculation, only a basic preprocessing for denoising and signal alignment is necessary. In order to check the validity of the method independently of the classifier, four different classifiers are used: Logistic Regression with L2 Regularization (L2 RLR), Adaptive Neural Network Classifier (ANNC), Support Vector Machine (SSVM), and Bagging classifier (BAGG). The main classification results for VF detection (including flutter episodes) are 95.56% sensitivity and 98.8% specificity, 88.80% sensitivity and 99.5% specificity for ventricular tachycardia (VT), 98.98% sensitivity and 97.7% specificity for normal sinus, and 96.87% sensitivity and 99.55% specificity for other rhythms. Results shows that using t-f data representations to feed classifiers provide superior performance values than the feature selection strategies used in previous works. It opens the door to be used in any other detection applications. Copyright © 2017 Elsevier B.V. All rights reserved.

The impact of signal normalization on seizure detection using line length features.

PubMed

Logesparan, Lojini; Rodriguez-Villegas, Esther; Casson, Alexander J

2015-10-01

Accurate automated seizure detection remains a desirable but elusive target for many neural monitoring systems. While much attention has been given to the different feature extractions that can be used to highlight seizure activity in the EEG, very little formal attention has been given to the normalization that these features are routinely paired with. This normalization is essential in patient-independent algorithms to correct for broad-level differences in the EEG amplitude between people, and in patient-dependent algorithms to correct for amplitude variations over time. It is crucial, however, that the normalization used does not have a detrimental effect on the seizure detection process. This paper presents the first formal investigation into the impact of signal normalization techniques on seizure discrimination performance when using the line length feature to emphasize seizure activity. Comparing five normalization methods, based upon the mean, median, standard deviation, signal peak and signal range, we demonstrate differences in seizure detection accuracy (assessed as the area under a sensitivity-specificity ROC curve) of up to 52 %. This is despite the same analysis feature being used in all cases. Further, changes in performance of up to 22 % are present depending on whether the normalization is applied to the raw EEG itself or directly to the line length feature. Our results highlight the median decaying memory as the best current approach for providing normalization when using line length features, and they quantify the under-appreciated challenge of providing signal normalization that does not impair seizure detection algorithm performance.

Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records.

PubMed

Soguero-Ruiz, Cristina; Hindberg, Kristian; Rojo-Alvarez, Jose Luis; Skrovseth, Stein Olav; Godtliebsen, Fred; Mortensen, Kim; Revhaug, Arthur; Lindsetmo, Rolv-Ole; Augestad, Knut Magne; Jenssen, Robert

2016-09-01

The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, little effort has been invested toward feature selection and the features' corresponding medical interpretation. In this study, we focus on the task of early detection of anastomosis leakage (AL), a severe complication after elective surgery for colorectal cancer (CRC) surgery, using free text extracted from EHRs. We use a bag-of-words model to investigate the potential for feature selection strategies. The purpose is earlier detection of AL and prediction of AL with data generated in the EHR before the actual complication occur. Due to the high dimensionality of the data, we derive feature selection strategies using the robust support vector machine linear maximum margin classifier, by investigating: 1) a simple statistical criterion (leave-one-out-based test); 2) an intensive-computation statistical criterion (Bootstrap resampling); and 3) an advanced statistical criterion (kernel entropy). Results reveal a discriminatory power for early detection of complications after CRC (sensitivity 100%; specificity 72%). These results can be used to develop prediction models, based on EHR data, that can support surgeons and patients in the preoperative decision making phase.

Electrochemical sensing of heavy metal ions with inorganic, organic and bio-materials.

PubMed

Cui, Lin; Wu, Jie; Ju, Huangxian

2015-01-15

As heavy metal ions severely harm human health, it is important to develop simple, sensitive and accurate methods for their detection in environment and food. Electrochemical detection featured with short analytical time, low power cost, high sensitivity and easy adaptability for in-situ measurement is one of the most developed methods. This review introduces briefly the recent achievements in electrochemical sensing of heavy metal ions with inorganic, organic and bio-materials modified electrodes. In particular, the unique properties of inorganic nanomaterials, organic small molecules or their polymers, enzymes and nucleic acids for detection of heavy metal ions are highlighted. By employing some representative examples, the design and sensing mechanisms of these electrodes are discussed. Copyright © 2014 Elsevier B.V. All rights reserved.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns.

PubMed

Haghnegahdar, A A; Kolahi, S; Khojastepour, L; Tajeripour, F

2018-03-01

Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.

Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy.

PubMed

Olsen, Anders Vinther; Stephansen, Jens; Leary, Eileen; Peppard, Paul E; Sheungshul, Hong; Jennum, Poul Jørgen; Sorensen, Helge; Mignot, Emmanuel

2017-04-15

Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized that sleep stage dissociations can be objectively detected through the analysis of nocturnal Polysomnography (PSG) data, and that those affecting REM sleep can be used as a diagnostic feature for narcolepsy. A Linear Discriminant Analysis (LDA) model using 38 features extracted from EOG, EMG and EEG was used in control subjects to select features differentiating wake, stage N1, N2, N3 and REM sleep. Sleep stage differentiation was next represented in a 2D projection. Features characteristic of sleep stage differences were estimated from the residual sleep stage probability in the 2D space. Using this model we evaluated PSG data from NT1 and non-narcoleptic subjects. An LDA classifier was used to determine the best separation plane. This method replicates the specificity/sensitivity from the training set to the validation set better than many other methods. Eight prominent features could differentiate narcolepsy and controls in the validation dataset. Using a composite measure and a specificity cut off 95% in the training dataset, sensitivity was 43%. Specificity/sensitivity was 94%/38% in the validation set. Using hypersomnia subjects, specificity/sensitivity was 84%/15%. Analyzing treated narcoleptics the specificity/sensitivity was 94%/10%. Sleep stage dissociation can be used for the diagnosis of narcolepsy. However the use of some medications and presence of undiagnosed hypersomnolence patients impacts the result. Copyright © 2017 Elsevier B.V. All rights reserved.

Sequential structural damage diagnosis algorithm using a change point detection method

NASA Astrophysics Data System (ADS)

Noh, H.; Rajagopal, R.; Kiremidjian, A. S.

2013-11-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

A triphenylamine-functionalized luminescent sensor for efficient p-nitroaniline detection.

PubMed

Ji, Ning-Ning; Shi, Zhi-Qiang; Hu, Hai-Liang; Zheng, He-Gen

2018-05-14

The combination of π-conjugated fluorophores within a hybrid system gives rise to a triphenylamine-functionalized material [Zn(bpba)(NO3)] (1) (Hbpba = 4-(bis(4-(pyridin-4-yl)phenyl)amino)benzoic acid). Compound 1 features a 2D + 2D → 2D parallel polycatenation structure with 63-hcb net. Photophysical studies revealed that the title phase showed superior sensitivity towards p-nitroaniline (p-NA) with a low detection limit (down to ∼0.10 ppm). Specifically, following a new detection route, vapor-sensing experiments using a saturated ethanol solution of nitroaromatic isomers have been established for the first time. Highly sensitive and selective detection of p-NA by the proposed material with a rapid response time (t = 30 s, QE > 90.0%) as compared to that via the control isomers (t = 60s, QE < 6.0%) demonstrates an attractive feasible route and a promising luminescent sensor for nitroaromatic detection.

Interferometric Reflectance Imaging Sensor (IRIS)—A Platform Technology for Multiplexed Diagnostics and Digital Detection

PubMed Central

Avci, Oguzhan; Lortlar Ünlü, Nese; Yalçın Özkumur, Ayça; Ünlü, M. Selim

2015-01-01

Over the last decade, the growing need in disease diagnostics has stimulated rapid development of new technologies with unprecedented capabilities. Recent emerging infectious diseases and epidemics have revealed the shortcomings of existing diagnostics tools, and the necessity for further improvements. Optical biosensors can lay the foundations for future generation diagnostics by providing means to detect biomarkers in a highly sensitive, specific, quantitative and multiplexed fashion. Here, we review an optical sensing technology, Interferometric Reflectance Imaging Sensor (IRIS), and the relevant features of this multifunctional platform for quantitative, label-free and dynamic detection. We discuss two distinct modalities for IRIS: (i) low-magnification (ensemble biomolecular mass measurements) and (ii) high-magnification (digital detection of individual nanoparticles) along with their applications, including label-free detection of multiplexed protein chips, measurement of single nucleotide polymorphism, quantification of transcription factor DNA binding, and high sensitivity digital sensing and characterization of nanoparticles and viruses. PMID:26205273

AE (Acoustic Emission) for Flip-Chip CGA/FCBGA Defect Detection

NASA Technical Reports Server (NTRS)

Ghaffarian, Reza

2014-01-01

C-mode scanning acoustic microscopy (C-SAM) is a nondestructive inspection technique that uses ultrasound to show the internal feature of a specimen. A very high or ultra-high-frequency ultrasound passes through a specimen to produce a visible acoustic microimage (AMI) of its inner features. As ultrasound travels into a specimen, the wave is absorbed, scattered or reflected. The response is highly sensitive to the elastic properties of the materials and is especially sensitive to air gaps. This specific characteristic makes AMI the preferred method for finding "air gaps" such as delamination, cracks, voids, and porosity. C-SAM analysis, which is a type of AMI, was widely used in the past for evaluation of plastic microelectronic circuits, especially for detecting delamination of direct die bonding. With the introduction of the flip-chip die attachment in a package; its use has been expanded to nondestructive characterization of the flip-chip solder bumps and underfill. Figure 1.1 compares visual and C-SAM inspection approaches for defect detection, especially for solder joint interconnections and hidden defects. C-SAM is specifically useful for package features like internal cracks and delamination. C-SAM not only allows for the visualization of the interior features, it has the ability to produce images on layer-by-layer basis. Visual inspection; however, is only superior to C-SAM for the exposed features including solder dewetting, microcracks, and contamination. Ideally, a combination of various inspection techniques - visual, optical and SEM microscopy, C-SAM, and X-ray - need to be performed in order to assure quality at part, package, and system levels. This reports presents evaluations performed on various advanced packages/assemblies, especially the flip-chip die version of ball grid array/column grid array (BGA/CGA) using C-SAM equipment. Both external and internal equipment was used for evaluation. The outside facility provided images of the key features that could be detected using the most advanced C-SAM equipment with a skilled operator. Investigation continued using in-house equipment with its limitations. For comparison, representative X-rays of the assemblies were also gathered to show key defect detection features of these non-destructive techniques. Key images gathered and compared are: Compared the images of 2D X-ray and C-SAM for a plastic LGA assembly showing features that could be detected by either NDE technique. For this specific case, X-ray was a clear winner. Evaluated flip-chip CGA and FCBGA assemblies with and without heat sink by C-SAM. Only the FCCGA package that had no heat sink could be fully analyzed for underfill and bump quality. Cross-sectional microscopy did not revealed peripheral delamination features detected by C-SAM. Analyzed a number of fine pitch PBGA assemblies by C-SAM. Even though the internal features of the package assemblies could be detected, C-SAM was unable to detect solder joint failure at either the package or board level. Twenty times touch ups by solder iron with 700degF tip temperature, each with about 5 second duration, did not induce defects to be detected by C-SAM images. Other techniques need to be considered to induce known defects for characterization. Given NASA's emphasis on the use of microelectronic packages and assemblies and quality assurance on workmanship defect detection, understanding key features of various inspection systems that detect defects in the early stages of package and assembly is critical to developing approaches that will minimize future failures. Additional specific, tailored non-destructive inspection approaches could enable low-risk insertion of these advanced electronic packages having hidden and fine features.

Sensitivity to feature displacement in familiar and unfamiliar faces: beyond the internal/external feature distinction.

PubMed

Brooks, Kevin R; Kemp, Richard I

2007-01-01

Previous studies of face recognition and of face matching have shown a general improvement for the processing of internal features as a face becomes more familiar to the participant. In this study, we used a psychophysical two-alternative forced-choice paradigm to investigate thresholds for the detection of a displacement of the eyes, nose, mouth, or ears for familiar and unfamiliar faces. No clear division between internal and external features was observed. Rather, for familiar (compared to unfamiliar) faces participants were more sensitive to displacements of internal features such as the eyes or the nose; yet, for our third internal feature-the mouth no such difference was observed. Despite large displacements, many subjects were unable to perform above chance when stimuli involved shifts in the position of the ears. These results are consistent with the proposal that familiarity effects may be mediated by the construction of a robust representation of a face, although the involvement of attention in the encoding of face stimuli cannot be ruled out. Furthermore, these effects are mediated by information from a spatial configuration of features, rather than by purely feature-based information.

Dynamic Metasurface Aperture as Smart Around-the-Corner Motion Detector.

PubMed

Del Hougne, Philipp; F Imani, Mohammadreza; Sleasman, Timothy; Gollub, Jonah N; Fink, Mathias; Lerosey, Geoffroy; Smith, David R

2018-04-25

Detecting and analysing motion is a key feature of Smart Homes and the connected sensor vision they embrace. At present, most motion sensors operate in line-of-sight Doppler shift schemes. Here, we propose an alternative approach suitable for indoor environments, which effectively constitute disordered cavities for radio frequency (RF) waves; we exploit the fundamental sensitivity of modes of such cavities to perturbations, caused here by moving objects. We establish experimentally three key features of our proposed system: (i) ability to capture the temporal variations of motion and discern information such as periodicity ("smart"), (ii) non line-of-sight motion detection, and (iii) single-frequency operation. Moreover, we explain theoretically and demonstrate experimentally that the use of dynamic metasurface apertures can substantially enhance the performance of RF motion detection. Potential applications include accurately detecting human presence and monitoring inhabitants' vital signs.

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique

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

Teramoto, Atsushi, E-mail: teramoto@fujita-hu.ac.jp; Fujita, Hiroshi; Yamamuro, Osamu

Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using anmore » active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors’ ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images.« less

Magnetic field feature extraction and selection for indoor location estimation.

PubMed

Galván-Tejada, Carlos E; García-Vázquez, Juan Pablo; Brena, Ramon F

2014-06-20

User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user's location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.

Nuclear magnetic resonance detection and spectroscopy of single proteins using quantum logic.

PubMed

Lovchinsky, I; Sushkov, A O; Urbach, E; de Leon, N P; Choi, S; De Greve, K; Evans, R; Gertner, R; Bersin, E; Müller, C; McGuinness, L; Jelezko, F; Walsworth, R L; Park, H; Lukin, M D

2016-02-19

Nuclear magnetic resonance spectroscopy is a powerful tool for the structural analysis of organic compounds and biomolecules but typically requires macroscopic sample quantities. We use a sensor, which consists of two quantum bits corresponding to an electronic spin and an ancillary nuclear spin, to demonstrate room temperature magnetic resonance detection and spectroscopy of multiple nuclear species within individual ubiquitin proteins attached to the diamond surface. Using quantum logic to improve readout fidelity and a surface-treatment technique to extend the spin coherence time of shallow nitrogen-vacancy centers, we demonstrate magnetic field sensitivity sufficient to detect individual proton spins within 1 second of integration. This gain in sensitivity enables high-confidence detection of individual proteins and allows us to observe spectral features that reveal information about their chemical composition. Copyright © 2016, American Association for the Advancement of Science.

CRIM-TRACK: sensor system for detection of criminal chemical substances

NASA Astrophysics Data System (ADS)

Munk, Jens K.; Buus, Ole T.; Larsen, Jan; Dossi, Eleftheria; Tatlow, Sol; Lässig, Lina; Sandström, Lars; Jakobsen, Mogens H.

2015-10-01

Detection of illegal compounds requires a reliable, selective and sensitive detection device. The successful device features automated target acquisition, identification and signal processing. It is portable, fast, user friendly, sensitive, specific, and cost efficient. LEAs are in need of such technology. CRIM-TRACK is developing a sensing device based on these requirements. We engage highly skilled specialists from research institutions, industry, SMEs and LEAs and rely on a team of end users to benefit maximally from our prototypes. Currently we can detect minute quantities of drugs, explosives and precursors thereof in laboratory settings. Using colorimetric technology we have developed prototypes that employ disposable sensing chips. Ease of operation and intuitive sensor response are highly prioritized features that we implement as we gather data to feed into machine learning. With machine learning our ability to detect threat compounds amidst harmless substances improves. Different end users prefer their equipment optimized for their specific field. In an explosives-detecting scenario, the end user may prefer false positives over false negatives, while the opposite may be true in a drug-detecting scenario. Such decisions will be programmed to match user preference. Sensor output can be as detailed as the sensor allows. The user can be informed of the statistics behind the detection, identities of all detected substances, and quantities thereof. The response can also be simplified to "yes" vs. "no". The technology under development in CRIM-TRACK will provide custom officers, police and other authorities with an effective tool to control trafficking of illegal drugs and drug precursors.

Portable SERS sensor for malachite green and other small dye molecules

NASA Astrophysics Data System (ADS)

Qiu, Suyan; Zhao, Fusheng; Li, Jingting; Shih, Wei-Chuan

2017-02-01

Sensitive detection of specific chemicals on site can be extremely powerful in many fields. Owing to its molecular fingerprinting capability, surface-enhanced Raman scattering has been one of the technological contenders. In this paper, we describe the novel use of DNA topological nanostructure on nanoporous gold nanoparticle (NPG-NP) array chip for chemical sensing. NPG-NP features large surface area and high-density plasmonic field enhancement known as "hotspots". Hence, NPG-NP array chip has found many applications in nanoplasmonic sensor development. This technique can provide novel label-free molecular sensing capability and enables high sensitivity and specificity detection using a portable Raman spectrometer.

Horseradish peroxidase-labeled oligonucleotides and fluorescent tyramides for rapid detection of chromosome-specific repeat sequences.

PubMed

van Gijlswijk, R P; Wiegant, J; Vervenne, R; Lasan, R; Tanke, H J; Raap, A K

1996-01-01

We present a sensitive and rapid fluorescence in situ hybridization (FISH) strategy for detecting chromosome-specific repeat sequences. It uses horseradish peroxidase (HRP)-labeled oligonucleotide sequences in combination with fluorescent tyramide-based detection. After in situ hybridization, the HRP conjugated to the oligonucleotide probe is used to deposit fluorescently labeled tyramide molecules at the site of hybridization. The method features full chemical synthesis of probes, strong FISH signals, and short processing periods, as well as multicolor capabilities.

Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features

NASA Astrophysics Data System (ADS)

Purwanti, Endah; Calista, Evelyn

2017-05-01

Leukemia is a type of cancer which is caused by malignant neoplasms in leukocyte cells. Leukemia disease which can cause death quickly enough for the sufferer is a type of acute lymphocyte leukemia (ALL). In this study, we propose automatic detection of lymphocyte leukemia through classification of lymphocyte cell images obtained from peripheral blood smear single cell. There are two main objectives in this study. The first is to extract featuring cells. The second objective is to classify the lymphocyte cells into two classes, namely normal and abnormal lymphocytes. In conducting this study, we use combination of shape feature and histogram feature, and the classification algorithm is k-nearest Neighbour with k variation is 1, 3, 5, 7, 9, 11, 13, and 15. The best level of accuracy, sensitivity, and specificity in this study are 90%, 90%, and 90%, and they were obtained from combined features of area-perimeter-mean-standard deviation with k=7.

A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats.

PubMed

Tejedor, Javier; Macias-Guarasa, Javier; Martins, Hugo F; Piote, Daniel; Pastor-Graells, Juan; Martin-Lopez, Sonia; Corredera, Pedro; Gonzalez-Herraez, Miguel

2017-02-12

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

EEG-based driver fatigue detection using hybrid deep generic model.

PubMed

Phyo Phyo San; Sai Ho Ling; Rifai Chai; Tran, Yvonne; Craig, Ashley; Hung Nguyen

2016-08-01

Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

Covariance of dynamic strain responses for structural damage detection

NASA Astrophysics Data System (ADS)

Li, X. Y.; Wang, L. X.; Law, S. S.; Nie, Z. H.

2017-10-01

A new approach to address the practical problems with condition evaluation/damage detection of structures is proposed based on the distinct features of a new damage index. The covariance of strain response function (CoS) is a function of modal parameters of the structure. A local stiffness reduction in structure would cause monotonous increase in the CoS. Its sensitivity matrix with respect to local damages of structure is negative and narrow-banded. The damage extent can be estimated with an approximation to the sensitivity matrix to decouple the identification equations. The CoS sensitivity can be calibrated in practice from two previous states of measurements to estimate approximately the damage extent of a structure. A seven-storey plane frame structure is numerically studied to illustrate the features of the CoS index and the proposed method. A steel circular arch in the laboratory is tested. Natural frequencies changed due to damage in the arch and the damage occurrence can be judged. However, the proposed CoS method can identify not only damage happening but also location, even damage extent without need of an analytical model. It is promising for structural condition evaluation of selected components.

Fatigue crack detection by nonlinear spectral correlation with a wideband input

NASA Astrophysics Data System (ADS)

Liu, Peipei; Sohn, Hoon

2017-04-01

Due to crack-induced nonlinearity, ultrasonic wave can distort, create accompanying harmonics, multiply waves of different frequencies, and, under resonance conditions, change resonance frequencies as a function of driving amplitude. All these nonlinear ultrasonic features have been widely studied and proved capable of detecting fatigue crack at its very early stage. However, in noisy environment, the nonlinear features might be drown in the noise, therefore it is difficult to extract those features using a conventional spectral density function. In this study, nonlinear spectral correlation is defined as a new nonlinear feature, which considers not only nonlinear modulations in ultrasonic waves but also spectral correlation between the nonlinear modulations. The proposed nonlinear feature is associated with the following two advantages: (1) stationary noise in the ultrasonic waves has little effect on nonlinear spectral correlation; and (2) the contrast of nonlinear spectral correlation between damage and intact conditions can be enhanced simply by using a wideband input. To validate the proposed nonlinear feature, micro fatigue cracks are introduced to aluminum plates by repeated tensile loading, and the experiment is conducted using surface-mounted piezoelectric transducers for ultrasonic wave generation and measurement. The experimental results confirm that the nonlinear spectral correlation can successfully detect fatigue crack with a higher sensitivity than the classical nonlinear coefficient.

Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone

PubMed Central

Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

2013-01-01

Objective Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Design Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. Patients Sixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m2, m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. Measurements and Results To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise). Conclusions Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients. PMID:24391903

Automatic detection of whole night snoring events using non-contact microphone.

PubMed

Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

2013-01-01

Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2), m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise). Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2017-05-01

Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics

DOEpatents

Ellingson, W.A.; Brada, M.P.

1995-06-20

A laser is used in a non-destructive manner to detect surface and near-subsurface defects in dense ceramics and particularly in ceramic bodies with complex shapes such as ceramic bearings, turbine blades, races, and the like. The laser`s wavelength is selected based upon the composition of the ceramic sample and the laser can be directed on the sample while the sample is static or in dynamic rotate or translate motion. Light is scattered off surface and subsurface defects using a preselected polarization. The change in polarization angle is used to select the depth and characteristics of surface/subsurface defects. The scattered light is detected by an optical train consisting of a charge coupled device (CCD), or vidicon, television camera which, in turn, is coupled to a video monitor and a computer for digitizing the image. An analyzing polarizer in the optical train allows scattered light at a given polarization angle to be observed for enhancing sensitivity to either surface or near-subsurface defects. Application of digital image processing allows subtraction of digitized images in near real-time providing enhanced sensitivity to subsurface defects. Storing known ``feature masks`` of identified defects in the computer and comparing the detected scatter pattern (Fourier images) with the stored feature masks allows for automatic classification of detected defects. 29 figs.

Computerized detection of leukocytes in microscopic leukorrhea images.

PubMed

Zhang, Jing; Zhong, Ya; Wang, Xiangzhou; Ni, Guangming; Du, Xiaohui; Liu, Juanxiu; Liu, Lin; Liu, Yong

2017-09-01

Detection of leukocytes is critical for the routine leukorrhea exam, which is widely used in gynecological examinations. An elevated vaginal leukocyte count in women with bacterial vaginosis is a strong predictor of vaginal or cervical infections. In the routine leukorrhea exam, the counting of leukocytes is primarily performed by manual techniques. However, the viewing and counting of leukocytes from multiple high-power viewing fields on a glass slide under a microscope leads to subjectivity, low efficiency, and low accuracy. To date, many biological cells in stool, blood, and breast cancer have been studied to realize computerized detection; however, the detection of leukocytes in microscopic leukorrhea images has not been studied. Thus, there is an increasing need for computerized detection of leukocytes. There are two key processes in the computerized detection of leukocytes in digital image processing. One is segmentation; the other is intelligent classification. In this paper, we propose a combined ensemble to detect leukocytes in the microscopic leukorrhea image. After image segmentation and selecting likely leukocyte subimages, we obtain the leukocyte candidates. Then, for intelligent classification, we adopt two methods: feature extraction and classification by a support vector machine (SVM); applying a modified convolutional neural network (CNN) to the larger subimages. If different methods classify a candidate in the same category, the process is finished. If not, the outputs of the methods are provided to a classifier to further classify the candidate. After acquiring leukocyte candidates, we attempted three methods to perform classification. The first approach using features and SVM achieved 88% sensitivity, 97% specificity, and 92.5% accuracy. The second method using CNN achieved 95% sensitivity, 84% specificity, and 89.5% accuracy. Then, in the combination approach, we achieved 92% sensitivity, 95% specificity, and 93.5% accuracy. Finally, the images with marked and counted leukocytes were obtained. A novel computerized detection system was developed for automated detection of leukocytes in microscopic images. Different methods resulted in comparable overall qualities by enabling computerized detection of leukocytes. The proposed approach further improved the performance. This preliminary study proves the feasibility of computerized detection of leukocytes in clinical use. © 2017 American Association of Physicists in Medicine.

Automated detection of neovascularization for proliferative diabetic retinopathy screening.

PubMed

Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

2016-08-01

Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.

Transmission Characteristics of Variably Protease-Sensitive Prionopathy

PubMed Central

Notari, Silvio; Xiao, Xiangzhu; Espinosa, Juan Carlos; Cohen, Yvonne; Qing, Liuting; Aguilar-Calvo, Patricia; Kofskey, Diane; Cali, Ignazio; Cracco, Laura; Kong, Qingzhong; Torres, Juan Maria

2014-01-01

Variably protease-sensitive prionopathy (VPSPr), a recently identified and seemingly sporadic human prion disease, is distinct from Creutzfeldt-Jakob disease (CJD) but shares features of Gerstmann-Sträussler-Scheinker disease (GSS). However, contrary to exclusively inherited GSS, no prion protein (PrP) gene variations have been detected in VPSPr, suggesting that VPSPr might be the long-sought sporadic form of GSS. The VPSPr atypical features raised the issue of transmissibility, a prototypical property of prion diseases. We inoculated VPSPr brain homogenate into transgenic mice expressing various levels of human PrP (PrPC). On first passage, 54% of challenged mice showed histopathologic lesions, and 34% harbored abnormal PrP similar to that of VPSPr. Surprisingly, no prion disease was detected on second passage. We concluded that VPSPr is transmissible; thus, it is an authentic prion disease. However, we speculate that normal human PrPC is not an efficient conversion substrate (or mouse brain not a favorable environment) and therefore cannot sustain replication beyond the first passage. PMID:25418590

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.

Early Detection of Human Focal Seizures Based on Cortical Multiunit Activity

PubMed Central

Park, Yun S.; Hochberg, Leigh R.; Eskandar, Emad N.; Cash, Sydney S.; Truccolo, Wilson

2014-01-01

Approximately 50 million people in the world suffer from epileptic seizures. Reliable early seizure detection could bring significantly beneficial therapeutic alternatives. In recent decades, most approaches have relied on scalp EEG and intracranial EEG signals, but practical early detection for closed-loop seizure control remains challenging. In this study, we present preliminary analyses of an early detection approach based on intracortical neuronal multiunit activity (MUA) recorded from a 96-microelectrode array (MEA). The approach consists of (1) MUA detection from broadband field potentials recorded at 30 kHz by the MEA; (2) MUA feature extraction; (3) cost-sensitive support vector machine classification of ictal and interictal samples; and (4) Kalman-filtering postprocessing. MUA was here defined as the number of threshold crossing (spike counts) applied to the 300 Hz – 6 kHz bandpass filtered local field potentials in 0.1 sec time windows. MUA features explored in this study included the mean, variance, and Fano-factor, computed across the MEA channels. In addition, we used the leading eigenvalues of MUA spatial and temporal correlation matrices computed in 1-sec moving time windows. We assessed the seizure detection approach on out-of-sample data from one-participant recordings with six seizure events and 4.73-hour interictal data. The proposed MUA-based detection approach yielded a 100% sensitivity (6/6) and no false positives, and a latency of 4.17 ± 2.27 sec (mean ± SD) with respect to ECoG-identified seizure onsets. These preliminary results indicate intracortical MUA may be a useful signal for early detection of human epileptic seizures. PMID:25571313

Heart sounds analysis using probability assessment.

PubMed

Plesinger, F; Viscor, I; Halamek, J; Jurco, J; Jurak, P

2017-07-31

This paper describes a method for automated discrimination of heart sounds recordings according to the Physionet Challenge 2016. The goal was to decide if the recording refers to normal or abnormal heart sounds or if it is not possible to decide (i.e. 'unsure' recordings). Heart sounds S1 and S2 are detected using amplitude envelopes in the band 15-90 Hz. The averaged shape of the S1/S2 pair is computed from amplitude envelopes in five different bands (15-90 Hz; 55-150 Hz; 100-250 Hz; 200-450 Hz; 400-800 Hz). A total of 53 features are extracted from the data. The largest group of features is extracted from the statistical properties of the averaged shapes; other features are extracted from the symmetry of averaged shapes, and the last group of features is independent of S1 and S2 detection. Generated features are processed using logical rules and probability assessment, a prototype of a new machine-learning method. The method was trained using 3155 records and tested on 1277 hidden records. It resulted in a training score of 0.903 (sensitivity 0.869, specificity 0.937) and a testing score of 0.841 (sensitivity 0.770, specificity 0.913). The revised method led to a test score of 0.853 in the follow-up phase of the challenge. The presented solution achieved 7th place out of 48 competing entries in the Physionet Challenge 2016 (official phase). In addition, the PROBAfind software for probability assessment was introduced.

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

PubMed

Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

2015-07-01

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Investigation of contact acoustic nonlinearities on metal and composite airframe structures via intensity based health monitoring.

PubMed

Romano, P Q; Conlon, S C; Smith, E C

2013-01-01

Nonlinear structural intensity (NSI) and nonlinear structural surface intensity (NSSI) based damage detection techniques were improved and extended to metal and composite airframe structures. In this study, the measurement of NSI maps at sub-harmonic frequencies was completed to provide enhanced understanding of the energy flow characteristics associated with the damage induced contact acoustic nonlinearity mechanism. Important results include NSI source localization visualization at ultra-subharmonic (nf/2) frequencies, and damage detection results utilizing structural surface intensity in the nonlinear domain. A detection metric relying on modulated wave spectroscopy was developed and implemented using the NSSI feature. The data fusion of the intensity formulation provided a distinct advantage, as both the single interrogation frequency NSSI and its modulated wave extension (NSSI-MW) exhibited considerably higher sensitivities to damage than using single-sensor (strain or acceleration) nonlinear detection metrics. The active intensity based techniques were also extended to composite materials, and results show both NSSI and NSSI-MW can be used to detect damage in the bond line of an integrally stiffened composite plate structure with high sensitivity. Initial damage detection measurements made on an OH-58 tailboom (Penn State Applied Research Laboratory, State College, PA) indicate the techniques can be transitioned to complex airframe structures achieving high detection sensitivities with minimal sensors and actuators.

Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image

NASA Astrophysics Data System (ADS)

Sebatubun, M. M.; Haryawan, C.; Windarta, B.

2018-03-01

Lung cancer causes a high mortality rate in the world than any other cancers. That can be minimised if the symptoms and cancer cells have been detected early. One of the techniques used to detect lung cancer is by computed tomography (CT) scan. CT scan images have been used in this study to identify one of the lesion characteristics named ground glass opacity (GGO). It has been used to determine the level of malignancy of the lesion. There were three phases in identifying GGO: image cropping, feature extraction using grey level co-occurrence matrices (GLCM) and classification using Naïve Bayes Classifier. In order to improve the classification results, the most significant feature was sought by feature selection using gain ratio evaluation. Based on the results obtained, the most significant features could be identified by using feature selection method used in this research. The accuracy rate increased from 83.33% to 91.67%, the sensitivity from 82.35% to 94.11% and the specificity from 84.21% to 89.47%.

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing.

PubMed

Weis, Christian; Hess, Andreas; Budinsky, Lubos; Fabry, Ben

2015-01-01

The migration of cells within a living organism can be observed with magnetic resonance imaging (MRI) in combination with iron oxide nanoparticles as an intracellular contrast agent. This method, however, suffers from low sensitivity and specificty. Here, we developed a quantitative non-invasive in-vivo cell localization method using contrast enhanced multiparametric MRI and support vector machines (SVM) based post-processing. Imaging phantoms consisting of agarose with compartments containing different concentrations of cancer cells labeled with iron oxide nanoparticles were used to train and evaluate the SVM for cell localization. From the magnitude and phase data acquired with a series of T2*-weighted gradient-echo scans at different echo-times, we extracted features that are characteristic for the presence of superparamagnetic nanoparticles, in particular hyper- and hypointensities, relaxation rates, short-range phase perturbations, and perturbation dynamics. High detection quality was achieved by SVM analysis of the multiparametric feature-space. The in-vivo applicability was validated in animal studies. The SVM detected the presence of iron oxide nanoparticles in the imaging phantoms with high specificity and sensitivity with a detection limit of 30 labeled cells per mm3, corresponding to 19 μM of iron oxide. As proof-of-concept, we applied the method to follow the migration of labeled cancer cells injected in rats. The combination of iron oxide labeled cells, multiparametric MRI and a SVM based post processing provides high spatial resolution, specificity, and sensitivity, and is therefore suitable for non-invasive in-vivo cell detection and cell migration studies over prolonged time periods.

A nanoscale Zr-based fluorescent metal-organic framework for selective and sensitive detection of hydrogen sulfide

NASA Astrophysics Data System (ADS)

Li, Yanping; Zhang, Xin; Zhang, Ling; Jiang, Ke; Cui, Yuanjing; Yang, Yu; Qian, Guodong

2017-11-01

Hydrogen sulfide (H2S) has been commonly viewed as a gas signaling molecule in various physiological and pathological processes. However, the highly efficient H2S detection still remains challenging. Herein, we designed a new robust nano metal-organic framework (MOF) UiO-66-CH=CH2 as a fluorescent probe for rapid, sensitive and selective detection of biological H2S. UiO-66-CH=CH2 was prepared by heating ZrCl4 and 2-vinylterephthalic acid via a simple method. UiO-66-CH=CH2 displayed fluorescence quenching to H2S and kept excellent selectivity in the presence of biological relevant analytes especially the cysteine and glutathione. This MOF-based probe also exhibited fast response (10 s) and high sensitivity with a detection limit of 6.46 μM which was within the concentration range of biological H2S in living system. Moreover, this constructed MOF featured water-stability, nanoscale (20-30 nm) and low toxicity, which made it a promising candidate for biological H2S sensing.

Preparation of highly stable fullerene C60 decorated graphene oxide nanocomposite and its sensitive electrochemical detection of dopamine in rat brain and pharmaceutical samples.

PubMed

Thirumalraj, Balamurugan; Palanisamy, Selvakumar; Chen, Shen-Ming; Lou, Bih-Show

2016-01-15

The research community has continuously paid much attention on the preparation of hybrid of carbon nanomaterials owing to combine their unique properties. Herein, we report the preparation of highly stable fullerene C60 (C60) wrapped graphene oxide (GO) nanocomposite by using a simple sonication method. The fabricated GO-C60 nanocomposite modified glassy carbon electrode shows a good sensitivity and lower oxidation overpotential towards dopamine (DA) than that of pristine GO and C60. The fabricated sensor detects the DA in the linear response range of 0.02-73.5μM. The limit of detection is estimated to be 0.008μM based on 3σ with a sensitivity of 4.23μAμM(-1)cm(-2). The fabricated sensor also exhibits other features such as good selectivity, stability, reproducibility and repeatability. The proposed sensor exhibits good practicality towards the detection of DA in rat brain and commercial DA injection samples. Copyright © 2015 Elsevier Inc. All rights reserved.

Improved Portable Ultrasonic Leak Detectors

NASA Technical Reports Server (NTRS)

Youngquist, Robert C.; Moerk, John S.; Haskell, William D.; Cox, Robert B.; Polk, Jimmy D.; Strobel, James P.; Luaces, Frank

1995-01-01

Improved portable ultrasonic leak detector features three interchangeable ultrasonic-transducer modules, each suited for operation in unique noncontact or contact mode. One module equipped with ultrasound-collecting horn for use in scanning to detect leaks from distance; horn provides directional sensitivity pattern with sensitivity multiplied by factor of about 6 in forward direction. Another module similar, does not include horn; this module used for scanning close to suspected leak, where proximity of leak more than offsets loss of sensitivity occasioned by lack of horn. Third module designed to be pressed against leaking vessel; includes rugged stainless-steel shell. Improved detectors perform significantly better, smaller, more rugged, and greater sensitivity.

Wavelength-modulated differential photothermal radiometry: Theory and experimental applications to glucose detection in water

NASA Astrophysics Data System (ADS)

Mandelis, Andreas; Guo, Xinxin

2011-10-01

A differential photothermal radiometry method, wavelength-modulated differential photothermal radiometry (WM-DPTR), has been developed theoretically and experimentally for noninvasive, noncontact biological analyte detection, such as blood glucose monitoring. WM-DPTR features analyte specificity and sensitivity by combining laser excitation by two out-of-phase modulated beams at wavelengths near the peak and the base line of a prominent and isolated mid-IR analyte absorption band (here the carbon-oxygen-carbon bond in the pyran ring of the glucose molecule). A theoretical photothermal model of WM-DPTR signal generation and detection has been developed. Simulation results on water-glucose phantoms with the human blood range (0-300 mg/dl) glucose concentration demonstrated high sensitivity and resolution to meet wide clinical detection requirements. The model has also been validated by experimental data of the glucose-water system obtained using WM-DPTR.

Hippocampus shape analysis for temporal lobe epilepsy detection in magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Kohan, Zohreh; Azmi, Reza

2016-03-01

There are evidences in the literature that Temporal Lobe Epilepsy (TLE) causes some lateralized atrophy and deformation on hippocampus and other substructures of the brain. Magnetic Resonance Imaging (MRI), due to high-contrast soft tissue imaging, is one of the most popular imaging modalities being used in TLE diagnosis and treatment procedures. Using an algorithm to help clinicians for better and more effective shape deformations analysis could improve the diagnosis and treatment of the disease. In this project our purpose is to design, implement and test a classification algorithm for MRIs based on hippocampal asymmetry detection using shape and size-based features. Our method consisted of two main parts; (1) shape feature extraction, and (2) image classification. We tested 11 different shape and size features and selected four of them that detect the asymmetry in hippocampus significantly in a randomly selected subset of the dataset. Then, we employed a support vector machine (SVM) classifier to classify the remaining images of the dataset to normal and epileptic images using our selected features. The dataset contains 25 patient images in which 12 cases were used as a training set and the rest 13 cases for testing the performance of classifier. We measured accuracy, specificity and sensitivity of, respectively, 76%, 100%, and 70% for our algorithm. The preliminary results show that using shape and size features for detecting hippocampal asymmetry could be helpful in TLE diagnosis in MRI.

Sonographic features of thyroid nodules that may help distinguish clinically atypical subacute thyroiditis from thyroid malignancy.

PubMed

Pan, Fu-shun; Wang, Wei; Wang, Yan; Xu, Ming; Liang, Jin-yu; Zheng, Yan-ling; Xie, Xiao-yan; Li, Xiao-xi

2015-04-01

The purpose of this study was to evaluate sonographic features for distinguishing clinically atypical subacute thyroiditis from malignant thyroid nodules. A total of 165 hypoechoic thyroid nodules without calcification in 135 patients with histologic diagnosis were included in this study. These nodules were classified into 2 groups: a thyroiditis group (55 nodules in 36 patients) and a malignancy group (110 nodules in 99 patients). The sonographic features of the groups were retrospectively reviewed. No significant differences were detected for the variables of marked echogenicity, a taller-than-wide shape, and mixed vascularity. However, a poorly defined margin was detected more frequently in the thyroiditis group than the malignancy group (P < .05); it yielded a high capability for differential diagnosis of atypical subacute thyroiditis, with sensitivity and specificity of 87.3% and 80.9%, respectively. Centripetal reduction echogenicity was observed exclusively in the thyroiditis group, with high specificity (100%) but low sensitivity (21.8%) for atypical subacute thyroiditis diagnosis. All of the thyroiditis nodules with a positive color signal showed noninternal vascularity (negative predictive value, 100%). There is a considerable overlap between the sonographic features of atypical subacute thyroiditis and thyroid malignancy. However, the margin, echogenicity, and vascularity type are helpful indicators for differential diagnosis of atypical subacute thyroiditis. © 2015 by the American Institute of Ultrasound in Medicine.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns

PubMed Central

Haghnegahdar, A.A.; Kolahi, S.; Khojastepour, L.; Tajeripour, F.

2018-01-01

Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages. PMID:29732343

Quantification of photoacoustic microscopy images for ovarian cancer detection

NASA Astrophysics Data System (ADS)

Wang, Tianheng; Yang, Yi; Alqasemi, Umar; Kumavor, Patrick D.; Wang, Xiaohong; Sanders, Melinda; Brewer, Molly; Zhu, Quing

2014-03-01

In this paper, human ovarian tissues with malignant and benign features were imaged ex vivo by using an opticalresolution photoacoustic microscopy (OR-PAM) system. Several features were quantitatively extracted from PAM images to describe photoacoustic signal distributions and fluctuations. 106 PAM images from 18 human ovaries were classified by applying those extracted features to a logistic prediction model. 57 images from 9 ovaries were used as a training set to train the logistic model, and 49 images from another 9 ovaries were used to test our prediction model. We assumed that if one image from one malignant ovary was classified as malignant, it is sufficient to classify this ovary as malignant. For the training set, we achieved 100% sensitivity and 83.3% specificity; for testing set, we achieved 100% sensitivity and 66.7% specificity. These preliminary results demonstrate that PAM could be extremely valuable in assisting and guiding surgeons for in vivo evaluation of ovarian tissue.

Evaluation Methodology between Globalization and Localization Features Approaches for Skin Cancer Lesions Classification

NASA Astrophysics Data System (ADS)

Ahmed, H. M.; Al-azawi, R. J.; Abdulhameed, A. A.

2018-05-01

Huge efforts have been put in the developing of diagnostic methods to skin cancer disease. In this paper, two different approaches have been addressed for detection the skin cancer in dermoscopy images. The first approach uses a global method that uses global features for classifying skin lesions, whereas the second approach uses a local method that uses local features for classifying skin lesions. The aim of this paper is selecting the best approach for skin lesion classification. The dataset has been used in this paper consist of 200 dermoscopy images from Pedro Hispano Hospital (PH2). The achieved results are; sensitivity about 96%, specificity about 100%, precision about 100%, and accuracy about 97% for globalization approach while, sensitivity about 100%, specificity about 100%, precision about 100%, and accuracy about 100% for Localization Approach, these results showed that the localization approach achieved acceptable accuracy and better than globalization approach for skin cancer lesions classification.

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network.

PubMed

Chi, Jianning; Walia, Ekta; Babyn, Paul; Wang, Jimmy; Groot, Gary; Eramian, Mark

2017-08-01

With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into "malignant" and "benign" cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.

A highly sensitive and flexible pressure sensor with electrodes and elastomeric interlayer containing silver nanowires.

PubMed

Wang, Jun; Jiu, Jinting; Nogi, Masaya; Sugahara, Tohru; Nagao, Shijo; Koga, Hirotaka; He, Peng; Suganuma, Katsuaki

2015-02-21

The next-generation application of pressure sensors is gradually being extended to include electronic artificial skin (e-skin), wearable devices, humanoid robotics and smart prosthetics. In these advanced applications, high sensing capability is an essential feature for high performance. Although surface patterning treatments and some special elastomeric interlayers have been applied to improve sensitivity, the process is complex and this inevitably raises the cost and is an obstacle to large-scale production. In the present study a simple printing process without complex patterning has been used for constructing the sensor, and an interlayer is employed comprising elastomeric composites filled with silver nanowires. By increasing the relative permittivity, εr, of the composite interlayer induced by compression at high nanowire concentration, it has been possible to achieve a maximum sensitivity of 5.54 kPa(-1). The improvement in sensitivity did not sacrifice or undermine the other features of the sensor. Thanks to the silver nanowire electrodes, the sensor is flexible and stable after 200 cycles at a bending radius of 2 mm, and exhibits outstanding reproducibility without hysteresis under similar pressure pulses. The sensor has been readily integrated onto an adhesive bandage and has been successful in detecting human movements. In addition to measuring pressure in direct contact, non-contact pressures such as air flow can also be detected.

Listening to Limericks: A Pupillometry Investigation of Perceivers’ Expectancy

PubMed Central

Scheepers, Christoph; Mohr, Sibylle; Fischer, Martin H.; Roberts, Andrew M.

2013-01-01

What features of a poem make it captivating, and which cognitive mechanisms are sensitive to these features? We addressed these questions experimentally by measuring pupillary responses of 40 participants who listened to a series of Limericks. The Limericks ended with either a semantic, syntactic, rhyme or metric violation. Compared to a control condition without violations, only the rhyme violation condition induced a reliable pupillary response. An anomaly-rating study on the same stimuli showed that all violations were reliably detectable relative to the control condition, but the anomaly induced by rhyme violations was perceived as most severe. Together, our data suggest that rhyme violations in Limericks may induce an emotional response beyond mere anomaly detection. PMID:24086417

Lowering the Spectral Detection Threshold for Molecular Impurities in Gas Mixtures by Interference Multiplexing

NASA Astrophysics Data System (ADS)

Ivanov, M. P.; Tolmachev, Yu. A.

2018-05-01

We consider the most feasible ways to significantly improve the sensitivity of spectroscopic methods for detection and measurement of trace concentrations of greenhouse gas molecules in the atmosphere. The proposed methods are based on combining light fluxes from a number of spectral components of the specified molecule on the same photodetector, taking into account the characteristic features of the transmission spectrum of devices utilizing multipath interference effects.

Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG.

PubMed

Shanir, P P Muhammed; Khan, Kashif Ahmad; Khan, Yusuf Uzzaman; Farooq, Omar; Adeli, Hojjat

2017-12-01

Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.

Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.

PubMed

Bourke, Alan K; Klenk, Jochen; Schwickert, Lars; Aminian, Kamiar; Ihlen, Espen A F; Mellone, Sabato; Helbostad, Jorunn L; Chiari, Lorenzo; Becker, Clemens

2016-08-01

Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.

Computer-Aided Diagnostic (CAD) Scheme by Use of Contralateral Subtraction Technique

NASA Astrophysics Data System (ADS)

Nagashima, Hiroyuki; Harakawa, Tetsumi

We developed a computer-aided diagnostic (CAD) scheme for detection of subtle image findings of acute cerebral infarction in brain computed tomography (CT) by using a contralateral subtraction technique. In our computerized scheme, the lateral inclination of image was first corrected automatically by rotating and shifting. The contralateral subtraction image was then derived by subtraction of reversed image from original image. Initial candidates for acute cerebral infarctions were identified using the multiple-thresholding and image filtering techniques. As the 1st step for removing false positive candidates, fourteen image features were extracted in each of the initial candidates. Halfway candidates were detected by applying the rule-based test with these image features. At the 2nd step, five image features were extracted using the overlapping scale with halfway candidates in interest slice and upper/lower slice image. Finally, acute cerebral infarction candidates were detected by applying the rule-based test with five image features. The sensitivity in the detection for 74 training cases was 97.4% with 3.7 false positives per image. The performance of CAD scheme for 44 testing cases had an approximate result to training cases. Our CAD scheme using the contralateral subtraction technique can reveal suspected image findings of acute cerebral infarctions in CT images.

Automatic spatiotemporal matching of detected pleural thickenings

NASA Astrophysics Data System (ADS)

Chaisaowong, Kraisorn; Keller, Simon Kai; Kraus, Thomas

2014-01-01

Pleural thickenings can be found in asbestos exposed patient's lung. Non-invasive diagnosis including CT imaging can detect aggressive malignant pleural mesothelioma in its early stage. In order to create a quantitative documentation of automatic detected pleural thickenings over time, the differences in volume and thickness of the detected thickenings have to be calculated. Physicians usually estimate the change of each thickening via visual comparison which provides neither quantitative nor qualitative measures. In this work, automatic spatiotemporal matching techniques of the detected pleural thickenings at two points of time based on the semi-automatic registration have been developed, implemented, and tested so that the same thickening can be compared fully automatically. As result, the application of the mapping technique using the principal components analysis turns out to be advantageous than the feature-based mapping using centroid and mean Hounsfield Units of each thickening, since the resulting sensitivity was improved to 98.46% from 42.19%, while the accuracy of feature-based mapping is only slightly higher (84.38% to 76.19%).

Simultaneous determination of indoor ammonia pollution and its biological metabolite in the human body with a recyclable nanocrystalline lanthanide-functionalized MOF.

PubMed

Hao, Ji-Na; Yan, Bing

2016-02-07

A Eu(3+) post-functionalized metal-organic framework of nanosized Ga(OH)bpydc(Eu(3+)@Ga(OH)bpydc, 1a) with intense luminescence is synthesized and characterized. Luminescence measurements reveal that 1a can detect ammonia gas selectively and sensitively among various indoor air pollutants. 1a can simultaneously determine a biological ammonia metabolite (urinary urea) in the human body, which is a rare example of a luminescent sensor that can monitor pollutants in the environment and also detect their biological markers. Furthermore, 1a exhibits appealing features including high selectivity and sensitivity, fast response, simple and quick regeneration, and excellent recyclability.

Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color

PubMed Central

Stoecker, William V.; Gupta, Kapil; Stanley, R. Joe; Moss, Randy H.; Shrestha, Bijaya

2011-01-01

Background Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area). Method Using both relative and absolute colors, blotches are detected in this research automatically by using thresholds in the red and green color planes. Several blotch indices are computed, including the scaled distance between the largest blotch centroid and the lesion centroid, ratio of total blotch areas to lesion area, ratio of largest blotch area to lesion area, total number of blotches, size of largest blotch, and irregularity of largest blotch. Results The effectiveness of the absolute and relative color blotch features was examined for melanoma/benign lesion discrimination over a dermoscopy image set containing 165 melanomas (151 invasive melanomas and 14 melanomas in situ) and 347 benign lesions (124 nevocellular nevi without dysplasia and 223 dysplastic nevi) using a leave-one-out neural network approach. Receiver operating characteristic curve results are shown, highlighting the sensitivity and specificity of melanoma detection. Statistical analysis of the blotch features are also presented. Conclusion Neural network and statistical analysis showed that the blotch detection method was somewhat more effective using relative color than using absolute color. The relative-color blotch detection method gave a diagnostic accuracy of about 77%. PMID:15998328

Quantitative analysis of arterial flow properties for detection of non-calcified plaques in ECG-gated coronary CT angiography

NASA Astrophysics Data System (ADS)

Wei, Jun; Zhou, Chuan; Chan, Heang-Ping; Chughtai, Aamer; Agarwal, Prachi; Kuriakose, Jean; Hadjiiski, Lubomir; Patel, Smita; Kazerooni, Ella

2015-03-01

We are developing a computer-aided detection system to assist radiologists in detection of non-calcified plaques (NCPs) in coronary CT angiograms (cCTA). In this study, we performed quantitative analysis of arterial flow properties in each vessel branch and extracted flow information to differentiate the presence and absence of stenosis in a vessel segment. Under rest conditions, blood flow in a single vessel branch was assumed to follow Poiseuille's law. For a uniform pressure distribution, two quantitative flow features, the normalized arterial compliance per unit length (Cu) and the normalized volumetric flow (Q) along the vessel centerline, were calculated based on the parabolic Poiseuille solution. The flow features were evaluated for a two-class classification task to differentiate NCP candidates obtained by prescreening as true NCPs and false positives (FPs) in cCTA. For evaluation, a data set of 83 cCTA scans was retrospectively collected from 83 patient files with IRB approval. A total of 118 NCPs were identified by experienced cardiothoracic radiologists. The correlation between the two flow features was 0.32. The discriminatory ability of the flow features evaluated as the area under the ROC curve (AUC) was 0.65 for Cu and 0.63 for Q in comparison with AUCs of 0.56-0.69 from our previous luminal features. With stepwise LDA feature selection, volumetric flow (Q) was selected in addition to three other luminal features. With FROC analysis, the test results indicated a reduction of the FP rates to 3.14, 1.98, and 1.32 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. The study indicated that quantitative blood flow analysis has the potential to provide useful features for the detection of NCPs in cCTA.

Sensitive SERS detection of lead ions via DNAzyme based quadratic signal amplification.

PubMed

Tian, Aihua; Liu, Yu; Gao, Jian

2017-08-15

Highly sensitive detection of Pb 2+ is very necessary for water quality control, clinical toxicology, and industrial monitoring. In this work, a simple and novel DNAzyme-based SERS quadratic amplification method is developed for the detection of Pb 2+ . This strategy possesses some remarkable features compared to the conventional DNAzyme-based SERS methods, which are as follows: (i) Coupled DNAzyme-activated hybridization chain reaction (HCR) with bio barcodes; a quadratic amplification method is designed using the unique catalytic selectivity of DNAzyme. The SERS signal is significantly amplified. This method is rapid with a detection time of 2h. (ii) The problem of high background induced by excess bio barcodes is circumvented by using magnetic beads (MBs) as the carrier of signal-output products, and this sensing system is simple in design and can easily be carried out by simple mixing and incubation. Given the unique and attractive characteristics, a simple and universal strategy is designed to accomplish sensitive detection of Pb 2+ . The detection limit of Pb 2+ via SERS detection is 70 fM, with the linear range from 1.0×10 -13 M to 1.0×10 -7 M. The method can be further extended to the quantitative detection of a variety of targets by replacing the lead-responsive DNAzyme with other functional DNA. Copyright © 2017 Elsevier B.V. All rights reserved.

Comprehensive Review of Human Sapoviruses

PubMed Central

Katayama, Kazuhiko; Saif, Linda J.

2015-01-01

SUMMARY Sapoviruses cause acute gastroenteritis in humans and animals. They belong to the genus Sapovirus within the family Caliciviridae. They infect and cause disease in humans of all ages, in both sporadic cases and outbreaks. The clinical symptoms of sapovirus gastroenteritis are indistinguishable from those caused by noroviruses, so laboratory diagnosis is essential to identify the pathogen. Sapoviruses are highly diverse genetically and antigenically. Currently, reverse transcription-PCR (RT-PCR) assays are widely used for sapovirus detection from clinical specimens due to their high sensitivity and broad reactivity as well as the lack of sensitive assays for antigen detection or cell culture systems for the detection of infectious viruses. Sapoviruses were first discovered in 1976 by electron microscopy in diarrheic samples of humans. To date, sapoviruses have also been detected from several animals: pigs, mink, dogs, sea lions, and bats. In this review, we focus on genomic and antigenic features, molecular typing/classification, detection methods, and clinical and epidemiological profiles of human sapoviruses. PMID:25567221

INVESTIGATION OF OPEN-PATH FTIR FOR FAST DEPLOYMENT EMERGENCY RESPONSE TO CHEMICAL THREATS AND ACCIDENTS.

EPA Science Inventory

We have performed a series of experiments to determine the tradeoff in detection sensitivity for implementing design features for an Open-Path Fourier Transform Infrared (OP-FTIR) chemical analyzer that would be quick to deploy under emergency response conditions. The fast-deplo...

High sensitivity and high Q-factor nanoslotted parallel quadrabeam photonic crystal cavity for real-time and label-free sensing

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

Yang, Daquan; State Key Laboratory of Information Photonics and Optical Communications, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876; School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138

We experimentally demonstrate a label-free sensor based on nanoslotted parallel quadrabeam photonic crystal cavity (NPQC). The NPQC possesses both high sensitivity and high Q-factor. We achieved sensitivity (S) of 451 nm/refractive index unit and Q-factor >7000 in water at telecom wavelength range, featuring a sensor figure of merit >2000, an order of magnitude improvement over the previous photonic crystal sensors. In addition, we measured the streptavidin-biotin binding affinity and detected 10 ag/mL concentrated streptavidin in the phosphate buffered saline solution.

Recent developments in optical detection methods for microchip separations.

PubMed

Götz, Sebastian; Karst, Uwe

2007-01-01

This paper summarizes the features and performances of optical detection systems currently applied in order to monitor separations on microchip devices. Fluorescence detection, which delivers very high sensitivity and selectivity, is still the most widely applied method of detection. Instruments utilizing laser-induced fluorescence (LIF) and lamp-based fluorescence along with recent applications of light-emitting diodes (LED) as excitation sources are also covered in this paper. Since chemiluminescence detection can be achieved using extremely simple devices which no longer require light sources and optical components for focusing and collimation, interesting approaches based on this technique are presented, too. Although UV/vis absorbance is a detection method that is commonly used in standard desktop electrophoresis and liquid chromatography instruments, it has not yet reached the same level of popularity for microchip applications. Current applications of UV/vis absorbance detection to microchip separations and innovative approaches that increase sensitivity are described. This article, which contains 85 references, focuses on developments and applications published within the last three years, points out exciting new approaches, and provides future perspectives on this field.

Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning

PubMed Central

Wang, Shijun; Yao, Jianhua; Petrick, Nicholas; Summers, Ronald M.

2010-01-01

Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per scan rate of 5, the sensitivity of the SVM using the combined features improved from 0.77 (Group A) and 0.73 (Group B) to 0.83 (p ≤ 0.01). PMID:20953299

Algorithm for automatic analysis of electro-oculographic data

PubMed Central

2013-01-01

Background Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets. Methods The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks. Results The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate. Conclusion The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics. PMID:24160372

Algorithm for automatic analysis of electro-oculographic data.

PubMed

Pettersson, Kati; Jagadeesan, Sharman; Lukander, Kristian; Henelius, Andreas; Haeggström, Edward; Müller, Kiti

2013-10-25

Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets. The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks. The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate. The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

Molecular beacon probes-base multiplex NASBA Real-time for detection of HIV-1 and HCV.

PubMed

Mohammadi-Yeganeh, S; Paryan, M; Mirab Samiee, S; Kia, V; Rezvan, H

2012-06-01

Developed in 1991, nucleic acid sequence-based amplification (NASBA) has been introduced as a rapid molecular diagnostic technique, where it has been shown to give quicker results than PCR, and it can also be more sensitive. This paper describes the development of a molecular beacon-based multiplex NASBA assay for simultaneous detection of HIV-1 and HCV in plasma samples. A well-conserved region in the HIV-1 pol gene and 5'-NCR of HCV genome were used for primers and molecular beacon design. The performance features of HCV/HIV-1 multiplex NASBA assay including analytical sensitivity and specificity, clinical sensitivity and clinical specificity were evaluated. The analysis of scalar concentrations of the samples indicated that the limit of quantification of the assay was <1000 copies/ml for HIV-1 and <500 copies/ml for HCV with 95% confidence interval. Multiplex NASBA assay showed a 98% sensitivity and 100% specificity. The analytical specificity study with BLAST software demonstrated that the primers do not attach to any other sequences except for that of HIV-1 or HCV. The primers and molecular beacon probes detected all HCV genotypes and all major variants of HIV-1. This method may represent a relatively inexpensive isothermal method for detection of HIV-1/HCV co-infection in monitoring of patients.

Optimizing computer-aided colonic polyp detection for CT colonography by evolving the Pareto front1

PubMed Central

Li, Jiang; Huang, Adam; Yao, Jack; Liu, Jiamin; Van Uitert, Robert L.; Petrick, Nicholas; Summers, Ronald M.

2009-01-01

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6–9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p=7.78×10−5) for the size category of 6–9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p=7.95×10−5) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95%CI [0.75%, 16%], p=0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists. PMID:19235388

Spectral feature variations in x-ray diffraction imaging systems

NASA Astrophysics Data System (ADS)

Wolter, Scott D.; Greenberg, Joel A.

2016-05-01

Materials with different atomic or molecular structures give rise to unique scatter spectra when measured by X-ray diffraction. The details of these spectra, though, can vary based on both intrinsic (e.g., degree of crystallinity or doping) and extrinsic (e.g., pressure or temperature) conditions. While this sensitivity is useful for detailed characterizations of the material properties, these dependences make it difficult to perform more general classification tasks, such as explosives threat detection in aviation security. A number of challenges, therefore, currently exist for reliable substance detection including the similarity in spectral features among some categories of materials combined with spectral feature variations from materials processing and environmental factors. These factors complicate the creation of a material dictionary and the implementation of conventional classification and detection algorithms. Herein, we report on two prominent factors that lead to variations in spectral features: crystalline texture and temperature variations. Spectral feature comparisons between materials categories will be described for solid metallic sheet, aqueous liquids, polymer sheet, and metallic, organic, and inorganic powder specimens. While liquids are largely immune to texture effects, they are susceptible to temperature changes that can modify their density or produce phase changes. We will describe in situ temperature-dependent measurement of aqueous-based commercial goods in the temperature range of -20°C to 35°C.

An ultra low power feature extraction and classification system for wearable seizure detection.

PubMed

Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

2015-01-01

In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work.

Partial polygon pruning of hydrographic features in automated generalization

USGS Publications Warehouse

Stum, Alexander K.; Buttenfield, Barbara P.; Stanislawski, Larry V.

2017-01-01

This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservoir arms, stream channels that are too narrow at the target scale, and islands that begin to coalesce—entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the U.S. Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This paper describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the United States.

Sensitivity and specificity of clinical, histologic, and immunologic features in the diagnosis of paraneoplastic pemphigus.

PubMed

Joly, P; Richard, C; Gilbert, D; Courville, P; Chosidow, O; Roujeau, J C; Beylot-Barry, M; D'incan, M; Martel, P; Lauret, P; Tron, F

2000-10-01

Paraneoplastic pemphigus (PNP) is an autoimmune blistering disease characterized by the production of autoantibodies mainly directed against proteins of the plakin family. An overlapping distribution of autoantibody specificities has been recently reported between PNP, pemphigus vulgaris (PV), and pemphigus foliaceus (PF), which suggests a relationship between the different types of pemphigus. Our purpose was to evaluate the sensitivity and the specificity of clinical, histologic, and immunologic features in the diagnosis of PNP. The clinical, histologic, and immunologic features of 22 PNP patients were retrospectively reviewed and compared with those of 81 PV and PF patients without neoplasia and of 8 PV and 4 PF patients with various neoplasms. One clinical and 2 biologic features had both high sensitivity (82%-86%) and high specificity (83%-100%) whatever the control group considered: (1) association with a lymphoproliferative disorder, (2) indirect immunofluorescence (IIF) labeling of rat bladder, and (3) recognition of the envoplakin and/or periplakin bands in immunoblotting. Two clinicopathologic and two biologic features had high specificity (87%-100%) but poor sensitivity (27%-59%): (1) clinical presentation associating erosive oral lesions with erythema multiforme-like, bullous pemphigoid-like, or lichen planus-like cutaneous lesions; (2) histologic picture of suprabasal acantholysis with keratinocyte necrosis, interface changes, or lichenoid infiltrate; (3) presence of both anti-epithelial cell surface and anti-basement membrane zone antibodies by IIF; and (4) recognition of the desmoplakin I and/or BPAG1 bands in immunoblotting. Interestingly, 45% of patients with PNP presented initially with isolated oral erosions that were undistinguishable from those seen in PV patients, and 27% had histologic findings of only suprabasal acantholysis, which was in accordance with the frequent detection of anti-desmoglein 3 antibodies in PNP sera. The association with a lymphoproliferative disorder, the IIF labeling of rat bladder, and the immunoblotting recognition of envoplakin and/or periplakin are both sensitive and specific features in the diagnosis of PNP.

Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems

PubMed Central

2012-01-01

Background Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. Method We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks. Results Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. Conclusion The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems. PMID:22336100

Patient-Specific Early Seizure Detection from Scalp EEG

PubMed Central

Minasyan, Georgiy R.; Chatten, John B.; Chatten, Martha Jane; Harner, Richard N.

2010-01-01

Objective Develop a method for automatic detection of seizures prior to or immediately after clinical onset using features derived from scalp EEG. Methods This detection method is patient-specific. It uses recurrent neural networks and a variety of input features. For each patient we trained and optimized the detection algorithm for two cases: 1) during the period immediately preceding seizure onset, and 2) during the period immediately following seizure onset. Continuous scalp EEG recordings (duration 15 – 62 h, median 25 h) from 25 patients, including a total of 86 seizures, were used in this study. Results Pre-onset detection was successful in 14 of the 25 patients. For these 14 patients, all of the testing seizures were detected prior to seizure onset with a median pre-onset time of 51 sec and false positive rate was 0.06/h. Post-onset detection had 100% sensitivity, 0.023/hr false positive rate and median delay of 4 sec after onset. Conclusions The unique results of this study relate to pre-onset detection. Significance Our results suggest that reliable pre-onset seizure detection may be achievable for a significant subset of epilepsy patients without use of invasive electrodes. PMID:20461014

Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans

NASA Astrophysics Data System (ADS)

Ramachandran S., Sindhu; George, Jose; Skaria, Shibon; V. V., Varun

2018-02-01

Lung cancer is the leading cause of cancer related deaths in the world. The survival rate can be improved if the presence of lung nodules are detected early. This has also led to more focus being given to computer aided detection (CAD) and diagnosis of lung nodules. The arbitrariness of shape, size and texture of lung nodules is a challenge to be faced when developing these detection systems. In the proposed work we use convolutional neural networks to learn the features for nodule detection, replacing the traditional method of handcrafting features like geometric shape or texture. Our network uses the DetectNet architecture based on YOLO (You Only Look Once) to detect the nodules in CT scans of lung. In this architecture, object detection is treated as a regression problem with a single convolutional network simultaneously predicting multiple bounding boxes and class probabilities for those boxes. By performing training using chest CT scans from Lung Image Database Consortium (LIDC), NVIDIA DIGITS and Caffe deep learning framework, we show that nodule detection using this single neural network can result in reasonably low false positive rates with high sensitivity and precision.

Detection of explosive cough events in audio recordings by internal sound analysis.

PubMed

Rocha, B M; Mendes, L; Couceiro, R; Henriques, J; Carvalho, P; Paiva, R P

2017-07-01

We present a new method for the discrimination of explosive cough events, which is based on a combination of spectral content descriptors and pitch-related features. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 9 features is extracted for each event. Two data sets, recorded using electronic stethoscopes and comprising a total of 46 healthy subjects and 13 patients, were employed to evaluate the method. The proposed feature set is compared to three other sets of descriptors: a baseline, a combination of both sets, and an automatic selection of the best 10 features from both sets. The combined feature set yields good results on the cross-validated database, attaining a sensitivity of 92.3±2.3% and a specificity of 84.7±3.3%. Besides, this feature set seems to generalize well when it is trained on a small data set of patients, with a variety of respiratory and cardiovascular diseases, and tested on a bigger data set of mostly healthy subjects: a sensitivity of 93.4% and a specificity of 83.4% are achieved in those conditions. These results demonstrate that complementing the proposed feature set with a baseline set is a promising approach.

Utilizing spatial and spectral features of photoacoustic imaging for ovarian cancer detection and diagnosis

NASA Astrophysics Data System (ADS)

Li, Hai; Kumavor, Patrick; Salman Alqasemi, Umar; Zhu, Quing

2015-01-01

A composite set of ovarian tissue features extracted from photoacoustic spectral data, beam envelope, and co-registered ultrasound and photoacoustic images are used to characterize malignant and normal ovaries using logistic and support vector machine (SVM) classifiers. Normalized power spectra were calculated from the Fourier transform of the photoacoustic beamformed data, from which the spectral slopes and 0-MHz intercepts were extracted. Five features were extracted from the beam envelope and another 10 features were extracted from the photoacoustic images. These 17 features were ranked by their p-values from t-tests on which a filter type of feature selection method was used to determine the optimal feature number for final classification. A total of 169 samples from 19 ex vivo ovaries were randomly distributed into training and testing groups. Both classifiers achieved a minimum value of the mean misclassification error when the seven features with lowest p-values were selected. Using these seven features, the logistic and SVM classifiers obtained sensitivities of 96.39±3.35% and 97.82±2.26%, and specificities of 98.92±1.39% and 100%, respectively, for the training group. For the testing group, logistic and SVM classifiers achieved sensitivities of 92.71±3.55% and 92.64±3.27%, and specificities of 87.52±8.78% and 98.49±2.05%, respectively.

Measurement of "total" microcystins using the MMPB/LC/MS ...

EPA Pesticide Factsheets

The detection and quantification of microcystins, a family of toxins associated with harmful algal blooms, is complicated by their structural diversity and a lack of commercially available analytical standards for method development. As a result, most detection methods have focused on either a subset of microcystin congeners, as in US EPA Method 544, or on techniques which are sensitive to structural features common to most microcystins, as in the anti-ADDA ELISA method. A recent development has been the use of 2-methyl-3-methoxy-4-phenylbutyric acid (MMPB), which is produced by chemical oxidation the ADDA moiety in most microcystin congeners, as a proxy for the sum of congeners present. Conditions for the MMPB derivatization were evaluated and applied to water samples obtained from various HAB impacted surface waters, and results were compared with congener-based LC/MS/MS and ELISA methods. The detection and quantification of microcystins, a family of toxins associated with harmful algal blooms, is complicated by their structural diversity and a lack of commercially available analytical standards for method development. As a result, most detection methods have focused on either a subset of microcystin congeners, as in US EPA Method 544, or on techniques which are sensitive to structural features common to most microcystins, as in the anti-ADDA ELISA method. A recent development has been the use of 2-methyl-3-methoxy-4-phenylbutyric acid (MMPB), which is produce

Reduction of lymph tissue false positives in pulmonary embolism detection

NASA Astrophysics Data System (ADS)

Ghanem, Bernard; Liang, Jianming; Bi, Jinbo; Salganicoff, Marcos; Krishnan, Arun

2008-03-01

Pulmonary embolism (PE) is a serious medical condition, characterized by the partial/complete blockage of an artery within the lungs. We have previously developed a fast yet effective approach for computer aided detection of PE in computed topographic pulmonary angiography (CTPA),1 which is capable of detecting both acute and chronic PEs, achieving a benchmark performance of 78% sensitivity at 4 false positives (FPs) per volume. By reviewing the FPs generated by this system, we found the most dominant type of FP, roughly one third of all FPs, to be lymph/connective tissue. In this paper, we propose a novel approach that specifically aims at reducing this FP type. Our idea is to explicitly exploit the anatomical context configuration of PE and lymph tissue in the lungs: a lymph FP connects to the airway and is located outside the artery, while a true PE should not connect to the airway and must be inside the artery. To realize this idea, given a detected candidate (i.e. a cluster of suspicious voxels), we compute a set of contextual features, including its distance to the airway based on local distance transform and its relative position to the artery based on fast tensor voting and Hessian "vesselness" scores. Our tests on unseen cases show that these features can reduce the lymph FPs by 59%, while improving the overall sensitivity by 3.4%.

Flexible hemispheric microarrays of highly pressure-sensitive sensors based on breath figure method.

PubMed

Wang, Zhihui; Zhang, Ling; Liu, Jin; Jiang, Hao; Li, Chunzhong

2018-05-30

Recently, flexible pressure sensors featuring high sensitivity, broad sensing range and real-time detection have aroused great attention owing to their crucial role in the development of artificial intelligent devices and healthcare systems. Herein, highly sensitive pressure sensors based on hemisphere-microarray flexible substrates are fabricated via inversely templating honeycomb structures deriving from a facile and static breath figure process. The interlocked and subtle microstructures greatly improve the sensing characteristics and compressibility of the as-prepared pressure sensor, endowing it a sensitivity as high as 196 kPa-1 and a wide pressure sensing range (0-100 kPa), as well as other superior performance, including a lower detection limit of 0.5 Pa, fast response time (<26 ms) and high reversibility (>10 000 cycles). Based on the outstanding sensing performance, the potential capability of our pressure sensor in capturing physiological information and recognizing speech signals has been demonstrated, indicating promising application in wearable and intelligent electronics.

Textile Pressure Mapping Sensor for Emotional Touch Detection in Human-Robot Interaction

PubMed Central

Cruz Zurian, Heber; Atefi, Seyed Reza; Seoane Martinez, Fernando; Lukowicz, Paul

2017-01-01

In this paper, we developed a fully textile sensing fabric for tactile touch sensing as the robot skin to detect human-robot interactions. The sensor covers a 20-by-20 cm2 area with 400 sensitive points and samples at 50 Hz per point. We defined seven gestures which are inspired by the social and emotional interactions of typical people to people or pet scenarios. We conducted two groups of mutually blinded experiments, involving 29 participants in total. The data processing algorithm first reduces the spatial complexity to frame descriptors, and temporal features are calculated through basic statistical representations and wavelet analysis. Various classifiers are evaluated and the feature calculation algorithms are analyzed in details to determine each stage and segments’ contribution. The best performing feature-classifier combination can recognize the gestures with a 93.3% accuracy from a known group of participants, and 89.1% from strangers. PMID:29120389

Textile Pressure Mapping Sensor for Emotional Touch Detection in Human-Robot Interaction.

PubMed

Zhou, Bo; Altamirano, Carlos Andres Velez; Zurian, Heber Cruz; Atefi, Seyed Reza; Billing, Erik; Martinez, Fernando Seoane; Lukowicz, Paul

2017-11-09

In this paper, we developed a fully textile sensing fabric for tactile touch sensing as the robot skin to detect human-robot interactions. The sensor covers a 20-by-20 cm 2 area with 400 sensitive points and samples at 50 Hz per point. We defined seven gestures which are inspired by the social and emotional interactions of typical people to people or pet scenarios. We conducted two groups of mutually blinded experiments, involving 29 participants in total. The data processing algorithm first reduces the spatial complexity to frame descriptors, and temporal features are calculated through basic statistical representations and wavelet analysis. Various classifiers are evaluated and the feature calculation algorithms are analyzed in details to determine each stage and segments' contribution. The best performing feature-classifier combination can recognize the gestures with a 93 . 3 % accuracy from a known group of participants, and 89 . 1 % from strangers.

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett's esophagus

NASA Astrophysics Data System (ADS)

Muldoon, Timothy J.; Thekkek, Nadhi; Roblyer, Darren; Maru, Dipen; Harpaz, Noam; Potack, Jonathan; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2010-03-01

Early detection of neoplasia in patients with Barrett's esophagus is essential to improve outcomes. The aim of this ex vivo study was to evaluate the ability of high-resolution microendoscopic imaging and quantitative image analysis to identify neoplastic lesions in patients with Barrett's esophagus. Nine patients with pathologically confirmed Barrett's esophagus underwent endoscopic examination with biopsies or endoscopic mucosal resection. Resected fresh tissue was imaged with fiber bundle microendoscopy; images were analyzed by visual interpretation or by quantitative image analysis to predict whether the imaged sites were non-neoplastic or neoplastic. The best performing pair of quantitative features were chosen based on their ability to correctly classify the data into the two groups. Predictions were compared to the gold standard of histopathology. Subjective analysis of the images by expert clinicians achieved average sensitivity and specificity of 87% and 61%, respectively. The best performing quantitative classification algorithm relied on two image textural features and achieved a sensitivity and specificity of 87% and 85%, respectively. This ex vivo pilot trial demonstrates that quantitative analysis of images obtained with a simple microendoscope system can distinguish neoplasia in Barrett's esophagus with good sensitivity and specificity when compared to histopathology and to subjective image interpretation.

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade.

PubMed

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-08-14

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade.

Exploring the capabilities of support vector machines in detecting silent data corruptions

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

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based onmore » different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.« less

Exploring the capabilities of support vector machines in detecting silent data corruptions

DOE PAGES

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo; ...

2018-02-01

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based onmore » different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.« less

Ultra-sensitive chemical and biological analysis via specialty fibers with built-in microstructured optofluidic channels.

PubMed

Zhang, Nan; Li, Kaiwei; Cui, Ying; Wu, Zhifang; Shum, Perry Ping; Auguste, Jean-Louis; Dinh, Xuan Quyen; Humbert, Georges; Wei, Lei

2018-02-13

All-in-fiber optofluidics is an analytical tool that provides enhanced sensing performance with simplified analyzing system design. Currently, its advance is limited either by complicated liquid manipulation and light injection configuration or by low sensitivity resulting from inadequate light-matter interaction. In this work, we design and fabricate a side-channel photonic crystal fiber (SC-PCF) and exploit its versatile sensing capabilities in in-line optofluidic configurations. The built-in microfluidic channel of the SC-PCF enables strong light-matter interaction and easy lateral access of liquid samples in these analytical systems. In addition, the sensing performance of the SC-PCF is demonstrated with methylene blue for absorptive molecular detection and with human cardiac troponin T protein by utilizing a Sagnac interferometry configuration for ultra-sensitive and specific biomolecular specimen detection. Owing to the features of great flexibility and compactness, high-sensitivity to the analyte variation, and efficient liquid manipulation/replacement, the demonstrated SC-PCF offers a generic solution to be adapted to various fiber-waveguide sensors to detect a wide range of analytes in real time, especially for applications from environmental monitoring to biological diagnosis.

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

PubMed Central

Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

2013-01-01

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

Bifurcation-enhanced ultrahigh sensitivity of a buckled cantilever

PubMed Central

An, Sangmin; Kim, Bongsu; Kwon, Soyoung; Moon, Geol; Lee, Manhee

2018-01-01

Buckling, first introduced by Euler in 1744 [Euler L (1744) Opera Omnia I 24:231], a sudden mechanical sideways deflection of a structural member under compressive stress, represents a bifurcation in the solution to the equations of static equilibrium. Although it has been investigated in diverse research areas, such a common nonlinear phenomenon may be useful to devise a unique mechanical sensor that addresses the still-challenging features, such as the enhanced sensitivity and polarization-dependent detection capability. We demonstrate the bifurcation-enhanced sensitive measurement of mechanical vibrations using the nonlinear buckled cantilever tip in ambient conditions. The cantilever, initially buckled with its tip pinned, flips its buckling near the bifurcation point (BP), where the buckled tip becomes softened. The enhanced mechanical sensitivity results from the increasing fluctuations, unlike the typical linear sensors, which facilitate the noise-induced buckling-to-flipping transition of the softened cantilever. This allows the in situ continuous or repeated single-shot detection of the surface acoustic waves of different polarizations without any noticeable wear of the tip. We obtained the sensitivity above 106 V(m/s)−1, a 1,000-fold enhancement over the conventional seismometers. Our results lead to development of mechanical sensors of high sensitivity, reproducibility, and durability, which may be applied to detect, e.g., the directional surface waves on the laboratory as well as the geological scale. PMID:29511105

Context-specific effects of musical expertise on audiovisual integration

PubMed Central

Bishop, Laura; Goebl, Werner

2014-01-01

Ensemble musicians exchange auditory and visual signals that can facilitate interpersonal synchronization. Musical expertise improves how precisely auditory and visual signals are perceptually integrated and increases sensitivity to asynchrony between them. Whether expertise improves sensitivity to audiovisual asynchrony in all instrumental contexts or only in those using sound-producing gestures that are within an observer's own motor repertoire is unclear. This study tested the hypothesis that musicians are more sensitive to audiovisual asynchrony in performances featuring their own instrument than in performances featuring other instruments. Short clips were extracted from audio-video recordings of clarinet, piano, and violin performances and presented to highly-skilled clarinetists, pianists, and violinists. Clips either maintained the audiovisual synchrony present in the original recording or were modified so that the video led or lagged behind the audio. Participants indicated whether the audio and video channels in each clip were synchronized. The range of asynchronies most often endorsed as synchronized was assessed as a measure of participants' sensitivities to audiovisual asynchrony. A positive relationship was observed between musical training and sensitivity, with data pooled across stimuli. While participants across expertise groups detected asynchronies most readily in piano stimuli and least readily in violin stimuli, pianists showed significantly better performance for piano stimuli than for either clarinet or violin. These findings suggest that, to an extent, the effects of expertise on audiovisual integration can be instrument-specific; however, the nature of the sound-producing gestures that are observed has a substantial effect on how readily asynchrony is detected as well. PMID:25324819

Device for Detection of Explosives, Nuclear and Other Hazardous Materials in Luggage and Cargo Containers

NASA Astrophysics Data System (ADS)

Kuznetsov, Andrey; Evsenin, Alexey; Gorshkov, Igor; Osetrov, Oleg; Vakhtin, Dmitry

2009-12-01

Device for detection of explosives, radioactive and heavily shielded nuclear materials in luggage and cargo containers based on Nanosecond Neutron Analysis/Associated Particles Technique (NNA/APT) is under construction. Detection module consists of a small neutron generator with built-in position-sensitive detector of associated alpha-particles, and several scintillator-based gamma-ray detectors. Explosives and other hazardous chemicals are detected by analyzing secondary high-energy gamma-rays from reactions of fast neutrons with materials inside a container. The same gamma-ray detectors are used to detect unshielded radioactive and nuclear materials. An array of several neutron detectors is used to detect fast neutrons from induced fission of nuclear materials. Coincidence and timing analysis allows one to discriminate between fission neutrons and scattered probing neutrons. Mathematical modeling by MCNP5 and MCNP-PoliMi codes was used to estimate the sensitivity of the device and its optimal configuration. Comparison of the features of three gamma detector types—based on BGO, NaI and LaBr3 crystals is presented.

High-sensitivity acoustic sensors from nanofibre webs.

PubMed

Lang, Chenhong; Fang, Jian; Shao, Hao; Ding, Xin; Lin, Tong

2016-03-23

Considerable interest has been devoted to converting mechanical energy into electricity using polymer nanofibres. In particular, piezoelectric nanofibres produced by electrospinning have shown remarkable mechanical energy-to-electricity conversion ability. However, there is little data for the acoustic-to-electric conversion of electrospun nanofibres. Here we show that electrospun piezoelectric nanofibre webs have a strong acoustic-to-electric conversion ability. Using poly(vinylidene fluoride) as a model polymer and a sensor device that transfers sound directly to the nanofibre layer, we show that the sensor devices can detect low-frequency sound with a sensitivity as high as 266 mV Pa(-1). They can precisely distinguish sound waves in low to middle frequency region. These features make them especially suitable for noise detection. Our nanofibre device has more than five times higher sensitivity than a commercial piezoelectric poly(vinylidene fluoride) film device. Electrospun piezoelectric nanofibres may be useful for developing high-performance acoustic sensors.

High-sensitivity acoustic sensors from nanofibre webs

PubMed Central

Lang, Chenhong; Fang, Jian; Shao, Hao; Ding, Xin; Lin, Tong

2016-01-01

Considerable interest has been devoted to converting mechanical energy into electricity using polymer nanofibres. In particular, piezoelectric nanofibres produced by electrospinning have shown remarkable mechanical energy-to-electricity conversion ability. However, there is little data for the acoustic-to-electric conversion of electrospun nanofibres. Here we show that electrospun piezoelectric nanofibre webs have a strong acoustic-to-electric conversion ability. Using poly(vinylidene fluoride) as a model polymer and a sensor device that transfers sound directly to the nanofibre layer, we show that the sensor devices can detect low-frequency sound with a sensitivity as high as 266 mV Pa−1. They can precisely distinguish sound waves in low to middle frequency region. These features make them especially suitable for noise detection. Our nanofibre device has more than five times higher sensitivity than a commercial piezoelectric poly(vinylidene fluoride) film device. Electrospun piezoelectric nanofibres may be useful for developing high-performance acoustic sensors. PMID:27005010

Automatic Detection of Diabetic Retinopathy and Age-Related Macular Degeneration in Digital Fundus Images

PubMed Central

Barriga, E. Simon; Murray, Victor; Nemeth, Sheila; Crammer, Robert; Bauman, Wendall; Zamora, Gilberto; Pattichis, Marios S.; Soliz, Peter

2011-01-01

Purpose. To describe and evaluate the performance of an algorithm that automatically classifies images with pathologic features commonly found in diabetic retinopathy (DR) and age-related macular degeneration (AMD). Methods. Retinal digital photographs (N = 2247) of three fields of view (FOV) were obtained of the eyes of 822 patients at two centers: The Retina Institute of South Texas (RIST, San Antonio, TX) and The University of Texas Health Science Center San Antonio (UTHSCSA). Ground truth was provided for the presence of pathologic conditions, including microaneurysms, hemorrhages, exudates, neovascularization in the optic disc and elsewhere, drusen, abnormal pigmentation, and geographic atrophy. The algorithm was used to report on the presence or absence of disease. A detection threshold was applied to obtain different values of sensitivity and specificity with respect to ground truth and to construct a receiver operating characteristic (ROC) curve. Results. The system achieved an average area under the ROC curve (AUC) of 0.89 for detection of DR and of 0.92 for detection of sight-threatening DR (STDR). With a fixed specificity of 0.50, the system's sensitivity ranged from 0.92 for all DR cases to 1.00 for clinically significant macular edema (CSME). Conclusions. A computer-aided algorithm was trained to detect different types of pathologic retinal conditions. The cases of hard exudates within 1 disc diameter (DD) of the fovea (surrogate for CSME) were detected with very high accuracy (sensitivity = 1, specificity = 0.50), whereas mild nonproliferative DR was the most challenging condition (sensitivity= 0.92, specificity = 0.50). The algorithm was also tested on images with signs of AMD, achieving a performance of AUC of 0.84 (sensitivity = 0.94, specificity = 0.50). PMID:21666234

Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images.

PubMed

Agurto, Carla; Barriga, E Simon; Murray, Victor; Nemeth, Sheila; Crammer, Robert; Bauman, Wendall; Zamora, Gilberto; Pattichis, Marios S; Soliz, Peter

2011-07-29

To describe and evaluate the performance of an algorithm that automatically classifies images with pathologic features commonly found in diabetic retinopathy (DR) and age-related macular degeneration (AMD). Retinal digital photographs (N = 2247) of three fields of view (FOV) were obtained of the eyes of 822 patients at two centers: The Retina Institute of South Texas (RIST, San Antonio, TX) and The University of Texas Health Science Center San Antonio (UTHSCSA). Ground truth was provided for the presence of pathologic conditions, including microaneurysms, hemorrhages, exudates, neovascularization in the optic disc and elsewhere, drusen, abnormal pigmentation, and geographic atrophy. The algorithm was used to report on the presence or absence of disease. A detection threshold was applied to obtain different values of sensitivity and specificity with respect to ground truth and to construct a receiver operating characteristic (ROC) curve. The system achieved an average area under the ROC curve (AUC) of 0.89 for detection of DR and of 0.92 for detection of sight-threatening DR (STDR). With a fixed specificity of 0.50, the system's sensitivity ranged from 0.92 for all DR cases to 1.00 for clinically significant macular edema (CSME). A computer-aided algorithm was trained to detect different types of pathologic retinal conditions. The cases of hard exudates within 1 disc diameter (DD) of the fovea (surrogate for CSME) were detected with very high accuracy (sensitivity = 1, specificity = 0.50), whereas mild nonproliferative DR was the most challenging condition (sensitivity = 0.92, specificity = 0.50). The algorithm was also tested on images with signs of AMD, achieving a performance of AUC of 0.84 (sensitivity = 0.94, specificity = 0.50).

Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.

PubMed

Jaya, T; Dheeba, J; Singh, N Albert

2015-12-01

Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.

Integration of a highly ordered gold nanowires array with glucose oxidase for ultra-sensitive glucose detection.

PubMed

Cui, Jiewu; Adeloju, Samuel B; Wu, Yucheng

2014-01-27

A highly sensitive amperometric nanobiosensor has been developed by integration of glucose oxidase (GO(x)) with a gold nanowires array (AuNWA) by cross-linking with a mixture of glutaraldehyde (GLA) and bovine serum albumin (BSA). An initial investigation of the morphology of the synthesized AuNWA by field emission scanning electron microscopy (FESEM) and field emission transmission electron microscopy (FETEM) revealed that the nanowires array was highly ordered with rough surface, and the electrochemical features of the AuNWA with/without modification were also investigated. The integrated AuNWA-BSA-GLA-GO(x) nanobiosensor with Nafion membrane gave a very high sensitivity of 298.2 μA cm(-2) mM(-1) for amperometric detection of glucose, while also achieving a low detection limit of 0.1 μM, and a wide linear range of 5-6000 μM. Furthermore, the nanobiosensor exhibited excellent anti-interference ability towards uric acid (UA) and ascorbic acid (AA) with the aid of Nafion membrane, and the results obtained for the analysis of human blood serum indicated that the device is capable of glucose detection in real samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Sensing Using Rare-Earth-Doped Upconversion Nanoparticles

PubMed Central

Hao, Shuwei; Chen, Guanying; Yang, Chunhui

2013-01-01

Optical sensing plays an important role in theranostics due to its capability to detect hint biochemical entities or molecular targets as well as to precisely monitor specific fundamental psychological processes. Rare-earth (RE) doped upconversion nanoparticles (UCNPs) are promising for these endeavors due to their unique frequency converting capability; they emit efficient and sharp visible or ultraviolet (UV) luminescence via use of ladder-like energy levels of RE ions when excited at near infrared (NIR) light that are silent to tissues. These features allow not only a high penetration depth in biological tissues but also a high detection sensitivity. Indeed, the energy transfer between UCNPs and biomolecular or chemical indicators provide opportunities for high-sensitive bio- and chemical-sensing. A temperature-sensitive change of the intensity ratio between two close UC bands promises them for use in temperature mapping of a single living cell. In this work, we review recent investigations on using UCNPs for the detection of biomolecules (avidin, ATP, etc.), ions (cyanide, mecury, etc.), small gas molecules (oxygen, carbon dioxide, ammonia, etc.), as well as for in vitro temperature sensing. We also briefly summarize chemical methods in synthesizing UCNPs of high efficiency that are important for the detection limit. PMID:23650480

A novel feature ranking method for prediction of cancer stages using proteomics data

PubMed Central

Saghapour, Ehsan; Sehhati, Mohammadreza

2017-01-01

Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice. PMID:28934234

A new rhodamine-based colorimetric chemosensor for naked-eye detection of Cu2 + in aqueous solution

NASA Astrophysics Data System (ADS)

Hu, Yang; Zhang, Jing; Lv, Yuan-Zheng; Huang, Xiao-Huan; Hu, Sheng-li

2016-03-01

A new colorimetric probe 1 based on rhodamine B lactam was developed for naked-eye detection of Cu2 +. The optical feature of 1 for Cu2 + was investigated by UV-vis absorption spectroscopy. Upon the addition of Cu2 +, the 1 displayed a distinct color change from colorless to pink, which can be directly detected by the naked eye. The stoichiometry of 1 to Cu2 + complex was found to be 1:1 and the naked-eye detection limit was determined as low as 2 μM. The results suggest that the probe 1 may provide a convenient method for visual detection of Cu2 + with high sensitivity.

BCC skin cancer diagnosis based on texture analysis techniques

NASA Astrophysics Data System (ADS)

Chuang, Shao-Hui; Sun, Xiaoyan; Chang, Wen-Yu; Chen, Gwo-Shing; Huang, Adam; Li, Jiang; McKenzie, Frederic D.

2011-03-01

In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% and 95%, respectively.

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images.

PubMed

Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin

2017-01-01

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.

Earthquake Damage Assessment over Port-au-Prince (Haiti) by Fusing Optical and SAR Data

NASA Astrophysics Data System (ADS)

Romaniello, V.; Piscini, A.; Bignami, C.; Anniballe, R.; Pierdicca, N.; Stramondo, S.

2016-08-01

This work proposes methodologies aiming at evaluating the sensitivity of optical and SAR change features obtained from satellite images with respect to the damage grade. The proposed methods are derived from the literature ([1], [2], [3], [4]) and the main novelty concerns the estimation of these change features at object scale.The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010.The analysis of change detection indicators is based on ground truth information collected during a post- earthquake survey. We have generated the damage map of Port-au-Prince by considering a set of polygons extracted from the open source Open Street Map geo- database. The resulting damage map was calculated in terms of collapse ratio [5].We selected some features having a good sensitivity with damage at object scale [6]: the Normalised Difference Index, the Kullback-Libler Divergence, the Mutual Information and the Intensity Correlation Difference.The Naive Bayes and the Support Vector Machine classifiers were used to evaluate the goodness of these features. The classification results demonstrate that the simultaneous use of several change features from EO observations can improve the damage estimation at object scale.

Automatic detection and classification of artifacts in single-channel EEG.

PubMed

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

Drowsiness detection using heart rate variability.

PubMed

Vicente, José; Laguna, Pablo; Bartra, Ariadna; Bailón, Raquel

2016-06-01

It is estimated that 10-30 % of road fatalities are related to drowsy driving. Driver's drowsiness detection based on biological and vehicle signals is being studied in preventive car safety. Autonomous nervous system activity, which can be measured noninvasively from the heart rate variability (HRV) signal obtained from surface electrocardiogram, presents alterations during stress, extreme fatigue and drowsiness episodes. We hypothesized that these alterations manifest on HRV and thus could be used to detect driver's drowsiness. We analyzed three driving databases in which drivers presented different sleep-deprivation levels, and in which each driving minute was annotated as drowsy or awake. We developed two different drowsiness detectors based on HRV. While the drowsiness episodes detector assessed each minute of driving as "awake" or "drowsy" with seven HRV derived features (positive predictive value 0.96, sensitivity 0.59, specificity 0.98 on 3475 min of driving), the sleep-deprivation detector discerned if a driver was suitable for driving or not, at driving onset, as function of his sleep-deprivation state. Sleep-deprivation state was estimated from the first three minutes of driving using only one HRV feature (positive predictive value 0.80, sensitivity 0.62, specificity 0.88 on 30 drivers). Incorporating drowsiness assessment based on HRV signal may add significant improvements to existing car safety systems.

Quantitative analysis of ex vivo colorectal epithelium using an automated feature extraction algorithm for microendoscopy image data

PubMed Central

Prieto, Sandra P.; Lai, Keith K.; Laryea, Jonathan A.; Mizell, Jason S.; Muldoon, Timothy J.

2016-01-01

Abstract. Qualitative screening for colorectal polyps via fiber bundle microendoscopy imaging has shown promising results, with studies reporting high rates of sensitivity and specificity, as well as low interobserver variability with trained clinicians. A quantitative image quality control and image feature extraction algorithm (QFEA) was designed to lessen the burden of training and provide objective data for improved clinical efficacy of this method. After a quantitative image quality control step, QFEA extracts field-of-view area, crypt area, crypt circularity, and crypt number per image. To develop and validate this QFEA, a training set of microendoscopy images was collected from freshly resected porcine colon epithelium. The algorithm was then further validated on ex vivo image data collected from eight human subjects, selected from clinically normal appearing regions distant from grossly visible tumor in surgically resected colorectal tissue. QFEA has proven flexible in application to both mosaics and individual images, and its automated crypt detection sensitivity ranges from 71 to 94% despite intensity and contrast variation within the field of view. It also demonstrates the ability to detect and quantify differences in grossly normal regions among different subjects, suggesting the potential efficacy of this approach in detecting occult regions of dysplasia. PMID:27335893

Detection of Fundus Lesions Using Classifier Selection

NASA Astrophysics Data System (ADS)

Nagayoshi, Hiroto; Hiramatsu, Yoshitaka; Sako, Hiroshi; Himaga, Mitsutoshi; Kato, Satoshi

A system for detecting fundus lesions caused by diabetic retinopathy from fundus images is being developed. The system can screen the images in advance in order to reduce the inspection workload on doctors. One of the difficulties that must be addressed in completing this system is how to remove false positives (which tend to arise near blood vessels) without decreasing the detection rate of lesions in other areas. To overcome this difficulty, we developed classifier selection according to the position of a candidate lesion, and we introduced new features that can distinguish true lesions from false positives. A system incorporating classifier selection and these new features was tested in experiments using 55 fundus images with some lesions and 223 images without lesions. The results of the experiments confirm the effectiveness of the proposed system, namely, degrees of sensitivity and specificity of 98% and 81%, respectively.

Automated Detection of Diabetic Retinopathy using Deep Learning.

PubMed

Lam, Carson; Yi, Darvin; Guo, Margaret; Lindsey, Tony

2018-01-01

Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.

A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

PubMed Central

Zhu, Bohui; Ding, Yongsheng; Hao, Kuangrong

2013-01-01

This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. PMID:23690875

Comparison of imaging characteristics of multiple-beam equalization and storage phosphor direct digitizer radiographic systems

NASA Astrophysics Data System (ADS)

Sankaran, A.; Chuang, Keh-Shih; Yonekawa, Hisashi; Huang, H. K.

1992-06-01

The imaging characteristics of two chest radiographic equipment, Advanced Multiple Beam Equalization Radiography (AMBER) and Konica Direct Digitizer [using a storage phosphor (SP) plate] systems have been compared. The variables affecting image quality and the computer display/reading systems used are detailed. Utilizing specially designed wedge, geometric, and anthropomorphic phantoms, studies were conducted on: exposure and energy response of detectors; nodule detectability; different exposure techniques; various look- up tables (LUTs), gray scale displays and laser printers. Methods for scatter estimation and reduction were investigated. It is concluded that AMBER with screen-film and equalization techniques provides better nodule detectability than SP plates. However, SP plates have other advantages such as flexibility in the selection of exposure techniques, image processing features, and excellent sensitivity when combined with optimum reader operating modes. The equalization feature of AMBER provides better nodule detectability under the denser regions of the chest. Results of diagnostic accuracy are demonstrated with nodule detectability plots and analysis of images obtained with phantoms.

Direct detection of light dark matter and solar neutrinos via color center production in crystals

NASA Astrophysics Data System (ADS)

Budnik, Ranny; Cheshnovsky, Ori; Slone, Oren; Volansky, Tomer

2018-07-01

We propose a new low-threshold direct-detection concept for dark matter and for coherent nuclear scattering of solar neutrinos, based on the dissociation of atoms and subsequent creation of color center type defects within a lattice. The novelty in our approach lies in its ability to detect single defects in a macroscopic bulk of material. This class of experiments features ultra-low energy thresholds which allows for the probing of dark matter as light as O (10) MeV through nuclear scattering. Another feature of defect creation in crystals is directional information, which presents as a spectacular signal and a handle on background reduction in the form of daily modulation of the interaction rate. We discuss the envisioned setup and detection technique, as well as background reduction. We further calculate the expected rates for dark matter and solar neutrinos in two example crystals for which available data exists, demonstrating the prospective sensitivity of such experiments.

A Smart Capsule System for Automated Detection of Intestinal Bleeding Using HSL Color Recognition

PubMed Central

Liu, Hongying; Yan, Xueping; Jia, Ziru; Pi, Xitian

2016-01-01

There are no ideal means for the diagnosis of intestinal bleeding diseases as of now, particularly in the small intestine. This study investigated an intelligent intestinal bleeding detection capsule system based on color recognition. After the capsule is swallowed, the bleeding detection module (containing a color-sensitive adsorptive film that changes color when absorbing intestinal juice,) is used to identify intestinal bleeding features. A hue-saturation-light color space method can be applied to detect bleeding according to the range of H and S values of the film color. Once bleeding features are recognized, a wireless transmission module is activated immediately to send an alarm signal to the outside; an in vitro module receives the signal and sends an alarm. The average power consumption of the entire capsule system is estimated to be about 2.1mW. Owing to its simplicity, reliability, and effectiveness, this system represents a new approach to the clinical diagnosis of intestinal bleeding diseases. PMID:27902728

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

PubMed Central

Irusta, Unai; Morgado, Eduardo; Aramendi, Elisabete; Ayala, Unai; Wik, Lars; Kramer-Johansen, Jo; Eftestøl, Trygve; Alonso-Atienza, Felipe

2016-01-01

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s. PMID:27441719

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

PubMed

Figuera, Carlos; Irusta, Unai; Morgado, Eduardo; Aramendi, Elisabete; Ayala, Unai; Wik, Lars; Kramer-Johansen, Jo; Eftestøl, Trygve; Alonso-Atienza, Felipe

2016-01-01

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s.

Pulsed helium ionization detection system

DOEpatents

Ramsey, R.S.; Todd, R.A.

1985-04-09

A helium ionization detection system is provided which produces stable operation of a conventional helium ionization detector while providing improved sensitivity and linearity. Stability is improved by applying pulsed dc supply voltage across the ionization detector, thereby modifying the sampling of the detectors output current. A unique pulse generator is used to supply pulsed dc to the detector which has variable width and interval adjust features that allows up to 500 V to be applied in pulse widths ranging from about 150 nsec to about dc conditions.

Pulsed helium ionization detection system

DOEpatents

Ramsey, Roswitha S.; Todd, Richard A.

1987-01-01

A helium ionization detection system is provided which produces stable operation of a conventional helium ionization detector while providing improved sensitivity and linearity. Stability is improved by applying pulsed dc supply voltage across the ionization detector, thereby modifying the sampling of the detectors output current. A unique pulse generator is used to supply pulsed dc to the detector which has variable width and interval adjust features that allows up to 500 V to be applied in pulse widths ranging from about 150 nsec to about dc conditions.

Time stamping of single optical photons with 10 ns resolution

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

Chakaberia, Irakli; Cotlet, Mircea; Fisher-Levine, Merlin

High spatial and temporal resolution are key features for many modern applications, e.g. mass spectrometry, probing the structure of materials via neutron scattering, studying molecular structure, etc. Fast imaging also provides the capability of coincidence detection, and the further addition of sensitivity to single optical photons with the capability of timestamping them further broadens the field of potential applications. Here, photon counting is already widely used in X-ray imaging, where the high energy of the photons makes their detection easier.

Time stamping of single optical photons with 10 ns resolution

DOE PAGES

Chakaberia, Irakli; Cotlet, Mircea; Fisher-Levine, Merlin; ...

2017-05-08

High spatial and temporal resolution are key features for many modern applications, e.g. mass spectrometry, probing the structure of materials via neutron scattering, studying molecular structure, etc. Fast imaging also provides the capability of coincidence detection, and the further addition of sensitivity to single optical photons with the capability of timestamping them further broadens the field of potential applications. Here, photon counting is already widely used in X-ray imaging, where the high energy of the photons makes their detection easier.

Standoff detection: distinction of bacteria by hyperspectral laser induced fluorescence

NASA Astrophysics Data System (ADS)

Walter, Arne; Duschek, Frank; Fellner, Lea; Grünewald, Karin M.; Hausmann, Anita; Julich, Sandra; Pargmann, Carsten; Tomaso, Herbert; Handke, Jürgen

2016-05-01

Sensitive detection and rapid identification of hazardous bioorganic material with high sensitivity and specificity are essential topics for defense and security. A single method can hardly cover these requirements. While point sensors allow a highly specific identification, they only provide localized information and are comparatively slow. Laser based standoff systems allow almost real-time detection and classification of potentially hazardous material in a wide area and can provide information on how the aerosol may spread. The coupling of both methods may be a promising solution to optimize the acquisition and identification of hazardous substances. The capability of the outdoor LIF system at DLR Lampoldshausen test facility as an online classification tool has already been demonstrated. Here, we present promising data for further differentiation among bacteria. Bacteria species can express unique fluorescence spectra after excitation at 280 nm and 355 nm. Upon deactivation, the spectral features change depending on the deactivation method.

Hybrid electro-optical nanosystem for neurons investigation

NASA Astrophysics Data System (ADS)

Miu, Mihaela; Kleps, Irina; Craciunoiu, Florea; Simion, Monica; Bragaru, Adina; Ignat, Teodora

2010-11-01

The scope of this paper is development of a new laboratory-on-a-chip (LOC) device for biomedical studies consisting of a microfluidic system coupled to microelectronic/optical transducers with nanometric features, commonly called biosensors. The proposed device is a hybrid system with sensing element on silicon (Si) chip and microfluidic system on polydimethylsiloxane (PDMS) substrates, taking into accounts their particular advantages. Different types of nanoelectrode arrays, positioned in the reactor, have been investigated as sensitive elements for electrical detection and the recording of neuron extracellular electric activity has been monitorized in parallel with whole-cell patch-clamp membrane current. Moreover, using an additional porosification process the sensing element became efficient for optical detection also. The preliminary test results demonstrate the functionality of the proposed design and also the fabrication technology, the devices bringing advantages in terms enhancement of sensitivity in both optoelectronic detection schemes.

Parahydrogen-enhanced zero-field nuclear magnetic resonance

NASA Astrophysics Data System (ADS)

Theis, T.; Ganssle, P.; Kervern, G.; Knappe, S.; Kitching, J.; Ledbetter, M. P.; Budker, D.; Pines, A.

2011-07-01

Nuclear magnetic resonance, conventionally detected in magnetic fields of several tesla, is a powerful analytical tool for the determination of molecular identity, structure and function. With the advent of prepolarization methods and detection schemes using atomic magnetometers or superconducting quantum interference devices, interest in NMR in fields comparable to the Earth's magnetic field and below (down to zero field) has been revived. Despite the use of superconducting quantum interference devices or atomic magnetometers, low-field NMR typically suffers from low sensitivity compared with conventional high-field NMR. Here we demonstrate direct detection of zero-field NMR signals generated through parahydrogen-induced polarization, enabling high-resolution NMR without the use of any magnets. The sensitivity is sufficient to observe spectra exhibiting 13C-1H scalar nuclear spin-spin couplings (known as J couplings) in compounds with 13C in natural abundance, without the need for signal averaging. The resulting spectra show distinct features that aid chemical fingerprinting.

Feasibility of spectral-domain phase-sensitive optical coherence tomography for middle ear vibrometry

PubMed Central

Nguyen-Huynh, Anh; Wang, Ruikang K.; Jacques, Steven L.; Choudhury, Niloy; Nuttall, Alfred L.

2012-01-01

Abstract. We describe a novel application of spectral-domain phase-sensitive optical coherence tomography (SD PS-OCT) to detect the tiny motions of the middle ear structures, such as the tympanic membrane and ossicular chain, and their morphological features for differential diagnosis of CHL. This technique has the potential to provide meaningful vibration of ossicles with a vibration sensitivity of ∼0.5  nm at 1 kHz of acoustic stimulation. To the best of our knowledge, this is the first demonstration of depth-resolved vibration imaging of ossicles with a PS-OCT system at a nanometer scale. PMID:22734728

Advanced materials for improving biosensing performances of propagating and localized plasmonic transducers

NASA Astrophysics Data System (ADS)

Manera, M. G.; Colombelli, A.; Convertino, A.; Rella, S.; De Lorenzis, E.; Taurino, A.; Malitesta, C.; Rella, R.

2015-05-01

Among all transduction methodologies reported in the field of solid state optical chemical sensors, the attention has been focused onto the optical sensing characterization by using propagating and localized surface plasmon resonance (SPR) techniques. The research in this field is always oriented in the improvement of the sensing features in terms of sensitivity and limits of detection. To this purpose different strategies have been proposed to realize advanced materials for high sensitive plasmonic devices. In this work nanostructured silica nanowires decorated by gold nanoparticles and active magneto-plasmonic transductors are considered as new biosensing transductors useful to increase the performance of sensitive devices.

Temporal Structure and Complexity Affect Audio-Visual Correspondence Detection

PubMed Central

Denison, Rachel N.; Driver, Jon; Ruff, Christian C.

2013-01-01

Synchrony between events in different senses has long been considered the critical temporal cue for multisensory integration. Here, using rapid streams of auditory and visual events, we demonstrate how humans can use temporal structure (rather than mere temporal coincidence) to detect multisensory relatedness. We find psychophysically that participants can detect matching auditory and visual streams via shared temporal structure for crossmodal lags of up to 200 ms. Performance on this task reproduced features of past findings based on explicit timing judgments but did not show any special advantage for perfectly synchronous streams. Importantly, the complexity of temporal patterns influences sensitivity to correspondence. Stochastic, irregular streams – with richer temporal pattern information – led to higher audio-visual matching sensitivity than predictable, rhythmic streams. Our results reveal that temporal structure and its complexity are key determinants for human detection of audio-visual correspondence. The distinctive emphasis of our new paradigms on temporal patterning could be useful for studying special populations with suspected abnormalities in audio-visual temporal perception and multisensory integration. PMID:23346067

Translational Research and Plasma Proteomic in Cancer.

PubMed

Santini, Annamaria Chiara; Giovane, Giancarlo; Auletta, Adelaide; Di Carlo, Angelina; Fiorelli, Alfonso; Cito, Letizia; Astarita, Carlo; Giordano, Antonio; Alfano, Roberto; Feola, Antonia; Di Domenico, Marina

2016-04-01

Proteomics is a recent field of research in molecular biology that can help in the fight against cancer through the search for biomarkers that can detect this disease in the early stages of its development. Proteomic is a speedily growing technology, also thanks to the development of even more sensitive and fast mass spectrometry analysis. Although this technique is the most widespread for the discovery of new cancer biomarkers, it still suffers of a poor sensitivity and insufficient reproducibility, essentially due to the tumor heterogeneity. Common technical shortcomings include limitations in the sensitivity of detecting low abundant biomarkers and possible systematic biases in the observed data. Current research attempts are trying to develop high-resolution proteomic instrumentation for high-throughput monitoring of protein changes that occur in cancer. In this review, we describe the basic features of the proteomic tools which have proven to be useful in cancer research, showing their advantages and disadvantages. The application of these proteomic tools could provide early biomarkers detection in various cancer types and could improve the understanding the mechanisms of tumor growth and dissemination. © 2015 Wiley Periodicals, Inc.

Aptamer binding to celiac disease-triggering hydrophobic proteins: a sensitive gluten detection approach.

PubMed

Amaya-González, Sonia; de-Los-Santos-Álvarez, Noemí; Miranda-Ordieres, Arturo J; Lobo-Castañón, M Jesús

2014-03-04

Celiac disease represents a significant public health problem in large parts of the world. A major hurdle in the effective management of the disease by celiac sufferers is the sensitivity of the current available methods for assessing gluten contents in food. In response, we report a highly sensitive approach for gluten analysis using aptamers as specific receptors. Gliadins, a fraction of gluten proteins, are the main constituent responsible for triggering the disease. However, they are highly hydrophobic and large molecules, regarded as difficult targets for in vitro evolution of aptamers without nucleobase modification. We describe the successful selection of aptamers for these water insoluble prolamins that was achieved choosing the immunodominant apolar peptide from α2-gliadin as a target for selection. All aptamers evolved are able to bind the target in its native environment within the natural protein. The best nonprotein receptor is the basis for an electrochemical competitive enzyme-linked assay on magnetic particles, which allows the measurement of as low as 0.5 ppb of gliadin standard (0.5 ppm of gluten). Reference immunoassay for detecting the same target has a limit of detection of 3 ppm, 6 times less sensitive than this method. Importantly, it also displays high specificity, detecting the other three prolamins toxic for celiac patients and not showing cross-reactivity to nontoxic proteins such as maize, soya, and rice. These features make the proposed method a valuable tool for gluten detection in foods.

ZIF-67 derived porous Co3O4 hollow nanopolyhedron functionalized solution-gated graphene transistors for simultaneous detection of glucose and uric acid in tears.

PubMed

Xiong, Can; Zhang, Tengfei; Kong, Weiyu; Zhang, Zhixiang; Qu, Hao; Chen, Wei; Wang, Yanbo; Luo, Linbao; Zheng, Lei

2018-03-15

Biomarkers in tears have attracted much attention in daily healthcare sensing and monitoring. Here, highly sensitive sensors for simultaneous detection of glucose and uric acid are successfully constructed based on solution-gated graphene transistors (SGGTs) with two separate Au gate electrodes, modified with GOx-CHIT and BSA-CHIT respectively. The sensitivity of the SGGT is dramatically improved by co-modifying the Au gate with ZIF-67 derived porous Co 3 O 4 hollow nanopolyhedrons. The sensing mechanism for glucose sensor is attributed to the reaction of H 2 O 2 generated by the oxidation of glucose near the gate, while the sensing mechanism for uric acid is due to the direct electro-oxidation of uric acid molecules on the gate. The optimized glucose and uric acid sensors show the detection limits both down to 100nM, far beyond the sensitivity required for non-invasive detection of glucose and uric acid in tears. The glucose and uric acid in real tear samples was quantitatively detected at 323.2 ± 16.1μM and 98.5 ± 16.3μM by using the functionalized SGGT device. Due to the low-cost, high-biocompatibility and easy-fabrication features of the ZIF-67 derived porous Co 3 O 4 hollow nanopolyhedron, they provide excellent electrocatalytic nanomaterials for enhancing sensitivity of SGGTs for a broad range of disease-related biomarkers. Copyright © 2017 Elsevier B.V. All rights reserved.

The future of primordial features with large-scale structure surveys

NASA Astrophysics Data System (ADS)

Chen, Xingang; Dvorkin, Cora; Huang, Zhiqi; Namjoo, Mohammad Hossein; Verde, Licia

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopic and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.

The future of primordial features with large-scale structure surveys

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

Chen, Xingang; Namjoo, Mohammad Hossein; Dvorkin, Cora

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopicmore » and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.« less

Neural basis for dynamic updating of object representation in visual working memory.

PubMed

Takahama, Sachiko; Miyauchi, Satoru; Saiki, Jun

2010-02-15

In real world, objects have multiple features and change dynamically. Thus, object representations must satisfy dynamic updating and feature binding. Previous studies have investigated the neural activity of dynamic updating or feature binding alone, but not both simultaneously. We investigated the neural basis of feature-bound object representation in a dynamically updating situation by conducting a multiple object permanence tracking task, which required observers to simultaneously process both the maintenance and dynamic updating of feature-bound objects. Using an event-related design, we separated activities during memory maintenance and change detection. In the search for regions showing selective activation in dynamic updating of feature-bound objects, we identified a network during memory maintenance that was comprised of the inferior precentral sulcus, superior parietal lobule, and middle frontal gyrus. In the change detection period, various prefrontal regions, including the anterior prefrontal cortex, were activated. In updating object representation of dynamically moving objects, the inferior precentral sulcus closely cooperates with a so-called "frontoparietal network", and subregions of the frontoparietal network can be decomposed into those sensitive to spatial updating and feature binding. The anterior prefrontal cortex identifies changes in object representation by comparing memory and perceptual representations rather than maintaining object representations per se, as previously suggested. Copyright 2009 Elsevier Inc. All rights reserved.

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

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

Liu, Jiamin; Hoffman, Joanne; Zhao, Jocelyn

2016-07-15

Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifiermore » for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.« less

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

PubMed

Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.

PubMed

Gardner, G G; Keating, D; Williamson, T H; Elliott, A T

1996-11-01

To determine if neural networks can detect diabetic features in fundus images and compare the network against an ophthalmologist screening a set of fundus images. 147 diabetic and 32 normal images were captured from a fundus camera, stored on computer, and analysed using a back propagation neural network. The network was trained to recognise features in the retinal image. The effects of digital filtering techniques and different network variables were assessed. 200 diabetic and 101 normal images were then randomised and used to evaluate the network's performance for the detection of diabetic retinopathy against an ophthalmologist. Detection rates for the recognition of vessels, exudates, and haemorrhages were 91.7%, 93.1%, and 73.8% respectively. When compared with the results of the ophthalmologist, the network achieved a sensitivity of 88.4% and a specificity of 83.5% for the detection of diabetic retinopathy. Detection of vessels, exudates, and haemorrhages was possible, with success rates dependent upon preprocessing and the number of images used in training. When compared with the ophthalmologist, the network achieved good accuracy for the detection of diabetic retinopathy. The system could be used as an aid to the screening of diabetic patients for retinopathy.

Assessment of interplay between UV wavelengths, material surfaces and food residues in open surface hygiene validation.

PubMed

Abban, Stephen; Jakobsen, Mogens; Jespersen, Lene

2014-12-01

The use of UV-visible radiation for detecting invisible residue on different surfaces as a means of validating cleanliness was investigated. Wavelengths at 365, 395, 435, 445, 470 and 490 nm from a monochromator were used to detect residues of beef, chicken, apple, mango and skim milk. These were on three surfaces: aluminium, fibre re-enforced plastic (FRP; Q-Liner®) and stainless steel, pre- and post a cleaning step using commercial detergent. The area covered by residues as detected by specific wavelengths was compared statistically. The sensitivity of the wavelengths for detection differed significantly (p < 0.05) for various residues depending on the material surfaces. Generally, wavelengths 365-445 nm were consistently able to illuminate all residue before cleaning, though sensitivity varied, while 490 nm showed more of the surface structural features instead of residue. The 365-395 nm wavelengths were significantly more sensitive (p < 0.05) for detecting beef and chicken residues on aluminium and stainless steel both before and after cleaning. The 435-445 nm wavelengths were significantly more sensitive for detecting apple and mango residues on the FRP both before and after cleaning. It is important when UV-systems are used as real-time tools for assessing cleanliness of surfaces that the surface materials being illuminated are taken into account in the choice of lamp wavelength, in addition to expected residue. This will ensure higher confidence in results during the use of UV-light for real-time hygiene validation of surfaces.

Distinguishing bias from sensitivity effects in multialternative detection tasks.

PubMed

Sridharan, Devarajan; Steinmetz, Nicholas A; Moore, Tirin; Knudsen, Eric I

2014-08-21

Studies investigating the neural bases of cognitive phenomena increasingly employ multialternative detection tasks that seek to measure the ability to detect a target stimulus or changes in some target feature (e.g., orientation or direction of motion) that could occur at one of many locations. In such tasks, it is essential to distinguish the behavioral and neural correlates of enhanced perceptual sensitivity from those of increased bias for a particular location or choice (choice bias). However, making such a distinction is not possible with established approaches. We present a new signal detection model that decouples the behavioral effects of choice bias from those of perceptual sensitivity in multialternative (change) detection tasks. By formulating the perceptual decision in a multidimensional decision space, our model quantifies the respective contributions of bias and sensitivity to multialternative behavioral choices. With a combination of analytical and numerical approaches, we demonstrate an optimal, one-to-one mapping between model parameters and choice probabilities even for tasks involving arbitrarily large numbers of alternatives. We validated the model with published data from two ternary choice experiments: a target-detection experiment and a length-discrimination experiment. The results of this validation provided novel insights into perceptual processes (sensory noise and competitive interactions) that can accurately and parsimoniously account for observers' behavior in each task. The model will find important application in identifying and interpreting the effects of behavioral manipulations (e.g., cueing attention) or neural perturbations (e.g., stimulation or inactivation) in a variety of multialternative tasks of perception, attention, and decision-making. © 2014 ARVO.

Distinguishing bias from sensitivity effects in multialternative detection tasks

PubMed Central

Sridharan, Devarajan; Steinmetz, Nicholas A.; Moore, Tirin; Knudsen, Eric I.

2014-01-01

Studies investigating the neural bases of cognitive phenomena increasingly employ multialternative detection tasks that seek to measure the ability to detect a target stimulus or changes in some target feature (e.g., orientation or direction of motion) that could occur at one of many locations. In such tasks, it is essential to distinguish the behavioral and neural correlates of enhanced perceptual sensitivity from those of increased bias for a particular location or choice (choice bias). However, making such a distinction is not possible with established approaches. We present a new signal detection model that decouples the behavioral effects of choice bias from those of perceptual sensitivity in multialternative (change) detection tasks. By formulating the perceptual decision in a multidimensional decision space, our model quantifies the respective contributions of bias and sensitivity to multialternative behavioral choices. With a combination of analytical and numerical approaches, we demonstrate an optimal, one-to-one mapping between model parameters and choice probabilities even for tasks involving arbitrarily large numbers of alternatives. We validated the model with published data from two ternary choice experiments: a target-detection experiment and a length-discrimination experiment. The results of this validation provided novel insights into perceptual processes (sensory noise and competitive interactions) that can accurately and parsimoniously account for observers' behavior in each task. The model will find important application in identifying and interpreting the effects of behavioral manipulations (e.g., cueing attention) or neural perturbations (e.g., stimulation or inactivation) in a variety of multialternative tasks of perception, attention, and decision-making. PMID:25146574

A novel approach to mask defect inspection

NASA Astrophysics Data System (ADS)

Sagiv, Amir; Shirman, Yuri; Mangan, Shmoolik

2008-10-01

Memory chips, now constituting a major part of semiconductor market, posit a special challenge for inspection, as they are generally produced with the smallest half-pitch available with today's technology. This is true, in particular, to photomasks of advanced memory devices, which are at the forefront of the "low-k1" regime. In this paper we present a novel photomask inspection approach, that is particularly suitable for low-k1 layers of advanced memory chips, owing to their typical dense and periodic structure. The method we present can produce a very strong signal for small mask defects, by suppression of the modulation of the pattern's image. Unlike dark-field detection, however, here a single diffraction order associated with the pattern generates a constant "gray" background image, that is used for signal enhancement. We define the theoretical basis for the new detection technique, and show, both analytically and numerically, that it can easily achieve a detection line past the printability spec, and that in cases it is at least as sensitive as high-resolution based detection. We also demonstrate this claim experimentally on a customer mask, using the platform of Applied Material's newly released Aera2TM mask inspection tool. The high sensitivity demonstrates the important and often overlooked concept that resolution is not synonymous with sensitivity. The novel detection method is advantageous in several other aspects, such as the very simple implementation, the high throughput, and the relatively simple pre- and post-processing algorithms required for signal extraction. These features, and in particular the very high sensitivity, make this novel detection method an attractive inspection option for advanced memory devices.

Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography

NASA Astrophysics Data System (ADS)

Lesniak, J. M.; Hupse, R.; Blanc, R.; Karssemeijer, N.; Székely, G.

2012-08-01

False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05-1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.

A Small Mission Featuring an Imaging X-ray Polarimeter with High Sensitivity

NASA Technical Reports Server (NTRS)

Weisskopf, Martin C.; Baldini, Luca; Bellazini, Ronaldo; Brez, Alessandro; Costa, Enrico; Dissley, Richard; Elsner, Ronald; Fabiani, Sergio; Matt, Giorgio; Minuti, Massimo;

Hyperpolarized 15N-pyridine Derivatives as pH-Sensitive MRI Agents

PubMed Central

Jiang, Weina; Lumata, Lloyd; Chen, Wei; Zhang, Shanrong; Kovacs, Zoltan; Sherry, A. Dean; Khemtong, Chalermchai

2015-01-01

Highly sensitive MR imaging agents that can accurately and rapidly monitor changes in pH would have diagnostic and prognostic value for many diseases. Here, we report an investigation of hyperpolarized 15N-pyridine derivatives as ultrasensitive pH-sensitive imaging probes. These molecules are easily polarized to high levels using standard dynamic nuclear polarization (DNP) techniques and their 15N chemical shifts were found to be highly sensitive to pH. These probes displayed sharp 15N resonances and large differences in chemical shifts (Δδ >90 ppm) between their free base and protonated forms. These favorable features make these agents highly suitable candidates for the detection of small changes in tissue pH near physiological values. PMID:25774436

Large-Format AlGaN PIN Photodiode Arrays for UV Images

NASA Technical Reports Server (NTRS)

Aslam, Shahid; Franz, David

2010-01-01

A large-format hybridized AlGaN photodiode array with an adjustable bandwidth features stray-light control, ultralow dark-current noise to reduce cooling requirements, and much higher radiation tolerance than previous technologies. This technology reduces the size, mass, power, and cost of future ultraviolet (UV) detection instruments by using lightweight, low-voltage AlGaN detectors in a hybrid detector/multiplexer configuration. The solar-blind feature eliminates the need for additional visible light rejection and reduces the sensitivity of the system to stray light that can contaminate observations.

nanoCAGE reveals 5′ UTR features that define specific modes of translation of functionally related MTOR-sensitive mRNAs

PubMed Central

Gandin, Valentina; Masvidal, Laia; Hulea, Laura; Gravel, Simon-Pierre; Cargnello, Marie; McLaughlan, Shannon; Cai, Yutian; Balanathan, Preetika; Morita, Masahiro; Rajakumar, Arjuna; Furic, Luc; Pollak, Michael; Porco, John A.; St-Pierre, Julie; Pelletier, Jerry; Larsson, Ola; Topisirovic, Ivan

2016-01-01

The diversity of MTOR-regulated mRNA translation remains unresolved. Whereas ribosome-profiling suggested that MTOR almost exclusively stimulates translation of the TOP (terminal oligopyrimidine motif) and TOP-like mRNAs, polysome-profiling indicated that MTOR also modulates translation of mRNAs without the 5′ TOP motif (non-TOP mRNAs). We demonstrate that in ribosome-profiling studies, detection of MTOR-dependent changes in non-TOP mRNA translation was obscured by low sensitivity and methodology biases. Transcription start site profiling using nano-cap analysis of gene expression (nanoCAGE) revealed that not only do many MTOR-sensitive mRNAs lack the 5′ TOP motif but that 5′ UTR features distinguish two functionally and translationally distinct subsets of MTOR-sensitive mRNAs: (1) mRNAs with short 5′ UTRs enriched for mitochondrial functions, which require EIF4E but are less EIF4A1-sensitive; and (2) long 5′ UTR mRNAs encoding proliferation- and survival-promoting proteins, which are both EIF4E- and EIF4A1-sensitive. Selective inhibition of translation of mRNAs harboring long 5′ UTRs via EIF4A1 suppression leads to sustained expression of proteins involved in respiration but concomitant loss of those protecting mitochondrial structural integrity, resulting in apoptosis. Conversely, simultaneous suppression of translation of both long and short 5′ UTR mRNAs by MTOR inhibitors results in metabolic dormancy and a predominantly cytostatic effect. Thus, 5′ UTR features define different modes of MTOR-sensitive translation of functionally distinct subsets of mRNAs, which may explain the diverse impact of MTOR and EIF4A inhibitors on neoplastic cells. PMID:26984228

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

Automatic localization of cerebral cortical malformations using fractal analysis.

PubMed

De Luca, A; Arrigoni, F; Romaniello, R; Triulzi, F M; Peruzzo, D; Bertoldo, A

2016-08-21

Malformations of cortical development (MCDs) encompass a variety of brain disorders affecting the normal development and organization of the brain cortex. The relatively low incidence and the extreme heterogeneity of these disorders hamper the application of classical group level approaches for the detection of lesions. Here, we present a geometrical descriptor for a voxel level analysis based on fractal geometry, then define two similarity measures to detect the lesions at single subject level. The pipeline was applied to 15 normal children and nine pediatric patients affected by MCDs following two criteria, maximum accuracy (WACC) and minimization of false positives (FPR), and proved that our lesion detection algorithm is able to detect and locate abnormalities of the brain cortex with high specificity (WACC  =  85%, FPR  =  96%), sensitivity (WACC  =  83%, FPR  =  63%) and accuracy (WACC  =  85%, FPR  =  90%). The combination of global and local features proves to be effective, making the algorithm suitable for the detection of both focal and diffused malformations. Compared to other existing algorithms, this method shows higher accuracy and sensitivity.

Automatic localization of cerebral cortical malformations using fractal analysis

NASA Astrophysics Data System (ADS)

De Luca, A.; Arrigoni, F.; Romaniello, R.; Triulzi, F. M.; Peruzzo, D.; Bertoldo, A.

2016-08-01

Malformations of cortical development (MCDs) encompass a variety of brain disorders affecting the normal development and organization of the brain cortex. The relatively low incidence and the extreme heterogeneity of these disorders hamper the application of classical group level approaches for the detection of lesions. Here, we present a geometrical descriptor for a voxel level analysis based on fractal geometry, then define two similarity measures to detect the lesions at single subject level. The pipeline was applied to 15 normal children and nine pediatric patients affected by MCDs following two criteria, maximum accuracy (WACC) and minimization of false positives (FPR), and proved that our lesion detection algorithm is able to detect and locate abnormalities of the brain cortex with high specificity (WACC  =  85%, FPR  =  96%), sensitivity (WACC  =  83%, FPR  =  63%) and accuracy (WACC  =  85%, FPR  =  90%). The combination of global and local features proves to be effective, making the algorithm suitable for the detection of both focal and diffused malformations. Compared to other existing algorithms, this method shows higher accuracy and sensitivity.

Graphene Paper Decorated with a 2D Array of Dendritic Platinum Nanoparticles for Ultrasensitive Electrochemical Detection of Dopamine Secreted by Live Cells

PubMed Central

Zan, Xiaoli; Wang, Chenxu

2016-01-01

Abstract To circumvent the bottlenecks of non‐flexibility, low sensitivity, and narrow workable detection range of conventional biosensors for biological molecule detection (e.g., dopamine (DA) secreted by living cells), a new hybrid flexible electrochemical biosensor has been created by decorating closely packed dendritic Pt nanoparticles (NPs) on freestanding graphene paper. This innovative structural integration of ultrathin graphene paper and uniform 2D arrays of dendritic NPs by tailored wet chemical synthesis has been achieved by a modular strategy through a facile and delicately controlled oil–water interfacial assembly method, whereby the uniform distribution of catalytic dendritic NPs on the graphene paper is maximized. In this way, the performance is improved by several orders of magnitude. The developed hybrid electrode shows a high sensitivity of 2 μA cm−2 μm −1, up to about 33 times higher than those of conventional sensors, a low detection limit of 5 nm, and a wide linear range of 87 nm to 100 μm. These combined features enable the ultrasensitive detection of DA released from pheochromocytoma (PC 12) cells. The unique features of this flexible sensor can be attributed to the well‐tailored uniform 2D array of dendritic Pt NPs and the modular electrode assembly at the oil–water interface. Its excellent performance holds much promise for the future development of optimized flexible electrochemical sensors for a diverse range of electroactive molecules to better serve society. PMID:26918612

Graphene Paper Decorated with a 2D Array of Dendritic Platinum Nanoparticles for Ultrasensitive Electrochemical Detection of Dopamine Secreted by Live Cells.

PubMed

Zan, Xiaoli; Bai, Hongwei; Wang, Chenxu; Zhao, Faqiong; Duan, Hongwei

2016-04-04

To circumvent the bottlenecks of non-flexibility, low sensitivity, and narrow workable detection range of conventional biosensors for biological molecule detection (e.g., dopamine (DA) secreted by living cells), a new hybrid flexible electrochemical biosensor has been created by decorating closely packed dendritic Pt nanoparticles (NPs) on freestanding graphene paper. This innovative structural integration of ultrathin graphene paper and uniform 2D arrays of dendritic NPs by tailored wet chemical synthesis has been achieved by a modular strategy through a facile and delicately controlled oil-water interfacial assembly method, whereby the uniform distribution of catalytic dendritic NPs on the graphene paper is maximized. In this way, the performance is improved by several orders of magnitude. The developed hybrid electrode shows a high sensitivity of 2 μA cm(-2) μM(-1), up to about 33 times higher than those of conventional sensors, a low detection limit of 5 nM, and a wide linear range of 87 nM to 100 μM. These combined features enable the ultrasensitive detection of DA released from pheochromocytoma (PC 12) cells. The unique features of this flexible sensor can be attributed to the well-tailored uniform 2D array of dendritic Pt NPs and the modular electrode assembly at the oil-water interface. Its excellent performance holds much promise for the future development of optimized flexible electrochemical sensors for a diverse range of electroactive molecules to better serve society. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Heat-Energy Analysis for Solar Receivers

NASA Technical Reports Server (NTRS)

Lansing, F. L.

1982-01-01

Heat-energy analysis program (HEAP) solves general heat-transfer problems, with some specific features that are "custom made" for analyzing solar receivers. Can be utilized not only to predict receiver performance under varying solar flux, ambient temperature and local heat-transfer rates but also to detect locations of hotspots and metallurgical difficulties and to predict performance sensitivity of neighboring component parameters.

Clinical experience with a computer-aided diagnosis system for automatic detection of pulmonary nodules at spiral CT of the chest

NASA Astrophysics Data System (ADS)

Wormanns, Dag; Fiebich, Martin; Saidi, Mustafa; Diederich, Stefan; Heindel, Walter

2001-05-01

The purpose of the study was to evaluate a computer aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Two radiologists in consensus reported 88 consecutive spiral CT examinations. All examinations were reviewed using a UNIX-based CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm was designed to detect nodules with at least 5 mm diameter. The results of automatic nodule detection were compared to the consensus reporting of two radiologists as gold standard. Additional CAD findings were regarded as nodules initially missed by the radiologists or as false positive results. A total of 153 nodules were detected with all modalities (diameter: 85 nodules <5mm, 63 nodules 5-9 mm, 5 nodules >= 10 mm). Reasons for failure of automatic nodule detection were assessed. Sensitivity of radiologists for nodules >=5 mm was 85%, sensitivity of CAD was 38%. For nodules >=5 mm without pleural contact sensitivity was 84% for radiologists at 45% for CAD. CAD detected 15 (10%) nodules not mentioned in the radiologist's report but representing real nodules, among them 10 (15%) nodules with a diameter $GREW5 mm. Reasons for nodules missed by CAD include: exclusion because of morphological features during region analysis (33%), nodule density below the detection threshold (26%), pleural contact (33%), segmentation errors (5%) and other reasons (2%). CAD improves detection of pulmonary nodules at spiral CT significantly and is a valuable second opinion in a clinical setting for lung cancer screening. Optimization of region analysis and an appropriate density threshold have a potential for further improvement of automatic nodule detection.

Application of the laguerre deconvolution method for time-resolved fluorescence spectroscopy to the characterization of atherosclerotic plaques.

PubMed

Jo, J A; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Beseth, B; Dorafshar, A H; Reil, T; Baker, D; Freischlag, J; Marcu, L

2005-01-01

This study investigates the ability of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) to detect inflammation in atherosclerotic lesion, a key feature of plaque vulnerability. A total of 348 TR-LIFS measurements were taken from carotid plaques of 30 patients, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified as Early, Fibrotic/Calcified or Inflamed lesions. A stepwise linear discriminant analysis algorithm was developed using spectral and TR features (normalized intensity values and Laguerre expansion coefficients at discrete emission wavelengths, respectively). Features from only three emission wavelengths (390, 450 and 500 nm) were used in the classifier. The Inflamed lesions were discriminated with sensitivity > 80% and specificity > 90 %, when the Laguerre expansion coefficients were included in the feature space. These results indicate that TR-LIFS information derived from the Laguerre expansion coefficients at few selected emission wavelengths can discriminate inflammation in atherosclerotic plaques. We believe that TR-LIFS derived Laguerre expansion coefficients can provide a valuable additional dimension for the detection of vulnerable plaques.

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification.

PubMed

Marin, D; Gegundez-Arias, M E; Ponte, B; Alvarez, F; Garrido, J; Ortega, C; Vasallo, M J; Bravo, J M

2018-01-10

The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme. Graphical Abstract Diagnosis system of risk of diabetic macular edema (DME) based on exudate (Ex) detection in fundus images.

Detection of anti-nuclear antibody by immunofluorescence assay and enzyme immunoassay in childhood systemic lupus erythematosus: experience from Bangladesh.

PubMed

Dipti, Tanjeem Rabika; Azam, Mohammad Shaiful; Sattar, Mohammad Humayun; Rahman, Shahana Akhter

2012-02-01

Systemic lupus erythematosus (SLE) is a multisystem, chronic but often episodic, autoimmune disease that is characterized by the presence of antinuclear antibodies (ANA). The criteria set by American College of Rheumatology are widely used for diagnosis of SLE. Elevation of ANA titer is the most sensitive of the ACR criteria. There are different methods for detection of ANA. Indirect immunofluorescence (ANA-IFA) and enzyme immunoassay (ANA-EIA) are commonly used methods. The sensitivity of ANA-IFA using HEp-2 cell substrate is 90-100% in systemic rheumatic diseases. In Bangladesh most of the laboratories use ANA-EIA for detection of ANA. As the sensitivity of ANA-EIA is lower than ANA-IFA it might be that we are missing many cases of ANA positivity in childhood SLE cases. To detect ANA by immunofluorescence assay using HEp-2 cell substrate and enzyme immunoassay in childhood SLE and to compare the diagnostic performance of these methods. This is a cross-sectional analytical study. A total of 40 patients were enrolled. Among them 20 were childhood SLE cases. Another 20 patients of childhood rheumatic diseases other than SLE were taken as the disease control group. In childhood SLE cases, 100% were ANA-positive by IFA and 55% were ANA positive by EIA. The sensitivity of ANA-IFA was 100%. In contrast, sensitivity of ANA-EIA was 55%.   ANA-IFA is superior to ANA-EIA for detection of ANA in childhood SLE patients. ANA-IFA should be the primary screening test for children with clinical features suggestive of SLE. © 2011 The Authors. International Journal of Rheumatic Diseases © 2011 Asia Pacific League of Associations for Rheumatology and Blackwell Publishing Asia Pty Ltd.

Ultrasensitive, real-time trace gas detection using a high-power, multimode diode laser and cavity ringdown spectroscopy.

PubMed

Karpf, Andreas; Qiao, Yuhao; Rao, Gottipaty N

2016-06-01

We present a simplified cavity ringdown (CRD) trace gas detection technique that is insensitive to vibration, and capable of extremely sensitive, real-time absorption measurements. A high-power, multimode Fabry-Perot (FP) diode laser with a broad wavelength range (Δλ_laser∼0.6  nm) is used to excite a large number of cavity modes, thereby reducing the detector's susceptibility to vibration and making it well suited for field deployment. When detecting molecular species with broad absorption features (Δλ_absorption≫Δλ_laser), the laser's broad linewidth removes the need for precision wavelength stabilization. The laser's power and broad linewidth allow the use of on-axis cavity alignment, improving the signal-to-noise ratio while maintaining its vibration insensitivity. The use of an FP diode laser has the added advantages of being inexpensive, compact, and insensitive to vibration. The technique was demonstrated using a 1.1 W (λ=400  nm) diode laser to measure low concentrations of nitrogen dioxide (NO₂) in zero air. A sensitivity of 38 parts in 10¹² (ppt) was achieved using an integration time of 128 ms; for single-shot detection, 530 ppt sensitivity was demonstrated with a measurement time of 60 μs, which opens the door to sensitive measurements with extremely high temporal resolution; to the best of our knowledge, these are the highest speed measurements of NO₂ concentration using CRD spectroscopy. The reduced susceptibility to vibration was demonstrated by introducing small vibrations into the apparatus and observing that there was no measurable effect on the sensitivity of detection.

Comparison of Texture Features Used for Classification of Life Stages of Malaria Parasite.

PubMed

Bairagi, Vinayak K; Charpe, Kshipra C

2016-01-01

Malaria is a vector borne disease widely occurring at equatorial region. Even after decades of campaigning of malaria control, still today it is high mortality causing disease due to improper and late diagnosis. To prevent number of people getting affected by malaria, the diagnosis should be in early stage and accurate. This paper presents an automatic method for diagnosis of malaria parasite in the blood images. Image processing techniques are used for diagnosis of malaria parasite and to detect their stages. The diagnosis of parasite stages is done using features like statistical features and textural features of malaria parasite in blood images. This paper gives a comparison of the textural based features individually used and used in group together. The comparison is made by considering the accuracy, sensitivity, and specificity of the features for the same images in database.

Machine learning approach to automatic exudate detection in retinal images from diabetic patients

NASA Astrophysics Data System (ADS)

Sopharak, Akara; Dailey, Matthew N.; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Tom; Thet Nwe, Khine; Aye Moe, Yin

2010-01-01

Exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early detection of exudates could improve patients' chances to avoid blindness. In this paper, we present a series of experiments on feature selection and exudates classification using naive Bayes and support vector machine (SVM) classifiers. We first fit the naive Bayes model to a training set consisting of 15 features extracted from each of 115,867 positive examples of exudate pixels and an equal number of negative examples. We then perform feature selection on the naive Bayes model, repeatedly removing features from the classifier, one by one, until classification performance stops improving. To find the best SVM, we begin with the best feature set from the naive Bayes classifier, and repeatedly add the previously-removed features to the classifier. For each combination of features, we perform a grid search to determine the best combination of hyperparameters ν (tolerance for training errors) and γ (radial basis function width). We compare the best naive Bayes and SVM classifiers to a baseline nearest neighbour (NN) classifier using the best feature sets from both classifiers. We find that the naive Bayes and SVM classifiers perform better than the NN classifier. The overall best sensitivity, specificity, precision, and accuracy are 92.28%, 98.52%, 53.05%, and 98.41%, respectively.

A new rhodamine-based colorimetric chemosensor for naked-eye detection of Cu(2+) in aqueous solution.

PubMed

Hu, Yang; Zhang, Jing; Lv, Yuan-Zheng; Huang, Xiao-Huan; Hu, Sheng-Li

2016-03-15

A new colorimetric probe 1 based on rhodamine B lactam was developed for naked-eye detection of Cu(2+). The optical feature of 1 for Cu(2+) was investigated by UV-vis absorption spectroscopy. Upon the addition of Cu(2+), the 1 displayed a distinct color change from colorless to pink, which can be directly detected by the naked eye. The stoichiometry of 1 to Cu(2+) complex was found to be 1:1 and the naked-eye detection limit was determined as low as 2 μM. The results suggest that the probe 1 may provide a convenient method for visual detection of Cu(2+) with high sensitivity. Copyright © 2015 Elsevier B.V. All rights reserved.

Asymmetric exponential amplification reaction on a toehold/biotin featured template: an ultrasensitive and specific strategy for isothermal microRNAs analysis

PubMed Central

Chen, Jun; Zhou, Xueqing; Ma, Yingjun; Lin, Xiulian; Dai, Zong; Zou, Xiaoyong

2016-01-01

The sensitive and specific analysis of microRNAs (miRNAs) without using a thermal cycler instrument is significant and would greatly facilitate biological research and disease diagnostics. Although exponential amplification reaction (EXPAR) is the most attractive strategy for the isothermal analysis of miRNAs, its intrinsic limitations of detection efficiency and inevitable non-specific amplification critically restrict its use in analytical sensitivity and specificity. Here, we present a novel asymmetric EXPAR based on a new biotin/toehold featured template. A biotin tag was used to reduce the melting temperature of the primer/template duplex at the 5′ terminus of the template, and a toehold exchange structure acted as a filter to suppress the non-specific trigger of EXPAR. The asymmetric EXPAR exhibited great improvements in amplification efficiency and specificity as well as a dramatic extension of dynamic range. The limit of detection for the let-7a analysis was decreased to 6.02 copies (0.01 zmol), and the dynamic range was extended to 10 orders of magnitude. The strategy enabled the sensitive and accurate analysis of let-7a miRNA in human cancer tissues with clearly better precision than both standard EXPAR and RT-qPCR. Asymmetric EXPAR is expected to have an important impact on the development of simple and rapid molecular diagnostic applications for short oligonucleotides. PMID:27257058

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

PubMed

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

2018-01-01

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

Sensitive far uv spectrograph with a multispectral element microchannel plate detector for rocket-borne astronomy.

PubMed

Weiser, H; Vitz, R C; Moos, H W; Weinstein, A

1976-12-01

An evacuated high transmission prism spectrograph using a microchannel plate detection system with resistive strip readout was flown behind a precision pointing telescope on a sounding rocket. The construction, preparation, flight performance, and calibration stability of the system are discussed. Despite the adverse environmental conditions associated with sounding rocket flights, the microchannel detector system performed well. Far uv spectra (1160-1750 A) of stellar and planetary objects were obtained; spectral features with fluxes as low as 0.06 photons cm(-2) sec(-1) were detectable. This was achieved by operating the plates at lower than normal gains, using sensitive pulse counting electronics with both upper and lower limit discriminators, and maintaining the spectrograph and detector at a pressure of ~10(-6) Torr until reaching altitude.

Method and apparatus for real time weld monitoring

DOEpatents

Leong, Keng H.; Hunter, Boyd V.

1997-01-01

An improved method and apparatus are provided for real time weld monitoring. An infrared signature emitted by a hot weld surface during welding is detected and this signature is compared with an infrared signature emitted by the weld surface during steady state conditions. The result is correlated with weld penetration. The signal processing is simpler than for either UV or acoustic techniques. Changes in the weld process, such as changes in the transmitted laser beam power, quality or positioning of the laser beam, change the resulting weld surface features and temperature of the weld surface, thereby resulting in a change in the direction and amount of infrared emissions. This change in emissions is monitored by an IR sensitive detecting apparatus that is sensitive to the appropriate wavelength region for the hot weld surface.

IDH mutation assessment of glioma using texture features of multimodal MR images

NASA Astrophysics Data System (ADS)

Zhang, Xi; Tian, Qiang; Wu, Yu-Xia; Xu, Xiao-Pan; Li, Bao-Juan; Liu, Yi-Xiong; Liu, Yang; Lu, Hong-Bing

2017-03-01

Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade

PubMed Central

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-01-01

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade. PMID:26287200

Tip-enhanced near-field Raman spectroscopy with a scanning tunneling microscope and side-illumination optics.

PubMed

Yi, K J; He, X N; Zhou, Y S; Xiong, W; Lu, Y F

2008-07-01

Conventional Raman spectroscopy (RS) suffers from low spatial resolution and low detection sensitivity due to the optical diffraction limit and small interaction cross sections. It has been reported that a highly localized and significantly enhanced electromagnetic field could be generated in the proximity of a metallic tip illuminated by a laser beam. In this study, a tip-enhanced RS system was developed to both improve the resolution and enhance the detection sensitivity using the tip-enhanced near-field effects. This instrument, by combining RS with a scanning tunneling microscope and side-illumination optics, demonstrated significant enhancement on both optical sensitivity and spatial resolution using either silver (Ag)-coated tungsten (W) tips or gold (Au) tips. The sensitivity improvement was verified by observing the enhancement effects on silicon (Si) substrates. Lateral resolution was verified to be below 100 nm by mapping Ag nanostructures. By deploying the depolarization technique, an apparent enhancement of 175% on Si substrates was achieved. Furthermore, the developed instrument features fast and reliable optical alignment, versatile sample adaptability, and effective suppression of far-field signals.

Implementation of Novel Design Features for qPCR-Based eDNA Assessment

PubMed Central

Veldhoen, Nik; Hobbs, Jared; Ikonomou, Georgios; Hii, Michael; Lesperance, Mary; Helbing, Caren C.

2016-01-01

Environmental stewardship requires timely, accurate information related to the status of a given ecosystem and the species that occupy it. Recent advances in the application of the highly sensitive real-time quantitative polymerase chain reaction (qPCR) towards identification of constituents within environmental DNA (eDNA) now allow targeted detection of the presence of species-specific biological material within a localized geographic region. However, as with all molecular techniques predicated on the specificity and sensitivity of the PCR assay, careful validation of each eDNA qPCR assay in development must be performed both under controlled laboratory conditions and when challenged with field-derived eDNA samples. Such a step-wise approach forms the basis for incorporation of innovative qPCR design features that strengthen the implementation and interpretation of the eDNA assay. This includes empirical determination that the qPCR assay is refractory to the presence of human DNA and the use of a tripartite assay approach comprised of 1) a primer set targeting plant chloroplast that evaluates the presence of amplifiable DNA from field samples to increase confidence in a negative result, 2) an animal group primer set to increase confidence in the assay result, and 3) a species-specific primer set to assess presence of DNA from the target species. To demonstrate this methodology, we generated eDNA assays specific for the North American bullfrog (Lithobates (Rana) catesbeiana) and the Rocky Mountain tailed frog (Ascaphus montanus) and characterized each with respect to detection sensitivity and specificity with demonstrated performance in a field survey scenario. The qPCR design features presented herein address specific challenges of eDNA assays thereby increasing their interpretative power. PMID:27802293

Evaluation of wavelet spectral features in pathological detection and discrimination of yellow rust and powdery mildew in winter wheat with hyperspectral reflectance data

NASA Astrophysics Data System (ADS)

Shi, Yue; Huang, Wenjiang; Zhou, Xianfeng

2017-04-01

Hyperspectral absorption features are important indicators of characterizing plant biophysical variables for the automatic diagnosis of crop diseases. Continuous wavelet analysis has proven to be an advanced hyperspectral analysis technique for extracting absorption features; however, specific wavelet features (WFs) and their relationship with pathological characteristics induced by different infestations have rarely been summarized. The aim of this research is to determine the most sensitive WFs for identifying specific pathological lesions from yellow rust and powdery mildew in winter wheat, based on 314 hyperspectral samples measured in field experiments in China in 2002, 2003, 2005, and 2012. The resultant WFs could be used as proxies to capture the major spectral absorption features caused by infestation of yellow rust or powdery mildew. Multivariate regression analysis based on these WFs outperformed conventional spectral features in disease detection; meanwhile, a Fisher discrimination model exhibited considerable potential for generating separable clusters for each infestation. Optimal classification returned an overall accuracy of 91.9% with a Kappa of 0.89. This paper also emphasizes the WFs and their relationship with pathological characteristics in order to provide a foundation for the further application of this approach in monitoring winter wheat diseases at the regional scale.

Manhole Cover Detection Using Vehicle-Based Multi-Sensor Data

NASA Astrophysics Data System (ADS)

Ji, S.; Shi, Y.; Shi, Z.

2012-07-01

A new method combined wit multi-view matching and feature extraction technique is developed to detect manhole covers on the streets using close-range images combined with GPS/IMU and LINDAR data. The covers are an important target on the road traffic as same as transport signs, traffic lights and zebra crossing but with more unified shapes. However, the different shoot angle and distance, ground material, complex street scene especially its shadow, and cars in the road have a great impact on the cover detection rate. The paper introduces a new method in edge detection and feature extraction in order to overcome these difficulties and greatly improve the detection rate. The LIDAR data are used to do scene segmentation and the street scene and cars are excluded from the roads. And edge detection method base on canny which sensitive to arcs and ellipses is applied on the segmented road scene and the interesting areas contain arcs are extracted and fitted to ellipse. The ellipse are then resampled for invariance to shooting angle and distance and then are matched to adjacent images for further checking if covers and . More than 1000 images with different scenes are used in our tests and the detection rate is analyzed. The results verified our method have its advantages in correct covers detection in the complex street scene.

Early diagnosis of genital mucosal melanoma: how good are our dermoscopic criteria?

PubMed

Rogers, Tova; Pulitzer, Melissa; Marino, Maria L; Marghoob, Ashfaq A; Zivanovic, Oliver; Marchetti, Michael A

2016-10-01

There are limited studies on the dermoscopic features of mucosal melanoma, particularly early-stage lesions. Described criteria include the presence of blue, gray, or white colors, with a reported sensitivity of 100%. It is unclear if these features will aid in the detection of early mucosal melanoma or improve diagnostic accuracy compared to naked-eye examination alone. An Asian female in her fifties was referred for evaluation of an asymptomatic, irregularly pigmented patch of the clitoral hood and labia minora of unknown duration. Her past medical history was notable for Stage IV non-small cell lung cancer. She denied a personal or family history of skin cancer. Dermoscopic evaluation of the vulvar lesion revealed heterogeneous brown and black pigmentation mostly composed of thick lines. There were no other colors or structures present. As the differential diagnosis included vulvar melanosis and mucosal melanoma, the patient was recommended to undergo biopsy, which was delayed due to complications from her underlying lung cancer. Repeat dermoscopic imaging performed three months later revealed significant changes concerning for melanoma, including increase in size, asymmetric darkening, and the appearance of structureless areas and central blue and pink colors. Histopathological examination of a biopsy and subsequent resection confirmed the diagnosis of melanoma in situ. Previously described dermoscopic features for mucosal melanoma may not have high sensitivity for early melanomas. Additional studies are needed to define the dermoscopic characteristics of mucosal melanomas that aid in early detection. Health care providers should have a low threshold for biopsy of mucosal lesions that show any clinical or dermoscopic features of melanoma, especially in older women.

Assessing alternatives for directional detection of a halo of weakly interacting massive particles

NASA Astrophysics Data System (ADS)

Copi, Craig J.; Krauss, Lawrence M.; Simmons-Duffin, David; Stroiney, Steven R.

2007-01-01

The future of direct terrestrial WIMP detection lies on two fronts: new, much larger low background detectors sensitive to energy deposition, and detectors with directional sensitivity. The former can explore a large range of WIMP parameter space using well-tested technology while the latter may be necessary if one is to disentangle particle physics parameters from astrophysical halo parameters. Because directional detectors will be quite difficult to construct it is worthwhile exploring in advance generally which experimental features will yield the greatest benefits at the lowest costs. We examine the sensitivity of directional detectors with varying angular tracking resolution with and without the ability to distinguish forward versus backward recoils, and compare these to the sensitivity of a detector where the track is projected onto a two-dimensional plane. The latter detector regardless of where it is placed on the Earth, can be oriented to produce a significantly better discrimination signal than a 3D detector without this capability, and with sensitivity within a factor of 2 of a full 3D tracking detector. Required event rates to distinguish signals from backgrounds for a simple isothermal halo range from the low teens in the best case to many thousands in the worst.

Direct antigen detection from immunoprecipitated beads using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry; a new method for immunobeads-mass spectrometry (iMS).

PubMed

Shimada, Takashi; Toyama, Atsuhiko; Aoki, Chikage; Aoki, Yutaka; Tanaka, Koichi; Sato, Taka-Aki

2011-12-15

One-step detection of biological molecules is one of the principal techniques for clinical diagnosis, and the potential of mass spectrometry for biomarker detection has been a promising new approach in the field of medical sciences. We demonstrate here a new and high-sensitivity method that we termed immunobeads-mass spectrometry (iMS), which combines conventional immunoprecipitation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The key feature of iMS is the MS-compatible condition of immunoprecipitation using detergents with a monosaccaride-C8 alkyl chain or a disaccharide-C10 alkyl chain, and the minimized number of steps required for high-sensitivity detection of target peptides in serum or biological fluid. This was achieved by optimizing the wash buffer and subjecting the immunobeads directly to MALDI-TOF MS analysis. Using this method, we showed that 1 fmol of amyloid beta peptide spiked in serum was readily detectable, demonstrating the powerful tool of iMS as a biomarker detection method. Copyright © 2011 John Wiley & Sons, Ltd.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis.

PubMed

Couceiro, R; Carvalho, P; Paiva, R P; Henriques, J; Muehlsteff, J

2014-12-01

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Spectral Photosensitization of Optical Anisotropy in Solid Poly(Vinyl Cinnamate) Films

NASA Astrophysics Data System (ADS)

Kozenkov, V. M.; Spakhov, A. A.; Belyaev, V. V.; Chausov, D. N.; Chigrinov, V. G.

2018-04-01

The possibility and features of formation of sensitized photoinduced optical anisotropy in amorphous films of poly(vinyl cinnamate) and its derivative poly(vinyl-4-metoxicinnamate) under the action of polarized light (including light that is not absorbed by polymer macromolecules themselves) have been investigated. It is found that the effect of induced optical anisotropy is based on the transfer of electron excitation energy from donor (sensitizer) molecules to acceptor molecules and is observed in the course of phototopochemical biomolecular cyclization reaction of cinnamate fragments in polymer macromolecules. The detected photoinduced anisotropy in solid films of poly(vinyl cinnamate) and its derivative poly(vinyl-4-metoxicinnamate) ensures sensitized photo-orientation of low-molecular thermotropic liquid crystals.

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.

PubMed

Bogaarts, J G; Hilkman, D M W; Gommer, E D; van Kranen-Mastenbroek, V H J M; Reulen, J P H

2016-12-01

Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).

Carbon Nanomaterials Based Electrochemical Sensors/Biosensors for the Sensitive Detection of Pharmaceutical and Biological Compounds

PubMed Central

Adhikari, Bal-Ram; Govindhan, Maduraiveeran; Chen, Aicheng

2015-01-01

Electrochemical sensors and biosensors have attracted considerable attention for the sensitive detection of a variety of biological and pharmaceutical compounds. Since the discovery of carbon-based nanomaterials, including carbon nanotubes, C60 and graphene, they have garnered tremendous interest for their potential in the design of high-performance electrochemical sensor platforms due to their exceptional thermal, mechanical, electronic, and catalytic properties. Carbon nanomaterial-based electrochemical sensors have been employed for the detection of various analytes with rapid electron transfer kinetics. This feature article focuses on the recent design and use of carbon nanomaterials, primarily single-walled carbon nanotubes (SWCNTs), reduced graphene oxide (rGO), SWCNTs-rGO, Au nanoparticle-rGO nanocomposites, and buckypaper as sensing materials for the electrochemical detection of some representative biological and pharmaceutical compounds such as methylglyoxal, acetaminophen, valacyclovir, β-nicotinamide adenine dinucleotide hydrate (NADH), and glucose. Furthermore, the electrochemical performance of SWCNTs, rGO, and SWCNT-rGO for the detection of acetaminophen and valacyclovir was comparatively studied, revealing that SWCNT-rGO nanocomposites possess excellent electrocatalytic activity in comparison to individual SWCNT and rGO platforms. The sensitive, reliable and rapid analysis of critical disease biomarkers and globally emerging pharmaceutical compounds at carbon nanomaterials based electrochemical sensor platforms may enable an extensive range of applications in preemptive medical diagnostics. PMID:26404304

Silver nanoparticles deposited on graphene oxide for ultrasensitive surface-enhanced Raman scattering immunoassay of cancer biomarker.

PubMed

Yang, Lin; Zhen, Shu Jun; Li, Yuan Fang; Huang, Cheng Zhi

2018-06-14

Graphene oxide (GO) exhibits distinctive Raman scattering features for its high frequency D (disordered) and tangential modes (G-band), which are characteristically sharp at 1580 cm-1 and 1350 cm-1, respectively, but are too weak for sensitive quantitation purposes. By depositing silver nanoparticles on the surface of GO in this contribution, both D and G bands of GO become enhanced. The enzyme label of this method controls the dissolution of silver nanoparticles on the surface of GO through hydrogen peroxide which is produced by the oxidation of the enzyme substrate. With the dissolution of the silver nanoparticles a greatly decreased SERS signal of GO was obtained. This strategy involves dual signal amplification of the enzyme and nanocomposites to improve the detection sensitivity. As a proof of concept, prostate specific antigen (PSA), a biomarker for prostate cancer, is successfully detected as a target by forming a sandwich structure in immunoassay. The SERS immunoassay possesses excellent analytical performance in the range 0.5 pg mL-1 to 500 pg mL-1 with a limit of detection of 0.23 pg mL-1, making the detection of PSA serum samples from prostate cancer patients satisfactory, demonstrating that the sensitive enzyme-assisted dissolved AgNPs SERS immunoassay of PSA has potential applications in clinical diagnosis.

Development of real-time and lateral flow strip reverse transcription recombinase polymerase Amplification assays for rapid detection of peste des petits ruminants virus.

PubMed

Yang, Yang; Qin, Xiaodong; Song, Yiming; Zhang, Wei; Hu, Gaowei; Dou, Yongxi; Li, Yanmin; Zhang, Zhidong

2017-02-07

Peste des petits ruminants (PPR) is an economically important, Office International des Epizooties (OIE) notifiable, transboundary viral disease of small ruminants such as sheep and goat. PPR virus (PPRV), a negative-sense single-stranded RNA virus, is the causal agent of PPR. Therefore, sensitive, specific and rapid diagnostic assay for the detection of PPRV are necessary to accurately and promptly diagnose suspected case of PPR. In this study, reverse transcription recombinase polymerase amplification assays using real-time fluorescent detection (real-time RT-RPA assay) and lateral flow strip detection (LFS RT-RPA assay) were developed targeting the N gene of PPRV. The sensitivity of the developed real-time RT-RPA assay was as low as 100 copies per reaction within 7 min at 40 °C with 95% reliability; while the sensitivity of the developed LFS RT-RPA assay was as low as 150 copies per reaction at 39 °C in less than 25 min. In both assays, there were no cross-reactions with sheep and goat pox viruses, foot-and-mouth disease virus and Orf virus. These features make RPA assay promising candidates either in field use or as a point of care diagnostic technique.

Short non-coding RNAs as bacteria species identifiers detected by surface plasmon resonance enhanced common path interferometry

NASA Astrophysics Data System (ADS)

Greef, Charles; Petropavlovskikh, Viatcheslav; Nilsen, Oyvind; Khattatov, Boris; Plam, Mikhail; Gardner, Patrick; Hall, John

2008-04-01

Small non-coding RNA sequences have recently been discovered as unique identifiers of certain bacterial species, raising the possibility that they can be used as highly specific Biowarfare Agent detection markers in automated field deployable integrated detection systems. Because they are present in high abundance they could allow genomic based bacterial species identification without the need for pre-assay amplification. Further, a direct detection method would obviate the need for chemical labeling, enabling a rapid, efficient, high sensitivity mechanism for bacterial detection. Surface Plasmon Resonance enhanced Common Path Interferometry (SPR-CPI) is a potentially market disruptive, high sensitivity dual technology that allows real-time direct multiplex measurement of biomolecule interactions, including small molecules, nucleic acids, proteins, and microbes. SPR-CPI measures differences in phase shift of reflected S and P polarized light under Total Internal Reflection (TIR) conditions at a surface, caused by changes in refractive index induced by biomolecular interactions within the evanescent field at the TIR interface. The measurement is performed on a microarray of discrete 2-dimensional areas functionalized with biomolecule capture reagents, allowing simultaneous measurement of up to 100 separate analytes. The optical beam encompasses the entire microarray, allowing a solid state detector system with no scanning requirement. Output consists of simultaneous voltage measurements proportional to the phase differences resulting from the refractive index changes from each microarray feature, and is automatically processed and displayed graphically or delivered to a decision making algorithm, enabling a fully automatic detection system capable of rapid detection and quantification of small nucleic acids at extremely sensitive levels. Proof-of-concept experiments on model systems and cell culture samples have demonstrated utility of the system, and efforts are in progress for full development and deployment of the device. The technology has broad applicability as a universal detection platform for BWA detection, medical diagnostics, and drug discovery research, and represents a new class of instrumentation as a rapid, high sensitivity, label-free methodology.

Investigation of Advanced Radar Techniques for Atmospheric Hazard Detection with Airborne Weather Radar

NASA Technical Reports Server (NTRS)

Pazmany, Andrew L.

2014-01-01

In 2013 ProSensing Inc. conducted a study to investigate the hazard detection potential of aircraft weather radars with new measurement capabilities, such as multi-frequency, polarimetric and radiometric modes. Various radar designs and features were evaluated for sensitivity, measurement range and for detecting and quantifying atmospheric hazards in wide range of weather conditions. Projected size, weight, power consumption and cost of the various designs were also considered. Various cloud and precipitation conditions were modeled and used to conduct an analytic evaluation of the design options. This report provides an overview of the study and summarizes the conclusions and recommendations.

Automatic Detection of Atrial Fibrillation Using Basic Shannon Entropy of RR Interval Feature

NASA Astrophysics Data System (ADS)

Afdala, Adfal; Nuryani, Nuryani; Satriyo Nugroho, Anto

2017-01-01

Atrial Fibrillation is one of heart disease, that common characterized by irregularity heart beat. Atrial fibrillation leads to severe complications such as cardiac failure with the subsequent risk of a stroke. A method to detect atrial fibrillation is needed to prevent a risk of atrial fibrillation. This research uses data from physionet in atrial fibrillation database category. The performance of Shannon entropy has the highest accuracy if a threshold is 0.5 with accuracy 89.79%, sensitivity 91.04% and specificity 89.01%. Based on the result we get a conclusion, the ability of Shannon entropy to detect atrial fibrillation is good.

Automatic Detection of Lung and Liver Lesions in 3-D Positron Emission Tomography Images: A Pilot Study

NASA Astrophysics Data System (ADS)

Lartizien, Carole; Marache-Francisco, Simon; Prost, Rémy

2012-02-01

Positron emission tomography (PET) using fluorine-18 deoxyglucose (18F-FDG) has become an increasingly recommended tool in clinical whole-body oncology imaging for the detection, diagnosis, and follow-up of many cancers. One way to improve the diagnostic utility of PET oncology imaging is to assist physicians facing difficult cases of residual or low-contrast lesions. This study aimed at evaluating different schemes of computer-aided detection (CADe) systems for the guided detection and localization of small and low-contrast lesions in PET. These systems are based on two supervised classifiers, linear discriminant analysis (LDA) and the nonlinear support vector machine (SVM). The image feature sets that serve as input data consisted of the coefficients of an undecimated wavelet transform. An optimization study was conducted to select the best combination of parameters for both the SVM and the LDA. Different false-positive reduction (FPR) methods were evaluated to reduce the number of false-positive detections per image (FPI). This includes the removal of small detected clusters and the combination of the LDA and SVM detection maps. The different CAD schemes were trained and evaluated based on a simulated whole-body PET image database containing 250 abnormal cases with 1230 lesions and 250 normal cases with no lesion. The detection performance was measured on a separate series of 25 testing images with 131 lesions. The combination of the LDA and SVM score maps was shown to produce very encouraging detection performance for both the lung lesions, with 91% sensitivity and 18 FPIs, and the liver lesions, with 94% sensitivity and 10 FPIs. Comparison with human performance indicated that the different CAD schemes significantly outperformed human detection sensitivities, especially regarding the low-contrast lesions.

IceCube sensitivity for low-energy neutrinos from nearby supernovae

NASA Astrophysics Data System (ADS)

Abbasi, R.; Abdou, Y.; Abu-Zayyad, T.; Ackermann, M.; Adams, J.; Aguilar, J. A.; Ahlers, M.; Allen, M. M.; Altmann, D.; Andeen, K.; Auffenberg, J.; Bai, X.; Baker, M.; Barwick, S. W.; Baum, V.; Bay, R.; Bazo Alba, J. L.; Beattie, K.; Beatty, J. J.; Bechet, S.; Becker, J. K.; Becker, K. H.; Benabderrahmane, M. L.; Benzvi, S.; Berdermann, J.; Berghaus, P.; Berley, D.; Bernardini, E.; Bertrand, D.; Besson, D. Z.; Bindig, D.; Bissok, M.; Blaufuss, E.; Blumenthal, J.; Boersma, D. J.; Bohm, C.; Bose, D.; Böser, S.; Botner, O.; Brown, A. M.; Buitink, S.; Caballero-Mora, K. S.; Carson, M.; Chirkin, D.; Christy, B.; Clevermann, F.; Cohen, S.; Colnard, C.; Cowen, D. F.; Cruz Silva, A. H.; D'Agostino, M. V.; Danninger, M.; Daughhetee, J.; Davis, J. C.; de Clercq, C.; Degner, T.; Demirörs, L.; Descamps, F.; Desiati, P.; de Vries-Uiterweerd, G.; Deyoung, T.; Díaz-Vélez, J. C.; Dierckxsens, M.; Dreyer, J.; Dumm, J. P.; Dunkman, M.; Eisch, J.; Ellsworth, R. W.; Engdegård, O.; Euler, S.; Evenson, P. A.; Fadiran, O.; Fazely, A. R.; Fedynitch, A.; Feintzeig, J.; Feusels, T.; Filimonov, K.; Finley, C.; Fischer-Wasels, T.; Fox, B. D.; Franckowiak, A.; Franke, R.; Gaisser, T. K.; Gallagher, J.; Gerhardt, L.; Gladstone, L.; Glüsenkamp, T.; Goldschmidt, A.; Goodman, J. A.; Góra, D.; Grant, D.; Griesel, T.; Groß, A.; Grullon, S.; Gurtner, M.; Ha, C.; Haj Ismail, A.; Hallgren, A.; Halzen, F.; Han, K.; Hanson, K.; Heinen, D.; Helbing, K.; Hellauer, R.; Hickford, S.; Hill, G. C.; Hoffman, K. D.; Hoffmann, B.; Homeier, A.; Hoshina, K.; Huelsnitz, W.; Hülß, J.-P.; Hulth, P. O.; Hultqvist, K.; Hussain, S.; Ishihara, A.; Jakobi, E.; Jacobsen, J.; Japaridze, G. S.; Johansson, H.; Kampert, K.-H.; Kappes, A.; Karg, T.; Karle, A.; Kenny, P.; Kiryluk, J.; Kislat, F.; Klein, S. R.; Köhne, H.; Kohnen, G.; Kolanoski, H.; Köpke, L.; Kopper, S.; Koskinen, D. J.; Kowalski, M.; Kowarik, T.; Krasberg, M.; Kroll, G.; Kurahashi, N.; Kuwabara, T.; Labare, M.; Laihem, K.; Landsman, H.; Larson, M. J.; Lauer, R.; Lünemann, J.; Madsen, J.; Marotta, A.; Maruyama, R.; Mase, K.; Matis, H. S.; Meagher, K.; Merck, M.; Mészáros, P.; Meures, T.; Miarecki, S.; Middell, E.; Milke, N.; Miller, J.; Montaruli, T.; Morse, R.; Movit, S. M.; Nahnhauer, R.; Nam, J. W.; Naumann, U.; Nygren, D. R.; Odrowski, S.; Olivas, A.; Olivo, M.; O'Murchadha, A.; Panknin, S.; Paul, L.; Pérez de Los Heros, C.; Petrovic, J.; Piegsa, A.; Pieloth, D.; Porrata, R.; Posselt, J.; Price, P. B.; Przybylski, G. T.; Rawlins, K.; Redl, P.; Resconi, E.; Rhode, W.; Ribordy, M.; Richard, A. S.; Richman, M.; Rodrigues, J. P.; Rothmaier, F.; Rott, C.; Ruhe, T.; Rutledge, D.; Ruzybayev, B.; Ryckbosch, D.; Sander, H.-G.; Santander, M.; Sarkar, S.; Schatto, K.; Schmidt, T.; Schönwald, A.; Schukraft, A.; Schulte, L.; Schultes, A.; Schulz, O.; Schunck, M.; Seckel, D.; Semburg, B.; Seo, S. H.; Sestayo, Y.; Seunarine, S.; Silvestri, A.; Singh, K.; Slipak, A.; Spiczak, G. M.; Spiering, C.; Stamatikos, M.; Stanev, T.; Stezelberger, T.; Stokstad, R. G.; Stößl, A.; Strahler, E. A.; Ström, R.; Stüer, M.; Sullivan, G. W.; Swillens, Q.; Taavola, H.; Taboada, I.; Tamburro, A.; Tepe, A.; Ter-Antonyan, S.; Tilav, S.; Toale, P. A.; Toscano, S.; Tosi, D.; van Eijndhoven, N.; Vandenbroucke, J.; van Overloop, A.; van Santen, J.; Vehring, M.; Voge, M.; Walck, C.; Waldenmaier, T.; Wallraff, M.; Walter, M.; Weaver, Ch.; Wendt, C.; Westerhoff, S.; Whitehorn, N.; Wiebe, K.; Wiebusch, C. H.; Williams, D. R.; Wischnewski, R.; Wissing, H.; Wolf, M.; Wood, T. R.; Woschnagg, K.; Xu, C.; Xu, D. L.; Xu, X. W.; Yanez, J. P.; Yodh, G.; Yoshida, S.; Zarzhitsky, P.; Zoll, M.; IceCube Collaboration

2011-11-01

This paper describes the response of the IceCube neutrino telescope located at the geographic south pole to outbursts of MeV neutrinos from the core collapse of nearby massive stars. IceCube was completed in December 2010 forming a lattice of 5160 photomultiplier tubes that monitor a volume of ~1 km3 in the deep Antarctic ice for particle induced photons. The telescope was designed to detect neutrinos with energies greater than 100 GeV. Owing to subfreezing ice temperatures, the photomultiplier dark noise rates are particularly low. Hence IceCube can also detect large numbers of MeV neutrinos by observing a collective rise in all photomultiplier rates on top of the dark noise. With 2 ms timing resolution, IceCube can detect subtle features in the temporal development of the supernova neutrino burst. For a supernova at the galactic center, its sensitivity matches that of a background-free megaton-scale supernova search experiment. The sensitivity decreases to 20 standard deviations at the galactic edge (30 kpc) and 6 standard deviations at the Large Magellanic Cloud (50 kpc). IceCube is sending triggers from potential supernovae to the Supernova Early Warning System. The sensitivity to neutrino properties such as the neutrino hierarchy is discussed, as well as the possibility to detect the neutronization burst, a short outbreak of \\barνe's released by electron capture on protons soon after collapse. Tantalizing signatures, such as the formation of a quark star or a black hole as well as the characteristics of shock waves, are investigated to illustrate IceCube's capability for supernova detection.

IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae

NASA Technical Reports Server (NTRS)

Stamatikos, M.; Abbasi, R.; Berghaus, P.; Chirkin, D.; Desiati, P.; Diaz-Velez, J.; Dumm, J. P.; Eisch, J.; Feintzeig, J.; Hanson, K.;

Diagnostic power of longitudinal strain at rest for the detection of obstructive coronary artery disease in patients with type 2 diabetes mellitus.

PubMed

Zuo, Houjuan; Yan, Jiangtao; Zeng, Hesong; Li, Wenyu; Li, Pengcheng; Liu, Zhengxiang; Cui, Guanglin; Lv, Jiagao; Wang, Daowen; Wang, Hong

2015-01-01

Global longitudinal strain (GLS) measured by 2-D speckle-tracking echocardiography (2-D STE) at rest has been recognized as a sensitive parameter in the detection of significant coronary artery disease (CAD). However, the diagnostic power of 2-D STE in the detection of significant CAD in patients with diabetes mellitus is unknown. Two-dimensional STE features were studied in total of 143 consecutive patients who underwent echocardiography and coronary angiography. Left ventricular global and segmental peak systolic longitudinal strains (PSLSs) were quantified by speckle-tracking imaging. In the presence of obstructive CAD (defined as stenosis ≥75%), global PSLS was significantly lower in patients with diabetes mellitus than in patients without (16.65 ± 2.29% vs. 17.32 ± 2.27%, p < 0.05). Receiver operating characteristic analysis revealed that global PSLS could effectively detect obstructive CAD in patients without diabetes mellitus (cutoff value: -18.35%, sensitivity: 78.8%, specificity: 77.5%). However, global PSLS could detect obstructive CAD in diabetic patients at a lower cutoff value with inadequate sensitivity and specificity (cutoff value: -17.15%; sensitivity: 61.1%, specificity: 52.9%). In addition, the results for segmental PSLS were similar to those for global PSLS. In conclusion, global and segmental PSLSs at rest were significantly lower in patients with both obstructive CAD and diabetes mellitus than in patients with obstructive CAD only; thus, PSLSs at rest might not be a useful parameter in the detection of obstructive CAD in patients with diabetes mellitus. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Detection and recognition of simple spatial forms

NASA Technical Reports Server (NTRS)

Watson, A. B.

1983-01-01

A model of human visual sensitivity to spatial patterns is constructed. The model predicts the visibility and discriminability of arbitrary two-dimensional monochrome images. The image is analyzed by a large array of linear feature sensors, which differ in spatial frequency, phase, orientation, and position in the visual field. All sensors have one octave frequency bandwidths, and increase in size linearly with eccentricity. Sensor responses are processed by an ideal Bayesian classifier, subject to uncertainty. The performance of the model is compared to that of the human observer in detecting and discriminating some simple images.

Enabling systematic interrogation of protein-protein interactions in live cells with a versatile ultra-high-throughput biosensor platform | Office of Cancer Genomics

Cancer.gov

The vast datasets generated by next generation gene sequencing and expression profiling have transformed biological and translational research. However, technologies to produce large-scale functional genomics datasets, such as high-throughput detection of protein-protein interactions (PPIs), are still in early development. While a number of powerful technologies have been employed to detect PPIs, a singular PPI biosensor platform featured with both high sensitivity and robustness in a mammalian cell environment remains to be established.

Metacognitive Confidence Increases with, but Does Not Determine, Visual Perceptual Learning.

PubMed

Zizlsperger, Leopold; Kümmel, Florian; Haarmeier, Thomas

2016-01-01

While perceptual learning increases objective sensitivity, the effects on the constant interaction of the process of perception and its metacognitive evaluation have been rarely investigated. Visual perception has been described as a process of probabilistic inference featuring metacognitive evaluations of choice certainty. For visual motion perception in healthy, naive human subjects here we show that perceptual sensitivity and confidence in it increased with training. The metacognitive sensitivity-estimated from certainty ratings by a bias-free signal detection theoretic approach-in contrast, did not. Concomitant 3Hz transcranial alternating current stimulation (tACS) was applied in compliance with previous findings on effective high-low cross-frequency coupling subserving signal detection. While perceptual accuracy and confidence in it improved with training, there were no statistically significant tACS effects. Neither metacognitive sensitivity in distinguishing between their own correct and incorrect stimulus classifications, nor decision confidence itself determined the subjects' visual perceptual learning. Improvements of objective performance and the metacognitive confidence in it were rather determined by the perceptual sensitivity at the outset of the experiment. Post-decision certainty in visual perceptual learning was neither independent of objective performance, nor requisite for changes in sensitivity, but rather covaried with objective performance. The exact functional role of metacognitive confidence in human visual perception has yet to be determined.

Interpersonal sensitivity and persistent attenuated psychotic symptoms in adolescence.

PubMed

Masillo, Alice; Brandizzi, M; Valmaggia, L R; Saba, R; Lo Cascio, N; Lindau, J F; Telesforo, L; Venturini, P; Montanaro, D; Di Pietro, D; D'Alema, M; Girardi, P; Fiori Nastro, P

2018-03-01

Interpersonal sensitivity defines feelings of inner-fragility in the presence of others due to the expectation of criticism or rejection. Interpersonal sensitivity was found to be related to attenuated positive psychotic symptom during the prodromal phase of psychosis. The aims of this study were to examine if high level of interpersonal sensitivity at baseline are associated with the persistence of attenuated positive psychotic symptoms and general psychopathology at 18-month follow-up. A sample of 85 help-seeking individuals (mean age = 16.6, SD = 5.05) referred an Italian early detection project, completed the interpersonal sensitivity measure and the structured interview for prodromal symptoms (SIPS) at baseline and were assessed at 18-month follow-up using the SIPS. Results showed that individuals with high level of interpersonal sensitivity at baseline reported high level of attenuated positive psychotic symptoms (i.e., unusual thought content) and general symptoms (i.e., depression, irritability and low tolerance to daily stress) at follow-up. This study suggests that being "hypersensitive" to interpersonal interactions is a psychological feature associated with attenuated positive psychotic symptoms and general symptoms, such as depression and irritability, at 18-month follow-up. Assessing and treating inner-self fragilities may be an important step of early detection program to avoid the persistence of subtle but very distressing long-terms symptoms.

Olfactory sensitivity for sperm-attractant aromatic aldehydes: a comparative study in human subjects and spider monkeys.

PubMed

Kjeldmand, Luna; Salazar, Laura Teresa Hernandez; Laska, Matthias

2011-01-01

Using a three-alternative forced-choice ascending staircase procedure, we determined olfactory detection thresholds in 20 human subjects for seven aromatic aldehydes and compared them to those of four spider monkeys tested in parallel using an operant conditioning paradigm. With all seven odorants, both species detected concentrations <1 ppm, and with several odorants single individuals of both species even discriminated concentrations <1 ppb from the solvent. No generalizable species differences in olfactory sensitivity were found despite marked differences in neuroanatomical and genetic features. The across-odorant patterns of sensitivity correlated significantly between humans and spider monkeys, and both species were more sensitive to bourgeonal than to lilial, cyclamal, canthoxal, helional, lyral, and 3-phenylpropanal. No significant correlation between presence/absence of an oxygen-containing moiety attached to the benzene ring or presence/absence of an additional alkyl group next to the functional aldehyde group, and olfactory sensitivity was found in any of the species. However, the presence of a tertiary butyl group in para position (relative to the functional aldehyde group) combined with a lack of an additional alkyl group next to the functional aldehyde group may be responsible for the finding that both species were most sensitive to bourgeonal.

Development of a sensitive setup for laser spectroscopy studies of very exotic calcium isotopes

NASA Astrophysics Data System (ADS)

Garcia Ruiz, R. F.; Gorges, C.; Bissell, M.; Blaum, K.; Gins, W.; Heylen, H.; Koenig, K.; Kaufmann, S.; Kowalska, M.; Krämer, J.; Lievens, P.; Malbrunot-Ettenauer, S.; Neugart, R.; Neyens, G.; Nörtershäuser, W.; Yordanov, D. T.; Yang, X. F.

2017-04-01

An experimental setup for sensitive high-resolution measurements of hyperfine structure spectra of exotic calcium isotopes has been developed and commissioned at the COLLAPS beam line at ISOLDE, CERN. The technique is based on the radioactive detection of decaying isotopes after optical pumping and state selective neutralization (ROC) (Vermeeren et al 1992 Phys. Rev. Lett. 68 1679). The improvements and developments necessary to extend the applicability of the experimental technique to calcium isotopes produced at rates as low as few ions s-1 are discussed. Numerical calculations of laser-ion interaction and ion-beam simulations were explored to obtain the optimum performance of the experimental setup. Among the implemented features are a multi-step optical pumping region for sensitive measurements of isotopes with hyperfine splitting, a high-voltage platform for adequate control of low-energy ion beams and simultaneous β-detection of neutralized and remaining ions. The commissioning of the experimental setup, and the first online results on neutron-rich calcium isotopes are presented.

Quantum metrology with a single spin-3/2 defect in silicon carbide

NASA Astrophysics Data System (ADS)

Soykal, Oney O.; Reinecke, Thomas L.

We show that implementations for quantum sensing with exceptional sensitivity and spatial resolution can be made using the novel features of semiconductor high half-spin multiplet defects with easy-to-implement optical detection protocols. To achieve this, we use the spin- 3 / 2 silicon monovacancy deep center in hexagonal silicon carbide based on our rigorous derivation of this defect's ground state and of its electronic and optical properties. For a single VSi- defect, we obtain magnetic field sensitivities capable of detecting individual nuclear magnetic moments. We also show that its zero-field splitting has an exceptional strain and temperature sensitivity within the technologically desirable near-infrared window of biological systems. Other point defects, i.e. 3d transition metal or rare-earth impurities in semiconductors, may also provide similar opportunities in quantum sensing due to their similar high spin (S >= 3 / 2) configurations. This work was supported in part by ONR and by the Office of Secretary of Defense, Quantum Science and Engineering Program.

An ultrasensitive quartz crystal microbalance-micropillars based sensor for humidity detection

NASA Astrophysics Data System (ADS)

Wang, Pengtao; Su, Junwei; Su, Che-Fu; Dai, Wen; Cernigliaro, George; Sun, Hongwei

2014-06-01

A unique sensing device, which couples microscale pillars with quartz crystal microbalance (QCM) substrate to form a resonant system, is developed to achieve several orders of magnitude enhancement in sensitivity compared to conventional QCM sensors. In this research, Polymethyl Methacrylate (PMMA) micropillars are fabricated on a QCM substrate using nanoimprinting lithography. The effects of pillar geometry and physical properties, tuned by molecular weight (MW) of PMMA, on the resonant characteristics of QCM-micropillars device are systematically investigated. It is found that the resonant frequency shift increases with increasing MW. The coupled QCM-micropillars device displays nonlinear frequency response, which is opposite to the linear response of conventional QCM devices. In addition, a positive resonant frequency shift is captured near the resonant point of the coupled QCM-micropillars system. Humidity detection experiments show that compared to current nanoscale feature based QCM sensors, QCM-micropillars devices offer higher sensitivity and moderate response time. This research points to a novel way of improving sensitivity of acoustic wave sensors without the need for fabricating surface nanostructures.

MSM optical detector on the basis of II-type ZnSe/ZnTe superlattice

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

Kuznetzov, P. I., E-mail: pik218@ire216.msk.su; Averin, S. V., E-mail: sva278@ire216.msk.su; Zhitov, V. A.

2017-02-15

On the basis of a type-II ZnSe/ZnTe superlattice, a MSM (metal—semiconductor–metal) photodetector is fabricated and investigated. The detector features low dark currents and a high sensitivity. The spectral characteristic of the detector provides the possibility of the selective detection of three separate spectral portions of visible and near-infrared radiation.

Real-time self-networking radiation detector apparatus

DOEpatents

Kaplan, Edward [Stony Brook, NY; Lemley, James [Miller Place, NY; Tsang, Thomas Y [Holbrook, NY; Milian, Laurence W [East Patchogue, NY

2007-06-12

The present invention is for a radiation detector apparatus for detecting radiation sources present in cargo shipments. The invention includes the features of integrating a bubble detector sensitive to neutrons and a GPS system into a miniaturized package that can wirelessly signal the presence of radioactive material in shipping containers. The bubble density would be read out if such indicated a harmful source.

Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images.

PubMed

Mane, Vijay Mahadeo; Jadhav, D V

2017-05-24

Diabetic retinopathy (DR) is the most common diabetic eye disease. Doctors are using various test methods to detect DR. But, the availability of test methods and requirements of domain experts pose a new challenge in the automatic detection of DR. In order to fulfill this objective, a variety of algorithms has been developed in the literature. In this paper, we propose a system consisting of a novel sparking process and a holoentropy-based decision tree for automatic classification of DR images to further improve the effectiveness. The sparking process algorithm is developed for automatic segmentation of blood vessels through the estimation of optimal threshold. The holoentropy enabled decision tree is newly developed for automatic classification of retinal images into normal or abnormal using hybrid features which preserve the disease-level patterns even more than the signal level of the feature. The effectiveness of the proposed system is analyzed using standard fundus image databases DIARETDB0 and DIARETDB1 for sensitivity, specificity and accuracy. The proposed system yields sensitivity, specificity and accuracy values of 96.72%, 97.01% and 96.45%, respectively. The experimental result reveals that the proposed technique outperforms the existing algorithms.

Occurrence and clinical features of sensitization to Pityrosporum orbiculare and other allergens in children with atopic dermatitis.

PubMed

Lindgren, L; Wahlgren, C F; Johansson, S G; Wiklund, I; Nordvall, S L

1995-07-01

One hundred and nineteen consecutive cases of children with atopic dermatitis aged 4-16 years (73 girls) from a pediatric dermatology outpatient clinic were included in a study of atopic sensitization. Structured interviews and clinical investigations were performed. IgE antibodies to common inhalant allergens, Pityrosporum orbiculare, Candida albicans, Tricophyton rubrum and Staphylococcus aureus were detected. Specific IgE antibodies frequently occurred to pollens, animal epithelia, C. albicans, house dust mites and moulds, whereas specific IgE antibodies to potential skin allergens were less prevalent. Twenty-six children (21.8%) had IgE antibodies to P. orbiculare, 14 (11.8%) to T. rubrum and 3 (2.5%) to S. aureus. Atopic dermatitis in children with one or several RAST positivities was worse, with a more chronic course, higher total eczema score, more frequent distribution in the head-neck-face regions and more itch compared to the children without serum detectable IgE antibodies. Severe itch disturbing nightly sleep was the only clinical feature that characterised P. orbiculare-positive cases. Allergy to P. orbiculare appears to be of little importance in early childhood atopic dermatitis but is likely to carry a poor prognosis.

Atmospheric pressure chemical ionization studies of non-polar isomeric hydrocarbons using ion mobility spectrometry and mass spectrometry with different ionization techniques

NASA Technical Reports Server (NTRS)

Borsdorf, H.; Nazarov, E. G.; Eiceman, G. A.

2002-01-01

The ionization pathways were determined for sets of isomeric non-polar hydrocarbons (structural isomers, cis/trans isomers) using ion mobility spectrometry and mass spectrometry with different techniques of atmospheric pressure chemical ionization to assess the influence of structural features on ion formation. Depending on the structural features, different ions were observed using mass spectrometry. Unsaturated hydrocarbons formed mostly [M - 1]+ and [(M - 1)2H]+ ions while mainly [M - 3]+ and [(M - 3)H2O]+ ions were found for saturated cis/trans isomers using photoionization and 63Ni ionization. These ionization methods and corona discharge ionization were used for ion mobility measurements of these compounds. Different ions were detected for compounds with different structural features. 63Ni ionization and photoionization provide comparable ions for every set of isomers. The product ions formed can be clearly attributed to the structures identified. However, differences in relative abundance of product ions were found. Although corona discharge ionization permits the most sensitive detection of non-polar hydrocarbons, the spectra detected are complex and differ from those obtained with 63Ni ionization and photoionization. c. 2002 American Society for Mass Spectrometry.

Document reconstruction by layout analysis of snippets

NASA Astrophysics Data System (ADS)

Kleber, Florian; Diem, Markus; Sablatnig, Robert

2010-02-01

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document. Also skew detection of scanned documents is performed to support OCR algorithms that are sensitive to skew. In this paper document analysis is applied to snippets of torn documents to calculate features for the reconstruction. Documents can either be destroyed by the intention to make the printed content unavailable (e.g. tax fraud investigation, business crime) or due to time induced degeneration of ancient documents (e.g. bad storage conditions). Current reconstruction methods for manually torn documents deal with the shape, inpainting and texture synthesis techniques. In this paper the possibility of document analysis techniques of snippets to support the matching algorithm by considering additional features are shown. This implies a rotational analysis, a color analysis and a line detection. As a future work it is planned to extend the feature set with the paper type (blank, checked, lined), the type of the writing (handwritten vs. machine printed) and the text layout of a snippet (text size, line spacing). Preliminary results show that these pre-processing steps can be performed reliably on a real dataset consisting of 690 snippets.

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.

Sequential projection pursuit for optimised vibration-based damage detection in an experimental wind turbine blade

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2018-02-01

To advance the concept of smart structures in large systems, such as wind turbines (WTs), it is desirable to be able to detect structural damage early while using minimal instrumentation. Data-driven vibration-based damage detection methods can be competitive in that respect because global vibrational responses encompass the entire structure. Multivariate damage sensitive features (DSFs) extracted from acceleration responses enable to detect changes in a structure via statistical methods. However, even though such DSFs contain information about the structural state, they may not be optimised for the damage detection task. This paper addresses the shortcoming by exploring a DSF projection technique specialised for statistical structural damage detection. High dimensional initial DSFs are projected onto a low-dimensional space for improved damage detection performance and simultaneous computational burden reduction. The technique is based on sequential projection pursuit where the projection vectors are optimised one by one using an advanced evolutionary strategy. The approach is applied to laboratory experiments with a small-scale WT blade under wind-like excitations. Autocorrelation function coefficients calculated from acceleration signals are employed as DSFs. The optimal numbers of projection vectors are identified with the help of a fast forward selection procedure. To benchmark the proposed method, selections of original DSFs as well as principal component analysis scores from these features are additionally investigated. The optimised DSFs are tested for damage detection on previously unseen data from the healthy state and a wide range of damage scenarios. It is demonstrated that using selected subsets of the initial and transformed DSFs improves damage detectability compared to the full set of features. Furthermore, superior results can be achieved by projecting autocorrelation coefficients onto just a single optimised projection vector.

Computer-aided diagnosis of liver tumors on computed tomography images.

PubMed

Chang, Chin-Chen; Chen, Hong-Hao; Chang, Yeun-Chung; Yang, Ming-Yang; Lo, Chung-Ming; Ko, Wei-Chun; Lee, Yee-Fan; Liu, Kao-Lang; Chang, Ruey-Feng

2017-07-01

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation. The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713. Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system. Copyright © 2017 Elsevier B.V. All rights reserved.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

PubMed

Chu, Catherine J; Chan, Arthur; Song, Dan; Staley, Kevin J; Stufflebeam, Steven M; Kramer, Mark A

2017-02-01

High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. Copyright © 2016 Elsevier B.V. All rights reserved.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram

PubMed Central

Chu, Catherine. J.; Chan, Arthur; Song, Dan; Staley, Kevin J.; Stufflebeam, Steven M.; Kramer, Mark A.

2017-01-01

Summary Background High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. New Method The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Results We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. Comparison with Existing Method The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Conclusions Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. PMID:27988323

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.

PubMed

Min, Jianliang; Wang, Ping; Hu, Jianfeng

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1-2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Decision support system for diabetic retinopathy using discrete wavelet transform.

PubMed

Noronha, K; Acharya, U R; Nayak, K P; Kamath, S; Bhandary, S V

2013-03-01

Prolonged duration of the diabetes may affect the tiny blood vessels of the retina causing diabetic retinopathy. Routine eye screening of patients with diabetes helps to detect diabetic retinopathy at the early stage. It is very laborious and time-consuming for the doctors to go through many fundus images continuously. Therefore, decision support system for diabetic retinopathy detection can reduce the burden of the ophthalmologists. In this work, we have used discrete wavelet transform and support vector machine classifier for automated detection of normal and diabetic retinopathy classes. The wavelet-based decomposition was performed up to the second level, and eight energy features were extracted. Two energy features from the approximation coefficients of two levels and six energy values from the details in three orientations (horizontal, vertical and diagonal) were evaluated. These features were fed to the support vector machine classifier with various kernel functions (linear, radial basis function, polynomial of orders 2 and 3) to evaluate the highest classification accuracy. We obtained the highest average classification accuracy, sensitivity and specificity of more than 99% with support vector machine classifier (polynomial kernel of order 3) using three discrete wavelet transform features. We have also proposed an integrated index called Diabetic Retinopathy Risk Index using clinically significant wavelet energy features to identify normal and diabetic retinopathy classes using just one number. We believe that this (Diabetic Retinopathy Risk Index) can be used as an adjunct tool by the doctors during the eye screening to cross-check their diagnosis.

Friction Stir Weld Inspection Through Conductivity Imaging Using Shaped Field MWM(Registered Trademark) - Arrays

NASA Technical Reports Server (NTRS)

Goldfine, Neil; Grundy, David; Zilberstein, Vladimir; Kinchen, David G.; McCool, Alex (Technical Monitor)

2002-01-01

Friction Stir Welds (FSW) of Al 2195-T8 and Al 2219-T8, provided by Lockheed Martin Michoud Operations, were inspected for lack-of-penetration (LOP) defects using a custom designed MWM-Array, a multi-element eddy-current sensor. MWM (registered trademark) electrical conductivity mapping demonstrated high sensitivity to LOP as small as 0.75 mm (0.03 in.), as confirmed by metallographic data that characterized the extent of LOP defects. High sensitivity and high spatial resolution was achieved via a 37-element custom designed MWM-Array allowing LOP detection using the normalized longitudinal component of the MWM measured conductivity. This permitted both LOP detection and correlation of MWM conductivity features with the LOP defect size, as changes in conductivity were apparently associated with metallurgical features within the near-surface layer of the LOP defect zone. MWM conductivity mapping reveals information similar to macro-etching as the MWM-Array is sensitive to small changes in conductivity due to changes in microstructure associated with material thermal processing, in this case welding. The electrical conductivity measured on the root side of FSWs varies across the weld due to microstructural differences introduced by the FSW process, as well as those caused by planar flaws. Weld metal, i.e., dynamically recrystallized zone (DXZ), thermomechanically affected zone (TMZ), heat-affected zone (HAZ), and parent metal (PM) are all evident in the conductivity maps. While prior efforts had met with limited success for NDE (Nondestructive Evaluation) of dissimilar alloy, Al2219 to Al2195 FSW, the new custom designed multi-element MWM-Array achieved detection of all LOP defects even in dissimilar metal welds.

A novel restriction endonuclease GlaI for rapid and highly sensitive detection of DNA methylation coupled with isothermal exponential amplification reaction.

PubMed

Sun, Yueying; Sun, Yuanyuan; Tian, Weimin; Liu, Chenghui; Gao, Kejian; Li, Zhengping

2018-02-07

Sensitive and accurate detection of site-specific DNA methylation is of critical significance for early diagnosis of human diseases, especially cancers. Herein, for the first time we employ a novel methylation-dependent restriction endonuclease GlaI to detect site-specific DNA methylation in a highly specific and sensitive way by coupling with isothermal exponential amplification reaction (EXPAR). GlaI can only cut the methylated target site with excellent selectivity but leave the unmethylated DNA intact. Then the newly exposed end fragments of methylated DNA can trigger EXPAR for highly efficient signal amplification while the intact unmethylated DNA will not initiate EXPAR at all. As such, only the methylated DNA is quantitatively and faithfully reflected by the real-time fluorescence signal of the GlaI-EXPAR system, and the potential false positive interference from unmethylated DNA can be effectively eliminated. Therefore, by integrating the unique features of GlaI for highly specific methylation discrimination and EXPAR for rapid and powerful signal amplification, the elegant GlaI-EXPAR assay allows the direct quantification of methylated DNA with ultrahigh sensitivity and accuracy. The detection limit of methylated DNA target has been pushed down to the aM level and the whole detection process of GlaI-EXPAR can be accomplished within a short time of 2 h. More importantly, ultrahigh specificity is achieved and as low as 0.01% methylated DNA can be clearly identified in the presence of a large excess of unmethylated DNA. This GlaI-EXPAR is also demonstrated to be capable of determining site-specific DNA methylations in real genomic DNA samples. Sharing the distinct advantages of ultrahigh sensitivity, outstanding specificity and facile operation, this new GlaI-EXPAR strategy may provide a robust and reliable platform for the detection of site-specific DNA methylations with low abundances.

A Wireless Fully Passive Neural Recording Device for Unobtrusive Neuropotential Monitoring.

PubMed

Kiourti, Asimina; Lee, Cedric W L; Chae, Junseok; Volakis, John L

2016-01-01

We propose a novel wireless fully passive neural recording device for unobtrusive neuropotential monitoring. Previous work demonstrated the feasibility of monitoring emulated brain signals in a wireless fully passive manner. In this paper, we propose a novel realistic recorder that is significantly smaller and much more sensitive. The proposed recorder utilizes a highly efficient microwave backscattering method and operates without any formal power supply or regulating elements. Also, no intracranial wires or cables are required. In-vitro testing is performed inside a four-layer head phantom (skin, bone, gray matter, and white matter). Compared to our former implementation, the neural recorder proposed in this study has the following improved features: 1) 59% smaller footprint, 2) up to 20-dB improvement in neuropotential detection sensitivity, and 3) encapsulation in biocompatible polymer. For the first time, temporal emulated neuropotentials as low as 63 μVpp can be detected in a wireless fully passive manner. Remarkably, the high-sensitivity achieved in this study implies reading of most neural signals generated by the human brain. The proposed recorder brings forward transformational possibilities in wireless fully passive neural detection for a very wide range of applications (e.g., epilepsy, Alzheimer's, mental disorders, etc.).

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Calhoun, Vince D.

2013-01-01

Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398

PVT-NG sensor final report.

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

Mitchell, Dean James; Brusseau, Charles A.

2012-01-01

This document is a final report for the polyvinyl toluene (PVT) neutron-gamma (PVT-NG) project, which was sponsored by the Domestic Nuclear Detection Office (DNDO). The PVT-NG sensor uses PVT detectors for both gamma and neutron detection. The sensor exhibits excellent spectral resolution and gain stabilization, which are features that are beneficial for detection of both gamma-ray and neutron sources. In fact, the ability to perform isotope identification based on spectra that were measured by the PVT-NG sensor was demonstrated. As described in a previous report, the neutron sensitivity of the first version of the prototype was about 25% less thanmore » the DNDO requirement of 2.5 cps/ng for bare Cf-252. This document describes design modifications that were expected to improve the neutron sensitivity by about 50% relative to the PVT-NG prototype. However, the project was terminated before execution of the design modifications after portal vendors demonstrated other technologies that enable neutron detection without the use of He-3. Nevertheless, the PVT-NG sensor development demonstrated several performance goals that may be useful in future portal designs.« less

A flow cytometry-optimized assay using an SOS-green fluorescent protein (SOS-GFP) whole-cell biosensor for the detection of genotoxins in complex environments.

PubMed

Norman, Anders; Hansen, Lars Hestbjerg; Sørensen, Søren J

2006-02-28

Whole-cell biosensors have become popular tools for detection of ecotoxic compounds in environmental samples. We have developed an assay optimized for flow cytometry with detection of genotoxic compounds in mind. The assay features extended pre-incubation and a cell density of only 10(6)-10(7) cells/mL, and proved far more sensitive than a previously published assay using the same biosensor strain. By applying the SOS-green fluorescent protein (GFP) whole-cell biosensor directly to soil microcosms we were also able to evaluate both the applicability and sensitivity of a biosensor based on SOS-induction in whole soil samples. Soil microcosms were spiked with a dilution-series of crude broth extract from the mitomycin C-producing streptomycete Streptomyces caespitosus. Biosensors extracted from these microcosms after 1 day of incubation at 30 degrees C were easily distinguished from extracts of non-contaminated soil particles when using flow cytometry, and induction of the biosensor by mitomycin C was detectable at concentrations as low as 2.5 ng/g of soil.

Ultrasensitive Immunosensor for Cancer Biomarker Proteins using Gold Nanoparticle Film Electrodes and Multienzyme-Particle Amplification

PubMed Central

Mani, Vigneshwaran; Chikkaveeraiah, Bhaskara V.; Patel, Vyomesh; Gutkind, J. Silvio; Rusling, James F.

2009-01-01

A densely packed gold nanoparticle platform combined with a multiple-enzyme labeled detection antibody-magnetic bead bioconjugate was used as the basis for an ultrasensitive electrochemical immunosensor to detect cancer biomarkers in serum. Sensitivity was greatly amplified by synthesizing magnetic bioconjugates particles containing 7500 horseradish peroxidase (HRP) labels along with detection antibodies (Ab2) attached to activated carboxyl groups on 1 µm diameter magnetic beads. These sensors had sensitivity of 31.5 µA mL ng−1 and detection limit (DL) of 0.5 pg mL−1 for prostate specific antigen (PSA) in 10 µL of undiluted serum. This represents an ultralow mass DL of 5 fg PSA, eight fold better than a previously reported carbon nanotube (CNT) forest immunosensor featuring multiple labels on carbon nanotubes, and near or below the normal serum levels of most cancer biomarkers. Measurements of PSA in cell lysates and human serum of cancer patients gave excellent correlations with standard ELISA assays. These easily fabricated AuNP immunosensors show excellent promise for future fabrication of bioelectronic arrays. PMID:19216571

Isothermal Amplification Methods for the Detection of Nucleic Acids in Microfluidic Devices

PubMed Central

Zanoli, Laura Maria; Spoto, Giuseppe

2012-01-01

Diagnostic tools for biomolecular detection need to fulfill specific requirements in terms of sensitivity, selectivity and high-throughput in order to widen their applicability and to minimize the cost of the assay. The nucleic acid amplification is a key step in DNA detection assays. It contributes to improving the assay sensitivity by enabling the detection of a limited number of target molecules. The use of microfluidic devices to miniaturize amplification protocols reduces the required sample volume and the analysis times and offers new possibilities for the process automation and integration in one single device. The vast majority of miniaturized systems for nucleic acid analysis exploit the polymerase chain reaction (PCR) amplification method, which requires repeated cycles of three or two temperature-dependent steps during the amplification of the nucleic acid target sequence. In contrast, low temperature isothermal amplification methods have no need for thermal cycling thus requiring simplified microfluidic device features. Here, the use of miniaturized analysis systems using isothermal amplification reactions for the nucleic acid amplification will be discussed. PMID:25587397

A "signal on" protection-displacement-hybridization-based electrochemical hepatitis B virus gene sequence sensor with high sensitivity and peculiar adjustable specificity.

PubMed

Li, Fengqin; Xu, Yanmei; Yu, Xiang; Yu, Zhigang; He, Xunjun; Ji, Hongrui; Dong, Jinghao; Song, Yongbin; Yan, Hong; Zhang, Guiling

2016-08-15

One "signal on" electrochemical sensing strategy was constructed for the detection of a specific hepatitis B virus (HBV) gene sequence based on the protection-displacement-hybridization-based (PDHB) signaling mechanism. This sensing system is composed of three probes, one capturing probe (CP) and one assistant probe (AP) which are co-immobilized on the Au electrode surface, and one 3-methylene blue (MB) modified signaling probe (SP) free in the detection solution. One duplex are formed between AP and SP with the target, a specific HBV gene sequence, hybridizing with CP. This structure can drive the MB labels close to the electrode surface, thereby producing a large detection current. Two electrochemical testing techniques, alternating current voltammetry (ACV) and cyclic voltammetry (CV), were used for characterizing the sensor. Under the optimized conditions, the proposed sensor exhibits a high sensitivity with the detection limit of ∼5fM for the target. When used for the discrimination of point mutation, the sensor also features an outstanding ability and its peculiar high adjustability. Copyright © 2016 Elsevier B.V. All rights reserved.

Modelling of celestial backgrounds

NASA Astrophysics Data System (ADS)

Hickman, Duncan L.; Smith, Moira I.; Lim, Jae-Wan; Jeon, Yun-Ho

2018-05-01

For applications where a sensor's image includes the celestial background, stars and Solar System Bodies compromise the ability of the sensor system to correctly classify a target. Such false targets are particularly significant for the detection of weak target signatures which only have a small relative angular motion. The detection of celestial features is well established in the visible spectral band. However, given the increasing sensitivity and low noise afforded by emergent infrared focal plane array technology together with larger and more efficient optics, the signatures of celestial features can also impact performance at infrared wavelengths. A methodology has been developed which allows the rapid generation of celestial signatures in any required spectral band using star data from star catalogues and other open-source information. Within this paper, the radiometric calculations are presented to determine the irradiance values of stars and planets in any spectral band.

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Classification of wet aged related macular degeneration using optical coherence tomographic images

NASA Astrophysics Data System (ADS)

Haq, Anam; Mir, Fouwad Jamil; Yasin, Ubaid Ullah; Khan, Shoab A.

2013-12-01

Wet Age related macular degeneration (AMD) is a type of age related macular degeneration. In order to detect Wet AMD we look for Pigment Epithelium detachment (PED) and fluid filled region caused by choroidal neovascularization (CNV). This form of AMD can cause vision loss if not treated in time. In this article we have proposed an automated system for detection of Wet AMD in Optical coherence tomographic (OCT) images. The proposed system extracts PED and CNV from OCT images using segmentation and morphological operations and then detailed feature set are extracted. These features are then passed on to the classifier for classification. Finally performance measures like accuracy, sensitivity and specificity are calculated and the classifier delivering the maximum performance is selected as a comparison measure. Our system gives higher performance using SVM as compared to other methods.

Real-time Detection and Monitoring of Loop Mediated Amplification (LAMP) Reaction Using Self-quenching and De-quenching Fluorogenic Probes.

PubMed

Gadkar, Vijay J; Goldfarb, David M; Gantt, Soren; Tilley, Peter A G

2018-04-03

Loop-mediated isothermal amplification (LAMP) is an isothermal nucleic acid amplification (iNAAT) technique known for its simplicity, sensitivity and speed. Its low-cost feature has resulted in its wide scale application, especially in low resource settings. The major disadvantage of LAMP is its heavy reliance on indirect detection methods like turbidity and non-specific dyes, which often leads to the detection of false positive results. In the present work, we have developed a direct detection approach, whereby a labelled loop probe quenched in its unbound state, fluoresces only when bound to its target (amplicon). Henceforth, referred to as Fluorescence of Loop Primer Upon Self Dequenching-LAMP (FLOS-LAMP), it allows for the sequence-specific detection of LAMP amplicons. The FLOS-LAMP concept was validated for rapid detection of the human pathogen, Varicella-zoster virus, from clinical samples. The FLOS-LAMP had a limit of detection of 500 copies of the target with a clinical sensitivity and specificity of 96.8% and 100%, respectively. The high level of specificity is a major advance and solves one of the main shortcomings of the LAMP technology, i.e. false positives. Self-quenching/de-quenching probes were further used with other LAMP primer sets and different fluorophores, thereby demonstrating its versatility and adaptability.

A new scoring model for characterization of adnexal masses based on two-dimensional gray-scale and colour Doppler sonographic features

PubMed Central

Abbas, A.M.; Zahran, K.M.; Nasr, A.; Kamel, H.S.

2014-01-01

Objective: To determine the most discriminating two-dimensional gray-scale and colour Doppler sonographic features that allow differentiation between malignant and benign adnexal masses, and to develop a scoring model that would enable more accurate diagnosis with those features. Methods: A cross sectional prospective study was conducted on patients scheduled for surgery due to presence of adnexal masses at Woman’s Health Center, Assiut University, Egypt between October 2012 and October 2013. All patients were evaluated by 2D ultrasound for morphological features of the masses combined with colour Doppler examination of their vessels. The final diagnosis, based on histopathological analysis, was used as a gold standard. Results: One hundred forty-six patients were recruited, 104 with benign masses, 42 with malignant masses. Features that allowed statistically significant discrimination of benignity from malignancy were; volume of mass, type of mass, presence and thickness of septae, presence and length of papillary projections, location of vessels at colour Doppler and colour score. A scoring model was formulated combining these features together; Assiut Scoring Model (ASM). The cut-off level with the highest accuracy in detection of malignancy, was ≥6, had a sensitivity of 93.5% and specificity of 92.2%. Conclusion: Our Scoring Model; a multiparameter scoring using four gray-scale ultrasound and two colour Doppler features, had shown a high sensitivity and specificity for prediction of malignancy in adnexal masses compared with previous scoring systems. PMID:25009729

Dual-echo ASL based assessment of motor networks: a feasibility study

NASA Astrophysics Data System (ADS)

Storti, Silvia Francesca; Boscolo Galazzo, Ilaria; Pizzini, Francesca B.; Menegaz, Gloria

2018-04-01

Objective. Dual-echo arterial spin labeling (DE-ASL) technique has been recently proposed for the simultaneous acquisition of ASL and blood-oxygenation-level-dependent (BOLD)-functional magnetic resonance imaging (fMRI) data. The assessment of this technique in detecting functional connectivity at rest or during motor and motor imagery tasks is still unexplored both per-se and in comparison with conventional methods. The purpose is to quantify the sensitivity of the DE-ASL sequence with respect to the conventional fMRI sequence (cvBOLD) in detecting brain activations, and to assess and compare the relevance of node features in decoding the network structure. Approach. Thirteen volunteers were scanned acquiring a pseudo-continuous DE-ASL sequence from which the concomitant BOLD (ccBOLD) simultaneously to the ASL can be extracted. The approach consists of two steps: (i) model-based analyses for assessing brain activations at individual and group levels, followed by statistical analysis for comparing the activation elicited by the three sequences under two conditions (motor and motor imagery), respectively; (ii) brain connectivity graph-theoretical analysis for assessing and comparing the network models properties. Main results. Our results suggest that cvBOLD and ccBOLD have comparable sensitivity in detecting the regions involved in the active task, whereas ASL offers a higher degree of co-localization with smaller activation volumes. The connectivity results and the comparative analysis of node features across sequences revealed that there are no strong changes between rest and tasks and that the differences between the sequences are limited to few connections. Significance. Considering the comparable sensitivity of the ccBOLD and cvBOLD sequences in detecting activated brain regions, the results demonstrate that DE-ASL can be successfully applied in functional studies allowing to obtain both ASL and BOLD information within a single sequence. Further, DE-ASL is a powerful technique for research and clinical applications allowing to perform quantitative comparisons as well as to characterize functional connectivity.

Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topological soft gradient prescreening method and luminal analysis

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

Wei, Jun, E-mail: jvwei@umich.edu; Zhou, Chuan; Chan, Heang-Ping

2014-08-15

Purpose: The buildup of noncalcified plaques (NCPs) that are vulnerable to rupture in coronary arteries is a risk for myocardial infarction. Interpretation of coronary CT angiography (cCTA) to search for NCP is a challenging task for radiologists due to the low CT number of NCP, the large number of coronary arteries, and multiple phase CT acquisition. The authors conducted a preliminary study to develop machine learning method for automated detection of NCPs in cCTA. Methods: With IRB approval, a data set of 83 ECG-gated contrast enhanced cCTA scans with 120 NCPs was collected retrospectively from patient files. A multiscale coronarymore » artery response and rolling balloon region growing (MSCAR-RBG) method was applied to each cCTA volume to extract the coronary arterial trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for NCP candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. The NCP candidates were then characterized by a luminal analysis that used 3D geometric features to quantify the shape information and gray-level features to evaluate the density of the NCP candidates. With machine learning techniques, useful features were identified and combined into an NCP score to differentiate true NCPs from false positives (FPs). To evaluate the effectiveness of the image analysis methods, the authors performed tenfold cross-validation with the available data set. Receiver operating characteristic (ROC) analysis was used to assess the classification performance of individual features and the NCP score. The overall detection performance was estimated by free response ROC (FROC) analysis. Results: With our TSG prescreening method, a prescreening sensitivity of 92.5% (111/120) was achieved with a total of 1181 FPs (14.2 FPs/scan). On average, six features were selected during the tenfold cross-validation training. The average area under the ROC curve (AUC) value for training was 0.87 ± 0.01 and the AUC value for validation was 0.85 ± 0.01. Using the NCP score, FROC analysis of the validation set showed that the FP rates were reduced to 3.16, 1.90, and 1.39 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. Conclusions: The topological soft-gradient prescreening method in combination with the luminal analysis for FP reduction was effective for detection of NCPs in cCTA, including NCPs causing positive or negative vessel remodeling. The accuracy of vessel segmentation, tracking, and centerline identification has a strong impact on NCP detection. Studies are underway to further improve these techniques and reduce the FPs of the CADe system.« less

Imaging Lysosomal pH Alteration in Stressed Cells with a Sensitive Ratiometric Fluorescence Sensor.

PubMed

Xue, Zhongwei; Zhao, Hu; Liu, Jian; Han, Jiahuai; Han, Shoufa

2017-03-24

The organelle-specific pH is crucial for cell homeostasis. Aberrant pH of lysosomes has been manifested in myriad diseases. To probe lysosome responses to cell stress, we herein report the detection of lysosomal pH changes with a dual colored probe (CM-ROX), featuring a coumarin domain with "always-on" blue fluorescence and a rhodamine-lactam domain activatable to lysosomal acidity to give red fluorescence. With sensitive ratiometric signals upon subtle pH changes, CM-ROX enables discernment of lysosomal pH changes in cells undergoing autophagy, cell death, and viral infection.

The Effect of Experimental Variables on Industrial X-Ray Micro-Computed Sensitivity

NASA Technical Reports Server (NTRS)

Roth, Don J.; Rauser, Richard W.

2014-01-01

A study was performed on the effect of experimental variables on radiographic sensitivity (image quality) in x-ray micro-computed tomography images for a high density thin wall metallic cylinder containing micro-EDM holes. Image quality was evaluated in terms of signal-to-noise ratio, flaw detectability, and feature sharpness. The variables included: day-to-day reproducibility, current, integration time, voltage, filtering, number of frame averages, number of projection views, beam width, effective object radius, binning, orientation of sample, acquisition angle range (180deg to 360deg), and directional versus transmission tube.

Infrared techniques for comet observations

NASA Technical Reports Server (NTRS)

Hanner, Martha S.; Tokunaga, Alan T.

1991-01-01

The infrared spectral region (1-1000 microns) is important for studies of both molecules and solid grains in comets. Infrared astronomy is in the midst of a technological revolution, with the development of sensitive 2D arrays leading to IR cameras and spectrometers with vastly improved sensitivity and resolution. The Halley campaign gave us tantalizing first glimpses of the comet science possible with this new technology, evidenced, for example, by the many new spectral features detected in the infrared. The techniques of photometry, imaging, and spectroscopy are reviewed in this chapter and their status at the time of the Halley observations is described.

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

Yu, Shenglong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

Yu, Shenglong; Zhao, Hong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

Hierarchically Self-Assembled Star-Shaped ZnO Microparticles for Electrochemical Sensing of Amines.

PubMed

Du, Jianping; Huang, Xiaoxi; Zhao, Ruihua; Li, Jinping; Asefa, Tewodros

2016-06-06

Novel, hierarchically nanostructured, star-shaped ZnO (SSZ) microparticles are synthesized by a hydrothermal synthetic route. The SSZ microparticles serve as effective platforms for electrochemical detection of amines in solution. The morphology and structure of the materials are characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, Raman spectroscopy, and UV/Vis spectroscopy. The as-synthesized SSZ microparticles comprise self-assembled hexagonal prisms that possess nanometer and micrometer pores in their structure and on their surfaces-structural features that are conducive to sensing applications. An electrode fabricated by using the hierarchically nanostructured SSZ materials serve as a sensitive electrochemical sensor for detection of low concentrations of ethylenediamine, with a sensitivity of 2.98×10(-2)  mA cm(-2)  mm(-1) , a detection limit of 2.36×10(-2)  mm, and a short response time of 8 s. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Biophotonics determination of 6-benzylaminopurine (6-BAP) plant growth regulator using OFRR biosensor

NASA Astrophysics Data System (ADS)

Yang, Gilmo; Kang, Sukwon; Lee, Kangjin; Kim, Giyoung; Son, Jaeryong; Mo, Changyeun

2010-04-01

The identification of pesticide and 6-benzylaminopurine (6-BAP) plant growth regulator was carried out using a label-free opto-fluidic ring resonator (OFRR) biosensor. The OFRR sensing platform is a recent advancement in opto-fluidic technology that integrates photonic sensing technology with microfluidics. It features quick detection time, small sample volume, accurate quantitative and kinetic results. The most predominant advantage of the OFRR integrated with microfluidics is that we can potentially realize the multi-channel and portable biosensor that detects numerous analytes simultaneously. Antisera for immunoassay were raised in rabbits against the 6-BAP-BSA conjugate. Using the immunization protocol and unknown cytokinin reacting with same antibody, comparable sensitivity and specificity were obtained. 6-BAP antibody was routinely used for cytokinin analysis. A sensitive and simple OFRR method with a good linear relationship was developed for the determination of 6-BAP. The detection limit was also examined. The biosensor demonstrated excellent reproducibility when periodically exposed to 6-BAP.

MoO3/nano-Si heterostructure based highly sensitive and acetone selective sensor prototype: a key to non-invasive detection of diabetes.

PubMed

Dwivedi, Priyanka; Dhanekar, Saakshi; Das, Samaresh

2018-07-06

This paper presents the development of an extremely sensitive and selective acetone sensor prototype which can be used as a platform for non-invasive diabetes detection through exhaled human breath. The miniaturized sensors were produced in high yield with the use of standard microfabrication processes. The sensors were based on a heterostructure composed of MoO 3 and nano-porous silicon (NPS). Features like acetone selective, enhanced sensor response and 0.5 ppm detection limit were observed upon introduction of MoO 3 on the NPS. The sensors were found to be repeatable and stable for almost 1 year, as tested under humid conditions at room temperature. It was inferred that the interface resistance of MoO 3 and NPS played a key role in the sensing mechanism. With the use of breath analysis and lab-on-chip, medical diagnosis procedures can be simplified and provide solutions for point-of-care testing.

Thiophene-based rhodamine as selectivef luorescence probe for Fe(III) and Al(III) in living cells.

PubMed

Wang, Kun-Peng; Chen, Ju-Peng; Zhang, Si-Jie; Lei, Yang; Zhong, Hua; Chen, Shaojin; Zhou, Xin-Hong; Hu, Zhi-Qiang

2017-09-01

The thiophene-modified rhodamine 6G (GYJ) has been synthesized as a novel chemosensor. The sensor has sufficiently high selectivity and sensitivity for the detection of Fe 3+ and Al 3+ ions (M 3+ ) by fluorescence and ultraviolet spectroscopy with a strong ability for anti-interference performance. The binding ratio of M 3+ -GYJ complex was determined to be 2:1 according to the Job's plot. The binding constants for Fe 3+ and Al 3+ were calculated to be 3.91 × 10 8 and 5.26 × 10 8  M -2 , respectively. All these unique features made it particularly favorable for cellular imaging applications. The obvious fluorescence microscopy experiments demonstrated that the probes could contribute to the detection of Fe 3+ and Al 3+ in related cells and biological organs with satisfying resolution. Graphical abstract GYJ has high selectivity and sensitivity for the detection of Fe(III) and Al(III) with the binding ratio of 2:1.

Sensitive Nonenzymatic Electrochemical Glucose Detection Based on Hollow Porous NiO

NASA Astrophysics Data System (ADS)

He, Gege; Tian, Liangliang; Cai, Yanhua; Wu, Shenping; Su, Yongyao; Yan, Hengqing; Pu, Wanrong; Zhang, Jinkun; Li, Lu

2018-01-01

Transition metal oxides (TMOs) have attracted extensive research attentions as promising electrocatalytic materials. Despite low cost and high stability, the electrocatalytic activity of TMOs still cannot satisfy the requirements of applications. Inspired by kinetics, the design of hollow porous structure is considered as a promising strategy to achieve superior electrocatalytic performance. In this work, cubic NiO hollow porous architecture (NiO HPA) was constructed through coordinating etching and precipitating (CEP) principle followed by post calcination. Being employed to detect glucose, NiO HPA electrode exhibits outstanding electrocatalytic activity in terms of high sensitivity (1323 μA mM-1 cm-2) and low detection limit (0.32 μM). The excellent electrocatalytic activity can be ascribed to large specific surface area (SSA), ordered diffusion channels, and accelerated electron transfer rate derived from the unique hollow porous features. The results demonstrate that the NiO HPA could have practical applications in the design of nonenzymatic glucose sensors. The construction of hollow porous architecture provides an effective nanoengineering strategy for high-performance electrocatalysts.

Fluorescence detection of white-beam X-ray absorption anisotropy: towards element-sensitive projections of local atomic structure

PubMed Central

Korecki, P.; Tolkiehn, M.; Dąbrowski, K. M.; Novikov, D. V.

2011-01-01

Projections of the atomic structure around Nb atoms in a LiNbO3 single crystal were obtained from a white-beam X-ray absorption anisotropy (XAA) pattern detected using Nb K fluorescence. This kind of anisotropy results from the interference of X-rays inside a sample and, owing to the short coherence length of a white beam, is visible only at small angles around interatomic directions. Consequently, the main features of the recorded XAA corresponded to distorted real-space projections of dense-packed atomic planes and atomic rows. A quantitative analysis of XAA was carried out using a wavelet transform and allowed well resolved projections of Nb atoms to be obtained up to distances of 10 Å. The signal of nearest O atoms was detected indirectly by a comparison with model calculations. The measurement of white-beam XAA using characteristic radiation indicates the possibility of obtaining element-sensitive projections of the local atomic structure in more complex samples. PMID:21997909

Benzothiazole-Based AIEgen with Tunable Excited-State Intramolecular Proton Transfer and Restricted Intramolecular Rotation Processes for Highly Sensitive Physiological pH Sensing.

PubMed

Li, Kai; Feng, Qi; Niu, Guangle; Zhang, Weijie; Li, Yuanyuan; Kang, Miaomiao; Xu, Kui; He, Juan; Hou, Hongwei; Tang, Ben Zhong

2018-04-23

In this work, a benzothiazole-based aggregation-induced emission luminogen (AIEgen) of 2-(5-(4-carboxyphenyl)-2-hydroxyphenyl)benzothiazole (3) was designed and synthesized, which exhibited multifluorescence emissions in different dispersed or aggregated states based on tunable excited-state intramolecular proton transfer (ESIPT) and restricted intramolecular rotation (RIR) processes. 3 was successfully used as a ratiometric fluorescent chemosensor for the detection of pH, which exhibited reversible acid/base-switched yellow/cyan emission transition. More importantly, the pH jump of 3 was very precipitous from 7.0 to 8.0 with a midpoint of 7.5, which was well matched with the physiological pH. This feature makes 3 very suitable for the highly sensitive detection of pH fluctuation in biosamples and neutral water samples. 3 was also successfully used as a ratiometric fluorescence chemosensor for the detection of acidic and basic organic vapors in test papers.

Combined FLIM and reflectance confocal microscopy for epithelial imaging

NASA Astrophysics Data System (ADS)

Jabbour, Joey M.; Cheng, Shuna; Shrestha, Sebina; Malik, Bilal; Jo, Javier A.; Applegate, Brian; Maitland, Kristen C.

2012-03-01

Current methods for detection of oral cancer lack the ability to delineate between normal and precancerous tissue with adequate sensitivity and specificity. The usual diagnostic mechanism involves visual inspection and palpation followed by tissue biopsy and histopathology, a process both invasive and time-intensive. A more sensitive and objective screening method can greatly facilitate the overall process of detection of early cancer. To this end, we present a multimodal imaging system with fluorescence lifetime imaging (FLIM) for wide field of view guidance and reflectance confocal microscopy for sub-cellular resolution imaging of epithelial tissue. Moving from a 12 x 12 mm2 field of view with 157 ìm lateral resolution using FLIM to 275 x 200 μm2 with lateral resolution of 2.2 μm using confocal microscopy, hamster cheek pouch model is imaged both in vivo and ex vivo. The results indicate that our dual modality imaging system can identify and distinguish between different tissue features, and, therefore, can potentially serve as a guide in early oral cancer detection..

Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

PubMed

Ge, Jing; Zhang, Guoping

2015-01-01

Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.

PubMed

Al-Jarrah, Mohammad A; Shatnawi, Hadeel

2017-08-01

Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection of retinopathy progress in individuals with diabetes is critical for preventing visual loss. Non-proliferative DR (NPDR) is an early stage of DR. Moreover, NPDR can be classified into mild, moderate and severe. This paper proposes a novel morphology-based algorithm for detecting retinal lesions and classifying each case. First, the proposed algorithm detects the three DR lesions, namely haemorrhages, microaneurysms and exudates. Second, we defined and extracted a set of features from detected lesions. The set of selected feature emulates what physicians looked for in classifying NPDR case. Finally, we designed an artificial neural network (ANN) classifier with three layers to classify NPDR to normal, mild, moderate and severe. Bayesian regularisation and resilient backpropagation algorithms are used to train ANN. The accuracy for the proposed classifiers based on Bayesian regularisation and resilient backpropagation algorithms are 96.6 and 89.9, respectively. The obtained results are compared with results of the recent published classifier. Our proposed classifier outperforms the best in terms of sensitivity and specificity.

Colitis detection on abdominal CT scans by rich feature hierarchies

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Lay, Nathan; Wei, Zhuoshi; Lu, Le; Kim, Lauren; Turkbey, Evrim; Summers, Ronald M.

2016-03-01

Colitis is inflammation of the colon due to neutropenia, inflammatory bowel disease (such as Crohn disease), infection and immune compromise. Colitis is often associated with thickening of the colon wall. The wall of a colon afflicted with colitis is much thicker than normal. For example, the mean wall thickness in Crohn disease is 11-13 mm compared to the wall of the normal colon that should measure less than 3 mm. Colitis can be debilitating or life threatening, and early detection is essential to initiate proper treatment. In this work, we apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals to detect potential colitis on CT scans. Our method first generates around 3000 category-independent region proposals for each slice of the input CT scan using selective search. Then, a fixed-length feature vector is extracted from each region proposal using a CNN. Finally, each region proposal is classified and assigned a confidence score with linear SVMs. We applied the detection method to 260 images from 26 CT scans of patients with colitis for evaluation. The detection system can achieve 0.85 sensitivity at 1 false positive per image.

Multi-stream LSTM-HMM decoding and histogram equalization for noise robust keyword spotting.

PubMed

Wöllmer, Martin; Marchi, Erik; Squartini, Stefano; Schuller, Björn

2011-09-01

Highly spontaneous, conversational, and potentially emotional and noisy speech is known to be a challenge for today's automatic speech recognition (ASR) systems, which highlights the need for advanced algorithms that improve speech features and models. Histogram Equalization is an efficient method to reduce the mismatch between clean and noisy conditions by normalizing all moments of the probability distribution of the feature vector components. In this article, we propose to combine histogram equalization and multi-condition training for robust keyword detection in noisy speech. To better cope with conversational speaking styles, we show how contextual information can be effectively exploited in a multi-stream ASR framework that dynamically models context-sensitive phoneme estimates generated by a long short-term memory neural network. The proposed techniques are evaluated on the SEMAINE database-a corpus containing emotionally colored conversations with a cognitive system for "Sensitive Artificial Listening".

Extended Malus law with terahertz metallic metamaterials for sensitive detection with giant tunable quality factor

NASA Astrophysics Data System (ADS)

Romain, Xavier; Baida, Fadi; Boyer, Philippe

2016-07-01

We study a polarizer-analyzer mounting for the terahertz regime with perfectly conducting metallic polarizers made of a periodic subwavelength pattern. With a renewed Jones formalism, we analytically investigate the influence of the multiple reflections, which occur between the polarizer and the analyzer, on the transmission response. We demonstrate that this interaction leads to a modified transmission response: the extended Malus law. In addition, we show that the transmission response can be controlled by the distance between the polarizer and the analyzer. For particular setups, the mounting exhibits extremely sensitive transmission responses. This interesting feature can be employed for high-precision sensing and characterization applications. We specifically propose a general design for measuring the electro-optical response of materials in the terahertz domain allowing detection of refractive index variations as small as 10-5.

Imaging-based molecular barcoding with pixelated dielectric metasurfaces

NASA Astrophysics Data System (ADS)

Tittl, Andreas; Leitis, Aleksandrs; Liu, Mingkai; Yesilkoy, Filiz; Choi, Duk-Yong; Neshev, Dragomir N.; Kivshar, Yuri S.; Altug, Hatice

2018-06-01

Metasurfaces provide opportunities for wavefront control, flat optics, and subwavelength light focusing. We developed an imaging-based nanophotonic method for detecting mid-infrared molecular fingerprints and implemented it for the chemical identification and compositional analysis of surface-bound analytes. Our technique features a two-dimensional pixelated dielectric metasurface with a range of ultrasharp resonances, each tuned to a discrete frequency; this enables molecular absorption signatures to be read out at multiple spectral points, and the resulting information is then translated into a barcode-like spatial absorption map for imaging. The signatures of biological, polymer, and pesticide molecules can be detected with high sensitivity, covering applications such as biosensing and environmental monitoring. Our chemically specific technique can resolve absorption fingerprints without the need for spectrometry, frequency scanning, or moving mechanical parts, thereby paving the way toward sensitive and versatile miniaturized mid-infrared spectroscopy devices.

Polyethylenimine-coated Fe3O4 nanoparticles effectively quench fluorescent DNA, which can be developed as a novel platform for protein detection.

PubMed

Ma, Long; Sun, Nana; Zhang, Jinyan; Tu, Chunhao; Cao, Xiuqi; Duan, Demin; Diao, Aipo; Man, Shuli

2017-11-23

We report a novel assembly of polyethyleneimine (PEI)-coated Fe 3 O 4 nanoparticles (NPs) with single-stranded DNA (ssDNA), and the fluorescence of the dye labeled in the DNA is remarkably quenched. In the presence of a target protein, the protein-DNA aptamer mutual interaction releases the ssDNA from this assembly and hence restores the fluorescence. This feature could be adopted to develop an aptasensor for protein detection. As a proof-of-concept, for the first time, we have used this proposed sensing strategy to detect thrombin selectively and sensitively. Furthermore, simultaneous multiple detection of thrombin and lysozyme in a complex protein mixture has been proven to be possible.

Bifunctional fluorescent probes for detection of amyloid aggregates and reactive oxygen species

NASA Astrophysics Data System (ADS)

Needham, Lisa-Maria; Weber, Judith; Fyfe, James W. B.; Kabia, Omaru M.; Do, Dung T.; Klimont, Ewa; Zhang, Yu; Rodrigues, Margarida; Dobson, Christopher M.; Ghandi, Sonia; Bohndiek, Sarah E.; Snaddon, Thomas N.; Lee, Steven F.

2018-02-01

Protein aggregation into amyloid deposits and oxidative stress are key features of many neurodegenerative disorders including Parkinson's and Alzheimer's disease. We report here the creation of four highly sensitive bifunctional fluorescent probes, capable of H2O2 and/or amyloid aggregate detection. These bifunctional sensors use a benzothiazole core for amyloid localization and boronic ester oxidation to specifically detect H2O2. We characterized the optical properties of these probes using both bulk fluorescence measurements and single-aggregate fluorescence imaging, and quantify changes in their fluorescence properties upon addition of amyloid aggregates of α-synuclein and pathophysiological H2O2 concentrations. Our results indicate these new probes will be useful to detect and monitor neurodegenerative disease.

Bifunctional fluorescent probes for detection of amyloid aggregates and reactive oxygen species.

PubMed

Needham, Lisa-Maria; Weber, Judith; Fyfe, James W B; Kabia, Omaru M; Do, Dung T; Klimont, Ewa; Zhang, Yu; Rodrigues, Margarida; Dobson, Christopher M; Ghandi, Sonia; Bohndiek, Sarah E; Snaddon, Thomas N; Lee, Steven F

2018-02-01

Protein aggregation into amyloid deposits and oxidative stress are key features of many neurodegenerative disorders including Parkinson's and Alzheimer's disease. We report here the creation of four highly sensitive bifunctional fluorescent probes, capable of H 2 O 2 and/or amyloid aggregate detection. These bifunctional sensors use a benzothiazole core for amyloid localization and boronic ester oxidation to specifically detect H 2 O 2 . We characterized the optical properties of these probes using both bulk fluorescence measurements and single-aggregate fluorescence imaging, and quantify changes in their fluorescence properties upon addition of amyloid aggregates of α-synuclein and pathophysiological H 2 O 2 concentrations. Our results indicate these new probes will be useful to detect and monitor neurodegenerative disease.

Bifunctional fluorescent probes for detection of amyloid aggregates and reactive oxygen species

PubMed Central

Needham, Lisa-Maria; Weber, Judith; Fyfe, James W. B.; Kabia, Omaru M.; Do, Dung T.; Klimont, Ewa; Zhang, Yu; Rodrigues, Margarida; Dobson, Christopher M.; Ghandi, Sonia; Bohndiek, Sarah E.; Snaddon, Thomas N.

2018-01-01

Protein aggregation into amyloid deposits and oxidative stress are key features of many neurodegenerative disorders including Parkinson's and Alzheimer's disease. We report here the creation of four highly sensitive bifunctional fluorescent probes, capable of H2O2 and/or amyloid aggregate detection. These bifunctional sensors use a benzothiazole core for amyloid localization and boronic ester oxidation to specifically detect H2O2. We characterized the optical properties of these probes using both bulk fluorescence measurements and single-aggregate fluorescence imaging, and quantify changes in their fluorescence properties upon addition of amyloid aggregates of α-synuclein and pathophysiological H2O2 concentrations. Our results indicate these new probes will be useful to detect and monitor neurodegenerative disease. PMID:29515860

Real-time line-width measurements: a new feature for reticle inspection systems

NASA Astrophysics Data System (ADS)

Eran, Yair; Greenberg, Gad; Joseph, Amnon; Lustig, Cornel; Mizrahi, Eyal

1997-07-01

The significance of line width control in mask production has become greater with the lessening of defect size. There are two conventional methods used for controlling line widths dimensions which employed in the manufacturing of masks for sub micron devices. These two methods are the critical dimensions (CD) measurement and the detection of edge defects. Achieving reliable and accurate control of line width errors is one of the most challenging tasks in mask production. Neither of the two methods cited above (namely CD measurement and the detection of edge defects) guarantees the detection of line width errors with good sensitivity over the whole mask area. This stems from the fact that CD measurement provides only statistical data on the mask features whereas applying edge defect detection method checks defects on each edge by itself, and does not supply information on the combined result of error detection on two adjacent edges. For example, a combination of a small edge defect together with a CD non- uniformity which are both within the allowed tolerance, may yield a significant line width error, which will not be detected using the conventional methods (see figure 1). A new approach for the detection of line width errors which overcomes this difficulty is presented. Based on this approach, a new sensitive line width error detector was developed and added to Orbot's RT-8000 die-to-database reticle inspection system. This innovative detector operates continuously during the mask inspection process and scans (inspects) the entire area of the reticle for line width errors. The detection is based on a comparison of measured line width that are taken on both the design database and the scanned image of the reticle. In section 2, the motivation for developing this new detector is presented. The section covers an analysis of various defect types, which are difficult to detect using conventional edge detection methods or, alternatively, CD measurements. In section 3, the basic concept of the new approach is introduced together with a description of the new detector and its characteristics. In section 4, the calibration process that took place in order to achieve reliable and repeatable line width measurements is presented. The description of an experiments conducted in order to evaluate the sensitivity of the new detector is given in section 5, followed by a report of the results of this evaluation. The conclusions are presented in section 6.

Computer aided detection of clusters of microcalcifications on full field digital mammograms

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

Ge Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.

2006-08-15

We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from themore » artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.« less

Surface enhanced Raman scattering (SERS) spectra of trinitrotoluene in silver colloids prepared by microwave heating method.

PubMed

Zhang, Chunling; Wang, Kaijun; Han, Dejun; Pang, Qing

2014-03-25

Surface enhanced Raman spectroscopy (SERS) has been demonstrated for the detection of trace levels of explosives due to its high sensitivity, speed of detection and fingerprint feature. 2,4,6-Trinitrotoluene (TNT), a leading example of nitroaromatic explosives, is causing wide concern. In this study, SERS spectra of TNT solution in silver colloids have been successfully measured and a comparison was drawn with the normal Raman spectra of bulk TNT. The silver colloids were prepared by the microwave heating method and characterized by UV-Vis spectra and the scanning electron microscopy (SEM). NaCl and pH value have a great impact on SERS intensity of TNT, the corresponding experimental research results and theoretical interpretations were further illustrated to a certain extent. Moreover, the detection limit of TNT in aqueous solution was achieved as low as 10(-10) mol L(-1) and some preliminary experiments of detecting TNT vapor (about 10 μg/L) using SERS have been carried out. Our results demonstrated the potential of SERS for probing TNT with high sensitivity, and suggest SERS as a powerful method for detection of TNT and similar species at trace levels. Copyright © 2013 Elsevier B.V. All rights reserved.

Quantitative evaluation of skeletal muscle defects in second harmonic generation images.

PubMed

Liu, Wenhua; Raben, Nina; Ralston, Evelyn

2013-02-01

Skeletal muscle pathologies cause irregularities in the normally periodic organization of the myofibrils. Objective grading of muscle morphology is necessary to assess muscle health, compare biopsies, and evaluate treatments and the evolution of disease. To facilitate such quantitation, we have developed a fast, sensitive, automatic imaging analysis software. It detects major and minor morphological changes by combining texture features and Fourier transform (FT) techniques. We apply this tool to second harmonic generation (SHG) images of muscle fibers which visualize the repeating myosin bands. Texture features are then calculated by using a Haralick gray-level cooccurrence matrix in MATLAB. Two scores are retrieved from the texture correlation plot by using FT and curve-fitting methods. The sensitivity of the technique was tested on SHG images of human adult and infant muscle biopsies and of mouse muscle samples. The scores are strongly correlated to muscle fiber condition. We named the software MARS (muscle assessment and rating scores). It is executed automatically and is highly sensitive even to subtle defects. We propose MARS as a powerful and unbiased tool to assess muscle health.

Quantitative evaluation of skeletal muscle defects in second harmonic generation images

NASA Astrophysics Data System (ADS)

Liu, Wenhua; Raben, Nina; Ralston, Evelyn

2013-02-01

Skeletal muscle pathologies cause irregularities in the normally periodic organization of the myofibrils. Objective grading of muscle morphology is necessary to assess muscle health, compare biopsies, and evaluate treatments and the evolution of disease. To facilitate such quantitation, we have developed a fast, sensitive, automatic imaging analysis software. It detects major and minor morphological changes by combining texture features and Fourier transform (FT) techniques. We apply this tool to second harmonic generation (SHG) images of muscle fibers which visualize the repeating myosin bands. Texture features are then calculated by using a Haralick gray-level cooccurrence matrix in MATLAB. Two scores are retrieved from the texture correlation plot by using FT and curve-fitting methods. The sensitivity of the technique was tested on SHG images of human adult and infant muscle biopsies and of mouse muscle samples. The scores are strongly correlated to muscle fiber condition. We named the software MARS (muscle assessment and rating scores). It is executed automatically and is highly sensitive even to subtle defects. We propose MARS as a powerful and unbiased tool to assess muscle health.

Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer?

PubMed

Kim, Hae Young; Kim, Young Hoon; Yun, Gabin; Chang, Won; Lee, Yoon Jin; Kim, Bohyoung

2018-01-01

To retrospectively investigate whether texture features obtained from preoperative CT images of advanced gastric cancer (AGC) patients could be used for the prediction of occult peritoneal carcinomatosis (PC) detected during operation. 51 AGC patients with occult PC detected during operation from January 2009 to December 2012 were included as occult PC group. For the control group, other 51 AGC patients without evidence of distant metastasis including PC, and whose clinical T and N stage could be matched to those of the patients of the occult PC group, were selected from the period of January 2011 to July 2012. Each group was divided into test (n = 41) and validation cohort (n = 10). Demographic and clinical data of these patients were acquired from the hospital database. Texture features including average, standard deviation, kurtosis, skewness, entropy, correlation, and contrast were obtained from manually drawn region of interest (ROI) over the omentum on the axial CT image showing the omentum at its largest cross sectional area. After using Fisher's exact and Wilcoxon signed-rank test for comparison of the clinical and texture features between the two groups of the test cohort, conditional logistic regression analysis was performed to determine significant independent predictor for occult PC. Using the optimal cut-off value from receiver operating characteristic (ROC) analysis for the significant variables, diagnostic sensitivity and specificity were determined in the test cohort. The cut-off value of the significant variables obtained from the test cohort was then applied to the validation cohort. Bonferroni correction was used to adjust P value for multiple comparisons. Between the two groups, there was no significant difference in the clinical features. Regarding the texture features, the occult PC group showed significantly higher average, entropy, standard deviation, and significantly lower correlation (P value < 0.004 for all). Conditional logistic regression analysis demonstrated that entropy was significant independent predictor for occult PC. When the cut-off value of entropy (> 7.141) was applied to the validation cohort, sensitivity and specificity for the prediction of occult PC were 80% and 90%, respectively. For AGC patients whose PC cannot be detected with routine imaging such as CT, texture analysis may be a useful adjunct for the prediction of occult PC.

Compressed 3D and 2D digital images versus standard 3D slide film for the evaluation of glaucomatous optic nerve features.

PubMed

Sandhu, Simrenjeet; Rudnisky, Chris; Arora, Sourabh; Kassam, Faazil; Douglas, Gordon; Edwards, Marianne C; Verstraten, Karin; Wong, Beatrice; Damji, Karim F

2018-03-01

Clinicians can feel confident compressed three-dimensional digital (3DD) and two-dimensional digital (2DD) imaging evaluating important features of glaucomatous disc damage is comparable to the previous gold standard of stereoscopic slide film photography, supporting the use of digital imaging for teleglaucoma applications. To compare the sensitivity and specificity of 3DD and 2DD photography with stereo slide film in detecting glaucomatous optic nerve head features. This prospective, multireader validation study imaged and compressed glaucomatous, suspicious or normal optic nerves using a ratio of 16:1 into 3DD and 2DD (1024×1280 pixels) and compared both to stereo slide film. The primary outcome was vertical cup-to-disc ratio (VCDR) and secondary outcomes, including disc haemorrhage and notching, were also evaluated. Each format was graded randomly by four glaucoma specialists. A protocol was implemented for harmonising data including consensus-based interpretation as needed. There were 192 eyes imaged with each format. The mean VCDR for slide, 3DD and 2DD was 0.59±0.20, 0.60±0.18 and 0.62±0.17, respectively. The agreement of VCDR for 3DD versus film was κ=0.781 and for 2DD versus film was κ=0.69. Sensitivity (95.2%), specificity (95.2%) and area under the curve (AUC; 0.953) of 3DD imaging to detect notching were better (p=0.03) than for 2DD (90.5%; 88.6%; AUC=0.895). Similarly, sensitivity (77.8%), specificity (98.9%) and AUC (0.883) of 3DD to detect disc haemorrhage were better (p=0.049) than for 2DD (44.4%; 99.5%; AUC=0.72). There was no difference between 3DD and 2DD imaging in detecting disc tilt (p=0.7), peripapillary atrophy (p=0.16), grey crescent (p=0.1) or pallor (p=0.43), although 3D detected sloping better (p=0.013). Both 3DD and 2DD imaging demonstrates excellent reproducibility in comparison to stereo slide film with experts evaluating VCDR, notching and disc haemorrhage. 3DD in this study was slightly more accurate than 2DD for evaluating disc haemorrhage, notching and sloping. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Value of multiplex PCR to determine the bacterial and viral aetiology of pneumonia in school-age children.

PubMed

Aydemir, Yusuf; Aydemir, Özlem; Pekcan, Sevgi; Özdemir, Mehmet

2017-02-01

Conventional methods for the aetiological diagnosis of community-acquired pneumonia (CAP) are often insufficient owing to low sensitivity and the long wait for the results of culture and particularly serology, and it often these methods establish a diagnosis in only half of cases. To evaluate the most common bacterial and viral agents in CAP using a fast responsive PCR method and investigate the relationship between clinical/laboratory features and aetiology, thereby contributing to empirical antibiotic selection and reduction of treatment failure. In children aged 4-15 years consecutively admitted with a diagnosis of CAP, the 10 most commonly detected bacterial and 12 most commonly detected viral agents were investigated by induced sputum using bacterial culture and multiplex PCR methods. Clinical and laboratory features were compared between bacterial and viral pneumonia. In 78 patients, at least one virus was detected in 38 (48.7%) and at least one bacterium in 32 (41%). In addition, both bacteria and viruses were detected in 16 (20.5%) patients. Overall, the agent detection rate was 69.2%. The most common viruses were respiratory syncytial virus and influenza and the most frequently detected bacteria were S. pneumoniae and H. influenzae. PCR was superior to culture for bacterial isolation (41% vs 13%, respectively). Fever, wheezing and radiological features were not helpful in differentiating between bacterial and viral CAP. White blood cell count, CRP and ESR values were significantly higher in the bacterial/mixed aetiology group than in the viral aetiology group. In CAP, multiplex PCR is highly reliable, superior in detecting multiple pathogens and rapidly identifies aetiological agents. Clinical features are poor for differentiation between bacterial and viral infections. The use of PCR methods allow physicians to provide more appropriate antimicrobial therapy, resulting in a better response to treatment, and it may be possible for use as a routine service if costs can be reduced.

Multi-Stage System for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Feature reduction and payload location with WAM steganalysis

NASA Astrophysics Data System (ADS)

Ker, Andrew D.; Lubenko, Ivans

2009-02-01

WAM steganalysis is a feature-based classifier for detecting LSB matching steganography, presented in 2006 by Goljan et al. and demonstrated to be sensitive even to small payloads. This paper makes three contributions to the development of the WAM method. First, we benchmark some variants of WAM in a number of sets of cover images, and we are able to quantify the significance of differences in results between different machine learning algorithms based on WAM features. It turns out that, like many of its competitors, WAM is not effective in certain types of cover, and furthermore it is hard to predict which types of cover are suitable for WAM steganalysis. Second, we demonstrate that only a few the features used in WAM steganalysis do almost all of the work, so that a simplified WAM steganalyser can be constructed in exchange for a little less detection power. Finally, we demonstrate how the WAM method can be extended to provide forensic tools to identify the location (and potentially content) of LSB matching payload, given a number of stego images with payload placed in the same locations. Although easily evaded, this is a plausible situation if the same stego key is mistakenly re-used for embedding in multiple images.

Automatic detection of atrial fibrillation in cardiac vibration signals.

PubMed

Brueser, C; Diesel, J; Zink, M D H; Winter, S; Schauerte, P; Leonhardt, S

2013-01-01

We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique

PubMed Central

Fan, Mengbao; Wang, Qi; Cao, Binghua; Ye, Bo; Sunny, Ali Imam; Tian, Guiyun

2016-01-01

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances. PMID:27164112

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique.

PubMed

Fan, Mengbao; Wang, Qi; Cao, Binghua; Ye, Bo; Sunny, Ali Imam; Tian, Guiyun

2016-05-07

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances.

A Highly Sensitive Diagnostic System for Detecting Dengue Viruses Using the Interaction between a Sulfated Sugar Chain and a Virion.

PubMed

Saksono, Budi; Dewi, Beti Ernawati; Nainggolan, Leonardo; Suda, Yasuo

2015-01-01

We propose a novel method of detecting trace amounts of dengue virus (DENVs) from serum. Our method is based on the interaction between a sulfated sugar chain and a DENV surface glycoprotein. After capturing DENV with the sulfated sugar chain-immobilized gold nanoparticles (SGNPs), the resulting complex is precipitated and viral RNA content is measured using the reverse-transcription quantitative polymerase chain reaction SYBR Green I (RT-qPCR-Syb) method. Sugar chains that bind to DENVs were identified using the array-type sugar chain immobilized chip (Sugar Chip) and surface plasmon resonance (SPR) imaging. Heparin and low-molecular-weight dextran sulfate were identified as binding partners, and immobilized on gold nanoparticles to prepare 3 types of SGNPs. The capacity of these SGNPs to capture and concentrate trace amounts of DENVs was evaluated in vitro. The SGNP with greatest sensitivity was tested using clinical samples in Indonesia in 2013-2014. As a result, the novel method was able to detect low concentrations of DENVs using only 6 μL of serum, with similar sensitivity to that of a Qiagen RNA extraction kit using 140 μL of serum. In addition, this method allows for multiplex-like identification of serotypes of DENVs. This feature is important for good healthcare management of DENV infection in order to safely diagnose the dangerous, highly contagious disease quickly, with high sensitivity.

"Change deafness" arising from inter-feature masking within a single auditory object.

PubMed

Barascud, Nicolas; Griffiths, Timothy D; McAlpine, David; Chait, Maria

2014-03-01

Our ability to detect prominent changes in complex acoustic scenes depends not only on the ear's sensitivity but also on the capacity of the brain to process competing incoming information. Here, employing a combination of psychophysics and magnetoencephalography (MEG), we investigate listeners' sensitivity in situations when two features belonging to the same auditory object change in close succession. The auditory object under investigation is a sequence of tone pips characterized by a regularly repeating frequency pattern. Signals consisted of an initial, regularly alternating sequence of three short (60 msec) pure tone pips (in the form ABCABC…) followed by a long pure tone with a frequency that is either expected based on the on-going regular pattern ("LONG expected"-i.e., "LONG-expected") or constitutes a pattern violation ("LONG-unexpected"). The change in LONG-expected is manifest as a change in duration (when the long pure tone exceeds the established duration of a tone pip), whereas the change in LONG-unexpected is manifest as a change in both the frequency pattern and a change in the duration. Our results reveal a form of "change deafness," in that although changes in both the frequency pattern and the expected duration appear to be processed effectively by the auditory system-cortical signatures of both changes are evident in the MEG data-listeners often fail to detect changes in the frequency pattern when that change is closely followed by a change in duration. By systematically manipulating the properties of the changing features and measuring behavioral and MEG responses, we demonstrate that feature changes within the same auditory object, which occur close together in time, appear to compete for perceptual resources.

Thermography based diagnosis of ruptured anterior cruciate ligament (ACL) in canines

NASA Astrophysics Data System (ADS)

Lama, Norsang; Umbaugh, Scott E.; Mishra, Deependra; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

2016-09-01

Anterior cruciate ligament (ACL) rupture in canines is a common orthopedic injury in veterinary medicine. Veterinarians use both imaging and non-imaging methods to diagnose the disease. Common imaging methods such as radiography, computed tomography (CT scan) and magnetic resonance imaging (MRI) have some disadvantages: expensive setup, high dose of radiation, and time-consuming. In this paper, we present an alternative diagnostic method based on feature extraction and pattern classification (FEPC) to diagnose abnormal patterns in ACL thermograms. The proposed method was experimented with a total of 30 thermograms for each camera view (anterior, lateral and posterior) including 14 disease and 16 non-disease cases provided from Long Island Veterinary Specialists. The normal and abnormal patterns in thermograms are analyzed in two steps: feature extraction and pattern classification. Texture features based on gray level co-occurrence matrices (GLCM), histogram features and spectral features are extracted from the color normalized thermograms and the computed feature vectors are applied to Nearest Neighbor (NN) classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier with leave-one-out validation method. The algorithm gives the best classification success rate of 86.67% with a sensitivity of 85.71% and a specificity of 87.5% in ACL rupture detection using NN classifier for the lateral view and Norm-RGB-Lum color normalization method. Our results show that the proposed method has the potential to detect ACL rupture in canines.

Toward comprehensive detection of sight threatening retinal disease using a multiscale AM-FM methodology

NASA Astrophysics Data System (ADS)

Agurto, C.; Barriga, S.; Murray, V.; Murillo, S.; Zamora, G.; Bauman, W.; Pattichis, M.; Soliz, P.

2011-03-01

In the United States and most of the western world, the leading causes of vision impairment and blindness are age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma. In the last decade, research in automatic detection of retinal lesions associated with eye diseases has produced several automatic systems for detection and screening of AMD, DR, and glaucoma. However. advanced, sight-threatening stages of DR and AMD can present with lesions not commonly addressed by current approaches to automatic screening. In this paper we present an automatic eye screening system based on multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions that addresses not only the early stages, but also advanced stages of retinal and optic nerve disease. Ten different experiments were performed in which abnormal features such as neovascularization, drusen, exudates, pigmentation abnormalities, geographic atrophy (GA), and glaucoma were classified. The algorithm achieved an accuracy detection range of [0.77 to 0.98] area under the ROC curve for a set of 810 images. When set to a specificity value of 0.60, the sensitivity of the algorithm to the detection of abnormal features ranged between 0.88 and 1.00. Our system demonstrates that, given an appropriate training set, it is possible to use a unique algorithm to detect a broad range of eye diseases.

Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals

NASA Technical Reports Server (NTRS)

Thompson, David R.; Mandrake, Lukas; Green, Robert O.

2013-01-01

Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.

Phenobarbital reduces EEG amplitude and propagation of neonatal seizures but does not alter performance of automated seizure detection.

PubMed

Mathieson, Sean R; Livingstone, Vicki; Low, Evonne; Pressler, Ronit; Rennie, Janet M; Boylan, Geraldine B

2016-10-01

Phenobarbital increases electroclinical uncoupling and our preliminary observations suggest it may also affect electrographic seizure morphology. This may alter the performance of a novel seizure detection algorithm (SDA) developed by our group. The objectives of this study were to compare the morphology of seizures before and after phenobarbital administration in neonates and to determine the effect of any changes on automated seizure detection rates. The EEGs of 18 term neonates with seizures both pre- and post-phenobarbital (524 seizures) administration were studied. Ten features of seizures were manually quantified and summary measures for each neonate were statistically compared between pre- and post-phenobarbital seizures. SDA seizure detection rates were also compared. Post-phenobarbital seizures showed significantly lower amplitude (p<0.001) and involved fewer EEG channels at the peak of seizure (p<0.05). No other features or SDA detection rates showed a statistical difference. These findings show that phenobarbital reduces both the amplitude and propagation of seizures which may help to explain electroclinical uncoupling of seizures. The seizure detection rate of the algorithm was unaffected by these changes. The results suggest that users should not need to adjust the SDA sensitivity threshold after phenobarbital administration. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2017-07-01

Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.

Phase-specific Surround suppression in Mouse Primary Visual Cortex Correlates with Figure Detection Behavior Based on Phase Discontinuity.

PubMed

Li, Fengling; Jiang, Weiqian; Wang, Tian-Yi; Xie, Taorong; Yao, Haishan

2018-05-21

In the primary visual cortex (V1), neuronal responses to stimuli within the receptive field (RF) are modulated by stimuli in the RF surround. A common effect of surround modulation is surround suppression, which is dependent on the feature difference between stimuli within and surround the RF and is suggested to be involved in the perceptual phenomenon of figure-ground segregation. In this study, we examined the relationship between feature-specific surround suppression of V1 neurons and figure detection behavior based on figure-ground feature difference. We trained freely moving mice to perform a figure detection task using figure and ground gratings that differed in spatial phase. The performance of figure detection increased with the figure-ground phase difference, and was modulated by stimulus contrast. Electrophysiological recordings from V1 in head-fixed mice showed that the increase in phase difference between stimuli within and surround the RF caused a reduction in surround suppression, which was associated with an increase in V1 neural discrimination between stimuli with and without RF-surround phase difference. Consistent with the behavioral performance, the sensitivity of V1 neurons to RF-surround phase difference could be influenced by stimulus contrast. Furthermore, inhibiting V1 by optogenetically activating either parvalbumin (PV)- or somatostatin (SOM)-expressing inhibitory neurons both decreased the behavioral performance of figure detection. Thus, the phase-specific surround suppression in V1 represents a neural correlate of figure detection behavior based on figure-ground phase discontinuity. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Breast Cancer Detection with Reduced Feature Set.

PubMed

Mert, Ahmet; Kılıç, Niyazi; Bilgili, Erdem; Akan, Aydin

2015-01-01

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

UltraSensitive Mycotoxin Detection by STING Sensors

PubMed Central

Actis, Paolo; Jejelowo, Olufisayo; Pourmand, Nader

2010-01-01

Signal Transduction by Ion Nano Gating (STING) technology is a label-free biosensor capable of identifying DNA and proteins. Based on a functionalized quartz nanopipette, the STING sensor includes specific recognition elements for analyte discrimination based on size, shape and charge density. A key feature of this technology is that it doesn't require any nanofabrication facility; each nanopipette can be easily, reproducibly, and inexpensively fabricated and tailored at the bench, thus reducing the cost and the turnaround time. Here, we show that STING sensors are capable of the ultrasensitive detection of HT-2 toxin with a detection limit of 100 fg/ml and compare the STING capabilities with respect to conventional sandwich assay techniques. PMID:20829024

Atorvastatin effect evaluation based on feature combination of three-dimension ultrasound images

NASA Astrophysics Data System (ADS)

Luo, Yongkang; Ding, Mingyue

2016-03-01

In the past decades, stroke has become the worldwide common cause of death and disability. It is well known that ischemic stroke is mainly caused by carotid atherosclerosis. As an inexpensive, convenient and fast means of detection, ultrasound technology is applied widely in the prevention and treatment of carotid atherosclerosis. Recently, many studies have focused on how to quantitatively evaluate local arterial effects of medicine treatment for carotid diseases. So the evaluation method based on feature combination was proposed to detect potential changes in the carotid arteries after atorvastatin treatment. And the support vector machine (SVM) and 10-fold cross-validation protocol were utilized on a database of 5533 carotid ultrasound images of 38 patients (17 atorvastatin groups and 21 placebo groups) at baseline and after 3 months of the treatment. With combination optimization of many features (including morphological and texture features), the evaluation results of single feature and different combined features were compared. The experimental results showed that the performance of single feature is poor and the best feature combination have good recognition ability, with the accuracy 92.81%, sensitivity 80.95%, specificity 95.52%, positive predictive value 80.47%, negative predictive value 95.65%, Matthew's correlation coefficient 76.27%, and Youden's index 76.48%. And the receiver operating characteristic (ROC) curve was also performed well with 0.9663 of the area under the ROC curve (AUC), which is better than all the features with 0.9423 of the AUC. Thus, it is proved that this novel method can reliably and accurately evaluate the effect of atorvastatin treatment.

Assessing alternatives for directional detection of a halo of weakly interacting massive particles

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

Copi, Craig J.; Krauss, Lawrence M.; Department of Astronomy, Case Western Reserve University, 10900 Euclid Ave., Cleveland, Ohio 44106-7079

2007-01-15

The future of direct terrestrial WIMP detection lies on two fronts: new, much larger low background detectors sensitive to energy deposition, and detectors with directional sensitivity. The former can explore a large range of WIMP parameter space using well-tested technology while the latter may be necessary if one is to disentangle particle physics parameters from astrophysical halo parameters. Because directional detectors will be quite difficult to construct it is worthwhile exploring in advance generally which experimental features will yield the greatest benefits at the lowest costs. We examine the sensitivity of directional detectors with varying angular tracking resolution with andmore » without the ability to distinguish forward versus backward recoils, and compare these to the sensitivity of a detector where the track is projected onto a two-dimensional plane. The latter detector regardless of where it is placed on the Earth, can be oriented to produce a significantly better discrimination signal than a 3D detector without this capability, and with sensitivity within a factor of 2 of a full 3D tracking detector. Required event rates to distinguish signals from backgrounds for a simple isothermal halo range from the low teens in the best case to many thousands in the worst.« less

Improving Cyber-Security of Smart Grid Systems via Anomaly Detection and Linguistic Domain Knowledge

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

Ondrej Linda; Todd Vollmer; Milos Manic

The planned large scale deployment of smart grid network devices will generate a large amount of information exchanged over various types of communication networks. The implementation of these critical systems will require appropriate cyber-security measures. A network anomaly detection solution is considered in this work. In common network architectures multiple communications streams are simultaneously present, making it difficult to build an anomaly detection solution for the entire system. In addition, common anomaly detection algorithms require specification of a sensitivity threshold, which inevitably leads to a tradeoff between false positives and false negatives rates. In order to alleviate these issues, thismore » paper proposes a novel anomaly detection architecture. The designed system applies the previously developed network security cyber-sensor method to individual selected communication streams allowing for learning accurate normal network behavior models. Furthermore, the developed system dynamically adjusts the sensitivity threshold of each anomaly detection algorithm based on domain knowledge about the specific network system. It is proposed to model this domain knowledge using Interval Type-2 Fuzzy Logic rules, which linguistically describe the relationship between various features of the network communication and the possibility of a cyber attack. The proposed method was tested on experimental smart grid system demonstrating enhanced cyber-security.« less

Comparative Study of the Detection of Chromium Content in Rice Leaves by 532 nm and 1064 nm Laser-Induced Breakdown Spectroscopy

PubMed Central

Shen, Tingting; Ye, Lanhan; Kong, Wenwen; Wang, Wei; Liu, Xiaodan

2018-01-01

Fast detection of toxic metals in crops is important for monitoring pollution and ensuring food safety. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the chromium content in rice leaves. We investigated the influence of laser wavelength (532 nm and 1064 nm excitation), along with the variations of delay time, pulse energy, and lens-to-sample distance (LTSD), on the signal (sensitivity and stability) and plasma features (temperature and electron density). With the optimized experimental parameters, univariate analysis was used for quantifying the chromium content, and several preprocessing methods (including background normalization, area normalization, multiplicative scatter correction (MSC) transformation and standardized normal variate (SNV) transformation were used to further improve the analytical performance. The results indicated that 532 nm excitation showed better sensitivity than 1064 nm excitation, with a detection limit around two times lower. However, the prediction accuracy for both excitation wavelengths was similar. The best result, with a correlation coefficient of 0.9849, root-mean-square error of 3.89 mg/kg and detection limit of 2.72 mg/kg, was obtained using the SNV transformed signal (Cr I 425.43 nm) induced by 532 nm excitation. The results indicate the inspiring capability of LIBS for toxic metals detection in plant materials. PMID:29463032

WiFi-Based Real-Time Calibration-Free Passive Human Motion Detection.

PubMed

Gong, Liangyi; Yang, Wu; Man, Dapeng; Dong, Guozhong; Yu, Miao; Lv, Jiguang

2015-12-21

With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Previous wireless device-free passive human detection systems either rely on deploying specialized systems with dense transmitter-receiver links or elaborate off-line training process, which blocks rapid deployment and weakens system robustness. In the paper, we explore to research a novel fine-grained real-time calibration-free device-free passive human motion via physical layer information, which is independent of indoor scenarios and needs no prior-calibration and normal profile. We investigate sensitivities of amplitude and phase to human motion, and discover that phase feature is more sensitive to human motion, especially to slow human motion. Aiming at lightweight and robust device-free passive human motion detection, we develop two novel and practical schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize system design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve a high detection rate and low false positive rate.

WiFi-Based Real-Time Calibration-Free Passive Human Motion Detection †

PubMed Central

Gong, Liangyi; Yang, Wu; Man, Dapeng; Dong, Guozhong; Yu, Miao; Lv, Jiguang

2015-01-01

With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Previous wireless device-free passive human detection systems either rely on deploying specialized systems with dense transmitter-receiver links or elaborate off-line training process, which blocks rapid deployment and weakens system robustness. In the paper, we explore to research a novel fine-grained real-time calibration-free device-free passive human motion via physical layer information, which is independent of indoor scenarios and needs no prior-calibration and normal profile. We investigate sensitivities of amplitude and phase to human motion, and discover that phase feature is more sensitive to human motion, especially to slow human motion. Aiming at lightweight and robust device-free passive human motion detection, we develop two novel and practical schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize system design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve a high detection rate and low false positive rate. PMID:26703612

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

Evaluation of four novel isothermal amplification assays towards simple and rapid genotyping of chloroquine resistant Plasmodium falciparum.

PubMed

Chahar, Madhvi; Anvikar, Anup; Dixit, Rajnikant; Valecha, Neena

2018-07-01

Loop mediated isothermal amplification (LAMP) assay is sensitive, prompt, high throughput and field deployable technique for nucleic acid amplification under isothermal conditions. In this study, we have developed and optimized four different visualization methods of loop-mediated isothermal amplification (LAMP) assay to detect Pfcrt K76T mutants of P. falciparum and compared their important features for one-pot in-field applications. Even though all the four tested LAMP methods could successfully detect K76T mutants of P. falciparum, however considering the time, safety, sensitivity, cost and simplicity, the malachite green and HNB based methods were found more efficient. Among four different visual dyes uses to detect LAMP products accurately, hydroxynaphthol blue and malachite green could produce long stable color change and brightness in a close tube-based approach to prevent cross-contamination risk. Our results indicated that the LAMP offers an interesting novel and convenient best method for the rapid, sensitive, cost-effective, and fairly user friendly tool for detection of K76T mutants of P. falciparum and therefore presents an alternative to PCR-based assays. Based on our comparative analysis, better field based LAMP visualization method can be chosen easily for the monitoring of other important drug targets (Kelch13 propeller region). Copyright © 2018 Elsevier Inc. All rights reserved.

An optofluidic metasurface for lateral flow-through detection of breast cancer biomarker.

PubMed

Wang, Yifei; Ali, Md Azahar; Chow, Edmond K C; Dong, Liang; Lu, Meng

2018-06-01

The rapid growth of point-of-care tests demands for biosensors with high sensitivity and small size. This paper demonstrates an optofluidic metasurface that combines silicon-on-insulator (SOI) nanophotonics and nanofluidics to realize a high-performance, lateral flow-through biosensor. The metasurface is made of a periodic array of silicon nanoposts on an SOI substrate, and functionalized with specific receptor molecules. Bonding of a polydimethylsiloxane slab directly onto the surface results in an ultracompact biosensor, where analyte solutions are restricted to flow only in the space between the nanoposts. No flow exists above the nanoposts. This sensor design overcomes the issue with diffusion-limited detection of many other biosensors. The lateral flow-through feature, in conjunction with high-Q resonance modes associated with optical bound states of the metasurface, offers an improved sensitivity to subtle molecule-bonding induced changes in refractive index. The device exhibits a resonance mode around 1550 nm wavelength and provides an index sensitivity of 720 nm/RIU. Biosensing is conducted to detect the epidermal growth factor receptor 2 (ErbB2), a protein biomarker for early-stage breast cancer screening, by monitoring resonance wavelength shifts in response to specific analyte-ligand binding events at the metasurface. The limit of detection of the device is 0.7 ng mL -1 for ErbB2. Copyright © 2018 Elsevier B.V. All rights reserved.

Veterinary software application for comparison of thermograms for pathology evaluation

NASA Astrophysics Data System (ADS)

Pant, Gita; Umbaugh, Scott E.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

2017-09-01

The bilateral symmetry property in mammals allows for the detection of pathology by comparison of opposing sides. For any pathological disorder, thermal patterns differ compared to the normal body part. A software application for veterinary clinics has been under development to input two thermograms of body parts on both sides, one normal and the other unknown, and the application compares them based on extracted features and appropriate similarity and difference measures and outputs the likelihood of pathology. Here thermographic image data from 19° C to 40° C was linearly remapped to create images with 256 gray level values. Features were extracted from these images, including histogram, texture and spectral features. The comparison metrics used are the vector inner product, Tanimoto, Euclidean, city block, Minkowski and maximum value metric. Previous research with the anterior cruciate ligament (ACL) pathology in dogs suggested any thermogram variation below a threshold of 40% of Euclidean distance is normal and above 40% is abnormal. Here the 40% threshold was applied to a new ACL image set and achieved a sensitivity of 75%, an improvement from the 55% sensitivity of the previous work. With the new data set it was determined that using a threshold of 20% provided a much improved 92% sensitivity metric. However, this will require further research to determine the corresponding specificity success rate. Additionally, it was found that the anterior view provided better results than the lateral view. It was also determined that better results were obtained with all three feature sets than with just the histogram and texture sets. Further experiments are ongoing with larger image datasets, and pathologies, new features and comparison metric evaluation for determination of more accurate threshold values to separate normal and abnormal images.

Polymerase chain reaction with phase change as intrinsic thermal control

NASA Astrophysics Data System (ADS)

Hsieh, Yi-Fan; Yonezawa, Eri; Kuo, Long-Sheng; Yeh, Shiou-Hwei; Chen, Pei-Jer; Chen, Ping-Hei

2013-04-01

This research demonstrated that without any external temperature controller, the capillary convective polymerase chain reaction (ccPCR) powered by a candle can operate with the help of phase change. The candle ccPCR system productively amplified hepatitis B virus 122 base-pairs DNA fragment. The detection sensitivity can achieve at an initial DNA concentration to 5 copies per reaction. The results also show that the candle ccPCR system can operate functionally even the ambient temperature varies from 7 °C to 45 °C. These features imply that the candle ccPCR system can provide robust medical detection services.

Quantitative imaging features: extension of the oncology medical image database

NASA Astrophysics Data System (ADS)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Multiresolution Local Binary Pattern texture analysis for false positive reduction in computerized detection of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Choi, Jae Young; Kim, Dae Hoe; Choi, Seon Hyeong; Ro, Yong Man

2012-03-01

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Krishnan, M Muthu Rama; Molinari, Filippo; Zieleźnik, Witold; Bardales, Ricardo H; Witkowska, Agnieszka; Suri, Jasjit S

2014-02-01

Computer-aided diagnostic (CAD) techniques aid physicians in better diagnosis of diseases by extracting objective and accurate diagnostic information from medical data. Hashimoto thyroiditis is the most common type of inflammation of the thyroid gland. The inflammation changes the structure of the thyroid tissue, and these changes are reflected as echogenic changes on ultrasound images. In this work, we propose a novel CAD system (a class of systems called ThyroScan) that extracts textural features from a thyroid sonogram and uses them to aid in the detection of Hashimoto thyroiditis. In this paradigm, we extracted grayscale features based on stationary wavelet transform from 232 normal and 294 Hashimoto thyroiditis-affected thyroid ultrasound images obtained from a Polish population. Significant features were selected using a Student t test. The resulting feature vectors were used to build and evaluate the following 4 classifiers using a 10-fold stratified cross-validation technique: support vector machine, decision tree, fuzzy classifier, and K-nearest neighbor. Using 7 significant features that characterized the textural changes in the images, the fuzzy classifier had the highest classification accuracy of 84.6%, sensitivity of 82.8%, specificity of 87.0%, and a positive predictive value of 88.9%. The proposed ThyroScan CAD system uses novel features to noninvasively detect the presence of Hashimoto thyroiditis on ultrasound images. Compared to manual interpretations of ultrasound images, the CAD system offers a more objective interpretation of the nature of the thyroid. The preliminary results presented in this work indicate the possibility of using such a CAD system in a clinical setting after evaluating it with larger databases in multicenter clinical trials.

Study Sensitivity: Evaluating the Ability to Detect Effects in Systematic Reviews of Chemical Exposures

PubMed Central

Cooper, Glinda S.; Lunn, Ruth M.; Ågerstrand, Marlene; Glenn, Barbara S.; Kraft, Andrew D.; Luke, April M.; Ratcliffe, Jennifer M.

2016-01-01

A critical step in systematic reviews of potential health hazards is the structured evaluation of the strengths and weaknesses of the included studies; risk of bias is a term often used to represent this process, specifically with respect to the evaluation of systematic errors that can lead to inaccurate (biased) results (i.e. focusing on internal validity). Systematic review methods developed in the clinical medicine arena have been adapted for use in evaluating environmental health hazards; this expansion raises questions about the scope of risk of bias tools and the extent to which they capture the elements that can affect the interpretation of results from environmental and occupational epidemiology studies and in vivo animal toxicology studies, (the studies typically available for assessment of risk of chemicals). One such element, described here as “sensitivity”, is a measure of the ability of a study to detect a true effect or hazard. This concept is similar to the concept of the sensitivity of an assay; an insensitive study may fail to show a difference that truly exists, leading to a false conclusion of no effect. Factors relating to study sensitivity should be evaluated in a systematic manner with the same rigor as the evaluation of other elements within a risk of bias framework. We discuss the importance of this component for the interpretation of individual studies, examine approaches proposed or in use to address it, and describe how it relates to other evaluation components. The evaluation domains contained within a risk of bias tool can include, or can be modified to include, some features relating to study sensitivity; the explicit inclusion of these sensitivity criteria with the same rigor and at the same stage of study evaluation as other bias-related criteria can improve the evaluation process. In some cases, these and other features may be better addressed through a separate sensitivity domain. The combined evaluation of risk of bias and sensitivity can be used to identify the most informative studies, to evaluate the confidence of the findings from individual studies and to identify those study elements that may help to explain heterogeneity across the body of literature. PMID:27156196

Electrochemical detection for microscale analytical systems: a review.

PubMed

Wang, Joseph

2002-02-11

As the field of chip-based microscale systems continues its rapid growth, there are urgent needs for developing compatible detection modes. Electrochemistry detection offers considerable promise for such microfluidic systems, with features that include remarkable sensitivity, inherent miniaturization and portability, independence of optical path length or sample turbidity, low cost, low-power requirements and high compatibility with advanced micromachining and microfabrication technologies. This paper highlights recent advances, directions and key strategies in controlled-potential electrochemical detectors for miniaturized analytical systems. Subjects covered include the design and integration of the electrochemical detection system, its requirements and operational principles, common electrode materials, derivatization reactions, electrical-field decouplers, typical applications and future prospects. It is expected that electrochemical detection will become a powerful tool for microscale analytical systems and will facilitate the creation of truly portable (and possibly disposable) devices.

Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study

PubMed Central

2011-01-01

Background Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies. Methods Hidden Markov model (HMM) was developed to interpret the state transitions of the in vitro rat hippocampal slice local field potentials (LFPs) during seizure episodes. It can be used to estimate the probability of state transitions and the corresponding characteristics of each state. Wavelet features were clustered and used to differentiate the electrophysiological characteristics at each corresponding HMM states. Using unsupervised training method, the HMM and the clustering parameters were obtained simultaneously. The HMM states were then assigned to the electrophysiological data using expert guided technique. Minimum redundancy maximum relevance (mRMR) analysis and Akaike Information Criterion (AICc) were applied to reduce the effect of over-fitting. The sensitivity, specificity and optimality index of chronic seizure detection were compared for various HMM topologies. The ability of distinguishing early and late tonic firing patterns prior to chronic seizures were also evaluated. Results Significant improvement in state detection performance was achieved when additional wavelet coefficient rates of change information were used as features. The final HMM topology obtained using mRMR and AICc was able to detect non-ictal (interictal), early and late tonic firing, chronic seizures and postictal activities. A mean sensitivity of 95.7%, mean specificity of 98.9% and optimality index of 0.995 in the detection of chronic seizures was achieved. The detection of early and late tonic firing was validated with experimental intracellular electrical recordings of seizures. Conclusions The HMM implementation of a seizure dynamics detector is an improvement over existing approaches using visual detection and complexity measures. The subjectivity involved in partitioning the observed data prior to training can be eliminated. It can also decipher the probabilities of seizure state transitions using the magnitude and rate of change wavelet information of the LFPs. PMID:21504608

Quantitative analysis of the plain radiographic appearance of nonossifying fibroma.

PubMed

Friedland, J A; Reinus, W R; Fisher, A J; Wilson, A J

1995-08-01

To quantitate radiographic features that distinguish the plain radiographic appearance of nonossifying fibroma (NOF) from other solitary lesions of bone. Seven hundred nine cases of focal bone lesions, including 34 NOFs, were analyzed according to demographic, anatomic, and plain radiographic features. Vector analysis of groups of features was performed to determine those that are most sensitive and specific for the appearance of NOF in contrast to other lesions in the data base. The radiographic appearance of NOFs was most consistently a medullary based (97%), lytic lesion (100%) with geographic bone destruction (100%), marginal sclerosis (97%), and well-defined edges (94%). A statistically significant number of lesions were located in the distal aspect of long bones. Unicameral bone cyst shared the most radiographic features with the NOF. Vector analysis showed a large degree of overlap between NOF and other lesions such as aneurysmal bone cyst, chondromyxoid fibroma, and eosinophilic granuloma. The description that optimized sensitivity and prevalence for detection of NOF is a medullary based, ovoid lesion in the distal or proximal portions of a long bone with well-defined edges, a partial or complete rind of sclerosis, and absence of fallen fragment, periosteal reaction, and cortical disruption. The radiographic appearance of NOF is relatively nonspecific but, using vector analysis, can be better elucidated over current textbook descriptions.

Aptamer-conjugated nanoparticles for cancer cell detection.

PubMed

Medley, Colin D; Bamrungsap, Suwussa; Tan, Weihong; Smith, Joshua E

2011-02-01

Aptamer-conjugated nanoparticles (ACNPs) have been used for a variety of applications, particularly dual nanoparticles for magnetic extraction and fluorescent labeling. In this type of assay, silica-coated magnetic and fluorophore-doped silica nanoparticles are conjugated to highly selective aptamers to detect and extract targeted cells in a variety of matrixes. However, considerable improvements are required in order to increase the selectivity and sensitivity of this two-particle assay to be useful in a clinical setting. To accomplish this, several parameters were investigated, including nanoparticle size, conjugation chemistry, use of multiple aptamer sequences on the nanoparticles, and use of multiple nanoparticles with different aptamer sequences. After identifying the best-performing elements, the improvements made to this assay's conditional parameters were combined to illustrate the overall enhanced sensitivity and selectivity of the two-particle assay using an innovative multiple aptamer approach, signifying a critical feature in the advancement of this technique.

Hyperspectral imaging for melanoma screening

NASA Astrophysics Data System (ADS)

Martin, Justin; Krueger, James; Gareau, Daniel

2014-03-01

The 5-year survival rate for patients diagnosed with Melanoma, a deadly form of skin cancer, in its latest stages is about 15%, compared to over 90% for early detection and treatment. We present an imaging system and algorithm that can be used to automatically generate a melanoma risk score to aid clinicians in the early identification of this form of skin cancer. Our system images the patient's skin at a series of different wavelengths and then analyzes several key dermoscopic features to generate this risk score. We have found that shorter wavelengths of light are sensitive to information in the superficial areas of the skin while longer wavelengths can be used to gather information at greater depths. This accompanying diagnostic computer algorithm has demonstrated much higher sensitivity and specificity than the currently commercialized system in preliminary trials and has the potential to improve the early detection of melanoma.

Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson's disease.

PubMed

Iakovakis, Dimitrios; Hadjidimitriou, Stelios; Charisis, Vasileios; Bostantzopoulou, Sevasti; Katsarou, Zoe; Hadjileontiadis, Leontios J

2018-05-16

Parkinson's disease (PD) is a degenerative movement disorder causing progressive disability that severely affects patients' quality of life. While early treatment can produce significant benefits for patients, the mildness of many early signs combined with the lack of accessible high-frequency monitoring tools may delay clinical diagnosis. To meet this need, user interaction data from consumer technologies have recently been exploited towards unsupervised screening for PD symptoms in daily life. Similarly, this work proposes a method for detecting fine motor skills decline in early PD patients via analysis of patterns emerging from finger interaction with touchscreen smartphones during natural typing. Our approach relies on low-/higher-order statistical features of keystrokes timing and pressure variables, computed from short typing sessions. Features are fed into a two-stage multi-model classification pipeline that reaches a decision on the subject's status (PD patient/control) by gradually fusing prediction probabilities obtained for individual typing sessions and keystroke variables. This method achieved an AUC = 0.92 and 0.82/0.81 sensitivity/specificity (matched groups of 18 early PD patients/15 controls) with discriminant features plausibly correlating with clinical scores of relevant PD motor symptoms. These findings suggest an improvement over similar approaches, thereby constituting a further step towards unobtrusive early PD detection from routine activities.

Allergen Sensitization Pattern by Sex: A Cluster Analysis in Korea.

PubMed

Ohn, Jungyoon; Paik, Seung Hwan; Doh, Eun Jin; Park, Hyun-Sun; Yoon, Hyun-Sun; Cho, Soyun

2017-12-01

Allergens tend to sensitize simultaneously. Etiology of this phenomenon has been suggested to be allergen cross-reactivity or concurrent exposure. However, little is known about specific allergen sensitization patterns. To investigate the allergen sensitization characteristics according to gender. Multiple allergen simultaneous test (MAST) is widely used as a screening tool for detecting allergen sensitization in dermatologic clinics. We retrospectively reviewed the medical records of patients with MAST results between 2008 and 2014 in our Department of Dermatology. A cluster analysis was performed to elucidate the allergen-specific immunoglobulin (Ig)E cluster pattern. The results of MAST (39 allergen-specific IgEs) from 4,360 cases were analyzed. By cluster analysis, 39items were grouped into 8 clusters. Each cluster had characteristic features. When compared with female, the male group tended to be sensitized more frequently to all tested allergens, except for fungus allergens cluster. The cluster and comparative analysis results demonstrate that the allergen sensitization is clustered, manifesting allergen similarity or co-exposure. Only the fungus cluster allergens tend to sensitize female group more frequently than male group.

Improvement and implementation for Canny edge detection algorithm

NASA Astrophysics Data System (ADS)

Yang, Tao; Qiu, Yue-hong

2015-07-01

Edge detection is necessary for image segmentation and pattern recognition. In this paper, an improved Canny edge detection approach is proposed due to the defect of traditional algorithm. A modified bilateral filter with a compensation function based on pixel intensity similarity judgment was used to smooth image instead of Gaussian filter, which could preserve edge feature and remove noise effectively. In order to solve the problems of sensitivity to the noise in gradient calculating, the algorithm used 4 directions gradient templates. Finally, Otsu algorithm adaptively obtain the dual-threshold. All of the algorithm simulated with OpenCV 2.4.0 library in the environments of vs2010, and through the experimental analysis, the improved algorithm has been proved to detect edge details more effectively and with more adaptability.

Rapid and Sensitive Detection of H7N9 Avian Influenza Virus by Use of Reverse Transcription–Loop-Mediated Isothermal Amplification

PubMed Central

Zhang, Jinhai; Feng, Youjun; Hu, Dan; Lv, Heng; Zhu, Jing; Cao, Min; Zheng, Feng; Zhu, Jin; Gong, Xiufang; Hao, Lina; Srinivas, Swaminath; Ren, Hao; Qi, Zhongtian

2013-01-01

An epidemic of human H7N9 influenza virus infection recently emerged in China whose clinical features include high mortality and which has also resulted in serious economic loss. The novel reassortant avian-origin influenza A (H7N9) virus which was the causative agent of this epidemic raised the possibility of triggering a large-scale influenza pandemic worldwide. It seemed likely that fast molecular detection assays specific for this virus would be in great demand. Here, we report a one-step reverse transcription–loop-mediated isothermal amplification (RT-LAMP) method for rapid detection of the hemagglutinin (HA) and neuraminidase (NA) genes of H7N9 virus, the minimum detection limit of which was evaluated using in vitro RNA transcription templates. In total, 135 samples from clinical specimens (from either patients or poultry) were tested using this method in comparison with the real-time PCR recommended by the World Health Organization (WHO). Our results showed that (i) RT-LAMP-based trials can be completed in approximately 12 to 23 min and (ii) the detection limit for the H7 gene is around 10 copies per reaction, similar to that of the real-time PCR, whereas the detection limit for its counterpart the N9 gene is 5 copies per reaction, a 100-fold-higher sensitivity than the WHO-recommended method. Indeed, this excellent performance of our method was also validated by the results for a series of clinical specimens. Therefore, we believe that the simple, fast, and sensitive method of RT-LAMP might be widely applied for detection of H7N9 infections and may play a role in prevention of an influenza pandemic. PMID:24006004

Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics.

PubMed

Abdolali, Fatemeh; Zoroofi, Reza Aghaeizadeh; Otake, Yoshito; Sato, Yoshinobu

2017-02-01

Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor. Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features. This study demonstrated that our proposed methodology can improve the computer assisted diagnosis (CAD) performance by incorporating more discriminative features. Using orthogonalized SPHARM is promising in computerized cyst detection and may have a significant impact in future CAD systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Earthquake damage mapping by using remotely sensed data: the Haiti case study

NASA Astrophysics Data System (ADS)

Romaniello, Vito; Piscini, Alessandro; Bignami, Christian; Anniballe, Roberta; Stramondo, Salvatore

2017-01-01

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west-south-west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback-Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

PubMed Central

2014-01-01

Background The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Methods Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. Conclusions A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice. PMID:24981916

Nothing more than a pair of curvatures: A common mechanism for the detection of both radial and non-radial frequency patterns.

PubMed

Schmidtmann, Gunnar; Kingdom, Frederick A A

2017-05-01

Radial frequency (RF) patterns, which are sinusoidal modulations of a radius in polar coordinates, are commonly used to study shape perception. Previous studies have argued that the detection of RF patterns is either achieved globally by a specialized global shape mechanism, or locally using as cue the maximum tangent orientation difference between the RF pattern and the circle. Here we challenge both ideas and suggest instead a model that accounts not only for the detection of RF patterns but also for line frequency patterns (LF), i.e. contours sinusoidally modulated around a straight line. The model has two features. The first is that the detection of both RF and LF patterns is based on curvature differences along the contour. The second is that this curvature metric is subject to what we term the Curve Frequency Sensitivity Function, or CFSF, which is characterized by a flat followed by declining response to curvature as a function of modulation frequency, analogous to the modulation transfer function of the eye. The evidence that curvature forms the basis for detection is that at very low modulation frequencies (1-3 cycles for the RF pattern) there is a dramatic difference in thresholds between the RF and LF patterns, a difference however that disappears at medium and high modulation frequencies. The CFSF feature on the other hand explains why thresholds, rather than continuously declining with modulation frequency, asymptote at medium and high modulation frequencies. In summary, our analysis suggests that the detection of shape modulations is processed by a common curvature-sensitive mechanism that is subject to a shape-frequency-dependent transfer function. This mechanism is independent of whether the modulation is applied to a circle or a straight line. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Neuronal encoding of object and distance information: a model simulation study on naturalistic optic flow processing

PubMed Central

Hennig, Patrick; Egelhaaf, Martin

2011-01-01

We developed a model of the input circuitry of the FD1 cell, an identified motion-sensitive interneuron in the blowfly's visual system. The model circuit successfully reproduces the FD1 cell's most conspicuous property: its larger responses to objects than to spatially extended patterns. The model circuit also mimics the time-dependent responses of FD1 to dynamically complex naturalistic stimuli, shaped by the blowfly's saccadic flight and gaze strategy: the FD1 responses are enhanced when, as a consequence of self-motion, a nearby object crosses the receptive field during intersaccadic intervals. Moreover, the model predicts that these object-induced responses are superimposed by pronounced pattern-dependent fluctuations during movements on virtual test flights in a three-dimensional environment with systematic modifications of the environmental patterns. Hence, the FD1 cell is predicted to detect not unambiguously objects defined by the spatial layout of the environment, but to be also sensitive to objects distinguished by textural features. These ambiguous detection abilities suggest an encoding of information about objects—irrespective of the features by which the objects are defined—by a population of cells, with the FD1 cell presumably playing a prominent role in such an ensemble. PMID:22461769

Antisocial features and "faking bad": A critical note.

PubMed

Niesten, Isabella J M; Nentjes, Lieke; Merckelbach, Harald; Bernstein, David P

2015-01-01

We critically review the literature on antisocial personality features and symptom fabrication (i.e., faking bad; e.g., malingering). A widespread assumption is that these constructs are intimately related. Some studies have, indeed, found that antisocial individuals score higher on instruments detecting faking bad, but others have been unable to replicate this pattern. In addition, studies exploring whether antisocial individuals are especially talented in faking bad have generally come up with null results. The notion of an intrinsic link between antisocial features and faking bad is difficult to test and research in this domain is sensitive to selection bias. We argue that research on faking bad would profit from further theoretical articulation. One topic that deserves scrutiny is how antisocial features affect the cognitive dissonance typically induced by faking bad. We illustrate our points with preliminary data and discuss their implications. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

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

2014-01-01

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

[MRI semiotics features of experimental acute intracerebral hematomas].

PubMed

Burenchev, D V; Skvortsova, V I; Tvorogova, T V; Guseva, O I; Gubskiĭ, L V; Kupriianov, D A; Pirogov, Iu A

2009-01-01

The aim of this study was to assess the possibility of revealing intracerebral hematomas (ICH), using MRI, within the first hours after onset and to determine their MRI semiotics features. Thirty animals with experimental ICH were studied. A method of two-stage introduction of autologous blood was used to develop ICH as human spontaneous intracranial hematomas. Within 3-5h after blood introduction to the rat brain. The control MRI was performed in the 3rd and 7th days after blood injections. ICH were definitely identified in the first MRI scans. The MRI semiotics features of acute ICH and their transformations were assessed. The high sensitivity of MRI to ICH as well as the uniform manifestations in all animals were shown. In conclusion, the method has high specificity for acute ICH detection.

In vivo classification of human skin burns using machine learning and quantitative features captured by optical coherence tomography

NASA Astrophysics Data System (ADS)

Singla, Neeru; Srivastava, Vishal; Singh Mehta, Dalip

2018-02-01

We report the first fully automated detection of human skin burn injuries in vivo, with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Our proposed automated procedure entails building a machine-learning-based classifier by extracting quantitative features from normal and burn tissue images recorded by OCT. In this study, 56 samples (28 normal, 28 burned) were imaged by OCT and eight features were extracted. A linear model classifier was trained using 34 samples and 22 samples were used to test the model. Sensitivity of 91.6% and specificity of 90% were obtained. Our results demonstrate the capability of a computer-aided technique for accurately and automatically identifying burn tissue resection margins during surgical treatment.

Feature saliency and feedback information interactively impact visual category learning

PubMed Central

Hammer, Rubi; Sloutsky, Vladimir; Grill-Spector, Kalanit

2015-01-01

Visual category learning (VCL) involves detecting which features are most relevant for categorization. VCL relies on attentional learning, which enables effectively redirecting attention to object’s features most relevant for categorization, while ‘filtering out’ irrelevant features. When features relevant for categorization are not salient, VCL relies also on perceptual learning, which enables becoming more sensitive to subtle yet important differences between objects. Little is known about how attentional learning and perceptual learning interact when VCL relies on both processes at the same time. Here we tested this interaction. Participants performed VCL tasks in which they learned to categorize novel stimuli by detecting the feature dimension relevant for categorization. Tasks varied both in feature saliency (low-saliency tasks that required perceptual learning vs. high-saliency tasks), and in feedback information (tasks with mid-information, moderately ambiguous feedback that increased attentional load, vs. tasks with high-information non-ambiguous feedback). We found that mid-information and high-information feedback were similarly effective for VCL in high-saliency tasks. This suggests that an increased attentional load, associated with the processing of moderately ambiguous feedback, has little effect on VCL when features are salient. In low-saliency tasks, VCL relied on slower perceptual learning; but when the feedback was highly informative participants were able to ultimately attain the same performance as during the high-saliency VCL tasks. However, VCL was significantly compromised in the low-saliency mid-information feedback task. We suggest that such low-saliency mid-information learning scenarios are characterized by a ‘cognitive loop paradox’ where two interdependent learning processes have to take place simultaneously. PMID:25745404

Take a byte out of MEEF: VAMPIRE: Vehicle for Advanced Mask Pattern Inspection Readiness Evaluations

NASA Astrophysics Data System (ADS)

Badger, Karen D.; Rankin, Jed; Turley, Christina; Seki, Kazunori; Dechene, Dan J.; Abdelghany, Hesham

2016-09-01

MEEF, or Mask Error Enhancement Factor, is simply defined as the ratio of the change in printed wafer feature width to the change in mask feature width scaled to wafer level. It is important in chip manufacturing that leads to the amplification of mask errors, creating challenges with both achieving dimensional control tolerances and ensuring defect free masks, as measured by on-wafer image quality. As lithographic imaging continues to be stressed, using lower and lower k1 factor resolution enhancement techniques, the high MEEF areas present on advanced optical masks creates an environment where the need for increased mask defect sensitivity in high-MEEF areas becomes more and more critical. There are multiple approaches to mask inspection that may or may not provide enough sensitivity to detect all wafer-printable defects; the challenge in the application of these techniques is simultaneously maintaining an acceptable level of mask inspectability. The higher the MEEF, the harder the challenge will be to achieve and appropriate level of sensitivity while maintaining inspectability…and to do so on the geometries that matter. The predominant photomask fabrication inspection approach in use today compares the features on the reticle directly with the design database using high-NA optics. This approach has the ability to detect small defects, however, when inspecting aggressive OPC, it can lead to the over-detection of inconsequential, or nuisance defects. To minimize these nuisance detections, changing the sensitivity of the inspection can improve the inspectability of a mask inspected in high-NA mode, however, it leads to the inability to detect subtle, yet wafer-printable defects in High-MEEF geometry, due to the fact that this `desense' must be applied globally. There are also `lithography-emulating' approaches to inspection that use various means to provide high defect sensitivity and the ability to tolerate inconsequential, non-printing defects by using scanner-like conditions to determine which defects are wafer printable. This inspection technique is commonly referred to as being `lithography plane' or `litho plane,' since it's assessing the mask quality based on how the mask appears to the imaging optics during use, as proposed to traditional `reticle plane' inspection which is comparing the mask only with its target design. Regardless of how the defects are detected, the real question is when should they be detected? For larger technology nodes, defects are considered `statistical risks'…i.e., first they have to occur, and then they have to fall in high-MEEF areas in order to be of concern, and be below the detection limits of traditional reticle-plane inspection. In short, the `perfect storm' has to happen in order to miss printable defects using well-optimized traditional inspection approaches. The introduction of lithographic inspection techniques has revealed this statistical game is a much higher risk than originally estimated, in that very subtle waferprintable CD errors typically fall into the desense band for traditional reticle plane inspection. Because printability is largely influenced by MEEF, designs with high-MEEF values are at greater risk of traditional inspection missing printable CD errors. The question is… how high is high… and at what MEEF is optical inspection at the reticle plane sufficient? This paper will provide evaluation results for both reticle-plane and litho-plane inspections as they pertain to varying degrees of MEEF. A newly designed high-MEEF programmed defect test mask, named VAMPIRE, will be introduced. This test mask is based on 7 nm node technology and contains intentionally varying degrees of MEEF as well as a variety of programmed defects in high-MEEF environments…all of which have been verified for defect lithographic significance on a Zeiss AIMS system.

LacI(Ts)-Regulated Expression as an In Situ Intracellular Biomolecular Thermometer▿

PubMed Central

McCabe, K. M.; Lacherndo, E. J.; Albino-Flores, I.; Sheehan, E.; Hernandez, M.

2011-01-01

In response to needs for in situ thermometry, a temperature-sensitive vector was adapted to report changes in the intracellular heat content of Escherichia coli in near-real time. This model system utilized vectors expressing increasing quantities of β-galactosidase in response to stepwise temperature increases through a biologically relevant range (22 to 45°C). As judged by calibrated fluorometric and colorimetric reporters, both whole E. coli cells and lysates expressed significant repeatable changes in β-galactosidase activity that were sensitive to temperature changes of less than 1°C (35 to 45°C). This model system suggests that changes in cellular heat content can be detected independently of the medium in which cells are maintained, a feature of particular importance where the medium is heterogeneous or nonaqueous, or otherwise has a low heat transfer capacity. We report here that the intracellular temperature can be reliably obtained in near-real time using reliable fluorescent reporting systems from cellular scales, with a 20°C range of detection and at least 0.7°C sensitivity between 35 and 45°C. PMID:21378059

Characterization of the gas sensors based on polymer-coated resonant microcantilevers for the detection of volatile organic compounds.

PubMed

Dong, Ying; Gao, Wei; Zhou, Qin; Zheng, Yi; You, Zheng

2010-06-25

The gas sensors based on polymer-coated resonant microcantilevers for volatile organic compounds (VOCs) detection are investigated. A method to characterize the gas sensors through sensor calibration is proposed. The expressions for the estimation of the characteristic parameters are derived. The effect of the polymer coating location on the sensor's sensitivity is investigated and the formula to calculate the polymer-analyte partition coefficient without knowing the polymer coating features is presented for the first time. Three polymers: polyethyleneoxide (PEO), polyethylenevinylacetate (PEVA) and polyvinylalcohol (PVA) are used to perform the experiments. Six organic solvents: toluene, benzene, ethanol, acetone, hexane and octane are used as analytes. The response time, reversibility, hydrophilicity, sensitivity and selectivity of the polymer layers are discussed. According to the results, highly sensitive sensors for each of the analytes are proposed. Based on the characterization method, a convenient and flexible way to the construction of electric nose system by the polymer-coated resonant microcantilevers can be achieved. Copyright 2010 Elsevier B.V. All rights reserved.

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

Multichannel interictal spike activity detection using time-frequency entropy measure.

PubMed

Thanaraj, Palani; Parvathavarthini, B

2017-06-01

Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time-Frequency (T-F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T-F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T-F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

PubMed Central

Min, Jianliang; Wang, Ping

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver. PMID:29220351

Development of a Near-Ir Cavity Enhanced Absorption Spectrometer for the Detection of Atmospheric Oxidation Products and Organoamines

NASA Astrophysics Data System (ADS)

Eddingsaas, Nathan C.; Jewell, Breanna; Thurnherr, Emily

2014-06-01

An estimated 10,000 to 100,000 different compounds have been measured in the atmosphere, each one undergoes many oxidation reactions that may or may not degrade air quality. To date, the fate of even some of the most abundant hydrocarbons in the atmosphere is poorly understood. One difficulty is the detection of atmospheric oxidation products that are very labile and decompose during analysis. To study labile species under atmospheric conditions, a highly sensitive, non-destructive technique is needed. Here we describe a near-IR incoherent broadband cavity enhanced absorption spectroscopy (IBBCEAS) setup that we are developing to meet this end. We have chosen to utilize the near-IR, where vibrational overtone absorptions are observed, due to the clean spectral windows and better spectral separation of absorption features. In one spectral window we can simultaneously and continuously monitor the composition of alcohols, hydroperoxides, and carboxylic acids in an air mass. In addition, we have used our CEAS setup to detect organoamines. The long effective path length of CEAS allows for low detection limits, even of the overtone absorption features, at ppb and ppt levels.

Size-tunable copper nanocluster aggregates and their application in hydrogen sulfide sensing on paper-based devices

NASA Astrophysics Data System (ADS)

Chen, Po-Cheng; Li, Yu-Chi; Ma, Jia-Yin; Huang, Jia-Yu; Chen, Chien-Fu; Chang, Huan-Tsung

2016-04-01

Polystyrene sulfonate (PSS), a strong polyelectrolyte, was used to prepare red photoluminescent PSS-penicillamine (PA) copper (Cu) nanoclusters (NC) aggregates, which displayed high selectivity and sensitivity to the detection of hydrogen sulfide (H2S). The size of the PSS-PA-Cu NC aggregates could be readily controlled from 5.5 μm to 173 nm using different concentrations of PSS, which enabled better dispersity and higher sensitivity towards H2S. PSS-PA-Cu NC aggregates provided rapid H2S detection by using the strong Cu-S interaction to quench NC photoluminescence as a sensing mechanism. As a result, a detection limit of 650 nM, which is lower than the maximum level permitted in drinking water by the World Health Organization, was achieved for the analysis of H2S in spring-water samples. Moreover, highly dispersed PSS-PA-Cu NC aggregates could be incorporated into a plate-format paper-based analytical device which enables ultra-low sample volumes (5 μL) and feature shorter analysis times (30 min) compared to conventional solution-based methods. The advantages of low reagent consumption, rapid result readout, limited equipment, and long-term storage make this platform sensitive and simple enough to use without specialized training in resource constrained settings.

A new real-time method for investigation of affinity properties and binding kinetics of magnetic nanoparticles

NASA Astrophysics Data System (ADS)

Orlov, Alexey V.; Nikitin, Maxim P.; Bragina, Vera A.; Znoyko, Sergey L.; Zaikina, Marina N.; Ksenevich, Tatiana I.; Gorshkov, Boris G.; Nikitin, Petr I.

2015-04-01

A method for quantitative investigation of affinity constants of receptors immobilized on magnetic nanoparticles (MP) is developed based on spectral correlation interferometry (SCI). The SCI records with a picometer resolution the thickness changes of a layer of molecules or nanoparticles due to a biochemical reaction on a cover slip, averaged over the sensing area. The method is compatible with other types of sensing surfaces employed in biosensing. The measured values of kinetic association constants of magnetic nanoparticles are 4 orders of magnitude higher than those of molecular antibody association with antigen. The developed method also suggests highly sensitive detection of antigens in a wide dynamic range. The limit of detection of 92 pg/ml has been demonstrated for prostate-specific antigen (PSA) with 50-nm MP employed as labels, which produce 3-order amplification of the SCI signals. The calibration curve features high sensitivity (slope) of 3-fold signal raise per 10-fold increase of PSA concentration within 4-order dynamic range, which is an attractive compromise for precise quantitative and highly sensitive immunoassay. The proposed biosensing technique offers inexpensive disposable sensor chips of cover slips and represents an economically sound alternative to traditional immunoassays for disease diagnostics, detection of pathogens in food and environmental monitoring.

Fluorometric determination of nucleic acids based on the use of polydopamine nanotubes and target-induced strand displacement amplification.

PubMed

Ge, Jia; Bai, Dong-Mei; -Geng, Xin; Hu, Ya-Lei; Cai, Qi-Yong; Xing, Ke; Zhang, Lin; Li, Zhao-Hui

2018-01-10

The authors describe a fluorometric method for the quantitation of nucleic acids by combining (a) cycled strand displacement amplification, (b) the unique features of the DNA probe SYBR Green, and (c) polydopamine nanotubes. SYBR Green undergoes strong fluorescence enhancement upon intercalation into double-stranded DNA (dsDNA). The polydopamine nanotubes selectively adsorb single-stranded DNA (ssDNA) and molecular beacons. In the absence of target DNA, the molecular beacon, primer and SYBR Green are adsorbed on the surface of polydopamine nanotubes. This results in quenching of the fluorescence of SYBR Green, typically measured at excitation/emission wavelengths of 488/518 nm. Upon addition of analyte (target DNA) and polymerase, the stem of the molecular beacon is opened so that it can bind to the primer. This triggers target strand displacement polymerization, during which dsDNA is synthesized. The hybridized target is then displaced due to the strand displacement activity of the polymerase. The displaced target hybridizes with another molecular beacon. This triggers the next round of polymerization. Consequently, a large amount of dsDNA is formed which is detected by addition of SYBR Green. Thus, sensitive and selective fluorometric detection is realized. The fluorescent sensing strategy shows very good analytical performances towards DNA detection, such as a wide linear range from 0.05 to 25 nM with a low limit of detection of 20 pM. Graphical abstract Schematic of a fluorometric strategy for highly sensitive and selective determination of nucleic acids by combining strand displacement amplification and the unique features of SYBR Green I (SG) and polydopamine nanotubes.

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-imaging

PubMed Central

Wuttisarnwattana, Patiwet; Gargesha, Madhusudhana; Hof, Wouter van’t; Cooke, Kenneth R.

2016-01-01

With its single cell sensitivity over volumes as large as or larger than a mouse, cryo-imaging enables imaging of stem cell biodistribution, homing, engraftment, and molecular mechanisms. We developed and evaluated a highly automated software tool to detect fluorescently labeled stem cells within very large (~200GB) cryo-imaging datasets. Cell detection steps are: preprocess, remove immaterial regions, spatially filter to create features, identify candidate pixels, classify pixels using bagging decision trees, segment cell patches, and perform 3D labeling. There are options for analysis and visualization. To train the classifier, we created synthetic images by placing realistic digital cell models onto cryo-images of control mice devoid of cells. Very good cell detection results were (precision=98.49%, recall=99.97%) for synthetic cryo-images, (precision=97.81%, recall=97.71%) for manually evaluated, actual cryo-images, and <1% false positives in control mice. An α-multiplier applied to features allows one to correct for experimental variations in cell brightness due to labeling. On dim cells (37% of standard brightness), with correction, we improved recall (49.26%→99.36%) without a significant drop in precision (99.99%→99.75%). With tail vein injection, multipotent adult progenitor cells in a graft-versus-host-disease model in the first days post injection were predominantly found in lung, liver, spleen, and bone marrow. Distribution was not simply related to blood flow. The lung contained clusters of cells while other tissues contained single cells. Our methods provided stem cell distribution anywhere in mouse with single cell sensitivity. Methods should provide a rational means of evaluating dosing, delivery methods, cell enhancements, and mechanisms for therapeutic cells. PMID:26552080

Domain-general neural correlates of dependency formation: Using complex tones to simulate language.

PubMed

Brilmayer, Ingmar; Sassenhagen, Jona; Bornkessel-Schlesewsky, Ina; Schlesewsky, Matthias

2017-08-01

There is an ongoing debate whether the P600 event-related potential component following syntactic anomalies reflects syntactic processes per se, or if it is an instance of the P300, a domain-general ERP component associated with attention and cognitive reorientation. A direct comparison of both components is challenging because of the huge discrepancy in experimental designs and stimulus choice between language and 'classic' P300 experiments. In the present study, we develop a new approach to mimic the interplay of sequential position as well as categorical and relational information in natural language syntax (word category and agreement) in a non-linguistic target detection paradigm using musical instruments. Participants were instructed to (covertly) detect target tones which were defined by instrument change and pitch rise between subsequent tones at the last two positions of four-tone sequences. We analysed the EEG using event-related averaging and time-frequency decomposition. Our results show striking similarities to results obtained from linguistic experiments. We found a P300 that showed sensitivity to sequential position and a late positivity sensitive to stimulus type and position. A time-frequency decomposition revealed significant effects of sequential position on the theta band and a significant influence of stimulus type on the delta band. Our results suggest that the detection of non-linguistic targets defined via complex feature conjunctions in the present study and the detection of syntactic anomalies share the same underlying processes: attentional shift and memory based matching processes that act upon multi-feature conjunctions. We discuss the results as supporting domain-general accounts of the P600 during natural language comprehension. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of a real-time PCR assay for rectal screening of OXA-48-producing Enterobacteriaceae in a general intensive care unit of an endemic hospital.

PubMed

Fernández, J; Cunningham, S A; Fernández-Verdugo, A; Viña-Soria, L; Martín, L; Rodicio, M R; Escudero, D; Vazquez, F; Mandrekar, J N; Patel, R

2017-07-01

Carbapenemase-producing Enterobacteriaceae are increasing worldwide. Rectal screening for these bacteria can inform the management of infected and colonized patients, especially those admitted to intensive care units (ICUs). A laboratory developed, qualitative duplex real-time polymerase chain reaction assay for rapid detection of OXA-48-like and VIM producing Enterobacteriaceae, performed on rectal swabs, was designed and evaluated in an intensive care unit with endemic presence of OXA-48. During analytical assay validation, no cross-reactivity was observed and 100% sensitivity and specificity were obtained for both bla OXA-48-like and bla VIM in all spiked clinical samples. During the clinical part of the study, the global sensitivity and specificity of the real-time PCR assay for OXA-48 detection were 95.7% and 100% (P=0.1250), respectively, in comparison with culture; no VIM-producing Enterobacteriaceae were detected. Clinical features of patients in the ICU who were colonized or infected with OXA-48 producing Enterobacteriaceae, including outcome, were analyzed. Most had severe underlying conditions, and had risk factors for colonization with carbapenemase-producing Enterobacteriaceae before or during ICU admission, such as receiving previous antimicrobial therapy, prior healthcare exposure (including long-term care), chronic disease, immunosuppression and/or the presence of an intravascular catheter and/or mechanical ventilation device. The described real-time PCR assay is fast (~2-3hours, if DNA extraction is included), simple to perform and results are easy to interpret, features which make it applicable in the routine of clinical microbiology laboratories. Implementation in endemic hospitals could contribute to early detection of patients colonized by OXA-48 producing Enterobacteriaceae and prevention of their spread. Copyright © 2017 Elsevier Inc. All rights reserved.

Non-invasive Glucose Measurements Using Wavelength Modulated Differential Photothermal Radiometry (WM-DPTR)

NASA Astrophysics Data System (ADS)

Guo, X.; Mandelis, A.; Zinman, B.

2012-11-01

Wavelength-modulated differential laser photothermal radiometry (WM-DPTR) is introduced for potential development of clinically viable non-invasive glucose biosensors. WM-DPTR features unprecedented glucose-specificity and sensitivity by combining laser excitation by two out-of-phase modulated beams at wavelengths near the peak and the baseline of a prominent and isolated mid-IR glucose absorption band. Measurements on water-glucose phantoms (0 to 300 mg/dl glucose concentration) demonstrate high sensitivity to meet wide clinical detection requirements ranging from hypoglycemia to hyperglycemia. The measurement results have been validated by simulations based on fully developed WM-DPTR theory. For sensitive and accurate glucose measurements, the key is the selection and tight control of the intensity ratio and the phase shift of the two laser beams.

The Principle of the Micro-Electronic Neural Bridge and a Prototype System Design.

PubMed

Huang, Zong-Hao; Wang, Zhi-Gong; Lu, Xiao-Ying; Li, Wen-Yuan; Zhou, Yu-Xuan; Shen, Xiao-Yan; Zhao, Xin-Tai

2016-01-01

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

Contributions of individual face features to face discrimination.

PubMed

Logan, Andrew J; Gordon, Gael E; Loffler, Gunter

2017-08-01

Faces are highly complex stimuli that contain a host of information. Such complexity poses the following questions: (a) do observers exhibit preferences for specific information? (b) how does sensitivity to individual face parts compare? These questions were addressed by quantifying sensitivity to different face features. Discrimination thresholds were determined for synthetic faces under the following conditions: (i) 'full face': all face features visible; (ii) 'isolated feature': single feature presented in isolation; (iii) 'embedded feature': all features visible, but only one feature modified. Mean threshold elevations for isolated features, relative to full-faces, were 0.84x, 1.08, 2.12, 3.34, 4.07 and 4.47 for head-shape, hairline, nose, mouth, eyes and eyebrows respectively. Hence, when two full faces can be discriminated at threshold, the difference between the eyes is about four times less than what is required when discriminating between isolated eyes. In all cases, sensitivity was higher when features were presented in isolation than when they were embedded within a face context (threshold elevations of 0.94x, 1.74, 2.67, 2.90, 5.94 and 9.94). This reveals a specific pattern of sensitivity to face information. Observers are between two and four times more sensitive to external than internal features. The pattern for internal features (higher sensitivity for the nose, compared to mouth, eyes and eyebrows) is consistent with lower sensitivity for those parts affected by facial dynamics (e.g. facial expressions). That isolated features are easier to discriminate than embedded features supports a holistic face processing mechanism which impedes extraction of information about individual features from full faces. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic detection of red lesions in digital color fundus photographs.

PubMed

Niemeijer, Meindert; van Ginneken, Bram; Staal, Joes; Suttorp-Schulten, Maria S A; Abràmoff, Michael D

2005-05-01

The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important new contributions. The first contribution is a new red lesion candidate detection system based on pixel classification. Using this technique, vasculature and red lesions are separated from the background of the image. After removal of the connected vasculature the remaining objects are considered possible red lesions. Second, an extensive number of new features are added to those proposed by Spencer-Frame. The detected candidate objects are classified using all features and a k-nearest neighbor classifier. An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set. When determining whether an image contains red lesions the system achieves a sensitivity of 100% at a specificity of 87%. The method is compared with several different automatic systems and is shown to outperform them all. Performance is close to that of a human expert examining the images for the presence of red lesions.

Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone.

PubMed

Lahdenoja, Olli; Hurnanen, Tero; Iftikhar, Zuhair; Nieminen, Sami; Knuutila, Timo; Saraste, Antti; Kiviniemi, Tuomas; Vasankari, Tuija; Airaksinen, Juhani; Pankaala, Mikko; Koivisto, Tero

2018-01-01

We present a smartphone-only solution for the detection of atrial fibrillation (AFib), which utilizes the built-in accelerometer and gyroscope sensors [inertial measurement unit, (IMU)] in the detection. Depending on the patient's situation, it is possible to use the developed smartphone application either regularly or occasionally for making a measurement of the subject. The smartphone is placed on the chest of the patient who is adviced to lay down and perform a noninvasive recording, while no external sensors are needed. After that, the application determines whether the patient suffers from AFib or not. The presented method has high potential to detect paroxysmal ("silent") AFib from large masses. In this paper, we present the preprocessing, feature extraction, feature analysis, and classification results of the envisioned AFib detection system based on clinical data acquired with a standard mobile phone equipped with Google Android OS. Test data was gathered from 16 AFib patients (validated against ECG), as well as a control group of 23 healthy individuals with no diagnosed heart diseases. We obtained an accuracy of 97.4% in AFib versus healthy classification (a sensitivity of 93.8% and a specificity of 100%). Due to the wide availability of smart devices/sensors with embedded IMU, the proposed methods could potentially also scale to other domains such as embedded body-sensor networks.

Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications.

PubMed

Mordang, Jan-Jurre; Gubern-Mérida, Albert; den Heeten, Gerard; Karssemeijer, Nico

2016-04-01

In the past decades, computer-aided detection (CADe) systems have been developed to aid screening radiologists in the detection of malignant microcalcifications. These systems are useful to avoid perceptual oversights and can increase the radiologists' detection rate. However, due to the high number of false positives marked by these CADe systems, they are not yet suitable as an independent reader. Breast arterial calcifications (BACs) are one of the most frequent false positives marked by CADe systems. In this study, a method is proposed for the elimination of BACs as positive findings. Removal of these false positives will increase the performance of the CADe system in finding malignant microcalcifications. A multistage method is proposed for the removal of BAC findings. The first stage consists of a microcalcification candidate selection, segmentation and grouping of the microcalcifications, and classification to remove obvious false positives. In the second stage, a case-based selection is applied where cases are selected which contain BACs. In the final stage, BACs are removed from the selected cases. The BACs removal stage consists of a GentleBoost classifier trained on microcalcification features describing their shape, topology, and texture. Additionally, novel features are introduced to discriminate BACs from other positive findings. The CADe system was evaluated with and without BACs removal. Here, both systems were applied on a validation set containing 1088 cases of which 95 cases contained malignant microcalcifications. After bootstrapping, free-response receiver operating characteristics and receiver operating characteristics analyses were carried out. Performance between the two systems was compared at 0.98 and 0.95 specificity. At a specificity of 0.98, the sensitivity increased from 37% to 52% and the sensitivity increased from 62% up to 76% at a specificity of 0.95. Partial areas under the curve in the specificity range of 0.8-1.0 were significantly different between the system without BACs removal and the system with BACs removal, 0.129 ± 0.009 versus 0.144 ± 0.008 (p<0.05), respectively. Additionally, the sensitivity at one false positive per 50 cases and one false positive per 25 cases increased as well, 37% versus 51% (p<0.05) and 58% versus 67% (p<0.05) sensitivity, respectively. Additionally, the CADe system with BACs removal reduces the number of false positives per case by 29% on average. The same sensitivity at one false positive per 50 cases in the CADe system without BACs removal can be achieved at one false positive per 80 cases in the CADe system with BACs removal. By using dedicated algorithms to detect and remove breast arterial calcifications, the performance of CADe systems can be improved, in particular, at false positive rates representative for operating points used in screening.

Detections and Sensitive Upper Limits for Methane and Related Trace Gases on Mars during 2003-2014, and planned extensions in 2016

NASA Astrophysics Data System (ADS)

Mumma, Michael J.; Villanueva, Geronimo L.; Novak, Robert E.

2015-11-01

Five groups report methane detections on Mars; all results suggest local release and high temporal variability [1-7]. Our team searched for CH4 on many dates and seasons and detected it on several dates [1, 9, 10]. TLS (Curiosity rover) reported methane upper limits [6], and then detections [7] that were consistent in size with earlier reports and that also showed rapid modulation of CH4 abundance.[8] argued that absorption features assigned to Mars 12CH4 by [1] might instead be weak lines of terrestrial 13CH4. If not properly removed, terrestrial 13CH4 signatures would appear on the blue wing of terrestrial 12CH4 even when Mars is red-shifted - but they do not (Fig. S6 of [1]), demonstrating that terrestrial signatures were correctly removed. [9] demonstrated that including the dependence of δ13CH4 with altitude did not affect the residual features, nor did taking δ13CH4 as zero. Were δ13CH4 important, its omission would have overemphasized the depth of 13CH4 terrestrial absorption, introducing emission features in the residual spectra [1]. However, the residual features are seen in absorption, establishing their origin as non-terrestrial - [8] now agrees with this view.We later reported results for multiple organic gases (CH4, CH3OH, H2CO, C2H6, C2H2, C2H4), hydroperoxyl (HO2), three nitriles (N2O, NH3, HCN) and two chlorinated species (HCl, CH3Cl) [9]. Most of these species cannot be detected with current space assets, owing to instrumental limitations (e.g., spectral resolving power). However, the high resolution infrared spectrometers (NOMAD, ACS) on ExoMars 2016 (Trace Gas Orbiter) will begin measurements in late 2016. In solar occultation, TGO sensitivities will far exceed prior capabilities.We published detailed hemispheric maps of H2O and HDO on Mars, inferring the size of a lost early ocean [10]. In 2016, we plan to acquire 3-D spatial maps of HDO and H2O with ALMA, and improved maps of organics with iSHELL/NASA-IRTF.References: [1] Mumma et al. Sci09; [2] Formisano et al. Sci04; [3] Krasnopolsky et al. Icar04; [4] Fonti and Marzo A&A10 [5] Krasnopolsky, Icar12; [6] Webster et al. Sci13; [7] Webster et al. Sci15; [8] Zahnle et al. Icar11; [9] Villanueva et al. Icar13; [10] Villanueva et al. Sci15.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery.

PubMed

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W; Chen, Zhuo Georgia; Fei, Baowei

2015-01-01

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

Photonic Crystal Sensors Based on Porous Silicon

PubMed Central

Pacholski, Claudia

2013-01-01

Porous silicon has been established as an excellent sensing platform for the optical detection of hazardous chemicals and biomolecular interactions such as DNA hybridization, antigen/antibody binding, and enzymatic reactions. Its porous nature provides a high surface area within a small volume, which can be easily controlled by changing the pore sizes. As the porosity and consequently the refractive index of an etched porous silicon layer depends on the electrochemial etching conditions photonic crystals composed of multilayered porous silicon films with well-resolved and narrow optical reflectivity features can easily be obtained. The prominent optical response of the photonic crystal decreases the detection limit and therefore increases the sensitivity of porous silicon sensors in comparison to sensors utilizing Fabry-Pérot based optical transduction. Development of porous silicon photonic crystal sensors which allow for the detection of analytes by the naked eye using a simple color change or the fabrication of stacked porous silicon photonic crystals showing two distinct optical features which can be utilized for the discrimination of analytes emphasize its high application potential. PMID:23571671

Automated microaneurysm detection in diabetic retinopathy using curvelet transform

NASA Astrophysics Data System (ADS)

Ali Shah, Syed Ayaz; Laude, Augustinus; Faye, Ibrahima; Tang, Tong Boon

2016-10-01

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

Automated microaneurysm detection in diabetic retinopathy using curvelet transform.

PubMed

Ali Shah, Syed Ayaz; Laude, Augustinus; Faye, Ibrahima; Tang, Tong Boon

2016-10-01

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

NASA Astrophysics Data System (ADS)

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W.; Chen, Zhuo Georgia; Fei, Baowei

2015-12-01

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

Non-invasive health status detection system using Gabor filters based on facial block texture features.

PubMed

Shu, Ting; Zhang, Bob

2015-04-01

Blood tests allow doctors to check for certain diseases and conditions. However, using a syringe to extract the blood can be deemed invasive, slightly painful, and its analysis time consuming. In this paper, we propose a new non-invasive system to detect the health status (Healthy or Diseased) of an individual based on facial block texture features extracted using the Gabor filter. Our system first uses a non-invasive capture device to collect facial images. Next, four facial blocks are located on these images to represent them. Afterwards, each facial block is convolved with a Gabor filter bank to calculate its texture value. Classification is finally performed using K-Nearest Neighbor and Support Vector Machines via a Library for Support Vector Machines (with four kernel functions). The system was tested on a dataset consisting of 100 Healthy and 100 Diseased (with 13 forms of illnesses) samples. Experimental results show that the proposed system can detect the health status with an accuracy of 93 %, a sensitivity of 94 %, a specificity of 92 %, using a combination of the Gabor filters and facial blocks.

Detection of explosives by differential hyperspectral imaging

NASA Astrophysics Data System (ADS)

Dubroca, Thierry; Brown, Gregory; Hummel, Rolf E.

2014-02-01

Our team has pioneered an explosives detection technique based on hyperspectral imaging of surfaces. Briefly, differential reflectometry (DR) shines ultraviolet (UV) and blue light on two close-by areas on a surface (for example, a piece of luggage on a moving conveyer belt). Upon reflection, the light is collected with a spectrometer combined with a charge coupled device (CCD) camera. A computer processes the data and produces in turn differential reflection spectra taken from these two adjacent areas on the surface. This differential technique is highly sensitive and provides spectroscopic data of materials, particularly of explosives. As an example, 2,4,6-trinitrotoluene displays strong and distinct features in differential reflectograms near 420 and 250 nm, that is, in the near-UV region. Similar, but distinctly different features are observed for other explosives. Finally, a custom algorithm classifies the collected spectral data and outputs an acoustic signal if a threat is detected. This paper presents the complete DR hyperspectral imager which we have designed and built from the hardware to the software, complete with an analysis of the device specifications.

A smart phone-based pocket fall accident detection, positioning, and rescue system.

PubMed

Kau, Lih-Jen; Chen, Chih-Sheng

2015-01-01

We propose in this paper a novel algorithm as well as architecture for the fall accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a fall accident event is detected, the user's position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.

Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification.

PubMed

Riccardi, Alessandro; Petkov, Todor Sergueev; Ferri, Gianluca; Masotti, Matteo; Campanini, Renato

2011-04-01

The authors presented a novel system for automated nodule detection in lung CT exams. The approach is based on (1) a lung tissue segmentation preprocessing step, composed of histogram thresholding, seeded region growing, and mathematical morphology; (2) a filtering step, whose aim is the preliminary detection of candidate nodules (via 3D fast radial filtering) and estimation of their geometrical features (via scale space analysis); and (3) a false positive reduction (FPR) step, comprising a heuristic FPR, which applies thresholds based on geometrical features, and a supervised FPR, which is based on support vector machines classification, which in turn, is enhanced by a feature extraction algorithm based on maximum intensity projection processing and Zernike moments. The system was validated on 154 chest axial CT exams provided by the lung image database consortium public database. The authors obtained correct detection of 71% of nodules marked by all radiologists, with a false positive rate of 6.5 false positives per patient (FP/patient). A higher specificity of 2.5 FP/patient was reached with a sensitivity of 60%. An independent test on the ANODE09 competition database obtained an overall score of 0.310. The system shows a novel approach to the problem of lung nodule detection in CT scans: It relies on filtering techniques, image transforms, and descriptors rather than region growing and nodule segmentation, and the results are comparable to those of other recent systems in literature and show little dependency on the different types of nodules, which is a good sign of robustness.

Computer aided detection of brain micro-bleeds in traumatic brain injury

NASA Astrophysics Data System (ADS)

van den Heuvel, T. L. A.; Ghafoorian, M.; van der Eerden, A. W.; Goraj, B. M.; Andriessen, T. M. J. C.; ter Haar Romeny, B. M.; Platel, B.

2015-03-01

Brain micro-bleeds (BMBs) are used as surrogate markers for detecting diffuse axonal injury in traumatic brain injury (TBI) patients. The location and number of BMBs have been shown to influence the long-term outcome of TBI. To further study the importance of BMBs for prognosis, accurate localization and quantification are required. The task of annotating BMBs is laborious, complex and prone to error, resulting in a high inter- and intra-reader variability. In this paper we propose a computer-aided detection (CAD) system to automatically detect BMBs in MRI scans of moderate to severe neuro-trauma patients. Our method consists of four steps. Step one: preprocessing of the data. Both susceptibility (SWI) and T1 weighted MRI scans are used. The images are co-registered, a brain-mask is generated, the bias field is corrected, and the image intensities are normalized. Step two: initial candidates for BMBs are selected as local minima in the processed SWI scans. Step three: feature extraction. BMBs appear as round or ovoid signal hypo-intensities on SWI. Twelve features are computed to capture these properties of a BMB. Step four: Classification. To identify BMBs from the set of local minima using their features, different classifiers are trained on a database of 33 expert annotated scans and 18 healthy subjects with no BMBs. Our system uses a leave-one-out strategy to analyze its performance. With a sensitivity of 90% and 1.3 false positives per BMB, our CAD system shows superior results compared to state-of-the-art BMB detection algorithms (developed for non-trauma patients).

Aspects of the Application of Cavity Enhanced Spectroscopy to Nitrogen Oxides Detection

PubMed Central

Wojtas, Jacek; Mikolajczyk, Janusz; Bielecki, Zbigniew

2013-01-01

This article presents design issues of high-sensitive laser absorption spectroscopy systems for nitrogen oxides (NOx) detection. Examples of our systems and their investigation results are also described. The constructed systems use one of the most sensitive methods, cavity enhanced absorption spectroscopy (CEAS). They operate at different wavelength ranges using a blue—violet laser diode (410 nm) as well as quantum cascade lasers (5.27 μm and 4.53 μm). Each of them is configured as a one or two channel measurement device using, e.g., time division multiplexing and averaging. During the testing procedure, the main performance features such as detection limits and measurements uncertainties have been determined. The obtained results are 1 ppb NO2, 75 ppb NO and 45 ppb N2O. For all systems, the uncertainty of concentration measurements does not exceed a value of 13%. Some experiments with explosives are also discussed. A setup equipped with a concentrator of explosives vapours was used. The detection method is based either on the reaction of the sensors to the nitrogen oxides directly emitted by the explosives or on the reaction to the nitrogen oxides produced during thermal decomposition of explosive vapours. For TNT, PETN, RDX and HMX a detection limit better than 1 ng has been achieved. PMID:23752566

A novel strategy for screening-out raw milk contaminated with Mycobacterium bovis on dairy farms by double-tagging PCR and electrochemical genosensing.

PubMed

Lermo, Anabel; Liébana, Susana; Campoy, Susana; Fabiano, Silvia; García, M Inés; Soutullo, Adriana; Zumárraga, Martín J; Alegret, Salvador; Pividori, M Isabel

2010-06-01

A highly sensitive assay for rapidly screening-out Mycobacterium bovis in contaminated samples was developed based on electrochemical genosensing. The assay consists of specific amplification and double-tagging of the IS6110 fragment, highly related to M. bovis, followed by electrochemical detection of the amplified product. PCR amplification was carried out using a labeled set of primers and resulted in a amplicon tagged at each terminus with both biotin and digoxigenin. Two different electrochemical platforms for the detection of the double-tagged amplicon were evaluated: (i) an avidin biocomposite (Av-GEB) and (ii) a magneto sensor (m-GEC) combined with streptavidin magnetic beads. In both cases, the double- tagged amplicon was immobilized through its biotinylated end and electrochemically detected, using an antiDig-HRP conjugate, through its digoxigenin end. The assay was determined to be highly sensitive, based on the detection of 620 and 10 fmol of PCR amplicon using the Av-GEB and m-GEC strategies, respectively. Moreover, the m-GEC assay showed promising features for the detection of M. bovis on dairy farms by screening for the presence of the bacterium's DNA in milk samples. The obtained results are discussed and compared with respect to those of inter-laboratory PCR assays and tuberculin skin testing.

Sensitivity and specificity of Addenbrooke's Cognitive Examination, Mattis Dementia Rating Scale, Frontal Assessment Battery and Mini Mental State Examination for diagnosing dementia in Parkinson's disease.

PubMed

Kaszás, B; Kovács, N; Balás, I; Kállai, J; Aschermann, Z; Kerekes, Z; Komoly, S; Nagy, F; Janszky, J; Lucza, T; Karádi, K

2012-06-01

Among the non-motor features of Parkinson's disease (PD), cognitive impairment is one of the most troublesome problems. Highly sensitive and specific screening instruments for detecting dementia in PD (PDD) are required in the clinical practice. In our study we evaluated the sensitivity and specificity of different neuropsychological tests (Addenbrooke's Cognitive Examination, ACE; Frontal Assessment Battery, FAB and Mattis Dementia Rating Scale, MDRS) in 73 Parkinson's disease patients without depression. By receiver operating characteristic curve analysis, these screening instruments were tested against the recently established clinical diagnostic criteria of PDD. Best cut-off score for ACE to identify PDD was 80 points (sensitivity = 74.0%, specificity = 78.1%). For FAB the most optimal cut-off value was 12 points (sensitivity = 66.3%, specificity = 72.2%); whereas for MDRS it was 125 points (sensitivity = 89.8%, specificity = 98.3%). Among the examined test batteries, MDRS had the best clinicometric profile for detecting PDD. Although the types of applied screening instruments might differ from movement disorder clinic to clinic within a country, determination of the most specific and sensitive test for the given population remains to be an important task. Our results demonstrated that the specificity and sensitivity of MDRS was better than those of ACE, FAB and MMSE in Hungary. However, further studies with larger sample size and more uniform criteria for participation are required to determine the most suitable screening instrument for cognitive impairment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multispectral processing based on groups of resolution elements

NASA Technical Reports Server (NTRS)

Richardson, W.; Gleason, J. M.

1975-01-01

Several nine-point rules are defined and compared with previously studied rules. One of the rules performed well in boundary areas, but with reduced efficiency in field interiors; another combined best performance on field interiors with good sensitivity to boundary detail. The basic threshold gradient and some modifications were investigated as a means of boundary point detection. The hypothesis testing methods of closed-boundary formation were also tested and evaluated. An analysis of the boundary detection problem was initiated, employing statistical signal detection and parameter estimation techniques to analyze various formulations of the problem. These formulations permit the atmospheric and sensor system effects on the data to be thoroughly analyzed. Various boundary features and necessary assumptions can also be investigated in this manner.

Electrochemical Biosensors for Rapid Detection of Foodborne Salmonella: A Critical Overview

PubMed Central

Cinti, Stefano; Volpe, Giulia; Piermarini, Silvia; Delibato, Elisabetta; Palleschi, Giuseppe

2017-01-01

Salmonella has represented the most common and primary cause of food poisoning in many countries for at least over 100 years. Its detection is still primarily based on traditional microbiological culture methods which are labor-intensive, extremely time consuming, and not suitable for testing a large number of samples. Accordingly, great efforts to develop rapid, sensitive and specific methods, easy to use, and suitable for multi-sample analysis, have been made and continue. Biosensor-based technology has all the potentialities to meet these requirements. In this paper, we review the features of the electrochemical immunosensors, genosensors, aptasensors and phagosensors developed in the last five years for Salmonella detection, focusing on the critical aspects of their application in food analysis. PMID:28820458

Disease-Related Detection with Electrochemical Biosensors: A Review.

PubMed

Huang, Ying; Xu, Jin; Liu, Junjie; Wang, Xiangyang; Chen, Bin

2017-10-17

Rapid diagnosis of diseases at their initial stage is critical for effective clinical outcomes and promotes general public health. Classical in vitro diagnostics require centralized laboratories, tedious work and large, expensive devices. In recent years, numerous electrochemical biosensors have been developed and proposed for detection of various diseases based on specific biomarkers taking advantage of their features, including sensitivity, selectivity, low cost and rapid response. This article reviews research trends in disease-related detection with electrochemical biosensors. Focus has been placed on the immobilization mechanism of electrochemical biosensors, and the techniques and materials used for the fabrication of biosensors are introduced in details. Various biomolecules used for different diseases have been listed. Besides, the advances and challenges of using electrochemical biosensors for disease-related applications are discussed.