An improved high order texture features extraction method with application to pathological diagnosis of colon lesions for CT colonography

NASA Astrophysics Data System (ADS)

Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Wang, Huafeng; Han, Fangfang; Zhu, Wei; Liang, Zhengrong

2014-03-01

Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic, is of fundamental importance for patient management. Image intensity based textural features have been recognized as a useful biomarker for the differentiation task. In this paper, we introduce high order texture features, beyond the intensity, such as gradient and curvature, for that task. Based on the Haralick texture analysis method, we introduce a virtual pathological method to explore the utility of texture features from high order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the random forest classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. The results show that after applying the high order features, the AUC was improved from 0.8069 to 0.8544 in differentiating non-neoplastic lesion from neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. The experimental results demonstrated that texture features from the higher order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography (CT) colonography for colorectal cancer screening by not only detecting polyps but also classifying them from optimal polyp management for the best outcome in personalized medicine.

Automatic segmentation of solitary pulmonary nodules based on local intensity structure analysis and 3D neighborhood features in 3D chest CT images

NASA Astrophysics Data System (ADS)

Chen, Bin; Kitasaka, Takayuki; Honma, Hirotoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi; Mori, Kensaku

2012-03-01

This paper presents a solitary pulmonary nodule (SPN) segmentation method based on local intensity structure analysis and neighborhood feature analysis in chest CT images. Automated segmentation of SPNs is desirable for a chest computer-aided detection/diagnosis (CAS) system since a SPN may indicate early stage of lung cancer. Due to the similar intensities of SPNs and other chest structures such as blood vessels, many false positives (FPs) are generated by nodule detection methods. To reduce such FPs, we introduce two features that analyze the relation between each segmented nodule candidate and it neighborhood region. The proposed method utilizes a blob-like structure enhancement (BSE) filter based on Hessian analysis to augment the blob-like structures as initial nodule candidates. Then a fine segmentation is performed to segment much more accurate region of each nodule candidate. FP reduction is mainly addressed by investigating two neighborhood features based on volume ratio and eigenvector of Hessian that are calculates from the neighborhood region of each nodule candidate. We evaluated the proposed method by using 40 chest CT images, include 20 standard-dose CT images that we randomly chosen from a local database and 20 low-dose CT images that were randomly chosen from a public database: LIDC. The experimental results revealed that the average TP rate of proposed method was 93.6% with 12.3 FPs/case.

Migraine disability assessment (MIDAS) score: relation to headache frequency, pain intensity, and headache symptoms.

PubMed

Stewart, Walter F; Lipton, Richard B; Kolodner, Ken

2003-03-01

To determine the extent to which variation in the Migraine Disability Assessment (MIDAS) score is associated with headache frequency, pain intensity, headache symptoms, gender, and employment status. The MIDAS questionnaire is a 7-item questionnaire (with 5 scored items) designed to measure headache-related disability, to improve physician-patient communication, and to identify patients with high treatment needs. Data from 3 population-based studies (total sample, n = 397) conducted in the United States and the United Kingdom were used to evaluate the relationship between headache features (attack frequency, pain intensity, pain quality, and associated symptoms) and MIDAS score. Data on headache features were collected by telephone using a standardized interview. The MIDAS questionnaire was completed shortly after the telephone interview. General linear models were used to determine the extent to which population variation in the MIDAS score was explained by headache features. Using linear regression, variables for all headache features (ie, headache frequency, pain intensity, pain quality, and associated symptoms) and demographic characteristics explained only 22% of the variation in MIDAS scores. Almost all (19.9%) the explained variance was accounted for by average pain intensity (12.0%), number of headache days (6.1%), and exacerbation of pain with movement (1.8%). When pain intensity and headache frequency were included in the model, no statistically significant differences in MIDAS scores were observed by gender or employment status. Although explaining only 2.1% of the variance, age was significantly associated with MIDAS scores, with those under 25 years demonstrating higher MIDAS scores than other age groups. No other variables (ie, frequency of occurrence of associated symptoms and other measures of quality of pain) were associated with MIDAS scores. Challenges to the utility of the MIDAS as a measure include whether headache-related disability is largely a function of other routine headache features and whether MIDAS is inherently biased based on work status and gender. While the MIDAS score was associated with headache frequency and average pain score, these two headache features explain only a modest proportion of the variation in MIDAS scores. Additionally, gender and work status were not related to MIDAS scores. These findings suggest that the MIDAS score captures information about disability that is not inherent to other headache features and is independent of gender and work status.

FEX: A Knowledge-Based System For Planimetric Feature Extraction

NASA Astrophysics Data System (ADS)

Zelek, John S.

1988-10-01

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

Image features dependant correlation-weighting function for efficient PRNU based source camera identification.

PubMed

Tiwari, Mayank; Gupta, Bhupendra

2018-04-01

For source camera identification (SCI), photo response non-uniformity (PRNU) has been widely used as the fingerprint of the camera. The PRNU is extracted from the image by applying a de-noising filter then taking the difference between the original image and the de-noised image. However, it is observed that intensity-based features and high-frequency details (edges and texture) of the image, effect quality of the extracted PRNU. This effects correlation calculation and creates problems in SCI. For solving this problem, we propose a weighting function based on image features. We have experimentally identified image features (intensity and high-frequency contents) effect on the estimated PRNU, and then develop a weighting function which gives higher weights to image regions which give reliable PRNU and at the same point it gives comparatively less weights to the image regions which do not give reliable PRNU. Experimental results show that the proposed weighting function is able to improve the accuracy of SCI up to a great extent. Copyright © 2018 Elsevier B.V. All rights reserved.

Voxel-based automated detection of focal cortical dysplasia lesions using diffusion tensor imaging and T2-weighted MRI data.

PubMed

Wang, Yanming; Zhou, Yawen; Wang, Huijuan; Cui, Jin; Nguchu, Benedictor Alexander; Zhang, Xufei; Qiu, Bensheng; Wang, Xiaoxiao; Zhu, Mingwang

2018-05-21

The aim of this study was to automatically detect focal cortical dysplasia (FCD) lesions in patients with extratemporal lobe epilepsy by relying on diffusion tensor imaging (DTI) and T2-weighted magnetic resonance imaging (MRI) data. We implemented an automated classifier using voxel-based multimodal features to identify gray and white matter abnormalities of FCD in patient cohorts. In addition to the commonly used T2-weighted image intensity feature, DTI-based features were also utilized. A Gaussian processes for machine learning (GPML) classifier was tested on 12 patients with FCD (8 with histologically confirmed FCD) scanned at 1.5 T and cross-validated using a leave-one-out strategy. Moreover, we compared the multimodal GPML paradigm's performance with that of single modal GPML and classical support vector machine (SVM). Our results demonstrated that the GPML performance on DTI-based features (mean AUC = 0.63) matches with the GPML performance on T2-weighted image intensity feature (mean AUC = 0.64). More promisingly, GPML yielded significantly improved performance (mean AUC = 0.76) when applying DTI-based features to multimodal paradigm. Based on the results, it can also be clearly stated that the proposed GPML strategy performed better and is robust to unbalanced dataset contrary to SVM that performed poorly (AUC = 0.69). Therefore, the GPML paradigm using multimodal MRI data containing DTI modality has promising result towards detection of the FCD lesions and provides an effective direction for future researches. Copyright © 2018 Elsevier Inc. All rights reserved.

A novel visual saliency analysis model based on dynamic multiple feature combination strategy

NASA Astrophysics Data System (ADS)

Lv, Jing; Ye, Qi; Lv, Wen; Zhang, Libao

2017-06-01

The human visual system can quickly focus on a small number of salient objects. This process was known as visual saliency analysis and these salient objects are called focus of attention (FOA). The visual saliency analysis mechanism can be used to extract the salient regions and analyze saliency of object in an image, which is time-saving and can avoid unnecessary costs of computing resources. In this paper, a novel visual saliency analysis model based on dynamic multiple feature combination strategy is introduced. In the proposed model, we first generate multi-scale feature maps of intensity, color and orientation features using Gaussian pyramids and the center-surround difference. Then, we evaluate the contribution of all feature maps to the saliency map according to the area of salient regions and their average intensity, and attach different weights to different features according to their importance. Finally, we choose the largest salient region generated by the region growing method to perform the evaluation. Experimental results show that the proposed model cannot only achieve higher accuracy in saliency map computation compared with other traditional saliency analysis models, but also extract salient regions with arbitrary shapes, which is of great value for the image analysis and understanding.

Bidirectional reflectance distribution function based surface modeling of non-Lambertian using intensity data of light detection and ranging.

PubMed

Li, Xiaolu; Liang, Yu; Xu, Lijun

2014-09-01

To provide a credible model for light detection and ranging (LiDAR) target classification, the focus of this study is on the relationship between intensity data of LiDAR and the bidirectional reflectance distribution function (BRDF). An integration method based on the built-in-lab coaxial laser detection system was advanced. A kind of intermediary BRDF model advanced by Schlick was introduced into the integration method, considering diffuse and specular backscattering characteristics of the surface. A group of measurement campaigns were carried out to investigate the influence of the incident angle and detection range on the measured intensity data. Two extracted parameters r and S(λ) are influenced by different surface features, which illustrate the surface features of the distribution and magnitude of reflected energy, respectively. The combination of two parameters can be used to describe the surface characteristics for target classification in a more plausible way.

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

NASA Astrophysics Data System (ADS)

Yang, Hongxin; Su, Fulin

2018-01-01

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

Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation.

PubMed

Wang, Lei; Zhang, Huimao; He, Kan; Chang, Yan; Yang, Xiaodong

2015-01-01

Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and 'vesselness values' from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.

Exact extraction method for road rutting laser lines

NASA Astrophysics Data System (ADS)

Hong, Zhiming

2018-02-01

This paper analyzes the importance of asphalt pavement rutting detection in pavement maintenance and pavement administration in today's society, the shortcomings of the existing rutting detection methods are presented and a new rutting line-laser extraction method based on peak intensity characteristic and peak continuity is proposed. The intensity of peak characteristic is enhanced by a designed transverse mean filter, and an intensity map of peak characteristic based on peak intensity calculation for the whole road image is obtained to determine the seed point of the rutting laser line. Regarding the seed point as the starting point, the light-points of a rutting line-laser are extracted based on the features of peak continuity, which providing exact basic data for subsequent calculation of pavement rutting depths.

Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

NASA Astrophysics Data System (ADS)

Mann, Kulwinder S.; Kaur, Sukhpreet

2017-06-01

There are various eye diseases in the patients suffering from the diabetes which includes Diabetic Retinopathy, Glaucoma, Hypertension etc. These all are the most common sight threatening eye diseases due to the changes in the blood vessel structure. The proposed method using supervised methods concluded that the segmentation of the retinal blood vessels can be performed accurately using neural networks training. It uses features which include Gray level features; Moment Invariant based features, Gabor filtering, Intensity feature, Vesselness feature for feature vector computation. Then the feature vector is calculated using only the prominent features.

Measurement of glucose concentration by image processing of thin film slides

NASA Astrophysics Data System (ADS)

Piramanayagam, Sankaranaryanan; Saber, Eli; Heavner, David

2012-02-01

Measurement of glucose concentration is important for diagnosis and treatment of diabetes mellitus and other medical conditions. This paper describes a novel image-processing based approach for measuring glucose concentration. A fluid drop (patient sample) is placed on a thin film slide. Glucose, present in the sample, reacts with reagents on the slide to produce a color dye. The color intensity of the dye formed varies with glucose at different concentration levels. Current methods use spectrophotometry to determine the glucose level of the sample. Our proposed algorithm uses an image of the slide, captured at a specific wavelength, to automatically determine glucose concentration. The algorithm consists of two phases: training and testing. Training datasets consist of images at different concentration levels. The dye-occupied image region is first segmented using a Hough based technique and then an intensity based feature is calculated from the segmented region. Subsequently, a mathematical model that describes a relationship between the generated feature values and the given concentrations is obtained. During testing, the dye region of a test slide image is segmented followed by feature extraction. These two initial steps are similar to those done in training. However, in the final step, the algorithm uses the model (feature vs. concentration) obtained from the training and feature generated from test image to predict the unknown concentration. The performance of the image-based analysis was compared with that of a standard glucose analyzer.

Extraction of edge-based and region-based features for object recognition

NASA Astrophysics Data System (ADS)

Coutts, Benjamin; Ravi, Srinivas; Hu, Gongzhu; Shrikhande, Neelima

1993-08-01

One of the central problems of computer vision is object recognition. A catalogue of model objects is described as a set of features such as edges and surfaces. The same features are extracted from the scene and matched against the models for object recognition. Edges and surfaces extracted from the scenes are often noisy and imperfect. In this paper algorithms are described for improving low level edge and surface features. Existing edge extraction algorithms are applied to the intensity image to obtain edge features. Initial edges are traced by following directions of the current contour. These are improved by using corresponding depth and intensity information for decision making at branch points. Surface fitting routines are applied to the range image to obtain planar surface patches. An algorithm of region growing is developed that starts with a coarse segmentation and uses quadric surface fitting to iteratively merge adjacent regions into quadric surfaces based on approximate orthogonal distance regression. Surface information obtained is returned to the edge extraction routine to detect and remove fake edges. This process repeats until no more merging or edge improvement can take place. Both synthetic (with Gaussian noise) and real images containing multiple object scenes have been tested using the merging criteria. Results appeared quite encouraging.

Feature-based Alignment of Volumetric Multi-modal Images

PubMed Central

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial AccelerometerWrist-Band.

PubMed

Mehrang, Saeed; Pietilä, Julia; Korhonen, Ilkka

2018-02-22

Wrist-worn sensors have better compliance for activity monitoring compared to hip, waist, ankle or chest positions. However, wrist-worn activity monitoring is challenging due to the wide degree of freedom for the hand movements, as well as similarity of hand movements in different activities such as varying intensities of cycling. To strengthen the ability of wrist-worn sensors in detecting human activities more accurately, motion signals can be complemented by physiological signals such as optical heart rate (HR) based on photoplethysmography. In this paper, an activity monitoring framework using an optical HR sensor and a triaxial wrist-worn accelerometer is presented. We investigated a range of daily life activities including sitting, standing, household activities and stationary cycling with two intensities. A random forest (RF) classifier was exploited to detect these activities based on the wrist motions and optical HR. The highest overall accuracy of 89.6 ± 3.9% was achieved with a forest of a size of 64 trees and 13-s signal segments with 90% overlap. Removing the HR-derived features decreased the classification accuracy of high-intensity cycling by almost 7%, but did not affect the classification accuracies of other activities. A feature reduction utilizing the feature importance scores of RF was also carried out and resulted in a shrunken feature set of only 21 features. The overall accuracy of the classification utilizing the shrunken feature set was 89.4 ± 4.2%, which is almost equivalent to the above-mentioned peak overall accuracy.

Estimating nonrigid motion from inconsistent intensity with robust shape features

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

Liu, Wenyang; Ruan, Dan, E-mail: druan@mednet.ucla.edu; Department of Radiation Oncology, University of California, Los Angeles, California 90095

2013-12-15

Purpose: To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Methods: Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, andmore » regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. Results: To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. Conclusions: The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.« less

Estimating nonrigid motion from inconsistent intensity with robust shape features.

PubMed

Liu, Wenyang; Ruan, Dan

2013-12-01

To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, and regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

NASA Astrophysics Data System (ADS)

Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

2017-08-01

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

Multiple-algorithm parallel fusion of infrared polarization and intensity images based on algorithmic complementarity and synergy

NASA Astrophysics Data System (ADS)

Zhang, Lei; Yang, Fengbao; Ji, Linna; Lv, Sheng

2018-01-01

Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.

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

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

PubMed Central

Gandomkar, Ziba; Brennan, Patrick C.; Mello-Thoms, Claudia

2017-01-01

Context: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Aims: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. Materials and Methods: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. Statistical Analysis Used: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. Results: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Conclusions: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects. PMID:28966834

Automatic measurement and representation of prosodic features

NASA Astrophysics Data System (ADS)

Ying, Goangshiuan Shawn

Effective measurement and representation of prosodic features of the acoustic signal for use in automatic speech recognition and understanding systems is the goal of this work. Prosodic features-stress, duration, and intonation-are variations of the acoustic signal whose domains are beyond the boundaries of each individual phonetic segment. Listeners perceive prosodic features through a complex combination of acoustic correlates such as intensity, duration, and fundamental frequency (F0). We have developed new tools to measure F0 and intensity features. We apply a probabilistic global error correction routine to an Average Magnitude Difference Function (AMDF) pitch detector. A new short-term frequency-domain Teager energy algorithm is used to measure the energy of a speech signal. We have conducted a series of experiments performing lexical stress detection on words in continuous English speech from two speech corpora. We have experimented with two different approaches, a segment-based approach and a rhythm unit-based approach, in lexical stress detection. The first approach uses pattern recognition with energy- and duration-based measurements as features to build Bayesian classifiers to detect the stress level of a vowel segment. In the second approach we define rhythm unit and use only the F0-based measurement and a scoring system to determine the stressed segment in the rhythm unit. A duration-based segmentation routine was developed to break polysyllabic words into rhythm units. The long-term goal of this work is to develop a system that can effectively detect the stress pattern for each word in continuous speech utterances. Stress information will be integrated as a constraint for pruning the word hypotheses in a word recognition system based on hidden Markov models.

Image segmentation using joint spatial-intensity-shape features: application to CT lung nodule segmentation

NASA Astrophysics Data System (ADS)

Ye, Xujiong; Siddique, Musib; Douiri, Abdel; Beddoe, Gareth; Slabaugh, Greg

2009-02-01

Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This paper presents a novel feature-guided method for the segmentation of 3D medical lesions. The proposed algorithm combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is integrated into the process of the segmentation algorithm. The joint spatial-intensity-shape features provide rich information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other anatomies, such as polyps in the colon.

Generalizations of the subject-independent feature set for music-induced emotion recognition.

PubMed

Lin, Yuan-Pin; Chen, Jyh-Horng; Duann, Jeng-Ren; Lin, Chin-Teng; Jung, Tzyy-Ping

2011-01-01

Electroencephalogram (EEG)-based emotion recognition has been an intensely growing field. Yet, how to achieve acceptable accuracy on a practical system with as fewer electrodes as possible is less concerned. This study evaluates a set of subject-independent features, based on differential power asymmetry of symmetric electrode pairs [1], with emphasis on its applicability to subject variability in music-induced emotion classification problem. Results of this study have evidently validated the feasibility of using subject-independent EEG features to classify four emotional states with acceptable accuracy in second-scale temporal resolution. These features could be generalized across subjects to detect emotion induced by music excerpts not limited to the music database that was used to derive the emotion-specific features.

Conceptual design of an intense positron source based on an LIA

NASA Astrophysics Data System (ADS)

Long, Ji-Dong; Yang, Zhen; Dong, Pan; Shi, Jin-Shui

2012-04-01

Accelerator based positron sources are widely used due to their high intensity. Most of these accelerators are RF accelerators. An LIA (linear induction accelerator) is a kind of high current pulsed accelerator used for radiography. A conceptual design of an intense pulsed positron source based on an LIA is presented in the paper. One advantage of an LIA is its pulsed power being higher than conventional accelerators, which means a higher amount of primary electrons for positron generations per pulse. Another advantage of an LIA is that it is very suitable to decelerate the positron bunch generated by bremsstrahlung pair process due to its ability to adjustably shape the voltage pulse. By implementing LIA cavities to decelerate the positron bunch before it is moderated, the positron yield could be greatly increased. These features may make the LIA based positron source become a high intensity pulsed positron source.

An image-processing method to detect sub-optical features based on understanding noise in intensity measurements.

PubMed

Bhatia, Tripta

2018-07-01

Accurate quantitative analysis of image data requires that we distinguish between fluorescence intensity (true signal) and the noise inherent to its measurements to the extent possible. We image multilamellar membrane tubes and beads that grow from defects in the fluid lamellar phase of the lipid 1,2-dioleoyl-sn-glycero-3-phosphocholine dissolved in water and water-glycerol mixtures by using fluorescence confocal polarizing microscope. We quantify image noise and determine the noise statistics. Understanding the nature of image noise also helps in optimizing image processing to detect sub-optical features, which would otherwise remain hidden. We use an image-processing technique "optimum smoothening" to improve the signal-to-noise ratio of features of interest without smearing their structural details. A high SNR renders desired positional accuracy with which it is possible to resolve features of interest with width below optical resolution. Using optimum smoothening, the smallest and the largest core diameter detected is of width [Formula: see text] and [Formula: see text] nm, respectively, discussed in this paper. The image-processing and analysis techniques and the noise modeling discussed in this paper can be used for detailed morphological analysis of features down to sub-optical length scales that are obtained by any kind of fluorescence intensity imaging in the raster mode.

Rainfall variability over the tropical Pacific from July 1987 through December 1991 as inferred via monthly estimates from SSM/I

NASA Technical Reports Server (NTRS)

Berg, Wesley; Avery, Susan K.

1994-01-01

Estimates of monthly rainfall have been computed over the tropical Pacific using passive microwave satellite observations from the Special Sensor Microwave/Imager (SSM/I) for the preiod from July 1987 through December 1991. The monthly estimates were calibrated using measurements from a network of Pacific atoll rain gauges and compared to other satellite-based rainfall estimation techniques. Based on these monthly estimates, an analysis of the variability of large-scale features over intraseasonal to interannual timescales has been performed. While the major precipitation features as well as the seasonal variability distributions show good agreement with expected values, the presence of a moderately intense El Nino during 1986-87 and an intense La Nina during 1988-89 highlights this time period.

Reflection symmetry detection using locally affine invariant edge correspondence.

PubMed

Wang, Zhaozhong; Tang, Zesheng; Zhang, Xiao

2015-04-01

Reflection symmetry detection receives increasing attentions in recent years. The state-of-the-art algorithms mainly use the matching of intensity-based features (such as the SIFT) within a single image to find symmetry axes. This paper proposes a novel approach by establishing the correspondence of locally affine invariant edge-based features, which are superior to the intensity based in the aspects that it is insensitive to illumination variations, and applicable to textureless objects. The locally affine invariance is achieved by simple linear algebra for efficient and robust computations, making the algorithm suitable for detections under object distortions like perspective projection. Commonly used edge detectors and a voting process are, respectively, used before and after the edge description and matching steps to form a complete reflection detection pipeline. Experiments are performed using synthetic and real-world images with both multiple and single reflection symmetry axis. The test results are compared with existing algorithms to validate the proposed method.

Skeletal muscle metastases on magnetic resonance imaging: analysis of 31 cases.

PubMed

Li, Qi; Wang, Lei; Pan, Shinong; Shu, Hong; Ma, Ying; Lu, Zaiming; Fu, Xihu; Jiang, Bo; Guo, Qiyong

2016-01-01

To investigate the magnetic resonance imaging (MRI) features of skeletal muscle metastases (SMM). The records of 31 patients with proven SMM were retrospectively reviewed. Clinical history, type of primary malignancy, location of metastases, and MRI features of SMM were evaluated. Based on MRI findings, SMM were divided into three MRI types. The correlation between MRI types with ages and pathology category, between MRI types of SMM and ages, as well as MRI types of SMM and pathology category were analysed with Spearman's rho. The most common primary tumour was genital tumour (25.8%) and bronchial carcinoma (19.4%), and the most common cell type was adenocarcinoma (58.1%). SMM were located in the iliopsoas muscle (26.3%), paravertebral muscles (21.1%), and upper extremity muscles (18.4%). MRI features: (1) Type-I localised lesions (12.90%), round-like mass limited to local regions with heterogeneous iso-signal intensity in T1WI and heterogeneous hyper-intensity in T2WI; (2) Type-II diffuse lesions without bone destruction (35.48%), abnormal diffuse swelling of the muscle with irregular boundaries and slightly hypo- to iso-intensity in T1WI and hyper-intensity in T2WI; and (3) Type-III diffuse lesions with bone destruction (51.61%), distinct irregular lump with iso-intensity in T1WI and heterogeneous hyper-intensity in T2WI with adjacent bone invasion. There was positive correlation between MRI types and ages (r = 0.431, p < 0.05). There were no significant differences of MRI types with pathology category (p > 0.05). SMM features on MRI can be broadly used to classify lesions, which is beneficial for SMM diagnosis.

Skeletal muscle metastases on magnetic resonance imaging: analysis of 31 cases

PubMed Central

Li, Qi; Wang, Lei; Shu, Hong; Ma, Ying; Lu, Zaiming; Fu, Xihu; Jiang, Bo; Guo, Qiyong

2016-01-01

Aim of the study To investigate the magnetic resonance imaging (MRI) features of skeletal muscle metastases (SMM). Material and methods The records of 31 patients with proven SMM were retrospectively reviewed. Clinical history, type of primary malignancy, location of metastases, and MRI features of SMM were evaluated. Based on MRI findings, SMM were divided into three MRI types. The correlation between MRI types with ages and pathology category, between MRI types of SMM and ages, as well as MRI types of SMM and pathology category were analysed with Spearman's rho. Results The most common primary tumour was genital tumour (25.8%) and bronchial carcinoma (19.4%), and the most common cell type was adenocarcinoma (58.1%). SMM were located in the iliopsoas muscle (26.3%), paravertebral muscles (21.1%), and upper extremity muscles (18.4%). MRI features: (1) Type-I localised lesions (12.90%), round-like mass limited to local regions with heterogeneous iso-signal intensity in T1WI and heterogeneous hyper-intensity in T2WI; (2) Type-II diffuse lesions without bone destruction (35.48%), abnormal diffuse swelling of the muscle with irregular boundaries and slightly hypo- to iso-intensity in T1WI and hyper-intensity in T2WI; and (3) Type-III diffuse lesions with bone destruction (51.61%), distinct irregular lump with iso-intensity in T1WI and heterogeneous hyper-intensity in T2WI with adjacent bone invasion. There was positive correlation between MRI types and ages (r = 0.431, p < 0.05). There were no significant differences of MRI types with pathology category (p > 0.05). Conclusions SMM features on MRI can be broadly used to classify lesions, which is beneficial for SMM diagnosis. PMID:27647989

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

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

Nigg, D.W.; Mitchell, H.E.; Harker, Y.D.

Therapeutically-useful epithermal-neutron beams for BNCT are currently generated by nuclear reactors. Various accelerator-based neutron sources for BNCT have been proposed and some low intensity prototypes of such sources, generally featuring the use of proton beams and beryllium or lithium targets have been constructed. This paper describes an alternate approach to the realization of a clinically useful accelerator-based source of epithermal neutrons for BNCT that reconciles the often conflicting objectives of target cooling, neutron beam intensity, and neutron beam spectral purity via a two stage photoneutron production process.

Towards a needle-free diagnosis of malaria: in vivo identification and classification of red and white blood cells containing haemozoin.

PubMed

Burnett, Jennifer L; Carns, Jennifer L; Richards-Kortum, Rebecca

2017-11-07

Optical detection of circulating haemozoin has been suggested as a needle free method to diagnose malaria using in vivo microscopy. Haemozoin is generated within infected red blood cells by the malaria parasite, serving as a highly specific, endogenous biomarker of malaria. However, phagocytosis of haemozoin by white blood cells which persist after the infection is resolved presents the potential for false positive diagnosis; therefore, the focus of this work is to identify a feature of the haemozoin signal to discriminate between infected red blood cells and haemozoin-containing white blood cells. Conventional brightfield microscopy of thin film blood smears was used to analyse haemozoin absorbance signal in vitro. Cell type and parasite maturity were morphologically determined using colocalized DAPI staining. The ability of features to discriminate between infected red blood cells and haemozoin-containing white blood cells was evaluated using images of smears from subjects infected with two species of Plasmodium, Plasmodium yoelii and Plasmodium falciparum. Discriminating features identified by blood smear microscopy were characterized in vivo in P. yoelii-infected mice. Two features of the haemozoin signal, haemozoin diameter and normalized intensity difference, were identified as potential parameters to differentiate infected red blood cells and haemozoin-containing white blood cells. Classification performance was evaluated using the area under the receiver operating characteristic curve, with area under the curve values of 0.89 for the diameter parameter and 0.85 for the intensity parameter when assessed in P. yoelii samples. Similar results were obtained from P. falciparum blood smears, showing an AUC of 0.93 or greater for both classification features. For in vivo investigations, the intensity-based metric was the best classifier, with an AUC of 0.91. This work demonstrates that size and intensity features of haemozoin absorbance signal collected by in vivo microscopy are effective classification metrics to discriminate infected red blood cells from haemozoin-containing white blood cells. This reduces the potential for false positive results associated with optical imaging strategies for in vivo diagnosis of malaria based on the endogenous biomarker haemozoin.

Rethinking the intensive care environment: considering nature in nursing practice.

PubMed

Minton, Claire; Batten, Lesley

2016-01-01

With consideration of an environmental concept, this paper explores evidence related to the negative impacts of the intensive care unit environment on patient outcomes and explores the potential counteracting benefits of 'nature-based' nursing interventions as a way to improve care outcomes. The impact of the environment in which a patient is nursed has long been recognised as one determinant in patient outcomes. Whilst the contemporary intensive care unit environment contains many features that support the provision of the intensive therapies the patient requires, it can also be detrimental, especially for long-stay patients. This narrative review considers theoretical and evidence-based literature that supports the adoption of nature-based nursing interventions in intensive care units. Research and theoretical literature from a diverse range of disciplines including nursing, medicine, psychology, architecture and environmental science were considered in relation to patient outcomes and intensive care nursing practice. There are many nature-based interventions that intensive care unit nurses can implement into their nursing practice to counteract environmental stressors. These interventions can also improve the environment for patients' families and nurses. Intensive care unit nurses must actively consider and manage the environment in which nursing occurs to facilitate the best patient outcomes. © 2015 John Wiley & Sons Ltd.

Surface-Constrained Volumetric Brain Registration Using Harmonic Mappings

PubMed Central

Joshi, Anand A.; Shattuck, David W.; Thompson, Paul M.; Leahy, Richard M.

2015-01-01

In order to compare anatomical and functional brain imaging data across subjects, the images must first be registered to a common coordinate system in which anatomical features are aligned. Intensity-based volume registration methods can align subcortical structures well, but the variability in sulcal folding patterns typically results in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces. This is achieved by first parameterizing and aligning the cortical surfaces using sulcal landmarks. We then use a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume. Finally, this mapping is refined using an intensity-based warp. We demonstrate the utility of the method by applying it to T1-weighted magnetic resonance images (MRI). We evaluate the performance of our proposed method relative to existing methods that use only intensity information; for this comparison we compute the inter-subject alignment of expert-labeled sub-cortical structures after registration. PMID:18092736

All-optical universal logic gates on nonlinear multimode interference coupler using tunable input intensity

NASA Astrophysics Data System (ADS)

Tajaldini, Mehdi; Jafri, Mohd Zubir Mat

2015-04-01

The theory of Nonlinear Modal Propagation Analysis Method (NMPA) have shown significant features of nonlinear multimode interference (MMI) coupler with compact dimension and when launched near the threshold of nonlinearity. Moreover, NMPA have the potential to allow studying the nonlinear MMI based the modal interference to explorer the phenomenon that what happen due to the natural of multimode region. Proposal of all-optical switch based NMPA has approved its capability to achieving the all-optical gates. All-optical gates have attracted increasing attention due to their practical utility in all-optical signal processing networks and systems. Nonlinear multimode interference devices could apply as universal all-optical gates due to significant features that NMPA introduce them. In this Paper, we present a novel Ultra-compact MMI coupler based on NMPA method in low intensity compared to last reports either as a novel design method and potential application for optical NAND, NOR as universal gates on single structure for Boolean logic signal processing devices and optimize their application via studding the contrast ratio between ON and OFF as a function of output width. We have applied NMPA for several applications so that the miniaturization in low nonlinear intensities is their main purpose.

Shadow-Based Vehicle Detection in Urban Traffic

PubMed Central

Ibarra-Arenado, Manuel; Tjahjadi, Tardi; Pérez-Oria, Juan; Robla-Gómez, Sandra; Jiménez-Avello, Agustín

2017-01-01

Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS. PMID:28448465

The Low Backscattering Objects Classification in Polsar Image Based on Bag of Words Model Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Yang, L.; Shi, L.; Li, P.; Yang, J.; Zhao, L.; Zhao, B.

2018-04-01

Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR) image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW) model using Support Vector Machine (SVM) to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT) points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

Multifractal modeling, segmentation, prediction, and statistical validation of posterior fossa tumors

NASA Astrophysics Data System (ADS)

Islam, Atiq; Iftekharuddin, Khan M.; Ogg, Robert J.; Laningham, Fred H.; Sivakumar, Bhuvaneswari

2008-03-01

In this paper, we characterize the tumor texture in pediatric brain magnetic resonance images (MRIs) and exploit these features for automatic segmentation of posterior fossa (PF) tumors. We focus on PF tumor because of the prevalence of such tumor in pediatric patients. Due to varying appearance in MRI, we propose to model the tumor texture with a multi-fractal process, such as a multi-fractional Brownian motion (mBm). In mBm, the time-varying Holder exponent provides flexibility in modeling irregular tumor texture. We develop a detailed mathematical framework for mBm in two-dimension and propose a novel algorithm to estimate the multi-fractal structure of tissue texture in brain MRI based on wavelet coefficients. This wavelet based multi-fractal feature along with MR image intensity and a regular fractal feature obtained using our existing piecewise-triangular-prism-surface-area (PTPSA) method, are fused in segmenting PF tumor and non-tumor regions in brain T1, T2, and FLAIR MR images respectively. We also demonstrate a non-patient-specific automated tumor prediction scheme based on these image features. We experimentally show the tumor discriminating power of our novel multi-fractal texture along with intensity and fractal features in automated tumor segmentation and statistical prediction. To evaluate the performance of our tumor prediction scheme, we obtain ROCs and demonstrate how sharply the curves reach the specificity of 1.0 sacrificing minimal sensitivity. Experimental results show the effectiveness of our proposed techniques in automatic detection of PF tumors in pediatric MRIs.

Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features

NASA Astrophysics Data System (ADS)

Gandomkar, Ziba; Tay, Kevin; Ryder, Will; Brennan, Patrick C.; Mello-Thoms, Claudia

2016-03-01

Radiologists' gaze-related parameters combined with image-based features were utilized to classify suspicious mammographic areas ultimately scored as True Positives (TP) and False Positives (FP). Eight breast radiologists read 120 two-view digital mammograms of which 59 had biopsy proven cancer. Eye tracking data was collected and nearby fixations were clustered together. Suspicious areas on mammograms were independently identified based on thresholding an intensity saliency map followed by automatic segmentation and pruning steps. For each radiologist reported area, radiologist's fixation clusters in the area, as well as neighboring suspicious areas within 2.5° of the center of fixation, were found. A 45-dimensional feature vector containing gaze parameters of the corresponding cluster along with image-based characteristics was constructed. Gaze parameters included total number of fixations in the cluster, dwell time, time to hit the cluster for the first time, maximum number of consecutive fixations, and saccade magnitude of the first fixation in the cluster. Image-based features consisted of intensity, shape, and texture descriptors extracted from the region around the suspicious area, its surrounding tissue, and the entire breast. For each radiologist, a userspecific Support Vector Machine (SVM) model was built to classify the reported areas as TPs or FPs. Leave-one-out cross validation was utilized to avoid over-fitting. A feature selection step was embedded in the SVM training procedure by allowing radial basis function kernels to have 45 scaling factors. The proposed method was compared with the radiologists' performance using the jackknife alternative free-response receiver operating characteristic (JAFROC). The JAFROC figure of merit increased significantly for six radiologists.

Intensity-Stabilized Fast-Scanned Direct Absorption Spectroscopy Instrumentation Based on a Distributed Feedback Laser with Detection Sensitivity down to 4 × 10−6

PubMed Central

Zhao, Gang; Tan, Wei; Jia, Mengyuan; Hou, Jiajuan; Ma, Weiguang; Dong, Lei; Zhang, Lei; Feng, Xiaoxia; Wu, Xuechun; Yin, Wangbao; Xiao, Liantuan; Axner, Ove; Jia, Suotang

2016-01-01

A novel, intensity-stabilized, fast-scanned, direct absorption spectroscopy (IS-FS-DAS) instrumentation, based on a distributed feedback (DFB) diode laser, is developed. A fiber-coupled polarization rotator and a fiber-coupled polarizer are used to stabilize the intensity of the laser, which significantly reduces its relative intensity noise (RIN). The influence of white noise is reduced by fast scanning over the spectral feature (at 1 kHz), followed by averaging. By combining these two noise-reducing techniques, it is demonstrated that direct absorption spectroscopy (DAS) can be swiftly performed down to a limit of detection (LOD) (1σ) of 4 × 10−6, which opens up a number of new applications. PMID:27657082

Discriminative analysis of lip motion features for speaker identification and speech-reading.

PubMed

Cetingül, H Ertan; Yemez, Yücel; Erzin, Engin; Tekalp, A Murat

2006-10-01

There have been several studies that jointly use audio, lip intensity, and lip geometry information for speaker identification and speech-reading applications. This paper proposes using explicit lip motion information, instead of or in addition to lip intensity and/or geometry information, for speaker identification and speech-reading within a unified feature selection and discrimination analysis framework, and addresses two important issues: 1) Is using explicit lip motion information useful, and, 2) if so, what are the best lip motion features for these two applications? The best lip motion features for speaker identification are considered to be those that result in the highest discrimination of individual speakers in a population, whereas for speech-reading, the best features are those providing the highest phoneme/word/phrase recognition rate. Several lip motion feature candidates have been considered including dense motion features within a bounding box about the lip, lip contour motion features, and combination of these with lip shape features. Furthermore, a novel two-stage, spatial, and temporal discrimination analysis is introduced to select the best lip motion features for speaker identification and speech-reading applications. Experimental results using an hidden-Markov-model-based recognition system indicate that using explicit lip motion information provides additional performance gains in both applications, and lip motion features prove more valuable in the case of speech-reading application.

A Set of Handwriting Features for Use in Automated Writer Identification.

PubMed

Miller, John J; Patterson, Robert Bradley; Gantz, Donald T; Saunders, Christopher P; Walch, Mark A; Buscaglia, JoAnn

2017-05-01

A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system described in this article has been implemented in a highly accurate and approximately language-independent biometric recognition system of writers of cursive documents. © 2017 American Academy of Forensic Sciences.

Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification.

PubMed

Hu, Jing; Zhang, Xiaolong; Liu, Xiaoming; Tang, Jinshan

2015-06-01

Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort; thus, the development of predictive models can be very helpful. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method is proposed for hot region prediction, which combines density-based incremental clustering with feature-based classification. The method uses density-based incremental clustering to obtain rough hot regions, and uses feature-based classification to remove the non-hot spot residues from the rough hot regions. Experimental results show that the proposed method significantly improves the prediction performance of hot regions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Shape based segmentation of MRIs of the bones in the knee using phase and intensity information

NASA Astrophysics Data System (ADS)

Fripp, Jurgen; Bourgeat, Pierrick; Crozier, Stuart; Ourselin, Sébastien

2007-03-01

The segmentation of the bones from MR images is useful for performing subsequent segmentation and quantitative measurements of cartilage tissue. In this paper, we present a shape based segmentation scheme for the bones that uses texture features derived from the phase and intensity information in the complex MR image. The phase can provide additional information about the tissue interfaces, but due to the phase unwrapping problem, this information is usually discarded. By using a Gabor filter bank on the complex MR image, texture features (including phase) can be extracted without requiring phase unwrapping. These texture features are then analyzed using a support vector machine classifier to obtain probability tissue matches. The segmentation of the bone is fully automatic and performed using a 3D active shape model based approach driven using gradient and texture information. The 3D active shape model is automatically initialized using a robust affine registration. The approach is validated using a database of 18 FLASH MR images that are manually segmented, with an average segmentation overlap (Dice similarity coefficient) of 0.92 compared to 0.9 obtained using the classifier only.

SU-F-R-30: Interscanner Variability of Radiomics Features in Computed Tomography (CT) Using a Standard ACR Phantom

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

Shafiq ul Hassan, M; Zhang, G; Moros, E

2016-06-15

Purpose: A simple approach to investigate Interscanner variability of Radiomics features in computed tomography (CT) using a standard ACR phantom. Methods: The standard ACR phantom was scanned on CT scanners from three different manufacturers. Scanning parameters of 120 KVp, 200 mA were used while slice thickness of 3.0 mm on two scanners and 3.27 mm on third scanner was used. Three spherical regions of interest (ROI) from water, medium density and high density inserts were contoured. Ninety four Radiomics features were extracted using an in-house program. These features include shape (11), intensity (22), GLCM (26), GLZSM (11), RLM (11), andmore » NGTDM (5) and 8 fractal dimensions features. To evaluate the Interscanner variability across three scanners, a coefficient of variation (COV) is calculated for each feature group. Each group is further classified according to the COV- by calculating the percentage of features in each of the following categories: COV less than 2%, between 2 and 10% and greater than 10%. Results: For all feature groups, similar trend was observed for three different inserts. Shape features were the most robust for all scanners as expected. 70% of the shape features had COV <2%. For intensity feature group, 2% COV varied from 9 to 32% for three scanners. All features in four groups GLCM, GLZSM, RLM and NGTDM were found to have Interscanner variability ≥2%. The fractal dimensions dependence for medium and high density inserts were similar while it was different for water inserts. Conclusion: We concluded that even for similar scanning conditions, Interscanner variability across different scanners was significant. The texture features based on GLCM, GLZSM, RLM and NGTDM are highly scanner dependent. Since the inserts of the ACR Phantom are not heterogeneous in HU values suggests that matrix based 2nd order features are highly affected by variation in noise. Research partly funded by NIH/NCI R01CA190105-01.« less

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

PubMed

Guo, Jian; Qian, Kun; Zhang, Gongxuan; Xu, Huijie; Schuller, Björn

2017-12-01

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

Visualizing dispersive features in 2D image via minimum gradient method

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

He, Yu; Wang, Yan; Shen, Zhi -Xun

Here, we developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative applicationmore » to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated.« less

Visualizing dispersive features in 2D image via minimum gradient method

DOE PAGES

He, Yu; Wang, Yan; Shen, Zhi -Xun

2017-07-24

Here, we developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative applicationmore » to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated.« less

Multi-center prediction of hemorrhagic transformation in acute ischemic stroke using permeability imaging features.

PubMed

Scalzo, Fabien; Alger, Jeffry R; Hu, Xiao; Saver, Jeffrey L; Dani, Krishna A; Muir, Keith W; Demchuk, Andrew M; Coutts, Shelagh B; Luby, Marie; Warach, Steven; Liebeskind, David S

2013-07-01

Permeability images derived from magnetic resonance (MR) perfusion images are sensitive to blood-brain barrier derangement of the brain tissue and have been shown to correlate with subsequent development of hemorrhagic transformation (HT) in acute ischemic stroke. This paper presents a multi-center retrospective study that evaluates the predictive power in terms of HT of six permeability MRI measures including contrast slope (CS), final contrast (FC), maximum peak bolus concentration (MPB), peak bolus area (PB), relative recirculation (rR), and percentage recovery (%R). Dynamic T2*-weighted perfusion MR images were collected from 263 acute ischemic stroke patients from four medical centers. An essential aspect of this study is to exploit a classifier-based framework to automatically identify predictive patterns in the overall intensity distribution of the permeability maps. The model is based on normalized intensity histograms that are used as input features to the predictive model. Linear and nonlinear predictive models are evaluated using a cross-validation to measure generalization power on new patients and a comparative analysis is provided for the different types of parameters. Results demonstrate that perfusion imaging in acute ischemic stroke can predict HT with an average accuracy of more than 85% using a predictive model based on a nonlinear regression model. Results also indicate that the permeability feature based on the percentage of recovery performs significantly better than the other features. This novel model may be used to refine treatment decisions in acute stroke. Copyright © 2013 Elsevier Inc. All rights reserved.

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

Chen, X; Zhou, Z; Thomas, K

Purpose: The goal of this work is to investigate the use of contrast enhanced computed tomographic (CT) features for the prediction of mutations of BAP1, PBRM1, and VHL genes in renal cell carcinoma (RCC). Methods: For this study, we used two patient databases with renal cell carcinoma (RCC). The first one consisted of 33 patients from our institution (UT Southwestern Medical Center, UTSW). The second one consisted of 24 patients from the Cancer Imaging Archive (TCIA), where each patient is connected by a unique identi?er to the tissue samples from the Cancer Genome Atlas (TCGA). From the contrast enhanced CTmore » image of each patient, tumor contour was first delineated by a physician. Geometry, intensity, and texture features were extracted from the delineated tumor. Based on UTSW dataset, we completed feature selection and trained a support vector machine (SVM) classifier to predict mutations of BAP1, PBRM1 and VHL genes. We then used TCIA-TCGA dataset to validate the predictive model build upon UTSW dataset. Results: The prediction accuracy of gene expression of TCIA-TCGA patients was 0.83 (20 of 24), 0.83 (20 of 24), and 0.75 (18 of 24) for BAP1, PBRM1, and VHL respectively. For BAP1 gene, texture feature was the most prominent feature type. For PBRM1 gene, intensity feature was the most prominent. For VHL gene, geometry, intensity, and texture features were all important. Conclusion: Using our feature selection strategy and models, we achieved predictive accuracy over 0.75 for all three genes under the condition of using patient data from one institution for training and data from other institutions for testing. These results suggest that radiogenomics can be used to aid in prognosis and used as convenient surrogates for expensive and time consuming gene assay procedures.« less

Analysis of Acoustic Features in Speakers with Cognitive Disorders and Speech Impairments

NASA Astrophysics Data System (ADS)

Saz, Oscar; Simón, Javier; Rodríguez, W. Ricardo; Lleida, Eduardo; Vaquero, Carlos

2009-12-01

This work presents the results in the analysis of the acoustic features (formants and the three suprasegmental features: tone, intensity and duration) of the vowel production in a group of 14 young speakers suffering different kinds of speech impairments due to physical and cognitive disorders. A corpus with unimpaired children's speech is used to determine the reference values for these features in speakers without any kind of speech impairment within the same domain of the impaired speakers; this is 57 isolated words. The signal processing to extract the formant and pitch values is based on a Linear Prediction Coefficients (LPCs) analysis of the segments considered as vowels in a Hidden Markov Model (HMM) based Viterbi forced alignment. Intensity and duration are also based in the outcome of the automated segmentation. As main conclusion of the work, it is shown that intelligibility of the vowel production is lowered in impaired speakers even when the vowel is perceived as correct by human labelers. The decrease in intelligibility is due to a 30% of increase in confusability in the formants map, a reduction of 50% in the discriminative power in energy between stressed and unstressed vowels and to a 50% increase of the standard deviation in the length of the vowels. On the other hand, impaired speakers keep good control of tone in the production of stressed and unstressed vowels.

EEG-based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

PubMed Central

Wade, Joshua W.; Key, Alexandra P.; Warren, Zachary E.; Sarkar, Nilanjan

2017-01-01

objective To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention. PMID:28422647

Surface characteristics modeling and performance evaluation of urban building materials using LiDAR data.

PubMed

Li, Xiaolu; Liang, Yu

2015-05-20

Analysis of light detection and ranging (LiDAR) intensity data to extract surface features is of great interest in remote sensing research. One potential application of LiDAR intensity data is target classification. A new bidirectional reflectance distribution function (BRDF) model is derived for target characterization of rough and smooth surfaces. Based on the geometry of our coaxial full-waveform LiDAR system, the integration method is improved through coordinate transformation to establish the relationship between the BRDF model and intensity data of LiDAR. A series of experiments using typical urban building materials are implemented to validate the proposed BRDF model and integration method. The fitting results show that three parameters extracted from the proposed BRDF model can distinguish the urban building materials from perspectives of roughness, specular reflectance, and diffuse reflectance. A comprehensive analysis of these parameters will help characterize surface features in a physically rigorous manner.

Method for a detailed measurement of image intensity nonuniformity in magnetic resonance imaging.

PubMed

Wang, Deming; Doddrell, David M

2005-04-01

In magnetic resonance imaging (MRI), the MR signal intensity can vary spatially and this spatial variation is usually referred to as MR intensity nonuniformity. Although the main source of intensity nonuniformity arises from B1 inhomogeneity of the coil acting as a receiver and/or transmitter, geometric distortion also alters the MR signal intensity. It is useful on some occasions to have these two different sources be separately measured and analyzed. In this paper, we present a practical method for a detailed measurement of the MR intensity nonuniformity. This method is based on the same three-dimensional geometric phantom that was recently developed for a complete measurement of the geometric distortion in MR systems. In this paper, the contribution to the intensity nonuniformity from the geometric distortion can be estimated and thus, it provides a mechanism for estimation of the intensity nonuniformity that reflects solely the spatial characteristics arising from B1. Additionally, a comprehensive scheme for characterization of the intensity nonuniformity based on the new measurement method is proposed. To demonstrate the method, the intensity nonuniformity in a 1.5 T Sonata MR system was measured and is used to illustrate the main features of the method.

A novel algorithm for reducing false arrhythmia alarms in intensive care units.

PubMed

Srivastava, Chandan; Sharma, Sonal; Jalali, Ali

2016-08-01

Alarm fatigue in intensive care units (ICU) is one of the top healthcare issues in the US. False alarms in ICU will decrease the quality of care and staff response time over the alarms. Normally, false alarm will cause desensitization of the clinical staff which leads to warnings and misleading, if the triggered alarm is true. In this study, we have proposed a multi-model ensemble approach to reduce the false alarm rate in monitoring systems. We have used 750 patient records from PhysioNet database. At First arrhythmia based features from electrocardiogram (ECG), arterial blood pressure (ABP) and photoplethysmogram (PPG) features were extracted from the records. Next, the dataset has been separated into two subsets on the basis of available features information. The first dataset (DS1) is the combination of ECG physiological, ABP and PPG features. Their correlation coefficient and p-values criteria have been applied for relevant alarm-wise feature-set selection, and random forest classifier was used for model development and validation. The threshold based approach was used on second dataset (DS2) which is the combination of arrhythmia, ABP and PPG features. The developed ensemble model is able to achieve sensitivity 83.33-100 % (average 95.56 %) being true alarms and suppress false alarms rate 66.67-89% (average 77.25%). The predictability of classifier shows the advantage to deal with unbalanced set of information, therefore overall model performance has reached to 83.96% accuracy.

SU-F-R-36: Validating Quantitative Radiomic Texture Features for Oncologic PET: A Digital Phantom Study

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

Yang, F; Yang, Y; Young, L

Purpose: Radiomic texture features derived from the oncologic PET have recently been brought under intense investigation within the context of patient stratification and treatment outcome prediction in a variety of cancer types; however, their validity has not yet been examined. This work is aimed to validate radiomic PET texture metrics through the use of realistic simulations in the ground truth setting. Methods: Simulation of FDG-PET was conducted by applying the Zubal phantom as an attenuation map to the SimSET software package that employs Monte Carlo techniques to model the physical process of emission imaging. A total of 15 irregularly-shaped lesionsmore » featuring heterogeneous activity distribution were simulated. For each simulated lesion, 28 texture features in relation to the intensity histograms (GLIH), grey-level co-occurrence matrices (GLCOM), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated and compared with their respective values extracted from the ground truth activity map. Results: In reference to the values from the ground truth images, texture parameters appearing on the simulated data varied with a range of 0.73–3026.2% for GLIH-based, 0.02–100.1% for GLCOM-based, 1.11–173.8% for GLNDM-based, and 0.35–66.3% for GLZSM-based. For majority of the examined texture metrics (16/28), their values on the simulated data differed significantly from those from the ground truth images (P-value ranges from <0.0001 to 0.04). Features not exhibiting significant difference comprised of GLIH-based standard deviation, GLCO-based energy and entropy, GLND-based coarseness and contrast, and GLZS-based low gray-level zone emphasis, high gray-level zone emphasis, short zone low gray-level emphasis, long zone low gray-level emphasis, long zone high gray-level emphasis, and zone size nonuniformity. Conclusion: The extent to which PET imaging disturbs texture appearance is feature-dependent and could be substantial. It is thus advised that use of PET texture parameters for predictive and prognostic measurements in oncologic setting awaits further systematic and critical evaluation.« less

Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions

NASA Astrophysics Data System (ADS)

Li, Ji; Ren, Fuji

Weblogs have greatly changed the communication ways of mankind. Affective analysis of blog posts is found valuable for many applications such as text-to-speech synthesis or computer-assisted recommendation. Traditional emotion recognition in text based on single-label classification can not satisfy higher requirements of affective computing. In this paper, the automatic identification of sentence emotion in weblogs is modeled as a multi-label text categorization task. Experiments are carried out on 12273 blog sentences from the Chinese emotion corpus Ren_CECps with 8-dimension emotion annotation. An ensemble algorithm RAKEL is used to recognize dominant emotions from the writer's perspective. Our emotion feature using detailed intensity representation for word emotions outperforms the other main features such as the word frequency feature and the traditional lexicon-based feature. In order to deal with relatively complex sentences, we integrate grammatical characteristics of punctuations, disjunctive connectives, modification relations and negation into features. It achieves 13.51% and 12.49% increases for Micro-averaged F1 and Macro-averaged F1 respectively compared to the traditional lexicon-based feature. Result shows that multiple-dimension emotion representation with grammatical features can efficiently classify sentence emotion in a multi-label problem.

A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.

PubMed

Echegaray, Sebastian; Nair, Viswam; Kadoch, Michael; Leung, Ann; Rubin, Daniel; Gevaert, Olivier; Napel, Sandy

2016-12-01

Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.

Investigations of image fusion

NASA Astrophysics Data System (ADS)

Zhang, Zhong

1999-12-01

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a single image which is more suitable for the purpose of human visual perception or further image processing tasks. In this thesis, a region-based fusion algorithm using the wavelet transform is proposed. The identification of important features in each image, such as edges and regions of interest, are used to guide the fusion process. The idea of multiscale grouping is also introduced and a generic image fusion framework based on multiscale decomposition is studied. The framework includes all of the existing multiscale-decomposition- based fusion approaches we found in the literature which did not assume a statistical model for the source images. Comparisons indicate that our framework includes some new approaches which outperform the existing approaches for the cases we consider. Registration must precede our fusion algorithms. So we proposed a hybrid scheme which uses both feature-based and intensity-based methods. The idea of robust estimation of optical flow from time- varying images is employed with a coarse-to-fine multi- resolution approach and feature-based registration to overcome some of the limitations of the intensity-based schemes. Experiments show that this approach is robust and efficient. Assessing image fusion performance in a real application is a complicated issue. In this dissertation, a mixture probability density function model is used in conjunction with the Expectation- Maximization algorithm to model histograms of edge intensity. Some new techniques are proposed for estimating the quality of a noisy image of a natural scene. Such quality measures can be used to guide the fusion. Finally, we study fusion of images obtained from several copies of a new type of camera developed for video surveillance. Our techniques increase the capability and reliability of the surveillance system and provide an easy way to obtain 3-D information of objects in the space monitored by the system.

The effects of variations in parameters and algorithm choices on calculated radiomics feature values: initial investigations and comparisons to feature variability across CT image acquisition conditions

NASA Astrophysics Data System (ADS)

Emaminejad, Nastaran; Wahi-Anwar, Muhammad; Hoffman, John; Kim, Grace H.; Brown, Matthew S.; McNitt-Gray, Michael

2018-02-01

Translation of radiomics into clinical practice requires confidence in its interpretations. This may be obtained via understanding and overcoming the limitations in current radiomic approaches. Currently there is a lack of standardization in radiomic feature extraction. In this study we examined a few factors that are potential sources of inconsistency in characterizing lung nodules, such as 1)different choices of parameters and algorithms in feature calculation, 2)two CT image dose levels, 3)different CT reconstruction algorithms (WFBP, denoised WFBP, and Iterative). We investigated the effect of variation of these factors on entropy textural feature of lung nodules. CT images of 19 lung nodules identified from our lung cancer screening program were identified by a CAD tool and contours provided. The radiomics features were extracted by calculating 36 GLCM based and 4 histogram based entropy features in addition to 2 intensity based features. A robustness index was calculated across different image acquisition parameters to illustrate the reproducibility of features. Most GLCM based and all histogram based entropy features were robust across two CT image dose levels. Denoising of images slightly improved robustness of some entropy features at WFBP. Iterative reconstruction resulted in improvement of robustness in a fewer times and caused more variation in entropy feature values and their robustness. Within different choices of parameters and algorithms texture features showed a wide range of variation, as much as 75% for individual nodules. Results indicate the need for harmonization of feature calculations and identification of optimum parameters and algorithms in a radiomics study.

Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Roychoudhury, Indranil; Balaban, Edward; Saxena, Abhinav

2010-01-01

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

Ranging through Gabor logons-a consistent, hierarchical approach.

PubMed

Chang, C; Chatterjee, S

1993-01-01

In this work, the correspondence problem in stereo vision is handled by matching two sets of dense feature vectors. Inspired by biological evidence, these feature vectors are generated by a correlation between a bank of Gabor sensors and the intensity image. The sensors consist of two-dimensional Gabor filters at various scales (spatial frequencies) and orientations, which bear close resemblance to the receptive field profiles of simple V1 cells in visual cortex. A hierarchical, stochastic relaxation method is then used to obtain the dense stereo disparities. Unlike traditional hierarchical methods for stereo, feature based hierarchical processing yields consistent disparities. To avoid false matchings due to static occlusion, a dual matching, based on the imaging geometry, is used.

Improving liver fibrosis diagnosis based on forward and backward second harmonic generation signals

NASA Astrophysics Data System (ADS)

Peng, Qiwen; Zhuo, Shuangmu; So, Peter T. C.; Yu, Hanry

2015-02-01

The correlation of forward second harmonic generation (SHG) signal and backward SHG signal in different liver fibrosis stages was investigated. We found that three features, including the collagen percentage for forward SHG, the collagen percentage for backward SHG, and the average intensity ratio of two kinds of SHG signals, can quantitatively stage liver fibrosis in thioacetamide-induced rat model. We demonstrated that the combination of all three features by using a support vector machine classification algorithm can provide a more accurate prediction than each feature alone in fibrosis diagnosis.

Serial clustering of extratropical cyclones and relationship with NAO and jet intensity based on the IMILAST cyclone database

NASA Astrophysics Data System (ADS)

Ulbrich, Sven; Pinto, Joaquim G.; Economou, Theodoros; Stephenson, David B.; Karremann, Melanie K.; Shaffrey, Len C.

2017-04-01

Cyclone families are a frequent synoptic weather feature in the Euro-Atlantic area, particularly during wintertime. Given appropriate large-scale conditions, such series (clusters) of storms may cause large socio-economic impacts and cumulative losses. Recent studies analyzing reanalysis data using single cyclone tracking methods have shown that serial clustering of cyclones occurs on both flanks and downstream regions of the North Atlantic storm track. Based on winter (DJF) cyclone counts from the IMILAST cyclone database, we explore the representation of serial clustering in the ERA-Interim period and its relationship with the NAO-phase and jet intensity. With this aim, clustering is estimated by the dispersion of winter (DJF) cyclone passages for each grid point over the Euro-Atlantic area. Results indicate that clustering over the Eastern North Atlantic and Western Europe can be identified for all methods, although the exact location and the dispersion magnitude may vary. The relationship between clustering and (i) the NAO-phase and (ii) jet intensity over the North Atlantic is statistically evaluated. Results show that the NAO-index and the jet intensity show a strong contribution to clustering, even though some spread is found between methods. We conclude that the general features of clustering of extratropical cyclones over the North Atlantic and Western Europe are robust to the choice of tracking method. The same is true for the influence of the NAO and jet intensity on cyclone dispersion.

[The research on separating and extracting overlapping spectral feature lines in LIBS using damped least squares method].

PubMed

Wang, Yin; Zhao, Nan-jing; Liu, Wen-qing; Yu, Yang; Fang, Li; Meng, De-shuo; Hu, Li; Zhang, Da-hai; Ma, Min-jun; Xiao, Xue; Wang, Yu; Liu, Jian-guo

2015-02-01

In recent years, the technology of laser induced breakdown spectroscopy has been developed rapidly. As one kind of new material composition detection technology, laser induced breakdown spectroscopy can simultaneously detect multi elements fast and simply without any complex sample preparation and realize field, in-situ material composition detection of the sample to be tested. This kind of technology is very promising in many fields. It is very important to separate, fit and extract spectral feature lines in laser induced breakdown spectroscopy, which is the cornerstone of spectral feature recognition and subsequent elements concentrations inversion research. In order to realize effective separation, fitting and extraction of spectral feature lines in laser induced breakdown spectroscopy, the original parameters for spectral lines fitting before iteration were analyzed and determined. The spectral feature line of' chromium (Cr I : 427.480 nm) in fly ash gathered from a coal-fired power station, which was overlapped with another line(FeI: 427.176 nm), was separated from the other one and extracted by using damped least squares method. Based on Gauss-Newton iteration, damped least squares method adds damping factor to step and adjust step length dynamically according to the feedback information after each iteration, in order to prevent the iteration from diverging and make sure that the iteration could converge fast. Damped least squares method helps to obtain better results of separating, fitting and extracting spectral feature lines and give more accurate intensity values of these spectral feature lines: The spectral feature lines of chromium in samples which contain different concentrations of chromium were separated and extracted. And then, the intensity values of corresponding spectral lines were given by using damped least squares method and least squares method separately. The calibration curves were plotted, which showed the relationship between spectral line intensity values and chromium concentrations in different samples. And then their respective linear correlations were compared. The experimental results showed that the linear correlation of the intensity values of spectral feature lines and the concentrations of chromium in different samples, which was obtained by damped least squares method, was better than that one obtained by least squares method. And therefore, damped least squares method was stable, reliable and suitable for separating, fitting and extracting spectral feature lines in laser induced breakdown spectroscopy.

A model of proto-object based saliency

PubMed Central

Russell, Alexander F.; Mihalaş, Stefan; von der Heydt, Rudiger; Niebur, Ernst; Etienne-Cummings, Ralph

2013-01-01

Organisms use the process of selective attention to optimally allocate their computational resources to the instantaneously most relevant subsets of a visual scene, ensuring that they can parse the scene in real time. Many models of bottom-up attentional selection assume that elementary image features, like intensity, color and orientation, attract attention. Gestalt psychologists, how-ever, argue that humans perceive whole objects before they analyze individual features. This is supported by recent psychophysical studies that show that objects predict eye-fixations better than features. In this report we present a neurally inspired algorithm of object based, bottom-up attention. The model rivals the performance of state of the art non-biologically plausible feature based algorithms (and outperforms biologically plausible feature based algorithms) in its ability to predict perceptual saliency (eye fixations and subjective interest points) in natural scenes. The model achieves this by computing saliency as a function of proto-objects that establish the perceptual organization of the scene. All computational mechanisms of the algorithm have direct neural correlates, and our results provide evidence for the interface theory of attention. PMID:24184601

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

PubMed Central

Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

2016-01-01

An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

Phenotypic feature quantification of patient derived 3D cancer spheroids in fluorescence microscopy image

NASA Astrophysics Data System (ADS)

Kang, Mi-Sun; Rhee, Seon-Min; Seo, Ji-Hyun; Kim, Myoung-Hee

2017-03-01

Patients' responses to a drug differ at the cellular level. Here, we present an image-based cell phenotypic feature quantification method for predicting the responses of patient-derived glioblastoma cells to a particular drug. We used high-content imaging to understand the features of patient-derived cancer cells. A 3D spheroid culture formation resembles the in vivo environment more closely than 2D adherent cultures do, and it allows for the observation of cellular aggregate characteristics. However, cell analysis at the individual level is more challenging. In this paper, we demonstrate image-based phenotypic screening of the nuclei of patient-derived cancer cells. We first stitched the images of each well of the 384-well plate with the same state. We then used intensity information to detect the colonies. The nuclear intensity and morphological characteristics were used for the segmentation of individual nuclei. Next, we calculated the position of each nucleus that is appeal of the spatial pattern of cells in the well environment. Finally, we compared the results obtained using 3D spheroid culture cells with those obtained using 2D adherent culture cells from the same patient being treated with the same drugs. This technique could be applied for image-based phenotypic screening of cells to determine the patient's response to the drug.

Efficient feature-based 2D/3D registration of transesophageal echocardiography to x-ray fluoroscopy for cardiac interventions

NASA Astrophysics Data System (ADS)

Hatt, Charles R.; Speidel, Michael A.; Raval, Amish N.

2014-03-01

We present a novel 2D/ 3D registration algorithm for fusion between transesophageal echocardiography (TEE) and X-ray fluoroscopy (XRF). The TEE probe is modeled as a subset of 3D gradient and intensity point features, which facilitates efficient 3D-to-2D perspective projection. A novel cost-function, based on a combination of intensity and edge features, evaluates the registration cost value without the need for time-consuming generation of digitally reconstructed radiographs (DRRs). Validation experiments were performed with simulations and phantom data. For simulations, in silica XRF images of a TEE probe were generated in a number of different pose configurations using a previously acquired CT image. Random misregistrations were applied and our method was used to recover the TEE probe pose and compare the result to the ground truth. Phantom experiments were performed by attaching fiducial markers externally to a TEE probe, imaging the probe with an interventional cardiac angiographic x-ray system, and comparing the pose estimated from the external markers to that estimated from the TEE probe using our algorithm. Simulations found a 3D target registration error of 1.08(1.92) mm for biplane (monoplane) geometries, while the phantom experiment found a 2D target registration error of 0.69mm. For phantom experiments, we demonstrated a monoplane tracking frame-rate of 1.38 fps. The proposed feature-based registration method is computationally efficient, resulting in near real-time, accurate image based registration between TEE and XRF.

Defect detection of castings in radiography images using a robust statistical feature.

PubMed

Zhao, Xinyue; He, Zaixing; Zhang, Shuyou

2014-01-01

One of the most commonly used optical methods for defect detection is radiographic inspection. Compared with methods that extract defects directly from the radiography image, model-based methods deal with the case of an object with complex structure well. However, detection of small low-contrast defects in nonuniformly illuminated images is still a major challenge for them. In this paper, we present a new method based on the grayscale arranging pairs (GAP) feature to detect casting defects in radiography images automatically. First, a model is built using pixel pairs with a stable intensity relationship based on the GAP feature from previously acquired images. Second, defects can be extracted by comparing the difference of intensity-difference signs between the input image and the model statistically. The robustness of the proposed method to noise and illumination variations has been verified on casting radioscopic images with defects. The experimental results showed that the average computation time of the proposed method in the testing stage is 28 ms per image on a computer with a Pentium Core 2 Duo 3.00 GHz processor. For the comparison, we also evaluated the performance of the proposed method as well as that of the mixture-of-Gaussian-based and crossing line profile methods. The proposed method achieved 2.7% and 2.0% false negative rates in the noise and illumination variation experiments, respectively.

Computation of reliable textural indices from multimodal brain MRI: suggestions based on a study of patients with diffuse intrinsic pontine glioma

NASA Astrophysics Data System (ADS)

Goya-Outi, Jessica; Orlhac, Fanny; Calmon, Raphael; Alentorn, Agusti; Nioche, Christophe; Philippe, Cathy; Puget, Stéphanie; Boddaert, Nathalie; Buvat, Irène; Grill, Jacques; Frouin, Vincent; Frouin, Frederique

2018-05-01

Few methodological studies regarding widely used textural indices robustness in MRI have been reported. In this context, this study aims to propose some rules to compute reliable textural indices from multimodal 3D brain MRI. Diagnosis and post-biopsy MR scans including T1, post-contrast T1, T2 and FLAIR images from thirty children with diffuse intrinsic pontine glioma (DIPG) were considered. The hybrid white stripe method was adapted to standardize MR intensities. Sixty textural indices were then computed for each modality in different regions of interest (ROI), including tumor and white matter (WM). Three types of intensity binning were compared : constant bin width and relative bounds; constant number of bins and relative bounds; constant number of bins and absolute bounds. The impact of the volume of the region was also tested within the WM. First, the mean Hellinger distance between patient-based intensity distributions decreased by a factor greater than 10 in WM and greater than 2.5 in gray matter after standardization. Regarding the binning strategy, the ranking of patients was highly correlated for 188/240 features when comparing with , but for only 20 when comparing with , and nine when comparing with . Furthermore, when using or texture indices reflected tumor heterogeneity as assessed visually by experts. Last, 41 features presented statistically significant differences between contralateral WM regions when ROI size slightly varies across patients, and none when using ROI of the same size. For regions with similar size, 224 features were significantly different between WM and tumor. Valuable information from texture indices can be biased by methodological choices. Recommendations are to standardize intensities in MR brain volumes, to use intensity binning with constant bin width, and to define regions with the same volumes to get reliable textural indices.

Decadal features of heavy rainfall events in eastern China

NASA Astrophysics Data System (ADS)

Chen, Huopo; Sun, Jianqi; Fan, Ke

2012-06-01

Based on daily precipitation data, the spatial-temporal features of heavy rainfall events (HREs) during 1960-2009 are investigated. The results indicate that the HREs experienced strong decadal variability in the past 50 years, and the decadal features varied across regions. More HRE days are observed in the 1960s, 1980s, and 1990s over Northeast China (NEC); in the 1960s, 1970s, and 1990s over North China (NC); in the early 1960s, 1980s, and 2000s over the Huaihe River basin (HR); in the 1970s-1990s over the mid-lower reaches of the Yangtze River valley (YR); and in the 1970s and 1990s over South China (SC). These decadal changes of HRE days in eastern China are closely associated with the decadal variations of water content and stratification stability of the local atmosphere. The intensity of HREs in each sub-region is also characterized by strong decadal variability. The HRE intensity and frequency co-vary on the long-term trend, and show consistent variability over NEC, NC, and YR, but inconsistent variability over SC and HR. Further analysis of the relationships between the annual rainfall and HRE frequency as well as intensity indicates that the HRE frequency is the major contributor to the total rainfall variability in eastern China, while the HRE intensity shows only relative weak contribution.

SAR image segmentation using skeleton-based fuzzy clustering

NASA Astrophysics Data System (ADS)

Cao, Yun Yi; Chen, Yan Qiu

2003-06-01

SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. The segmentation goal is achieved by unsupervised clustering upon characteristic descriptors extracted from local patches. The mixture model of characteristic descriptor, which combines intensity and texture feature, is investigated. The unsupervised algorithm is derived from the recently proposed Skeleton-Based Data Labeling method. Skeletons are constructed as prototypes of clusters to represent arbitrary latent structures in image data. Segmentation using Skeleton-Based Fuzzy Clustering is able to detect the types of surfaces appeared in SAR images automatically without any user input.

NIR small arms muzzle flash

NASA Astrophysics Data System (ADS)

Montoya, Joseph; Kennerly, Stephen; Rede, Edward

2010-04-01

Utilization of Near-Infrared (NIR) spectral features in a muzzle flash will allow for small arms detection using low cost silicon (Si)-based imagers. Detection of a small arms muzzle flash in a particular wavelength region is dependent on the intensity of that emission, the efficiency of source emission transmission through the atmosphere, and the relative intensity of the background scene. The NIR muzzle flash signature exists in the relatively large Si spectral response wavelength region of 300 nm-1100 nm, which allows for use of commercial-off-the-shelf (COTS) Si-based detectors. The alkali metal origin of the NIR spectral features in the 7.62 × 39-mm round muzzle flash is discussed, and the basis for the spectral bandwidth is examined, using a calculated Voigt profile. This report will introduce a model of the 7.62 × 39-mm NIR muzzle flash signature based on predicted source characteristics. Atmospheric limitations based on NIR spectral regions are investigated in relation to the NIR muzzle flash signature. A simple signal-to-clutter ratio (SCR) metric is used to predict sensor performance based on a model of radiance for the source and solar background and pixel registered image subtraction.

Brownian motion curve-based textural classification and its application in cancer diagnosis.

PubMed

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

2011-06-01

To develop an automated diagnostic methodology based on textural features of the oral mucosal epithelium to discriminate normal and oral submucous fibrosis (OSF). A total of 83 normal and 29 OSF images from histopathologic sections of the oral mucosa are considered. The proposed diagnostic mechanism consists of two parts: feature extraction using Brownian motion curve (BMC) and design ofa suitable classifier. The discrimination ability of the features has been substantiated by statistical tests. An error back-propagation neural network (BPNN) is used to classify OSF vs. normal. In development of an automated oral cancer diagnostic module, BMC has played an important role in characterizing textural features of the oral images. Fisher's linear discriminant analysis yields 100% sensitivity and 85% specificity, whereas BPNN leads to 92.31% sensitivity and 100% specificity, respectively. In addition to intensity and morphology-based features, textural features are also very important, especially in histopathologic diagnosis of oral cancer. In view of this, a set of textural features are extracted using the BMC for the diagnosis of OSF. Finally, a textural classifier is designed using BPNN, which leads to a diagnostic performance with 96.43% accuracy. (Anal Quant

Quality assessment of remote sensing image fusion using feature-based fourth-order correlation coefficient

NASA Astrophysics Data System (ADS)

Ma, Dan; Liu, Jun; Chen, Kai; Li, Huali; Liu, Ping; Chen, Huijuan; Qian, Jing

2016-04-01

In remote sensing fusion, the spatial details of a panchromatic (PAN) image and the spectrum information of multispectral (MS) images will be transferred into fused images according to the characteristics of the human visual system. Thus, a remote sensing image fusion quality assessment called feature-based fourth-order correlation coefficient (FFOCC) is proposed. FFOCC is based on the feature-based coefficient concept. Spatial features related to spatial details of the PAN image and spectral features related to the spectrum information of MS images are first extracted from the fused image. Then, the fourth-order correlation coefficient between the spatial and spectral features is calculated and treated as the assessment result. FFOCC was then compared with existing widely used indices, such as Erreur Relative Globale Adimensionnelle de Synthese, and quality assessed with no reference. Results of the fusion and distortion experiments indicate that the FFOCC is consistent with subjective evaluation. FFOCC significantly outperforms the other indices in evaluating fusion images that are produced by different fusion methods and that are distorted in spatial and spectral features by blurring, adding noise, and changing intensity. All the findings indicate that the proposed method is an objective and effective quality assessment for remote sensing image fusion.

Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease

PubMed Central

Gray, Katherine R.; Wolz, Robin; Heckemann, Rolf A.; Aljabar, Paul; Hammers, Alexander; Rueckert, Daniel

2012-01-01

Imaging biomarkers for Alzheimer’s disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer’s Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer’s disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer’s disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer’s disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials. PMID:22236449

Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open-curve active contour vessel tracing.

PubMed

Chen, Li; Mossa-Basha, Mahmud; Balu, Niranjan; Canton, Gador; Sun, Jie; Pimentel, Kristi; Hatsukami, Thomas S; Hwang, Jenq-Neng; Yuan, Chun

2018-06-01

To develop a quantitative intracranial artery measurement technique to extract comprehensive artery features from time-of-flight MR angiography (MRA). By semiautomatically tracing arteries based on an open-curve active contour model in a graphical user interface, 12 basic morphometric features and 16 basic intensity features for each artery were identified. Arteries were then classified as one of 24 types using prediction from a probability model. Based on the anatomical structures, features were integrated within 34 vascular groups for regional features of vascular trees. Eight 3D MRA acquisitions with intracranial atherosclerosis were assessed to validate this technique. Arterial tracings were validated by an experienced neuroradiologist who checked agreement at bifurcation and stenosis locations. This technique achieved 94% sensitivity and 85% positive predictive values (PPV) for bifurcations, and 85% sensitivity and PPV for stenosis. Up to 1,456 features, such as length, volume, and averaged signal intensity for each artery, as well as vascular group in each of the MRA images, could be extracted to comprehensively reflect characteristics, distribution, and connectivity of arteries. Length for the M1 segment of the middle cerebral artery extracted by this technique was compared with reviewer-measured results, and the intraclass correlation coefficient was 0.97. A semiautomated quantitative method to trace, label, and measure intracranial arteries from 3D-MRA was developed and validated. This technique can be used to facilitate quantitative intracranial vascular research, such as studying cerebrovascular adaptation to aging and disease conditions. Magn Reson Med 79:3229-3238, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Auditory emotion recognition impairments in Schizophrenia: Relationship to acoustic features and cognition

PubMed Central

Gold, Rinat; Butler, Pamela; Revheim, Nadine; Leitman, David; Hansen, John A.; Gur, Ruben; Kantrowitz, Joshua T.; Laukka, Petri; Juslin, Patrik N.; Silipo, Gail S.; Javitt, Daniel C.

2013-01-01

Objective Schizophrenia is associated with deficits in ability to perceive emotion based upon tone of voice. The basis for this deficit, however, remains unclear and assessment batteries remain limited. We evaluated performance in schizophrenia on a novel voice emotion recognition battery with well characterized physical features, relative to impairments in more general emotional and cognitive function. Methods We studied in a primary sample of 92 patients relative to 73 controls. Stimuli were characterized according to both intended emotion and physical features (e.g., pitch, intensity) that contributed to the emotional percept. Parallel measures of visual emotion recognition, pitch perception, general cognition, and overall outcome were obtained. More limited measures were obtained in an independent replication sample of 36 patients, 31 age-matched controls, and 188 general comparison subjects. Results Patients showed significant, large effect size deficits in voice emotion recognition (F=25.4, p<.00001, d=1.1), and were preferentially impaired in recognition of emotion based upon pitch-, but not intensity-features (group X feature interaction: F=7.79, p=.006). Emotion recognition deficits were significantly correlated with pitch perception impairments both across (r=56, p<.0001) and within (r=.47, p<.0001) group. Path analysis showed both sensory-specific and general cognitive contributions to auditory emotion recognition deficits in schizophrenia. Similar patterns of results were observed in the replication sample. Conclusions The present study demonstrates impairments in auditory emotion recognition in schizophrenia relative to acoustic features of underlying stimuli. Furthermore, it provides tools and highlights the need for greater attention to physical features of stimuli used for study of social cognition in neuropsychiatric disorders. PMID:22362394

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

PubMed Central

Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

2016-01-01

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions. PMID:28154470

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

PubMed

Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

2016-05-26

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

Constructing storyboards based on hierarchical clustering analysis

NASA Astrophysics Data System (ADS)

Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu

2005-07-01

There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.

Supervised pixel classification using a feature space derived from an artificial visual system

NASA Technical Reports Server (NTRS)

Baxter, Lisa C.; Coggins, James M.

1991-01-01

Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

Intensity Accents in French 2 Year Olds' Speech.

ERIC Educational Resources Information Center

Allen, George D.

The acoustic features and functions of accentuation in French are discussed, and features of accentuation in the speech of French 2-year-olds are explored. The four major acoustic features used to signal accentual distinctions are fundamental frequency of voicing, duration of segments and syllables, intensity of segments and syllables, and…

General fusion approaches for the age determination of latent fingerprint traces: results for 2D and 3D binary pixel feature fusion

NASA Astrophysics Data System (ADS)

Merkel, Ronny; Gruhn, Stefan; Dittmann, Jana; Vielhauer, Claus; Bräutigam, Anja

2012-03-01

Determining the age of latent fingerprint traces found at crime scenes is an unresolved research issue since decades. Solving this issue could provide criminal investigators with the specific time a fingerprint trace was left on a surface, and therefore would enable them to link potential suspects to the time a crime took place as well as to reconstruct the sequence of events or eliminate irrelevant fingerprints to ensure privacy constraints. Transferring imaging techniques from different application areas, such as 3D image acquisition, surface measurement and chemical analysis to the domain of lifting latent biometric fingerprint traces is an upcoming trend in forensics. Such non-destructive sensor devices might help to solve the challenge of determining the age of a latent fingerprint trace, since it provides the opportunity to create time series and process them using pattern recognition techniques and statistical methods on digitized 2D, 3D and chemical data, rather than classical, contact-based capturing techniques, which alter the fingerprint trace and therefore make continuous scans impossible. In prior work, we have suggested to use a feature called binary pixel, which is a novel approach in the working field of fingerprint age determination. The feature uses a Chromatic White Light (CWL) image sensor to continuously scan a fingerprint trace over time and retrieves a characteristic logarithmic aging tendency for 2D-intensity as well as 3D-topographic images from the sensor. In this paper, we propose to combine such two characteristic aging features with other 2D and 3D features from the domains of surface measurement, microscopy, photography and spectroscopy, to achieve an increase in accuracy and reliability of a potential future age determination scheme. Discussing the feasibility of such variety of sensor devices and possible aging features, we propose a general fusion approach, which might combine promising features to a joint age determination scheme in future. We furthermore demonstrate the feasibility of the introduced approach by exemplary fusing the binary pixel features based on 2D-intensity and 3D-topographic images of the mentioned CWL sensor. We conclude that a formula based age determination approach requires very precise image data, which cannot be achieved at the moment, whereas a machine learning based classification approach seems to be feasible, if an adequate amount of features can be provided.

Split ring resonator based THz-driven electron streak camera featuring femtosecond resolution

PubMed Central

Fabiańska, Justyna; Kassier, Günther; Feurer, Thomas

2014-01-01

Through combined three-dimensional electromagnetic and particle tracking simulations we demonstrate a THz driven electron streak camera featuring a temporal resolution on the order of a femtosecond. The ultrafast streaking field is generated in a resonant THz sub-wavelength antenna which is illuminated by an intense single-cycle THz pulse. Since electron bunches and THz pulses are generated with parts of the same laser system, synchronization between the two is inherently guaranteed. PMID:25010060

Weld radiograph enigmas

NASA Technical Reports Server (NTRS)

Jemian, Wartan A.

1986-01-01

Weld radiograph enigmas are features observed on X-ray radiographs of welds. Some of these features resemble indications of weld defects, although their origin is different. Since they are not understood, they are a source of concern. There is a need to identify their causes and especially to measure their effect on weld mechanical properties. A method is proposed whereby the enigmas can be evaluated and rated, in relation to the full spectrum of weld radiograph indications. Thie method involves a signature and a magnitude that can be used as a quantitive parameter. The signature is generated as the diference between the microdensitometer trace across the radiograph and the computed film intensity derived from a thickness scan along the corresponding region of the sample. The magnitude is the measured difference in intensity between the peak and base line values of the signature. The procedure is demonstated by comparing traces across radiographs of a weld sample before and after the introduction of a hole and by a system based on a MacIntosh mouse used for surface profiling.

Cluster Analysis of Weighted Bipartite Networks: A New Copula-Based Approach

PubMed Central

Chessa, Alessandro; Crimaldi, Irene; Riccaboni, Massimo; Trapin, Luca

2014-01-01

In this work we are interested in identifying clusters of “positional equivalent” actors, i.e. actors who play a similar role in a system. In particular, we analyze weighted bipartite networks that describes the relationships between actors on one side and features or traits on the other, together with the intensity level to which actors show their features. We develop a methodological approach that takes into account the underlying multivariate dependence among groups of actors. The idea is that positions in a network could be defined on the basis of the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Moreover, we propose a new clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clustering algorithm can be applied both to binary and real-valued matrices. We validate it with simulations and applications to real-world data. PMID:25303095

Bayesian Analysis of Hmi Images and Comparison to Tsi Variations and MWO Image Observables

NASA Astrophysics Data System (ADS)

Parker, D. G.; Ulrich, R. K.; Beck, J.; Tran, T. V.

2015-12-01

We have previously applied the Bayesian automatic classification system AutoClass to solar magnetogram and intensity images from the 150 Foot Solar Tower at Mount Wilson to identify classes of solar surface features associated with variations in total solar irradiance (TSI) and, using those identifications, modeled TSI time series with improved accuracy (r > 0.96). (Ulrich, et al, 2010) AutoClass identifies classes by a two-step process in which it: (1) finds, without human supervision, a set of class definitions based on specified attributes of a sample of the image data pixels, such as magnetic field and intensity in the case of MWO images, and (2) applies the class definitions thus found to new data sets to identify automatically in them the classes found in the sample set. HMI high resolution images capture four observables-magnetic field, continuum intensity, line depth and line width-in contrast to MWO's two observables-magnetic field and intensity. In this study, we apply AutoClass to the HMI observables for images from June, 2010 to December, 2014 to identify solar surface feature classes. We use contemporaneous TSI measurements to determine whether and how variations in the HMI classes are related to TSI variations and compare the characteristic statistics of the HMI classes to those found from MWO images. We also attempt to derive scale factors between the HMI and MWO magnetic and intensity observables.The ability to categorize automatically surface features in the HMI images holds out the promise of consistent, relatively quick and manageable analysis of the large quantity of data available in these images. Given that the classes found in MWO images using AutoClass have been found to improve modeling of TSI, application of AutoClass to the more complex HMI images should enhance understanding of the physical processes at work in solar surface features and their implications for the solar-terrestrial environment.Ulrich, R.K., Parker, D, Bertello, L. and Boyden, J. 2010, Solar Phys. , 261 , 11.

Effective Strategies for School-Based Early Childhood Centers. The Northwest Regional Educational Laboratory Program Report.

ERIC Educational Resources Information Center

Jewett, Janet

Effective strategies for developing early childhood centers in public schools are discussed in this paper, which draws from a research-based literature search and intensive case studies of six Northwest sites. The sites represent a range of rural, suburban, and urban programs; large and small schools; and a variety of program features. The sites…

Twilight Intensity Variation of the Infrared Hydroxyl Airglow

NASA Technical Reports Server (NTRS)

Lowe, R. P.; Gilbert, K. L.; Niciejewski, R. J.

1984-01-01

The vibration rotation bands of the hydroxyl radical are the strongest features in the night airglow and are exceeded in intensity in the dayglow only by the infrared atmospheric bands of oxygen. The variation of intensity during evening twilight is discussed. Using a ground-based Fourier Transform Spectrometer (FTS), hydroxyl intensity measurements as early as 3 deg solar depression were made. Models of the twilight behavior show that this should be sufficient to provide measurement of the main portion of the twilight intensity change. The instrument was equipped with a liquid nitrogen-cooled germanium detector whose high sensitivity combined with the efficiency of the FTS technique permits spectra of the region 1.1 to 1.6 microns at high signal-to-noise to be obtained in two minutes. The use of a polarizer at the entrance aperture of the instrument reduces the intensity of scattered sunlight by a factor of at least ten for zenith observations.

Web-based interactive visualization in a Grid-enabled neuroimaging application using HTML5.

PubMed

Siewert, René; Specovius, Svenja; Wu, Jie; Krefting, Dagmar

2012-01-01

Interactive visualization and correction of intermediate results are required in many medical image analysis pipelines. To allow certain interaction in the remote execution of compute- and data-intensive applications, new features of HTML5 are used. They allow for transparent integration of user interaction into Grid- or Cloud-enabled scientific workflows. Both 2D and 3D visualization and data manipulation can be performed through a scientific gateway without the need to install specific software or web browser plugins. The possibilities of web-based visualization are presented along the FreeSurfer-pipeline, a popular compute- and data-intensive software tool for quantitative neuroimaging.

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.

Exploring Specialized STEM High Schools: Three Dissertation Studies Examining Commonalities and Differences Across Six Case Studies

NASA Astrophysics Data System (ADS)

Tofel-Grehl, Colby

This dissertation is comprised of three independently conducted analyses of a larger investigation into the practices and features of specialized STEM high schools. While educators and policy makers advocate the development of many new specialized STEM high schools, little is known about the unique features and practices of these schools. The results of these manuscripts add to the literature exploring the promise of specialized STEM schools. Manuscript 1¹ is a qualitative investigation of the common features of STEM schools across multiple school model types. Schools were found to possess common cultural and academic features regardless of model type. Manuscript 2² builds on the findings of manuscript 1. With no meaningful differences found attributable to model type, the researchers used grounded theory to explore the relationships between observed differences among programs as related to the intensity of the STEM experience offered at schools. Schools were found to fall into two categories, high STEM intensity (HSI) and low STEM intensity (LSI), based on five major traits. Manuscript 3³ examines the commonalities and differences in classroom discourse and teachers' questioning techniques in STEM schools. It explicates these discursive practices in order to explore instructional practices across schools. It also examines factors that may influence classroom discourse such as discipline, level of teacher education, and course status as required or elective. Collectively, this research furthers the agenda of better understanding the potential advantages of specialized STEM high schools for preparing a future scientific workforce. ¹Tofel-Grehl, C., Callahan, C., & Gubbins, E. (2012). STEM high school communities: Common and differing features. Manuscript in preparation. ²Tofel-Grehl, C., Callahan, C., & Gubbins, E. (2012). Variations in the intensity of specialized science, technology, engineering, and mathematics (STEM) high schools. Manuscript in preparation. ³Tofel-Grehl, C., Callahan, C., & Gubbins, E. (2012). Comparative analyses of discourse in specialized STEM school classes. Manuscript in preparation.

Blood vessel segmentation in color fundus images based on regional and Hessian features.

PubMed

Shah, Syed Ayaz Ali; Tang, Tong Boon; Faye, Ibrahima; Laude, Augustinus

2017-08-01

To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis. Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel. The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy. Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.

Mutual information-based feature selection for radiomics

NASA Astrophysics Data System (ADS)

Oubel, Estanislao; Beaumont, Hubert; Iannessi, Antoine

2016-03-01

Background The extraction and analysis of image features (radiomics) is a promising field in the precision medicine era, with applications to prognosis, prediction, and response to treatment quantification. In this work, we present a mutual information - based method for quantifying reproducibility of features, a necessary step for qualification before their inclusion in big data systems. Materials and Methods Ten patients with Non-Small Cell Lung Cancer (NSCLC) lesions were followed over time (7 time points in average) with Computed Tomography (CT). Five observers segmented lesions by using a semi-automatic method and 27 features describing shape and intensity distribution were extracted. Inter-observer reproducibility was assessed by computing the multi-information (MI) of feature changes over time, and the variability of global extrema. Results The highest MI values were obtained for volume-based features (VBF). The lesion mass (M), surface to volume ratio (SVR) and volume (V) presented statistically significant higher values of MI than the rest of features. Within the same VBF group, SVR showed also the lowest variability of extrema. The correlation coefficient (CC) of feature values was unable to make a difference between features. Conclusions MI allowed to discriminate three features (M, SVR, and V) from the rest in a statistically significant manner. This result is consistent with the order obtained when sorting features by increasing values of extrema variability. MI is a promising alternative for selecting features to be considered as surrogate biomarkers in a precision medicine context.

Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.

PubMed

Kim, Hyungjin; Park, Chang Min; Lee, Myunghee; Park, Sang Joon; Song, Yong Sub; Lee, Jong Hyuk; Hwang, Eui Jin; Goo, Jin Mo

2016-01-01

To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two readers independently segmented the whole tumor volume. Fifteen radiomic features were extracted and compared among reconstruction algorithms. Intra- and inter-reader variability and inter-reconstruction algorithm variability were calculated using coefficients of variation (CVs) and then compared. Among the 15 features, 5 first-order tumor intensity features and 4 gray level co-occurrence matrix (GLCM)-based features showed significant differences (p<0.05) among reconstruction algorithms. As for the variability, effective diameter, sphericity, entropy, and GLCM entropy were the most robust features (CV≤5%). Inter-reader variability was larger than intra-reader or inter-reconstruction algorithm variability in 9 features. However, for entropy, homogeneity, and 4 GLCM-based features, inter-reconstruction algorithm variability was significantly greater than inter-reader variability (p<0.013). Most of the radiomic features were significantly affected by the reconstruction algorithms. Inter-reconstruction algorithm variability was greater than inter-reader variability for entropy, homogeneity, and GLCM-based features.

LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data.

PubMed

Koelmel, Jeremy P; Kroeger, Nicholas M; Ulmer, Candice Z; Bowden, John A; Patterson, Rainey E; Cochran, Jason A; Beecher, Christopher W W; Garrett, Timothy J; Yost, Richard A

2017-07-10

Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode. LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.

Autonomous rock detection on mars through region contrast

NASA Astrophysics Data System (ADS)

Xiao, Xueming; Cui, Hutao; Yao, Meibao; Tian, Yang

2017-08-01

In this paper, we present a new autonomous rock detection approach through region contrast. Unlike current state-of-art pixel-level rock segmenting methods, new method deals with this issue in region level, which will significantly reduce the computational cost. Image is firstly splitted into homogeneous regions based on intensity information and spatial layout. Considering the high-water memory constraints of onboard flight processor, only low-level features, average intensity and variation of superpixel, are measured. Region contrast is derived as the integration of intensity contrast and smoothness measurement. Rocks are then segmented from the resulting contrast map by an adaptive threshold. Since the merely intensity-based method may cause false detection in background areas with different illuminations from surroundings, a more reliable method is further proposed by introducing spatial factor and background similarity to the region contrast. Spatial factor demonstrates the locality of contrast, while background similarity calculates the probability of each subregion belonging to background. Our method is efficient in dealing with large images and only few parameters are needed. Preliminary experimental results show that our algorithm outperforms edge-based methods in various grayscale rover images.

An efficient direct method for image registration of flat objects

NASA Astrophysics Data System (ADS)

Nikolaev, Dmitry; Tihonkih, Dmitrii; Makovetskii, Artyom; Voronin, Sergei

2017-09-01

Image alignment of rigid surfaces is a rapidly developing area of research and has many practical applications. Alignment methods can be roughly divided into two types: feature-based methods and direct methods. Known SURF and SIFT algorithms are examples of the feature-based methods. Direct methods refer to those that exploit the pixel intensities without resorting to image features and image-based deformations are general direct method to align images of deformable objects in 3D space. Nevertheless, it is not good for the registration of images of 3D rigid objects since the underlying structure cannot be directly evaluated. In the article, we propose a model that is suitable for image alignment of rigid flat objects under various illumination models. The brightness consistency assumptions used for reconstruction of optimal geometrical transformation. Computer simulation results are provided to illustrate the performance of the proposed algorithm for computing of an accordance between pixels of two images.

The correlation study of parallel feature extractor and noise reduction approaches

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

Dewi, Deshinta Arrova; Sundararajan, Elankovan; Prabuwono, Anton Satria

2015-05-15

This paper presents literature reviews that show variety of techniques to develop parallel feature extractor and finding its correlation with noise reduction approaches for low light intensity images. Low light intensity images are normally displayed as darker images and low contrast. Without proper handling techniques, those images regularly become evidences of misperception of objects and textures, the incapability to section them. The visual illusions regularly clues to disorientation, user fatigue, poor detection and classification performance of humans and computer algorithms. Noise reduction approaches (NR) therefore is an essential step for other image processing steps such as edge detection, image segmentation,more » image compression, etc. Parallel Feature Extractor (PFE) meant to capture visual contents of images involves partitioning images into segments, detecting image overlaps if any, and controlling distributed and redistributed segments to extract the features. Working on low light intensity images make the PFE face challenges and closely depend on the quality of its pre-processing steps. Some papers have suggested many well established NR as well as PFE strategies however only few resources have suggested or mentioned the correlation between them. This paper reviews best approaches of the NR and the PFE with detailed explanation on the suggested correlation. This finding may suggest relevant strategies of the PFE development. With the help of knowledge based reasoning, computational approaches and algorithms, we present the correlation study between the NR and the PFE that can be useful for the development and enhancement of other existing PFE.« less

A films based approach to intensity imbalance correction for 65nm node c:PSM

NASA Astrophysics Data System (ADS)

Cottle, Rand; Sixt, Pierre; Lassiter, Matt; Cangemi, Marc; Martin, Patrick; Progler, Chris

2005-11-01

Intensity imbalance between the 0 and π phase features of c:PSM cause gate CD control and edge placement problems. Strategies such as undercut, selective biasing, and combinations of undercut and bias are currently used in production to mitigate these problems. However, there are drawbacks to these strategies such as space CD delta through pitch, gate CD control through defocus, design rule restrictions, and reticle manufacturability. This paper investigates the application of an innovative films-based approach to intensity balancing known as the Transparent Etch Stop Layer (TESL). TESL, in addition to providing a host of reticle quality and manufacturability benefits, also can be tuned to significantly reduce imbalance. Rigorous 3D vector simulations and experimental data compare through pitch and defocus performance of TESL and conventional c:PSM for 65nm design rules.

Auroral photometry from the atmosphere Explorer satellite

NASA Technical Reports Server (NTRS)

Rees, M. H.; Abreu, V. J.

1984-01-01

Attention is given to the ability of remote sensing from space to yield quantitative auroral and ionospheric parametrers, in view of the auroral measurements made during two passes of the Explorer C satellite over the Poker Flat Optical Observatory and the Chatanika Radar Facility. The emission rate of the N2(+) 4278 A band computed from intensity measurements of energetic auroral electrons has tracked the same spetral feature that was measured remotely from the satellite over two decades of intensity, providing a stringent test for the measurement of atmospheric scattering effects. It also verifies the absolute intensity with respect to ground-based photometric measurements. In situ satellite measurments of ion densities and ground based electron density profile radar measurements provide a consistent picture of the ionospheric response to auroral input, while also predicting the observed optical emission rate.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Deterministic object tracking using Gaussian ringlet and directional edge features

NASA Astrophysics Data System (ADS)

Krieger, Evan W.; Sidike, Paheding; Aspiras, Theus; Asari, Vijayan K.

2017-10-01

Challenges currently existing for intensity-based histogram feature tracking methods in wide area motion imagery (WAMI) data include object structural information distortions, background variations, and object scale change. These issues are caused by different pavement or ground types and from changing the sensor or altitude. All of these challenges need to be overcome in order to have a robust object tracker, while attaining a computation time appropriate for real-time processing. To achieve this, we present a novel method, Directional Ringlet Intensity Feature Transform (DRIFT), which employs Kirsch kernel filtering for edge features and a ringlet feature mapping for rotational invariance. The method also includes an automatic scale change component to obtain accurate object boundaries and improvements for lowering computation times. We evaluated the DRIFT algorithm on two challenging WAMI datasets, namely Columbus Large Image Format (CLIF) and Large Area Image Recorder (LAIR), to evaluate its robustness and efficiency. Additional evaluations on general tracking video sequences are performed using the Visual Tracker Benchmark and Visual Object Tracking 2014 databases to demonstrate the algorithms ability with additional challenges in long complex sequences including scale change. Experimental results show that the proposed approach yields competitive results compared to state-of-the-art object tracking methods on the testing datasets.

Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features

NASA Astrophysics Data System (ADS)

Andreasen, Daniel; Edmund, Jens M.; Zografos, Vasileios; Menze, Bjoern H.; Van Leemput, Koen

2016-03-01

In radiotherapy treatment planning that is only based on magnetic resonance imaging (MRI), the electron density information usually obtained from computed tomography (CT) must be derived from the MRI by synthesizing a so-called pseudo CT (pCT). This is a non-trivial task since MRI intensities are neither uniquely nor quantitatively related to electron density. Typical approaches involve either a classification or regression model requiring specialized MRI sequences to solve intensity ambiguities, or an atlas-based model necessitating multiple registrations between atlases and subject scans. In this work, we explore a machine learning approach for creating a pCT of the pelvic region from conventional MRI sequences without using atlases. We use a random forest provided with information about local texture, edges and spatial features derived from the MRI. This helps to solve intensity ambiguities. Furthermore, we use the concept of auto-context by sequentially training a number of classification forests to create and improve context features, which are finally used to train a regression forest for pCT prediction. We evaluate the pCT quality in terms of the voxel-wise error and the radiologic accuracy as measured by water-equivalent path lengths. We compare the performance of our method against two baseline pCT strategies, which either set all MRI voxels in the subject equal to the CT value of water, or in addition transfer the bone volume from the real CT. We show an improved performance compared to both baseline pCTs suggesting that our method may be useful for MRI-only radiotherapy.

Multiresolution texture models for brain tumor segmentation in MRI.

PubMed

Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

2011-01-01

In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

Wavelet energy-guided level set-based active contour: a segmentation method to segment highly similar regions.

PubMed

Achuthan, Anusha; Rajeswari, Mandava; Ramachandram, Dhanesh; Aziz, Mohd Ezane; Shuaib, Ibrahim Lutfi

2010-07-01

This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection. Copyright 2010 Elsevier Ltd. All rights reserved.

Meeting Report: Tissue-based Image Analysis.

PubMed

Saravanan, Chandra; Schumacher, Vanessa; Brown, Danielle; Dunstan, Robert; Galarneau, Jean-Rene; Odin, Marielle; Mishra, Sasmita

2017-10-01

Quantitative image analysis (IA) is a rapidly evolving area of digital pathology. Although not a new concept, the quantification of histological features on photomicrographs used to be cumbersome, resource-intensive, and limited to specialists and specialized laboratories. Recent technological advances like highly efficient automated whole slide digitizer (scanner) systems, innovative IA platforms, and the emergence of pathologist-friendly image annotation and analysis systems mean that quantification of features on histological digital images will become increasingly prominent in pathologists' daily professional lives. The added value of quantitative IA in pathology includes confirmation of equivocal findings noted by a pathologist, increasing the sensitivity of feature detection, quantification of signal intensity, and improving efficiency. There is no denying that quantitative IA is part of the future of pathology; however, there are also several potential pitfalls when trying to estimate volumetric features from limited 2-dimensional sections. This continuing education session on quantitative IA offered a broad overview of the field; a hands-on toxicologic pathologist experience with IA principles, tools, and workflows; a discussion on how to apply basic stereology principles in order to minimize bias in IA; and finally, a reflection on the future of IA in the toxicologic pathology field.

An experimental pilot study of response to invalidation in young women with features of borderline personality disorder.

PubMed

Woodberry, Kristen A; Gallo, Kaitlin P; Nock, Matthew K

2008-01-15

One of the leading biosocial theories of borderline personality disorder (BPD) suggests that individuals with BPD have biologically based abnormalities in emotion regulation contributing to more intense and rapid responses to emotional stimuli, in particular, invalidation [Linehan, M.M., 1993. Cognitive-Behavioral Treatment of Borderline Personality Disorder. Guilford, New York.]. This study used a 2 by 2 experimental design to test whether young women with features of BPD actually show increased physiological arousal in response to invalidation. Twenty-three women ages 18 to 29 who endorsed high levels of BPD symptoms and 18 healthy controls were randomly assigned to hear either a validating or invalidating comment during a frustrating task. Although we found preliminary support for differential response to these stimuli in self-report of valence, we found neither self-report nor physiological evidence of hyperarousal in the BPD features group, either at baseline or in response to invalidation. Interestingly, the BPD features group reported significantly lower comfort with emotion, and comfort was significantly associated with affective valence but not arousal. We discuss implications for understanding and responding to the affective intensity of this population.

Focus on Undergraduate Personal/Professional Preparation in Physical Education.

ERIC Educational Resources Information Center

Weinberg, Herman

A program is described that features an integrated course sequence and a continuous field-based experience. It focuses intensively on the individual needs and growth of the prospective physical education teacher. The primary components of the program are interrelated and designed to ensure that relevant content appears concurrently and…

How Does Professional Development Improve Teaching?

ERIC Educational Resources Information Center

Kennedy, Mary M.

2016-01-01

Professional development programs are based on different theories of how students learn and different theories of how teachers learn. Reviewers often sort programs according to design features such as program duration, intensity, or the use of specific techniques such as coaches or online lessons, but these categories do not illuminate the…

Improved superficial brain hemorrhage visualization in susceptibility weighted images by constrained minimum intensity projection

NASA Astrophysics Data System (ADS)

Castro, Marcelo A.; Pham, Dzung L.; Butman, John

2016-03-01

Minimum intensity projection is a technique commonly used to display magnetic resonance susceptibility weighted images, allowing the observer to better visualize hemorrhages and vasculature. The technique displays the minimum intensity in a given projection within a thick slab, allowing different connectivity patterns to be easily revealed. Unfortunately, the low signal intensity of the skull within the thick slab can mask superficial tissues near the skull base and other regions. Because superficial microhemorrhages are a common feature of traumatic brain injury, this effect limits the ability to proper diagnose and follow up patients. In order to overcome this limitation, we developed a method to allow minimum intensity projection to properly display superficial tissues adjacent to the skull. Our approach is based on two brain masks, the largest of which includes extracerebral voxels. The analysis of the rind within both masks containing the actual brain boundary allows reclassification of those voxels initially missed in the smaller mask. Morphological operations are applied to guarantee accuracy and topological correctness, and the mean intensity within the mask is assigned to all outer voxels. This prevents bone from dominating superficial regions in the projection, enabling superior visualization of cortical hemorrhages and vessels.

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

NASA Astrophysics Data System (ADS)

Leijenaar, Ralph T. H.; Nalbantov, Georgi; Carvalho, Sara; van Elmpt, Wouter J. C.; Troost, Esther G. C.; Boellaard, Ronald; Aerts, Hugo J. W. L.; Gillies, Robert J.; Lambin, Philippe

2015-08-01

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) RB, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, RB was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values-which was used as a surrogate for textural feature interpretation-between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

Three Years of TRMM Precipitation Features. Part 1; Radar, Radiometric, and Lightning Characteristics

NASA Technical Reports Server (NTRS)

Cecil, Daniel J.; Goodman, Steven J.; Boccippio, Dennis J.; Zipser, Edward J.; Nesbitt, Stephen W.

2004-01-01

During its first three years, the Tropical Rainfall Measuring Mission (TRMM) satellite observed nearly six million precipitation features. The population of precipitation features is sorted by lightning flash rate, minimum brightness temperature, maximum radar reflectivity, areal extent, and volumetric rainfall. For each of these characteristics, essentially describing the convective intensity or the size of the features, the population is broken into categories consisting of the top 0.001%, top 0.01%, top 0.1%, top 1%, top 2.4%, and remaining 97.6%. The set of 'weakest / smallest' features comprises 97.6% of the population because that fraction does not have detected lightning, with a minimum detectable flash rate 0.7 fl/min. The greatest observed flash rate is 1351 fl/min; the lowest brightness temperatures are 42 K (85-GHz) and 69 K (37- GHz). The largest precipitation feature covers 335,000 sq km and the greatest rainfall from an individual precipitation feature exceeds 2 x 10(exp 12) kg of water. There is considerable overlap between the greatest storms according to different measures of convective intensity. The largest storms are mostly independent of the most intense storms. The set of storms producing the most rainfall is a convolution of the largest and the most intense storms. This analysis is a composite of the global tropics and subtropics. Significant variability is known to exist between locations, seasons, and meteorological regimes. Such variability will be examined in Part II. In Part I, only a crude land / Ocean separation is made. The known differences in bulk lightning flash rates over land and Ocean result from at least two differences in the precipitation feature population: the frequency of occurrence of intense storms, and the magnitude of those intense storms that do occur. Even when restricted to storms with the same brightness temperature, same size, or same radar reflectivity aloft, the storms over water are considerably less likely to produce lightning than are comparable storms over land.

Three Years of TRMM Precipitation Features. Part 1; Radar, Radiometric, and Lightning Characteristics

NASA Technical Reports Server (NTRS)

Cecil, Daniel J.; Goodman, Steven J.; Boccippio, Dennis J.; Zipser, Edward J.; Nesbitt, Stephen W.

2005-01-01

During its first three years, the Tropical Rainfall Measuring Mission (TRMM) satellite observed nearly six million precipitation features. The population of precipitation features is sorted by lightning flash rate, minimum brightness temperature, maximum radar reflectivity. areal extent, and volumetric rainfall. For each of these characteristics, essentially describing the convective intensity or the size of the features, the population is broken into categories consisting of the top 0.001%, top 0.01%, top 0.1%, top 1%, top 2.4%. and remaining 97.6%. The set of weakest/smallest features composes 97.6% of the population because that fraction does not have detected lightning, with a minimum detectable flash rate of 0.7 flashes (fl) per minute. The greatest observed flash rate is 1351 fl per minute; the lowest brightness temperatures are 42 K (85 GHz) and 69 K (37 GHz). The largest precipitation feature covers 335 000 square kilometers and the greatest rainfall from an individual precipitation feature exceeds 2 x 10 kg per hour of water. There is considerable overlap between the greatest storms according to different measures of convective intensity. The largest storms are mostly independent of the most intense storms. The set of storms producing the most rainfall is a convolution of the largest and the most intense storms. This analysis is a composite of the global Tropics and subtropics. Significant variability is known to exist between locations. seasons, and meteorological regimes. Such variability will be examined in Part II. In Part I, only a crude land-ocean separation is made. The known differences in bulk lightning flash rates over land and ocean result from at least two differences in the precipitation feature population: the frequency of occurrence of intense storms and the magnitude of those intense storms that do occur. Even when restricted to storms with the same brightness temperature, same size, or same radar reflectivity aloft, the storms over water are considerably less likely to produce lightning than are comparable storms over land.

The locomotor activity of soccer players based on playing positions during the 2010 World Cup.

PubMed

Soroka, Andrzej

2018-06-01

The aim of this study was to define the locomotor activity of footballer players during the 2010 World Cup and to assess what differences existed among different playing positions. Research was conducted using research material collected from the Castrol Performance Index, a kinematic game analysis system that records player movements during a game by use of semi-automatic cameras. A total of 599 players who participated in the championships were analyzed. The results were evaluated using one-way analysis of variance (ANOVA) and a post-hoc test that calculated the Honestly Significant Difference (HSD) in order to determine which mean values significantly differed among the player positions. It was found that midfielders covered on average the largest distance during a match (10,777.6 m, P<0.001) as well as performing the most locomotor activity at high and sprint intensities (2936.8 m and 108.4 m, respectively). Additionally, midfielders also spent the largest amount of time at performing at a high intensity (10.6%). Strikers also featured high levels of the above parameters; the total length of distance covered with high intensities was found to be on average 2586.7 m, the distance covered at sprint intensity was 105 m. The footballers, playing at the championship level feature excellent locomotor preparation. This fact is undoubtedly supported by the aerobic training of high intensity. Such training allows footballers to extend the distance they cover during the match, increase the intensity of locomotor activities and sprint speed distance.

Computation of reliable textural indices from multimodal brain MRI: suggestions based on a study of patients with diffuse intrinsic pontine glioma.

PubMed

Goya-Outi, Jessica; Orlhac, Fanny; Calmon, Raphael; Alentorn, Agusti; Nioche, Christophe; Philippe, Cathy; Puget, Stéphanie; Boddaert, Nathalie; Buvat, Irène; Grill, Jacques; Frouin, Vincent; Frouin, Frederique

2018-05-10

Few methodological studies regarding widely used textural indices robustness in MRI have been reported. In this context, this study aims to propose some rules to compute reliable textural indices from multimodal 3D brain MRI. Diagnosis and post-biopsy MR scans including T1, post-contrast T1, T2 and FLAIR images from thirty children with diffuse intrinsic pontine glioma (DIPG) were considered. The hybrid white stripe method was adapted to standardize MR intensities. Sixty textural indices were then computed for each modality in different regions of interest (ROI), including tumor and white matter (WM). Three types of intensity binning were compared [Formula: see text]: constant bin width and relative bounds; [Formula: see text] constant number of bins and relative bounds; [Formula: see text] constant number of bins and absolute bounds. The impact of the volume of the region was also tested within the WM. First, the mean Hellinger distance between patient-based intensity distributions decreased by a factor greater than 10 in WM and greater than 2.5 in gray matter after standardization. Regarding the binning strategy, the ranking of patients was highly correlated for 188/240 features when comparing [Formula: see text] with [Formula: see text], but for only 20 when comparing [Formula: see text] with [Formula: see text], and nine when comparing [Formula: see text] with [Formula: see text]. Furthermore, when using [Formula: see text] or [Formula: see text] texture indices reflected tumor heterogeneity as assessed visually by experts. Last, 41 features presented statistically significant differences between contralateral WM regions when ROI size slightly varies across patients, and none when using ROI of the same size. For regions with similar size, 224 features were significantly different between WM and tumor. Valuable information from texture indices can be biased by methodological choices. Recommendations are to standardize intensities in MR brain volumes, to use intensity binning with constant bin width, and to define regions with the same volumes to get reliable textural indices.

An intensive combined training program modulates physical, physiological, biomotoric, and technical parameters in women basketball players.

PubMed

Kilinç, Fatih

2008-11-01

The purpose of this study was the investigation of the effects of an intensive combined training program based on the pretest scores of a university women's basketball team on their physical, physiological, biomotoric, and technical features. Twenty-four university volunteers were equally divided into two groups: an experiment group (intensive combined training group) and a control (technical training) group. The 10-week intensive combined training program was performed on the experiment group according to their pretest outcomes. Before and at the end of each period of training, which was scheduled four times a week, the physical, physiological, biomotoric, and technical performance of each subject were determined. With respect to the pre- and posttest measurements, the basketball group showed significant differences (p < 0.05) in girth measurements (shoulder, waist, hip, arm, thigh, and calf), in skinfold measurements (percent body fat), in physiological measurements (vital capacity and forced vital capacity), in biomotoric tests (right-left hand grip, dynamic and countermovement jump, sit-up, push-up, 1500-m endurance), and in technique tests (free and inside shooting). It can be concluded that a 10-week intensive combined training program performed on university women basketball players had a significant effect on improving their physical, physiological, biomotoric, and technical features. It proved to be highly recommendable for female basketball players who are preparing for short-term tournaments; the basketball group in this study won a championship.

Dynamic Analysis of Soil Erosion in Songhua River Watershed

NASA Astrophysics Data System (ADS)

Zhang, Yujuan; Li, Xiuhai; Wang, Qiang; Liu, Jiang; Liang, Xin; Li, Dan; Ni, Chundi; Liu, Yan

2018-01-01

In this paper, based on RS and GIS technology and Revised Universal Soil Loss Equation (RUSLE), the soil erosion dynamic changes during the two periods of 1990 and 2010 in Bin County was analyzed by using the Landsat TM data of the two periods, so as to reveal the soil erosion spatial distribution pattern and spatial and temporal dynamic evolution rule in the region. The results showed that: the overall patterns of soil erosion were basically the same in both periods, mainly featuring slight erosion and mild erosion, with the area proportions of 80.68% and 74.71% respectively. The slight and extremely intensive erosion changing rates showed a narrowing trend; mild, moderate and intensive erosion was increasing, with a trend of increased soil erosion; mild and intensive erosion were developing towards moderate erosion and moderate and extremely intensive erosion were progressing towards intensive erosion.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

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

Sadjadi, Seyedabdolreza; Zhang, Yong; Kwok, Sun, E-mail: sunkwok@hku.hk

The 3.3 μ m unidentified infrared emission feature is commonly attributed to the C–H stretching band of aromatic molecules. Astronomical observations have shown that this feature is composed of two separate bands at 3.28 and 3.30 μ m, and the origin of these two bands is unclear. In this paper, we perform vibrational analyses based on quantum mechanical calculations of 153 organic molecules, including both pure aromatic molecules and molecules with mixed aromatic/olefinic/aliphatic hydridizations. We find that many of the C–H stretching vibrational modes in polycyclic aromatic hydrocarbon (PAH) molecules are coupled. Even considering the uncoupled modes only, the correlationmore » between the band intensity ratios and the structure of the PAH molecule is not observed, and the 3.28 and 3.30 μ m features cannot be directly interpreted in the PAH model. Based on these results, the possible aromatic, olefinic, and aliphatic origins of the 3.3 μ m feature are discussed. We suggest that the 3.28 μ m feature is assigned to aromatic C–H stretch whereas the 3.30 μ m feature is olefinic. From the ratio of these two features, the relative olefinic to aromatic content of the carrier can be determined.« less

Deep learning based classification of breast tumors with shear-wave elastography.

PubMed

Zhang, Qi; Xiao, Yang; Dai, Wei; Suo, Jingfeng; Wang, Congzhi; Shi, Jun; Zheng, Hairong

2016-12-01

This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer. Copyright © 2016 Elsevier B.V. All rights reserved.

A CNN Regression Approach for Real-Time 2D/3D Registration.

PubMed

Shun Miao; Wang, Z Jane; Rui Liao

2016-05-01

In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow computation and 2) small capture range. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the digitally reconstructed radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. An automatic feature extraction step is introduced to calculate 3-D pose-indexed features that are sensitive to the variables to be regressed while robust to other factors. The CNN regressors are then trained for local zones and applied in a hierarchical manner to break down the complex regression task into multiple simpler sub-tasks that can be learned separately. Weight sharing is furthermore employed in the CNN regression model to reduce the memory footprint. The proposed approach has been quantitatively evaluated on 3 potential clinical applications, demonstrating its significant advantage in providing highly accurate real-time 2-D/3-D registration with a significantly enlarged capture range when compared to intensity-based methods.

Real-time microstructure imaging by Laue microdiffraction: A sample application in laser 3D printed Ni-based superalloys

DOE PAGES

Zhou, Guangni; Zhu, Wenxin; Shen, Hao; ...

2016-06-15

Synchrotron-based Laue microdiffraction has been widely applied to characterize the local crystal structure, orientation, and defects of inhomogeneous polycrystalline solids by raster scanning them under a micro/nano focused polychromatic X-ray probe. In a typical experiment, a large number of Laue diffraction patterns are collected, requiring novel data reduction and analysis approaches, especially for researchers who do not have access to fast parallel computing capabilities. In this article, a novel approach is developed by plotting the distributions of the average recorded intensity and the average filtered intensity of the Laue patterns. Visualization of the characteristic microstructural features is realized in realmore » time during data collection. As an example, this method is applied to image key features such as microcracks, carbides, heat affected zone, and dendrites in a laser assisted 3D printed Ni-based superalloy, at a speed much faster than data collection. Such analytical approach remains valid for a wide range of crystalline solids, and therefore extends the application range of the Laue microdiffraction technique to problems where real-time decision-making during experiment is crucial (for instance time-resolved non-reversible experiments).« less

Real-time microstructure imaging by Laue microdiffraction: A sample application in laser 3D printed Ni-based superalloys

PubMed Central

Zhou, Guangni; Zhu, Wenxin; Shen, Hao; Li, Yao; Zhang, Anfeng; Tamura, Nobumichi; Chen, Kai

2016-01-01

Synchrotron-based Laue microdiffraction has been widely applied to characterize the local crystal structure, orientation, and defects of inhomogeneous polycrystalline solids by raster scanning them under a micro/nano focused polychromatic X-ray probe. In a typical experiment, a large number of Laue diffraction patterns are collected, requiring novel data reduction and analysis approaches, especially for researchers who do not have access to fast parallel computing capabilities. In this article, a novel approach is developed by plotting the distributions of the average recorded intensity and the average filtered intensity of the Laue patterns. Visualization of the characteristic microstructural features is realized in real time during data collection. As an example, this method is applied to image key features such as microcracks, carbides, heat affected zone, and dendrites in a laser assisted 3D printed Ni-based superalloy, at a speed much faster than data collection. Such analytical approach remains valid for a wide range of crystalline solids, and therefore extends the application range of the Laue microdiffraction technique to problems where real-time decision-making during experiment is crucial (for instance time-resolved non-reversible experiments). PMID:27302087

On the use of feature selection to improve the detection of sea oil spills in SAR images

NASA Astrophysics Data System (ADS)

Mera, David; Bolon-Canedo, Veronica; Cotos, J. M.; Alonso-Betanzos, Amparo

2017-03-01

Fast and effective oil spill detection systems are crucial to ensure a proper response to environmental emergencies caused by hydrocarbon pollution on the ocean's surface. Typically, these systems uncover not only oil spills, but also a high number of look-alikes. The feature extraction is a critical and computationally intensive phase where each detected dark spot is independently examined. Traditionally, detection systems use an arbitrary set of features to discriminate between oil spills and look-alikes phenomena. However, Feature Selection (FS) methods based on Machine Learning (ML) have proved to be very useful in real domains for enhancing the generalization capabilities of the classifiers, while discarding the existing irrelevant features. In this work, we present a generic and systematic approach, based on FS methods, for choosing a concise and relevant set of features to improve the oil spill detection systems. We have compared five FS methods: Correlation-based feature selection (CFS), Consistency-based filter, Information Gain, ReliefF and Recursive Feature Elimination for Support Vector Machine (SVM-RFE). They were applied on a 141-input vector composed of features from a collection of outstanding studies. Selected features were validated via a Support Vector Machine (SVM) classifier and the results were compared with previous works. Test experiments revealed that the classifier trained with the 6-input feature vector proposed by SVM-RFE achieved the best accuracy and Cohen's kappa coefficient (87.1% and 74.06% respectively). This is a smaller feature combination with similar or even better classification accuracy than previous works. The presented finding allows to speed up the feature extraction phase without reducing the classifier accuracy. Experiments also confirmed the significance of the geometrical features since 75.0% of the different features selected by the applied FS methods as well as 66.67% of the proposed 6-input feature vector belong to this category.

An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine

NASA Astrophysics Data System (ADS)

Zhao, Lili; Yin, Jianping; Yuan, Lihuan; Liu, Qiang; Li, Kuan; Qiu, Minghui

2017-07-01

Automatic detection of abnormal cells from cervical smear images is extremely demanded in annual diagnosis of women's cervical cancer. For this medical cell recognition problem, there are three different feature sections, namely cytology morphology, nuclear chromatin pathology and region intensity. The challenges of this problem come from feature combination s and classification accurately and efficiently. Thus, we propose an efficient abnormal cervical cell detection system based on multi-instance extreme learning machine (MI-ELM) to deal with above two questions in one unified framework. MI-ELM is one of the most promising supervised learning classifiers which can deal with several feature sections and realistic classification problems analytically. Experiment results over Herlev dataset demonstrate that the proposed method outperforms three traditional methods for two-class classification in terms of well accuracy and less time.

CAPER 3.0: A Scalable Cloud-Based System for Data-Intensive Analysis of Chromosome-Centric Human Proteome Project Data Sets.

PubMed

Yang, Shuai; Zhang, Xinlei; Diao, Lihong; Guo, Feifei; Wang, Dan; Liu, Zhongyang; Li, Honglei; Zheng, Junjie; Pan, Jingshan; Nice, Edouard C; Li, Dong; He, Fuchu

2015-09-04

The Chromosome-centric Human Proteome Project (C-HPP) aims to catalog genome-encoded proteins using a chromosome-by-chromosome strategy. As the C-HPP proceeds, the increasing requirement for data-intensive analysis of the MS/MS data poses a challenge to the proteomic community, especially small laboratories lacking computational infrastructure. To address this challenge, we have updated the previous CAPER browser into a higher version, CAPER 3.0, which is a scalable cloud-based system for data-intensive analysis of C-HPP data sets. CAPER 3.0 uses cloud computing technology to facilitate MS/MS-based peptide identification. In particular, it can use both public and private cloud, facilitating the analysis of C-HPP data sets. CAPER 3.0 provides a graphical user interface (GUI) to help users transfer data, configure jobs, track progress, and visualize the results comprehensively. These features enable users without programming expertise to easily conduct data-intensive analysis using CAPER 3.0. Here, we illustrate the usage of CAPER 3.0 with four specific mass spectral data-intensive problems: detecting novel peptides, identifying single amino acid variants (SAVs) derived from known missense mutations, identifying sample-specific SAVs, and identifying exon-skipping events. CAPER 3.0 is available at http://prodigy.bprc.ac.cn/caper3.

Distributional fold change test – a statistical approach for detecting differential expression in microarray experiments

PubMed Central

2012-01-01

Background Because of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment. Results A new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best – on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets. Conclusions The distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set. PMID:23122055

A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features.

PubMed

Li, Liqi; Luo, Qifa; Xiao, Weidong; Li, Jinhui; Zhou, Shiwen; Li, Yongsheng; Zheng, Xiaoqi; Yang, Hua

2017-02-01

Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.

MRI-based quantification of Duchenne muscular dystrophy in a canine model

NASA Astrophysics Data System (ADS)

Wang, Jiahui; Fan, Zheng; Kornegay, Joe N.; Styner, Martin A.

2011-03-01

Duchenne muscular dystrophy (DMD) is a progressive and fatal X-linked disease caused by mutations in the DMD gene. Magnetic resonance imaging (MRI) has shown potential to provide non-invasive and objective biomarkers for monitoring disease progression and therapeutic effect in DMD. In this paper, we propose a semi-automated scheme to quantify MRI features of golden retriever muscular dystrophy (GRMD), a canine model of DMD. Our method was applied to a natural history data set and a hydrodynamic limb perfusion data set. The scheme is composed of three modules: pre-processing, muscle segmentation, and feature analysis. The pre-processing module includes: calculation of T2 maps, spatial registration of T2 weighted (T2WI) images, T2 weighted fat suppressed (T2FS) images, and T2 maps, and intensity calibration of T2WI and T2FS images. We then manually segment six pelvic limb muscles. For each of the segmented muscles, we finally automatically measure volume and intensity statistics of the T2FS images and T2 maps. For the natural history study, our results showed that four of six muscles in affected dogs had smaller volumes and all had higher mean intensities in T2 maps as compared to normal dogs. For the perfusion study, the muscle volumes and mean intensities in T2FS were increased in the post-perfusion MRI scans as compared to pre-perfusion MRI scans, as predicted. We conclude that our scheme successfully performs quantitative analysis of muscle MRI features of GRMD.

Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology

NASA Astrophysics Data System (ADS)

Sharma, Harshita; Zerbe, Norman; Heim, Daniel; Wienert, Stephan; Lohmann, Sebastian; Hellwich, Olaf; Hufnagl, Peter

2016-03-01

This paper describes a novel graph-based method for efficient representation and subsequent classification in histological whole slide images of gastric cancer. Her2/neu immunohistochemically stained and haematoxylin and eosin stained histological sections of gastric carcinoma are digitized. Immunohistochemical staining is used in practice by pathologists to determine extent of malignancy, however, it is laborious to visually discriminate the corresponding malignancy levels in the more commonly used haematoxylin and eosin stain, and this study attempts to solve this problem using a computer-based method. Cell nuclei are first isolated at high magnification using an automatic cell nuclei segmentation strategy, followed by construction of cell nuclei attributed relational graphs of the tissue regions. These graphs represent tissue architecture comprehensively, as they contain information about cell nuclei morphology as vertex attributes, along with knowledge of neighborhood in the form of edge linking and edge attributes. Global graph characteristics are derived and ensemble learning is used to discriminate between three types of malignancy levels, namely, non-tumor, Her2/neu positive tumor and Her2/neu negative tumor. Performance is compared with state of the art methods including four texture feature groups (Haralick, Gabor, Local Binary Patterns and Varma Zisserman features), color and intensity features, and Voronoi diagram and Delaunay triangulation. Texture, color and intensity information is also combined with graph-based knowledge, followed by correlation analysis. Quantitative assessment is performed using two cross validation strategies. On investigating the experimental results, it can be concluded that the proposed method provides a promising way for computer-based analysis of histopathological images of gastric cancer.

DMR 'Map of the Early Universe.'

NASA Technical Reports Server (NTRS)

2002-01-01

DMR 'Map of the Early Universe.' This false-color image shows tiny variations in the intensity of the cosmic microwave background measured in four years of observations by the Differential Microwave Radiometers on NASA's Cosmic Background Explorer (COBE). The cosmic microwave background is widely believed to be a remnant of the Big Bang; the blue and red spots correspond to regions of greater or lesser density in the early Universe. These 'fossilized' relics record the distribution of matter and energy in the early Universe before the matter became organized into stars and galaxies. While the initial discovery of variations in the intensity of the CMB (made by COBE in 1992) was based on a mathematical examination of the data, this picture of the sky from the full four-year mission gives an accurate visual impression of the data. The features traced in this map stretch across the visible Universe: the largest features seen by optical telescopes, such as the 'Great Wall' of galaxies, would fit neatly within the smallest feature in this map. (See Bennett et al. 1996, ApJ, 464, L1 and references therein for details.)

A practical salient region feature based 3D multi-modality registration method for medical images

NASA Astrophysics Data System (ADS)

Hahn, Dieter A.; Wolz, Gabriele; Sun, Yiyong; Hornegger, Joachim; Sauer, Frank; Kuwert, Torsten; Xu, Chenyang

2006-03-01

We present a novel representation of 3D salient region features and its integration into a hybrid rigid-body registration framework. We adopt scale, translation and rotation invariance properties of those intrinsic 3D features to estimate a transform between underlying mono- or multi-modal 3D medical images. Our method combines advantageous aspects of both feature- and intensity-based approaches and consists of three steps: an automatic extraction of a set of 3D salient region features on each image, a robust estimation of correspondences and their sub-pixel accurate refinement with outliers elimination. We propose a region-growing based approach for the extraction of 3D salient region features, a solution to the problem of feature clustering and a reduction of the correspondence search space complexity. Results of the developed algorithm are presented for both mono- and multi-modal intra-patient 3D image pairs (CT, PET and SPECT) that have been acquired for change detection, tumor localization, and time based intra-person studies. The accuracy of the method is clinically evaluated by a medical expert with an approach that measures the distance between a set of selected corresponding points consisting of both anatomical and functional structures or lesion sites. This demonstrates the robustness of the proposed method to image overlap, missing information and artefacts. We conclude by discussing potential medical applications and possibilities for integration into a non-rigid registration framework.

Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.

PubMed

Tustison, Nicholas J; Shrinidhi, K L; Wintermark, Max; Durst, Christopher R; Kandel, Benjamin M; Gee, James C; Grossman, Murray C; Avants, Brian B

2015-04-01

Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package--a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for "complete", "core", and "enhanced" tumor components, respectively.

Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients

NASA Astrophysics Data System (ADS)

Yang, Guang; Zhuang, Xiahai; Khan, Habib; Haldar, Shouvik; Nyktari, Eva; Li, Lei; Ye, Xujiong; Slabaugh, Greg; Wong, Tom; Mohiaddin, Raad; Keegan, Jennifer; Firmin, David

2017-03-01

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is an emerging non-invasive technique to image and quantify preablation native and post-ablation atrial scarring. Previous studies have reported that enhanced image intensities of the atrial scarring in the LGE CMRI inversely correlate with the left atrial endocardial voltage invasively obtained by electro-anatomical mapping. However, the reported reproducibility of using LGE CMRI to identify and quantify atrial scarring is variable. This may be due to two reasons: first, delineation of the left atrium (LA) and pulmonary veins (PVs) anatomy generally relies on manual operation that is highly subjective, and this could substantially affect the subsequent atrial scarring segmentation; second, simple intensity based image features may not be good enough to detect subtle changes in atrial scarring. In this study, we hypothesized that texture analysis can provide reliable image features for the LGE CMRI images subject to accurate and objective delineation of the heart anatomy based on a fully-automated whole heart segmentation (WHS) method. We tested the extracted texture features to differentiate between pre-ablation and post-ablation LGE CMRI studies in longstanding persistent atrial fibrillation patients. These patients often have extensive native scarring and differentiation from post-ablation scarring can be difficult. Quantification results showed that our method is capable of solving this classification task, and we can envisage further deployment of this texture analysis based method for other clinical problems using LGE CMRI.

Training for Efficiency: Work, Time, and Systems-Based Practice in Medical Residency

ERIC Educational Resources Information Center

Szymczak, Julia E.; Bosk, Charles L.

2012-01-01

Medical residency is a period of intense socialization with a heavy workload. Previous sociological studies have identified efficiency as a practical skill necessary for success. However, many contextual features of the training environment have undergone dramatic change since these studies were conducted. What are the consequences of these…

School Interventions for Attention Deficit Hyperactivity Disorder: Where to from Here?

ERIC Educational Resources Information Center

Barkley, Russell A.

2007-01-01

In this article, the author considers some issues concerning future research into school-based assessment and management of attention deficit hyperactivity disorder (ADHD). He focuses on a few issues of some relevance to school interventions for children and teens with ADHD. He features three articles that found that less intensive interventions…

Understanding Mental Health Intervention and Assessment within a Multi-Tiered Framework: Contemporary Science, Practice, and Policy

ERIC Educational Resources Information Center

Kilgus, Stephen P.; Reinke, Wendy M.; Jimerson, Shane R.

2015-01-01

This special topic section features research regarding practices that will support mental health service delivery within a school-based multitiered framework. The articles include data and discussions regarding the evaluation of universal, targeted, or intensive intervention addressing mental health concerns and assessment tools intended for use…

The Distribution of the Informative Intensity of the Text in Terms of its Structure (On Materials of the English Texts in the Mining Sphere)

NASA Astrophysics Data System (ADS)

Znikina, Ludmila; Rozhneva, Elena

2017-11-01

The article deals with the distribution of informative intensity of the English-language scientific text based on its structural features contributing to the process of formalization of the scientific text and the preservation of the adequacy of the text with derived semantic information in relation to the primary. Discourse analysis is built on specific compositional and meaningful examples of scientific texts taken from the mining field. It also analyzes the adequacy of the translation of foreign texts into another language, the relationships between elements of linguistic systems, the degree of a formal conformance, translation with the specific objectives and information needs of the recipient. Some key words and ideas are emphasized in the paragraphs of the English-language mining scientific texts. The article gives the characteristic features of the structure of paragraphs of technical text and examples of constructions in English scientific texts based on a mining theme with the aim to explain the possible ways of their adequate translation.

Polycyclic Aromatic Hydrocarbons with Aliphatic Sidegroups: Intensity Scaling for the C-H Stretching Modes and Astrophysical Implications

NASA Astrophysics Data System (ADS)

Yang, X. J.; Li, Aigen; Glaser, R.; Zhong, J. X.

2017-03-01

The so-called unidentified infrared emission (UIE) features at 3.3, 6.2, 7.7, 8.6, and 11.3 μ {{m}} ubiquitously seen in a wide variety of astrophysical regions are generally attributed to polycyclic aromatic hydrocarbon (PAH) molecules. Astronomical PAHs may have an aliphatic component, as revealed by the detection in many UIE sources of the aliphatic C-H stretching feature at 3.4 μ {{m}}. The ratio of the observed intensity of the 3.4 μ {{m}} feature to that of the 3.3 μ {{m}} aromatic C-H feature allows one to estimate the aliphatic fraction of the UIE carriers. This requires knowledge of the intrinsic oscillator strengths of the 3.3 μ {{m}} aromatic C-H stretch ({A}3.3) and the 3.4 μ {{m}} aliphatic C-H stretch ({A}3.4). Lacking experimental data on {A}3.3 and {A}3.4 for the UIE candidate materials, one often has to rely on quantum-chemical computations. Although the second-order Møller-Plesset (MP2) perturbation theory with a large basis set is more accurate than the B3LYP density functional theory, MP2 is computationally very demanding and impractical for large molecules. Based on methylated PAHs, we show here that, by scaling the band strengths computed at an inexpensive level (e.g., B3LYP/6-31G*), we are able to obtain band strengths as accurate as those computed at far more expensive levels (e.g., MP2/6-311+G(3df,3pd)). We calculate the model spectra of methylated PAHs and their cations excited by starlight of different spectral shapes and intensities. We find that {({I}3.4/{I}3.3)}{mod}, the ratio of the model intensity of the 3.4 μ {{m}} feature to that of the 3.3 μ {{m}} feature, is insensitive to the spectral shape and intensity of the exciting starlight. We derive a straightforward relation for determining the aliphatic fraction of the UIE carriers (I.e., the ratio of the number of C atoms in aliphatic units {N}{{C},{ali}} to that in aromatic rings {N}{{C},{aro}}) from the observed band ratios {({I}3.4/{I}3.3)}{obs}: {N}{{C},{ali}}/{N}{{C},{aro}}≈ 0.57× {({I}3.4/{I}3.3)}{obs} for neutrals and {N}{{C},{ali}}/{N}{{C},{aro}}≈ 0.26× {({I}3.4/{I}3.3)}{obs} for cations.

The structure of intense vorticity in homogeneous isotropic turbulence

NASA Technical Reports Server (NTRS)

Jimenez, J.; Wray, A. A.; Saffman, P. G.; Rogallo, R. S.

1992-01-01

The structure of the intense vorticity regions is studied in numerically simulated homogeneous, isotropic, equilibrium turbulent flow fields at four different Reynolds numbers in the range Re(sub lambda) = 36-171. In accordance with previous investigators, this vorticity is found to be organized in coherent, cylindrical or ribbon-like, vortices ('worms'). A statistical study suggests that they are just especially intense features of the background, O(omega'), vorticity. Their radii scale with the Kolmogorov microscale and their lengths with the integral scale of the flow. An interesting observation is that the Reynolds number based on the circulation of the intense vortices, gamma/nu, increases monotonically with Re(sub lambda), raising the question of the stability of the structures in the limit of Re(sub lambda) approaching infinity. One and two-dimensional statistics of vorticity and strain are presented; they are non-gaussian, and the behavior of their tails depends strongly on the Reynolds number. There is no evidence of convergence to a limiting distribution in our range of Re(sub lambda), even though the energy spectra and the energy dissipation rate show good asymptotic properties in the higher Reynolds number cases. Evidence is presented to show that worms are natural features of the flow and that they do not depend on the particular forcing scheme.

Conventional MRI features for predicting the clinical outcome of patients with invasive placenta

PubMed Central

Chen, Ting; Xu, Xiao-Quan; Shi, Hai-Bin; Yang, Zheng-Qiang; Zhou, Xin; Pan, Yi

2017-01-01

PURPOSE We aimed to evaluate whether morphologic magnetic resonance imaging (MRI) features could help to predict the maternal outcome after uterine artery embolization (UAE)-assisted cesarean section (CS) in patients with invasive placenta previa. METHODS We retrospectively reviewed the MRI data of 40 pregnant women who have undergone UAE-assisted cesarean section due to suspected high risk of massive hemorrhage caused by invasive placenta previa. Patients were divided into two groups based on the maternal outcome (good-outcome group: minor hemorrhage and uterus preserved; poor-outcome group: significant hemorrhage or emergency hysterectomy). Morphologic MRI features were compared between the two groups. Multivariate logistic regression analysis was used to identify the most valuable variables, and predictive value of the identified risk factor was determined. RESULTS Low signal intensity bands on T2-weighted imaging (P < 0.001), placenta percreta (P = 0.011), and placental cervical protrusion sign (P = 0.002) were more frequently observed in patients with poor outcome. Low signal intensity bands on T2-weighted imaging was the only significant predictor of poor maternal outcome in multivariate analysis (P = 0.020; odds ratio, 14.79), with 81.3% sensitivity and 84.3% specificity. CONCLUSION Low signal intensity bands on T2-weighted imaging might be a predictor of poor maternal outcome after UAE-assisted cesarean section in patients with invasive placenta previa. PMID:28345524

Estimation of T2* Relaxation Time of Breast Cancer: Correlation with Clinical, Imaging and Pathological Features

PubMed Central

Seo, Mirinae; Jahng, Geon-Ho; Sohn, Yu-Mee; Rhee, Sun Jung; Oh, Jang-Hoon; Won, Kyu-Yeoun

2017-01-01

Objective The purpose of this study was to estimate the T2* relaxation time in breast cancer, and to evaluate the association between the T2* value with clinical-imaging-pathological features of breast cancer. Materials and Methods Between January 2011 and July 2013, 107 consecutive women with 107 breast cancers underwent multi-echo T2*-weighted imaging on a 3T clinical magnetic resonance imaging system. The Student's t test and one-way analysis of variance were used to compare the T2* values of cancer for different groups, based on the clinical-imaging-pathological features. In addition, multiple linear regression analysis was performed to find independent predictive factors associated with the T2* values. Results Of the 107 breast cancers, 92 were invasive and 15 were ductal carcinoma in situ (DCIS). The mean T2* value of invasive cancers was significantly longer than that of DCIS (p = 0.029). Signal intensity on T2-weighted imaging (T2WI) and histologic grade of invasive breast cancers showed significant correlation with T2* relaxation time in univariate and multivariate analysis. Breast cancer groups with higher signal intensity on T2WI showed longer T2* relaxation time (p = 0.005). Cancer groups with higher histologic grade showed longer T2* relaxation time (p = 0.017). Conclusion The T2* value is significantly longer in invasive cancer than in DCIS. In invasive cancers, T2* relaxation time is significantly longer in higher histologic grades and high signal intensity on T2WI. Based on these preliminary data, quantitative T2* mapping has the potential to be useful in the characterization of breast cancer. PMID:28096732

SMART-COP: a tool for predicting the need for intensive respiratory or vasopressor support in community-acquired pneumonia.

PubMed

Charles, Patrick G P; Wolfe, Rory; Whitby, Michael; Fine, Michael J; Fuller, Andrew J; Stirling, Robert; Wright, Alistair A; Ramirez, Julio A; Christiansen, Keryn J; Waterer, Grant W; Pierce, Robert J; Armstrong, John G; Korman, Tony M; Holmes, Peter; Obrosky, D Scott; Peyrani, Paula; Johnson, Barbara; Hooy, Michelle; Grayson, M Lindsay

2008-08-01

Existing severity assessment tools, such as the pneumonia severity index (PSI) and CURB-65 (tool based on confusion, urea level, respiratory rate, blood pressure, and age >or=65 years), predict 30-day mortality in community-acquired pneumonia (CAP) and have limited ability to predict which patients will require intensive respiratory or vasopressor support (IRVS). The Australian CAP Study (ACAPS) was a prospective study of 882 episodes in which each patient had a detailed assessment of severity features, etiology, and treatment outcomes. Multivariate logistic regression was performed to identify features at initial assessment that were associated with receipt of IRVS. These results were converted into a simple points-based severity tool that was validated in 5 external databases, totaling 7464 patients. In ACAPS, 10.3% of patients received IRVS, and the 30-day mortality rate was 5.7%. The features statistically significantly associated with receipt of IRVS were low systolic blood pressure (2 points), multilobar chest radiography involvement (1 point), low albumin level (1 point), high respiratory rate (1 point), tachycardia (1 point), confusion (1 point), poor oxygenation (2 points), and low arterial pH (2 points): SMART-COP. A SMART-COP score of >or=3 points identified 92% of patients who received IRVS, including 84% of patients who did not need immediate admission to the intensive care unit. Accuracy was also high in the 5 validation databases. Sensitivities of PSI and CURB-65 for identifying the need for IRVS were 74% and 39%, respectively. SMART-COP is a simple, practical clinical tool for accurately predicting the need for IRVS that is likely to assist clinicians in determining CAP severity.

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

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

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties.

PubMed

Jacobs, Richard H A H; Haak, Koen V; Thumfart, Stefan; Renken, Remco; Henson, Brian; Cornelissen, Frans W

2016-01-01

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed-and presumably for this reason-the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities-including the aesthetic appreciation-are sufficiently universal to be predicted-with reasonable accuracy-based on the computed feature content of the textures.

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties

PubMed Central

Jacobs, Richard H. A. H.; Haak, Koen V.; Thumfart, Stefan; Renken, Remco; Henson, Brian; Cornelissen, Frans W.

2016-01-01

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed—and presumably for this reason—the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities—including the aesthetic appreciation—are sufficiently universal to be predicted—with reasonable accuracy—based on the computed feature content of the textures. PMID:27493628

Object-Based Arctic Sea Ice Feature Extraction through High Spatial Resolution Aerial photos

NASA Astrophysics Data System (ADS)

Miao, X.; Xie, H.

2015-12-01

High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice features, such as melt ponds, submerged ice, water, ice/snow, and pressure ridges, is time-consuming and labor-intensive. An object-based classification algorithm is developed to automatically extract sea ice features efficiently from aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the MIZ near the Alaska coast. The algorithm includes four steps: (1) the image segmentation groups the neighboring pixels into objects based on the similarity of spectral and textural information; (2) the random forest classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice), shadow, and ice/snow; (3) the polygon neighbor analysis separates melt ponds and submerged ice based on spatial relationship; and (4) pressure ridge features are extracted from shadow based on local illumination geometry. The producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection, and shadow shows a user's accuracy of 88.9% and producer's accuracies of 91.4%. Finally, pond density, pond fraction, ice floes, mean ice concentration, average ridge height, ridge profile, and ridge frequency are extracted from batch processing of aerial photos, and their uncertainties are estimated.

Land Covers Classification Based on Random Forest Method Using Features from Full-Waveform LIDAR Data

NASA Astrophysics Data System (ADS)

Ma, L.; Zhou, M.; Li, C.

2017-09-01

In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.

Geospatial Analytics in Retail Site Selection and Sales Prediction.

PubMed

Ting, Choo-Yee; Ho, Chiung Ching; Yee, Hui Jia; Matsah, Wan Razali

2018-03-01

Studies have shown that certain features from geography, demography, trade area, and environment can play a vital role in retail site selection, largely due to the impact they asserted on retail performance. Although the relevant features could be elicited by domain experts, determining the optimal feature set can be intractable and labor-intensive exercise. The challenges center around (1) how to determine features that are important to a particular retail business and (2) how to estimate retail sales performance given a new location? The challenges become apparent when the features vary across time. In this light, this study proposed a nonintervening approach by employing feature selection algorithms and subsequently sales prediction through similarity-based methods. The results of prediction were validated by domain experts. In this study, data sets from different sources were transformed and aggregated before an analytics data set that is ready for analysis purpose could be obtained. The data sets included data about feature location, population count, property type, education status, and monthly sales from 96 branches of a telecommunication company in Malaysia. The finding suggested that (1) optimal retail performance can only be achieved through fulfillment of specific location features together with the surrounding trade area characteristics and (2) similarity-based method can provide solution to retail sales prediction.

Transmission calculation and intensity suppression for a proton therapy system

NASA Astrophysics Data System (ADS)

Chen, Wei; Yang, Jun; Qin, Bin; Liang, ZhiKai; Chen, Qushan; Liu, Kaifeng; Li, Dong; Fan, Mingwu

2018-02-01

A proton therapy project HUST-PTF (HUST Proton Therapy Facility) based on a 250 MeV isochronous superconducting cyclotron is under development in Huazhong University of Science and Technology (HUST). In this paper we report the main design features of the beam line in HUST-PTF project. The energy selection system (ESS) for energy modulation is discussed in detail, including the collimators, momentum slit and transmission calculation. Due to significant difference among the transmissions of ESS for different energies, the intensity suppression scheme by defocusing beam at high energies on collimators in the beam line is proposed and discussed. Finally, the ratios of beam intensities between low and high energies are expected to be controlled within 10 to meet the clinical requirement, and the beam optics of each energy step after intensity suppression is studied respectively.

Processing of Signals from Fiber Bragg Gratings Using Unbalanced Interferometers

NASA Technical Reports Server (NTRS)

Adamovsky, Grigory; Juergens, Jeff; Floyd, Bertram

2005-01-01

Fiber Bragg gratings (FBG) have become preferred sensory structures in fiber optic sensing system. High sensitivity, embedability, and multiplexing capabilities make FBGs superior to other sensor configurations. The main feature of FBGs is that they respond in the wavelength domain with the wavelength of the returned signal as the indicator of the measured parameter. The wavelength is then converted to optical intensity by a photodetector to detect corresponding changes in intensity. This wavelength-to-intensity conversion is a crucial part in any FBG-based sensing system. Among the various types of wavelength-to-intensity converters, unbalanced interferometers are especially attractive because of their small weight and volume, lack of moving parts, easy integration, and good stability. In this paper we investigate the applicability of unbalanced interferometers to analyze signals reflected from Bragg gratings. Analytical and experimental data are presented.

Bayesian Analysis Of HMI Solar Image Observables And Comparison To TSI Variations And MWO Image Observables

NASA Astrophysics Data System (ADS)

Parker, D. G.; Ulrich, R. K.; Beck, J.

2014-12-01

We have previously applied the Bayesian automatic classification system AutoClass to solar magnetogram and intensity images from the 150 Foot Solar Tower at Mount Wilson to identify classes of solar surface features associated with variations in total solar irradiance (TSI) and, using those identifications, modeled TSI time series with improved accuracy (r > 0.96). (Ulrich, et al, 2010) AutoClass identifies classes by a two-step process in which it: (1) finds, without human supervision, a set of class definitions based on specified attributes of a sample of the image data pixels, such as magnetic field and intensity in the case of MWO images, and (2) applies the class definitions thus found to new data sets to identify automatically in them the classes found in the sample set. HMI high resolution images capture four observables-magnetic field, continuum intensity, line depth and line width-in contrast to MWO's two observables-magnetic field and intensity. In this study, we apply AutoClass to the HMI observables for images from May, 2010 to June, 2014 to identify solar surface feature classes. We use contemporaneous TSI measurements to determine whether and how variations in the HMI classes are related to TSI variations and compare the characteristic statistics of the HMI classes to those found from MWO images. We also attempt to derive scale factors between the HMI and MWO magnetic and intensity observables. The ability to categorize automatically surface features in the HMI images holds out the promise of consistent, relatively quick and manageable analysis of the large quantity of data available in these images. Given that the classes found in MWO images using AutoClass have been found to improve modeling of TSI, application of AutoClass to the more complex HMI images should enhance understanding of the physical processes at work in solar surface features and their implications for the solar-terrestrial environment. Ulrich, R.K., Parker, D, Bertello, L. and Boyden, J. 2010, Solar Phys. , 261 , 11.

The human footprint in the west: a large-scale analysis of anthropogenic impacts.

USGS Publications Warehouse

Leu, M.; Hanser, S.E.; Knick, S.T.

2008-01-01

Anthropogenic features such as urbanization, roads, and power lines, are increasing in western United States landscapes in response to rapidly growing human populations. However, their spatial effects have not been evaluated. Our goal was to model the human footprint across the western United States. We first delineated the actual area occupied by anthropogenic features, the physical effect area. Next, we developed the human footprint model based on the ecological effect area, the zone influenced by features beyond their physical presence, by combining seven input models: three models quantified top-down anthropogenic influences of synanthropic predators (avian predators, domestic dog and cat presence risk), and four models quantified bottom-up anthropogenic influences on habitat (invasion of exotic plants, human-caused fires, energy extraction, and anthropogenic wildland fragmentation). Using independent bird population data, we found bird abundance of four synanthropic species to correlate positively with human footprint intensity and negatively for three of the six species influenced by habitat fragmentation. We then evaluated the extent of the human footprint in relation to terrestrial (ecoregions) and aquatic systems (major rivers and lakes), regional management and conservation status, physical environment, and temporal changes in human actions. The physical effect area of anthropogenic features covered 13% of the western United States with agricultural land (9.8%) being most dominant. High-intensity human footprint areas (class 8–10) overlapped highly productive low-elevation private landholdings and covered 7% of the western United States compared to 48% for low-intensity areas (class 1–3), which were confined to low-productivity high-elevation federal landholdings. Areas within 1 km of rivers were more affected by the human footprint compared to lakes. Percentage human population growth was higher in low-intensity human footprint areas. The disproportional regional effects of the human footprint on landscapes in the western United States create a challenge to management of ecosystems and wildlife populations. Using footprint models, managers can plan land use actions, develop restoration scenarios, and identify areas of high conservation value at local landscapes within a regional context. Moreover, human footprint models serve as a tool to stratify landscapes for studies investigating floral and faunal response to human disturbance intensity gradients.

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

2017-04-01

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects.

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

PubMed

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

2016-01-01

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

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

PubMed

Tekwe, Carmen D; Carroll, Raymond J; Dabney, Alan R

2012-08-01

Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. ctekwe@stat.tamu.edu.

Auger Up-Conversion of Low-Intensity Infrared Light in Engineered Quantum Dots

DOE PAGES

Makarov, Nikolay S.; Lin, Qianglu; Pietryga, Jeffrey M.; ...

2016-11-29

One source of efficiency losses in photovoltaic cells is their transparency toward solar photons with energies below the band gap of the absorbing layer. This loss can be reduced using a process of up-conversion whereby two or more sub-band-gap photons generate a single above-gap exciton. Traditional approaches to up-conversion, such as nonlinear two-photon absorption (2PA) or triplet fusion, suffer from low efficiency at solar light intensities, a narrow absorption bandwidth, nonoptimal absorption energies, and difficulties for implementing in practical devices. We show that these deficiencies can be alleviated using the effect of Auger up-conversion in thick-shell PbSe/CdSe quantum dots. Thismore » process relies on Auger recombination whereby two low-energy, core-based excitons are converted into a single higher-energy, shell-based exciton. When compared to their monocomponent counterparts, the tailored PbSe/CdSe heterostructures feature enhanced absorption cross-sections, a higher efficiency of the “productive” Auger pathway involving re-excitation of a hole, and longer lifetimes of both core- and shell-localized excitons. These features lead to effective up-conversion cross-sections that are more than 6 orders of magnitude higher than for standard nonlinear 2PA, which allows for efficient up-conversion of continuous wave infrared light at intensities as low as a few watts per square centimeter.« less

Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition.

PubMed

Wang, Kun-Ching

2015-01-14

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer

NASA Astrophysics Data System (ADS)

Hao, Hongxia; Zhou, Zhiguo; Li, Shulong; Maquilan, Genevieve; Folkert, Michael R.; Iyengar, Puneeth; Westover, Kenneth D.; Albuquerque, Kevin; Liu, Fang; Choy, Hak; Timmerman, Robert; Yang, Lin; Wang, Jing

2018-05-01

Distant failure is the main cause of human cancer-related mortalities. To develop a model for predicting distant failure in non-small cell lung cancer (NSCLC) and cervix cancer (CC) patients, a shell feature, consisting of outer voxels around the tumor boundary, was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The utility of the shell was evaluated by the support vector machine classifier in comparison with intensity, geometry, gray level co-occurrence matrix-based texture, neighborhood gray tone difference matrix-based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and AUC. Collectively, the shell feature showed better predictive performance than all the other features for distant failure prediction in both NSCLC and CC cohorts.

Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI.

PubMed

Ahmed, Shaheen; Iftekharuddin, Khan M; Vossough, Arastoo

2011-03-01

Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.

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

PubMed

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

2009-08-04

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

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

NASA Astrophysics Data System (ADS)

Chirra, Prathyush; Leo, Patrick; Yim, Michael; Bloch, B. Nicolas; Rastinehad, Ardeshir R.; Purysko, Andrei; Rosen, Mark; Madabhushi, Anant; Viswanath, Satish

2018-02-01

The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.

Features in visual search combine linearly

PubMed Central

Pramod, R. T.; Arun, S. P.

2014-01-01

Single features such as line orientation and length are known to guide visual search, but relatively little is known about how multiple features combine in search. To address this question, we investigated how search for targets differing in multiple features (intensity, length, orientation) from the distracters is related to searches for targets differing in each of the individual features. We tested race models (based on reaction times) and co-activation models (based on reciprocal of reaction times) for their ability to predict multiple feature searches. Multiple feature searches were best accounted for by a co-activation model in which feature information combined linearly (r = 0.95). This result agrees with the classic finding that these features are separable i.e., subjective dissimilarity ratings sum linearly. We then replicated the classical finding that the length and width of a rectangle are integral features—in other words, they combine nonlinearly in visual search. However, to our surprise, upon including aspect ratio as an additional feature, length and width combined linearly and this model outperformed all other models. Thus, length and width of a rectangle became separable when considered together with aspect ratio. This finding predicts that searches involving shapes with identical aspect ratio should be more difficult than searches where shapes differ in aspect ratio. We confirmed this prediction on a variety of shapes. We conclude that features in visual search co-activate linearly and demonstrate for the first time that aspect ratio is a novel feature that guides visual search. PMID:24715328

The first TDDFT and MCD studies of free base triarylcorroles: a closer look into solvent-dependent UV-visible absorption.

PubMed

Ziegler, Christopher J; Sabin, Jared R; Geier, G Richard; Nemykin, Victor N

2012-05-16

Absorption spectra of several free base triarylcorroles were investigated by MCD spectroscopy. The MCD spectra exhibit unusual sign-reverse (positive-to-negative intensities in ascending energy) features in the Soret- and Q-type band regions, suggesting a rare ΔHOMO < ΔLUMO relationship between π and π* MOs in the corrole core. This journal is © The Royal Society of Chemistry 2012

Investigating the Effects of the Interaction Intensity in a Weak Measurement.

PubMed

Piacentini, Fabrizio; Avella, Alessio; Gramegna, Marco; Lussana, Rudi; Villa, Federica; Tosi, Alberto; Brida, Giorgio; Degiovanni, Ivo Pietro; Genovese, Marco

2018-05-03

Measurements are crucial in quantum mechanics, for fundamental research as well as for applicative fields like quantum metrology, quantum-enhanced measurements and other quantum technologies. In the recent years, weak-interaction-based protocols like Weak Measurements and Protective Measurements have been experimentally realized, showing peculiar features leading to surprising advantages in several different applications. In this work we analyze the validity range for such measurement protocols, that is, how the interaction strength affects the weak value extraction, by measuring different polarization weak values on heralded single photons. We show that, even in the weak interaction regime, the coupling intensity limits the range of weak values achievable, setting a threshold on the signal amplification effect exploited in many weak measurement based experiments.

TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

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

Krafft, S; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Briere, T

2015-06-15

Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. Amore » total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP modeling is warranted. This work was supported by the Rosalie B. Hite Fellowship in Cancer research awarded to SPK.« less

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

Raman spectral feature selection using ant colony optimization for breast cancer diagnosis.

PubMed

Fallahzadeh, Omid; Dehghani-Bidgoli, Zohreh; Assarian, Mohammad

2018-06-04

Pathology as a common diagnostic test of cancer is an invasive, time-consuming, and partially subjective method. Therefore, optical techniques, especially Raman spectroscopy, have attracted the attention of cancer diagnosis researchers. However, as Raman spectra contain numerous peaks involved in molecular bounds of the sample, finding the best features related to cancerous changes can improve the accuracy of diagnosis in this method. The present research attempted to improve the power of Raman-based cancer diagnosis by finding the best Raman features using the ACO algorithm. In the present research, 49 spectra were measured from normal, benign, and cancerous breast tissue samples using a 785-nm micro-Raman system. After preprocessing for removal of noise and background fluorescence, the intensity of 12 important Raman bands of the biological samples was extracted as features of each spectrum. Then, the ACO algorithm was applied to find the optimum features for diagnosis. As the results demonstrated, by selecting five features, the classification accuracy of the normal, benign, and cancerous groups increased by 14% and reached 87.7%. ACO feature selection can improve the diagnostic accuracy of Raman-based diagnostic models. In the present study, features corresponding to ν(C-C) αhelix proline, valine (910-940), νs(C-C) skeletal lipids (1110-1130), and δ(CH2)/δ(CH3) proteins (1445-1460) were selected as the best features in cancer diagnosis.

Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods

PubMed Central

Chasin, Rachel; Rumshisky, Anna; Uzuner, Ozlem; Szolovits, Peter

2014-01-01

Objective To evaluate state-of-the-art unsupervised methods on the word sense disambiguation (WSD) task in the clinical domain. In particular, to compare graph-based approaches relying on a clinical knowledge base with bottom-up topic-modeling-based approaches. We investigate several enhancements to the topic-modeling techniques that use domain-specific knowledge sources. Materials and methods The graph-based methods use variations of PageRank and distance-based similarity metrics, operating over the Unified Medical Language System (UMLS). Topic-modeling methods use unlabeled data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database to derive models for each ambiguous word. We investigate the impact of using different linguistic features for topic models, including UMLS-based and syntactic features. We use a sense-tagged clinical dataset from the Mayo Clinic for evaluation. Results The topic-modeling methods achieve 66.9% accuracy on a subset of the Mayo Clinic's data, while the graph-based methods only reach the 40–50% range, with a most-frequent-sense baseline of 56.5%. Features derived from the UMLS semantic type and concept hierarchies do not produce a gain over bag-of-words features in the topic models, but identifying phrases from UMLS and using syntax does help. Discussion Although topic models outperform graph-based methods, semantic features derived from the UMLS prove too noisy to improve performance beyond bag-of-words. Conclusions Topic modeling for WSD provides superior results in the clinical domain; however, integration of knowledge remains to be effectively exploited. PMID:24441986

Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

PubMed

Choi, Hyungwon; Kim, Sinae; Fermin, Damian; Tsou, Chih-Chiang; Nesvizhskii, Alexey I

2015-11-03

We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter. QPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories. This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015 Elsevier B.V. All rights reserved.

Unsupervised segmentation of lungs from chest radiographs

NASA Astrophysics Data System (ADS)

Ghosh, Payel; Antani, Sameer K.; Long, L. Rodney; Thoma, George R.

2012-03-01

This paper describes our preliminary investigations for deriving and characterizing coarse-level textural regions present in the lung field on chest radiographs using unsupervised grow-cut (UGC), a cellular automaton based unsupervised segmentation technique. The segmentation has been performed on a publicly available data set of chest radiographs. The algorithm is useful for this application because it automatically converges to a natural segmentation of the image from random seed points using low-level image features such as pixel intensity values and texture features. Our goal is to develop a portable screening system for early detection of lung diseases for use in remote areas in developing countries. This involves developing automated algorithms for screening x-rays as normal/abnormal with a high degree of sensitivity, and identifying lung disease patterns on chest x-rays. Automatically deriving and quantitatively characterizing abnormal regions present in the lung field is the first step toward this goal. Therefore, region-based features such as geometrical and pixel-value measurements were derived from the segmented lung fields. In the future, feature selection and classification will be performed to identify pathological conditions such as pulmonary tuberculosis on chest radiographs. Shape-based features will also be incorporated to account for occlusions of the lung field and by other anatomical structures such as the heart and diaphragm.

"Radio-oncomics" : The potential of radiomics in radiation oncology.

PubMed

Peeken, Jan Caspar; Nüsslin, Fridtjof; Combs, Stephanie E

2017-10-01

Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

3D Riesz-wavelet based Covariance descriptors for texture classification of lung nodule tissue in CT.

PubMed

Cirujeda, Pol; Muller, Henning; Rubin, Daniel; Aguilera, Todd A; Loo, Billy W; Diehn, Maximilian; Binefa, Xavier; Depeursinge, Adrien

2015-01-01

In this paper we present a novel technique for characterizing and classifying 3D textured volumes belonging to different lung tissue types in 3D CT images. We build a volume-based 3D descriptor, robust to changes of size, rigid spatial transformations and texture variability, thanks to the integration of Riesz-wavelet features within a Covariance-based descriptor formulation. 3D Riesz features characterize the morphology of tissue density due to their response to changes in intensity in CT images. These features are encoded in a Covariance-based descriptor formulation: this provides a compact and flexible representation thanks to the use of feature variations rather than dense features themselves and adds robustness to spatial changes. Furthermore, the particular symmetric definite positive matrix form of these descriptors causes them to lay in a Riemannian manifold. Thus, descriptors can be compared with analytical measures, and accurate techniques from machine learning and clustering can be adapted to their spatial domain. Additionally we present a classification model following a "Bag of Covariance Descriptors" paradigm in order to distinguish three different nodule tissue types in CT: solid, ground-glass opacity, and healthy lung. The method is evaluated on top of an acquired dataset of 95 patients with manually delineated ground truth by radiation oncology specialists in 3D, and quantitative sensitivity and specificity values are presented.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases.

PubMed

Ferreira Junior, José Raniery; Koenigkam-Santos, Marcel; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic; Azevedo-Marques, Paulo Mazzoncini de

2018-06-01

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning. Copyright © 2018 Elsevier B.V. All rights reserved.

[MRI findings of renal cell carcinoma associated with Xp11.2 translocations/TFE3 gene fusions].

PubMed

Zhong, Y; Wang, H Y; Chen, X; Guo, A T; Ma, L; Wang, Y W; Ye, H Y

2016-09-06

Objective: To analyze MRI findings of renal cell carcinoma associated with Xp11.2 translocation-TFE gene fusion(Xp11.2 RCC). Methods: MR imaging features of eleven patients with pathologically-proved Xp11.2 RCC were retrospectively analyzed from December 2008 to December 2015. The following MRI features of the lesions were analyzed in the study: location, maximal diameter, signal intensity, hemorrhage, necrosis, cystic change, enhancement features and metastasis. The data was analyzed by using t test. Results: Four men and seven women (mean age, 35.2 years; age range, 15-49 years) were included. Tumors occurred in the right kidney in 5 cases and the left kidney in 6 cases. On T 1 WI tumors showed heterogeneously hypo-intensity and iso-intensity, hyper-intensity in 10 cases, 1 cases, respectively. On T 2 WI tumors showed heterogeneously slight hypo-intensity, heterogeneously slight hyper-intensity and hyper-intensity in 6 cases, 4 cases, 1 case, respectively. On DWI tumors showed hyper-intensity and heterogeneously slight hype-intensity in 2 cases, 9 cases, respectively. ADC value of the tumors were statistically significant lower than that of renal cortex(×10 -3 mm 2 /s)(1.35±0.20 vs 2.09±0.11, P <0.05). Imaging findings were suggestive of hemorrhage( n =4) or necrosis ( n =1) or cystic change ( n =6) or lipid( n =1) in the tumors. On dynamic contrast-enhanced imaging, tumors showed lower signal intensity change (96%±93%, 110%±86% and 103%±46%, respectively) than did renal cortex (285%±109%, 254%±97% and 225%±90%, respectively) ( P <0.05). Tumor capsule showed in 7 cases. Enlarged lymph node was found in renal hilum in one case. Conclusion: MRI findings may show characteristic features of Xp11.2 RCC combined with patients' age and assist in preoperative correct diagnosis.

Machine learning to analyze images of shocked materials for precise and accurate measurements

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

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast imagesmore » of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.« less

An estimation methode for measurement of ultraviolet radiation during nondestructive testing

NASA Astrophysics Data System (ADS)

Hosseinipanah, M.; Movafeghi, A.; Farvadin, D.

2018-04-01

Dye penetrant testing and magnetic particle testing are among conventional NDT methods. For increased sensitivity, fluorescence dyes and particles can be used with ultraviolet (black) lights. UV flaw detection lights have different spectra. With the help of photo-filters, the output lights are transferred to UV-A and visible zones. UV-A light can be harmful to human eyes in some conditions. In this research, UV intensity and spectrum were obtained by a Radio-spectrometer for two different UV flaw detector lighting systems. According to the standards such as ASTM E709, UV intensity must be at least 10 W/m2 at a distance of 30 cm. Based on our measurements; these features not achieved in some lamps. On the other hand, intensity and effective intensity of UV lights must be below the some limits for prevention of unprotected eye damage. NDT centers are usually using some type of UV measuring devices. A method for the estimation of effective intensity of UV light has been proposed in this research.

The dynamical link between deep Atlantic extratropical cyclones and intense Mediterranean cyclones

NASA Astrophysics Data System (ADS)

Raveh-Rubin, Shira; Flaounas, Emmanouil

2017-04-01

Breaking of atmospheric Rossby waves has been previously shown to lead to intense Mediterranean cyclones, one of the most prominent environmental risks in the region. Wave breaking may be enhanced by warm conveyor belts (WCBs) associated with extratropical cyclones developing over the Atlantic Ocean. More precisely, WCBs supply the upper troposphere with air masses of low potential vorticity that, in turn, amplify ridges and thus favor Rossby wave breaking. This study identifies and validates the relevance of the mechanism that connects Atlantic cyclones and intense mature Mediterranean cyclones through ridge amplification by WCBs. Using ECMWF ERA-Interim reanalyses and a feature-based approach, we analyze the 200 most intense Mediterranean cyclones for the years 1989-2008 and show that their majority (181 cases) is indeed associated with this mechanism upstream. Results show that multiple Atlantic cyclones are associated with each case of intense Mediterranean cyclone downstream. Moreover, the associated Atlantic cyclones are particularly deep compared to climatology.

SVM based colon polyps classifier in a wireless active stereo endoscope.

PubMed

Ayoub, J; Granado, B; Mhanna, Y; Romain, O

2010-01-01

This work focuses on the recognition of three-dimensional colon polyps captured by an active stereo vision sensor. The detection algorithm consists of SVM classifier trained on robust feature descriptors. The study is related to Cyclope, this prototype sensor allows real time 3D object reconstruction and continues to be optimized technically to improve its classification task by differentiation between hyperplastic and adenomatous polyps. Experimental results were encouraging and show correct classification rate of approximately 97%. The work contains detailed statistics about the detection rate and the computing complexity. Inspired by intensity histogram, the work shows a new approach that extracts a set of features based on depth histogram and combines stereo measurement with SVM classifiers to correctly classify benign and malignant polyps.

Classification of melanoma lesions using sparse coded features and random forests

NASA Astrophysics Data System (ADS)

Rastgoo, Mojdeh; Lemaître, Guillaume; Morel, Olivier; Massich, Joan; Garcia, Rafael; Meriaudeau, Fabrice; Marzani, Franck; Sidibé, Désiré

2016-03-01

Malignant melanoma is the most dangerous type of skin cancer, yet it is the most treatable kind of cancer, conditioned by its early diagnosis which is a challenging task for clinicians and dermatologists. In this regard, CAD systems based on machine learning and image processing techniques are developed to differentiate melanoma lesions from benign and dysplastic nevi using dermoscopic images. Generally, these frameworks are composed of sequential processes: pre-processing, segmentation, and classification. This architecture faces mainly two challenges: (i) each process is complex with the need to tune a set of parameters, and is specific to a given dataset; (ii) the performance of each process depends on the previous one, and the errors are accumulated throughout the framework. In this paper, we propose a framework for melanoma classification based on sparse coding which does not rely on any pre-processing or lesion segmentation. Our framework uses Random Forests classifier and sparse representation of three features: SIFT, Hue and Opponent angle histograms, and RGB intensities. The experiments are carried out on the public PH2 dataset using a 10-fold cross-validation. The results show that SIFT sparse-coded feature achieves the highest performance with sensitivity and specificity of 100% and 90.3% respectively, with a dictionary size of 800 atoms and a sparsity level of 2. Furthermore, the descriptor based on RGB intensities achieves similar results with sensitivity and specificity of 100% and 71.3%, respectively for a smaller dictionary size of 100 atoms. In conclusion, dictionary learning techniques encode strong structures of dermoscopic images and provide discriminant descriptors.

Prediction of treatment response and metastatic disease in soft tissue sarcoma

NASA Astrophysics Data System (ADS)

Farhidzadeh, Hamidreza; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Raghavan, Meera.; Gatenby, Robert A.

2014-03-01

Soft tissue sarcomas (STS) are a heterogenous group of malignant tumors comprised of more than 50 histologic subtypes. Based on spatial variations of the tumor, predictions of the development of necrosis in response to therapy as well as eventual progression to metastatic disease are made. Optimization of treatment, as well as management of therapy-related side effects, may be improved using progression information earlier in the course of therapy. Multimodality pre- and post-gadolinium enhanced magnetic resonance images (MRI) were taken before and after treatment for 30 patients. Regional variations in the tumor bed were measured quantitatively. The voxel values from the tumor region were used as features and a fuzzy clustering algorithm was used to segment the tumor into three spatial regions. The regions were given labels of high, intermediate and low based on the average signal intensity of pixels from the post-contrast T1 modality. These spatially distinct regions were viewed as essential meta-features to predict the response of the tumor to therapy based on necrosis (dead tissue in tumor bed) and metastatic disease (spread of tumor to sites other than primary). The best feature was the difference in the number of pixels in the highest intensity regions of tumors before and after treatment. This enabled prediction of patients with metastatic disease and lack of positive treatment response (i.e. less necrosis). The best accuracy, 73.33%, was achieved by a Support Vector Machine in a leave-one-out cross validation on 30 cases predicting necrosis < 90% post treatment and metastasis.

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

NASA Astrophysics Data System (ADS)

Yang, Xue; Sun, Hao; Fu, Kun; Yang, Jirui; Sun, Xian; Yan, Menglong; Guo, Zhi

2018-01-01

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.

Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach

NASA Astrophysics Data System (ADS)

Wang, Chuang; Udupa, Jayaram K.; Tong, Yubing; Chen, Jerry; Venigalla, Sriram; Odhner, Dewey; Guzzo, Thomas J.; Christodouleas, John; Torigian, Drew A.

2018-02-01

Magnetic resonance imaging (MRI) is often used in clinical practice to stage patients with bladder cancer to help plan treatment. However, qualitative assessment of MR images is prone to inaccuracies, adversely affecting patient outcomes. In this paper, T2-weighted MR image-based quantitative features were extracted from the bladder wall in 65 patients with bladder cancer to classify them into two primary tumor (T) stage groups: group 1 - T stage < T2, with primary tumor locally confined to the bladder, and group 2 - T stage < T2, with primary tumor locally extending beyond the bladder. The bladder was divided into 8 sectors in the axial plane, where each sector has a corresponding reference standard T stage that is based on expert radiology qualitative MR image review and histopathologic results. The performance of the classification for correct assignment of T stage grouping was then evaluated at both the patient level and the sector level. Each bladder sector was divided into 3 shells (inner, middle, and outer), and 15,834 features including intensity features and texture features from local binary pattern and gray-level co-occurrence matrix were extracted from the 3 shells of each sector. An optimal feature set was selected from all features using an optimal biomarker approach. Nine optimal biomarker features were derived based on texture properties from the middle shell, with an area under the ROC curve of AUC value at the sector and patient level of 0.813 and 0.806, respectively.

Facial emotion recognition and borderline personality pathology.

PubMed

Meehan, Kevin B; De Panfilis, Chiara; Cain, Nicole M; Antonucci, Camilla; Soliani, Antonio; Clarkin, John F; Sambataro, Fabio

2017-09-01

The impact of borderline personality pathology on facial emotion recognition has been in dispute; with impaired, comparable, and enhanced accuracy found in high borderline personality groups. Discrepancies are likely driven by variations in facial emotion recognition tasks across studies (stimuli type/intensity) and heterogeneity in borderline personality pathology. This study evaluates facial emotion recognition for neutral and negative emotions (fear/sadness/disgust/anger) presented at varying intensities. Effortful control was evaluated as a moderator of facial emotion recognition in borderline personality. Non-clinical multicultural undergraduates (n = 132) completed a morphed facial emotion recognition task of neutral and negative emotional expressions across different intensities (100% Neutral; 25%/50%/75% Emotion) and self-reported borderline personality features and effortful control. Greater borderline personality features related to decreased accuracy in detecting neutral faces, but increased accuracy in detecting negative emotion faces, particularly at low-intensity thresholds. This pattern was moderated by effortful control; for individuals with low but not high effortful control, greater borderline personality features related to misattributions of emotion to neutral expressions, and enhanced detection of low-intensity emotional expressions. Individuals with high borderline personality features may therefore exhibit a bias toward detecting negative emotions that are not or barely present; however, good self-regulatory skills may protect against this potential social-cognitive vulnerability. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Efficient and robust model-to-image alignment using 3D scale-invariant features.

PubMed

Toews, Matthew; Wells, William M

2013-04-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

PubMed Central

Toews, Matthew; Wells, William M.

2013-01-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. PMID:23265799

Spatial variations of the 3 micron emission features within UV-excited nebulae - Photochemical evolution of interstellar polycyclic aromatic hydrocarbons

NASA Technical Reports Server (NTRS)

Geballe, T. R.; Tielens, A. G. G. M.; Allamandola, L. J.; Moorhouse, A.; Brand, P. W. J. L.

1989-01-01

Spectra at 3 microns have been obtained at several positions in the Orion Bar region and in the nebula surrounding HD 44179. Weak emission features at 3.40, 3.46, 3.51, and 3.57 microns are prominent in the Orion Bar region. The 3.40- and 3.51-micron features increase in intensity relative to the dominant 3.29-micron feature. The spectrum obtained in the Red Rectangle region 5 arcsecs north of HD 44179 are similar to those in the Orion Bar, with a weak, broad 3.40-micron feature at the position of HD 44179. The spatial behavior of the weak emission features is explained in terms of hot bands of the CH stretch and overtones, and combination bands of other fundamental vibrations in simple PAHs. Based on the susceptibility of PAHs to destruction by the far UV fields in both regions, PAH sizes are estimated at 20-50 carbon atoms.

Facial feature tracking: a psychophysiological measure to assess exercise intensity?

PubMed

Miles, Kathleen H; Clark, Bradley; Périard, Julien D; Goecke, Roland; Thompson, Kevin G

2018-04-01

The primary aim of this study was to determine whether facial feature tracking reliably measures changes in facial movement across varying exercise intensities. Fifteen cyclists completed three, incremental intensity, cycling trials to exhaustion while their faces were recorded with video cameras. Facial feature tracking was found to be a moderately reliable measure of facial movement during incremental intensity cycling (intra-class correlation coefficient = 0.65-0.68). Facial movement (whole face (WF), upper face (UF), lower face (LF) and head movement (HM)) increased with exercise intensity, from lactate threshold one (LT1) until attainment of maximal aerobic power (MAP) (WF 3464 ± 3364mm, P < 0.005; UF 1961 ± 1779mm, P = 0.002; LF 1608 ± 1404mm, P = 0.002; HM 849 ± 642mm, P < 0.001). UF movement was greater than LF movement at all exercise intensities (UF minus LF at: LT1, 1048 ± 383mm; LT2, 1208 ± 611mm; MAP, 1401 ± 712mm; P < 0.001). Significant medium to large non-linear relationships were found between facial movement and power output (r 2  = 0.24-0.31), HR (r 2  = 0.26-0.33), [La - ] (r 2  = 0.33-0.44) and RPE (r 2  = 0.38-0.45). The findings demonstrate the potential utility of facial feature tracking as a non-invasive, psychophysiological measure to potentially assess exercise intensity.

Clustering and classification of infrasonic events at Mount Etna using pattern recognition techniques

NASA Astrophysics Data System (ADS)

Cannata, A.; Montalto, P.; Aliotta, M.; Cassisi, C.; Pulvirenti, A.; Privitera, E.; Patanè, D.

2011-04-01

Active volcanoes generate sonic and infrasonic signals, whose investigation provides useful information for both monitoring purposes and the study of the dynamics of explosive phenomena. At Mt. Etna volcano (Italy), a pattern recognition system based on infrasonic waveform features has been developed. First, by a parametric power spectrum method, the features describing and characterizing the infrasound events were extracted: peak frequency and quality factor. Then, together with the peak-to-peak amplitude, these features constituted a 3-D ‘feature space’; by Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) three clusters were recognized inside it. After the clustering process, by using a common location method (semblance method) and additional volcanological information concerning the intensity of the explosive activity, we were able to associate each cluster to a particular source vent and/or a kind of volcanic activity. Finally, for automatic event location, clusters were used to train a model based on Support Vector Machine, calculating optimal hyperplanes able to maximize the margins of separation among the clusters. After the training phase this system automatically allows recognizing the active vent with no location algorithm and by using only a single station.

Quantitative evaluation of high-resolution features in images of negatively stained Tobacco Mosaic Virus.

PubMed

Chang, C F; Williams, R C; Grano, D A; Downing, K H; Glaeser, R M

1983-01-01

This study investigates the causes of the apparent differences between the optical diffraction pattern of a micrograph of a Tobacco Mosaic Virus (TMV) particle, the optical diffraction pattern of a ten-fold photographically averaged image, and the computed diffraction pattern of the original micrograph. Peak intensities along the layer lines in the transform of the averaged image appear to be quite unlike those in the diffraction pattern of the original micrograph, and the diffraction intensities for the averaged image extend to unexpectedly high resolution. A carefully controlled, quantitative comparison reveals, however, that the optical diffraction pattern of the original micrograph and that of the ten-fold averaged image are essentially equivalent. Using computer-based image processing, we discovered that the peak intensities on the 6th layer line have values very similar in magnitude to the neighboring noise, in contrast to what was expected from the optical diffraction pattern of the original micrograph. This discrepancy was resolved by recording a series of optical diffraction patterns when the original micrograph was immersed in oil. These patterns revealed the presence of a substantial phase grating effect, which exaggerated the peak intensities on the 6th layer line, causing an erroneous impression that the high resolution features possessed a good signal-to-noise ratio. This study thus reveals some pitfalls and misleading results that can be encountered when using optical diffraction patterns to evaluate image quality.

2-D multiline spectroscopy of the solar photosphere

NASA Astrophysics Data System (ADS)

Berrilli, F.; Consolini, G.; Pietropaolo, E.; Caccin, B.; Penza, V.; Lepreti, F.

2002-01-01

The structure and dynamics of the photosphere are investigated, with time series of broadband and monochromatic images of quiet granulation, at the solar disk center. Images were acquired with the IPM observing mode at the THEMIS telescope. Velocity and line center intensity fields, derived from the observation of three different photospheric lines, are used to study velocity and intensity patterns at different heights in the photosphere. Automatic segmentation procedures are applied to velocity and intensity frames to extract solar features, and to investigate the dependence of their properties at different scales and heights. We find a dependence of the statistical properties of upflow and downflow regions on the atmospheric height. Larger granules, passing through a great part of the photosphere, are used to investigate the damping of convective motions in stably stratified layers. The results suggest the occurrence of an intense braking in the deep photosphere (first ~ 120 km). Furthermore, we investigate the temporal and spatial evolution of velocity fields, deriving typical time scales of dynamical processes relative to different solar features. In particular, for two selected isolated exploders, we reveal a velocity deceleration in the central region since the early phase of their fragmentation. Based on observations made with THEMIS-CNRS/INSU-CNR operated on the island of Tenerife by THEMIS S.L. in the Spanish Observatorio del Teide of the Instituto de Astrofisica de Canarias.

Estimation of glacier surface motion by robust phase correlation and point like features of SAR intensity images

NASA Astrophysics Data System (ADS)

Fang, Li; Xu, Yusheng; Yao, Wei; Stilla, Uwe

2016-11-01

For monitoring of glacier surface motion in pole and alpine areas, radar remote sensing is becoming a popular technology accounting for its specific advantages of being independent of weather conditions and sunlight. In this paper we propose a method for glacier surface motion monitoring using phase correlation (PC) based on point-like features (PLF). We carry out experiments using repeat-pass TerraSAR X-band (TSX) and Sentinel-1 C-band (S1C) intensity images of the Taku glacier in Juneau icefield located in southeast Alaska. The intensity imagery is first filtered by an improved adaptive refined Lee filter while the effect of topographic reliefs is removed via SRTM-X DEM. Then, a robust phase correlation algorithm based on singular value decomposition (SVD) and an improved random sample consensus (RANSAC) algorithm is applied to sequential PLF pairs generated by correlation using a 2D sinc function template. The approaches for glacier monitoring are validated by both simulated SAR data and real SAR data from two satellites. The results obtained from these three test datasets confirm the superiority of the proposed approach compared to standard correlation-like methods. By the use of the proposed adaptive refined Lee filter, we achieve a good balance between the suppression of noise and the preservation of local image textures. The presented phase correlation algorithm shows the accuracy of better than 0.25 pixels, when conducting matching tests using simulated SAR intensity images with strong noise. Quantitative 3D motions and velocities of the investigated Taku glacier during a repeat-pass period are obtained, which allows a comprehensive and reliable analysis for the investigation of large-scale glacier surface dynamics.

Characterizing tobacco control mass media campaigns in England

PubMed Central

Langley, Tessa; Lewis, Sarah; McNeill, Ann; Gilmore, Anna; Szatkowski, Lisa; West, Robert; Sims, Michelle

2013-01-01

Aims To characterize publically funded tobacco control campaigns in England between 2004 and 2010 and to explore if they were in line with recommendations from the literature in terms of their content and intensity. International evidence suggests that campaigns which warn of the negative consequences of smoking and feature testimonials from real-life smokers are most effective, and that four exposures per head per month are required to reduce smoking prevalence. Design Characterization of tobacco control advertisements using a theoretically based framework designed to describe advertisement themes, informational and emotional content and style. Study of the intensity of advertising and exposure to different types of advertisement using data on population-level exposure to advertisements shown during the study period. Setting England. Measurements Television Ratings (TVRs), a standard measure of advertising exposure, were used to calculate exposure to each different campaign type. Findings A total of 89% of advertising was for smoking cessation; half of this advertising warned of the negative consequences of smoking, while half contained how-to-quit messages. Acted scenes featured in 72% of advertising, while only 17% featured real-life testimonials. Only 39% of months had at least four exposures to tobacco control campaigns per head. Conclusions A theory-driven approach enabled a systematic characterization of tobacco control advertisements in England. Between 2004 and 2010 only a small proportion of tobacco control advertisements utilized the most effective strategies—negative health effects messages and testimonials from real-life smokers. The intensity of campaigns was lower than international recommendations. PMID:23834209

Automatic analysis and quantification of fluorescently labeled synapses in microscope images

NASA Astrophysics Data System (ADS)

Yona, Shai; Katsman, Alex; Orenbuch, Ayelet; Gitler, Daniel; Yitzhaky, Yitzhak

2011-09-01

The purpose of this work is to classify and quantify synapses and their properties in the cultures of a mouse's hippocampus, from images acquired by a fluorescent microscope. Quantification features include the number of synapses, their intensity and their size characteristics. The images obtained by the microscope contain hundreds to several thousands of synapses with various elliptic-like shape features and intensities. These images also include other features such as glia cells and other biological objects beyond the focus plane; those features reduce the visibility of the synapses and interrupt the segmentation process. The proposed method comprises several steps, including background subtraction, identification of suspected centers of synapses as local maxima of small neighborhoods, evaluation of the tendency of objects to be synapses according to intensity properties at their larger neighborhoods, classification of detected synapses into categories as bulks or single synapses and finally, delimiting the borders of each synapse.

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

Altazi, B; Fernandez, D; Zhang, G

Purpose: Radiomics have shown potential for predicting treatment outcomes in several body sites. This study investigated the correlation between PET Radiomics features and treatment response of cervical cancer outcomes. Methods: our dataset consisted of a cohort of 79 patients diagnosed with cervical cancer, FIGO stage IB-IVA, age range 25–86 years, (median age at diagnosis: 50 years) all treated between: 2009–14 with external beam radiation therapy to a dose range between: 45–50.4 Gy (median= 45 Gy), concurrent cisplatin chemotherapy and MRI-based brachytherapy to a dose of 20–30 Gy (median= 28 Gy). Metabolic Tumor Volume (MTV) in patient’s primary site was delineatedmore » on pretreatment PET/CT by two board certified Radiation Oncologists. The features extracted from each patient’s volume were: 26 Co-occurrence matrix (COM) Feature, 11 Run-Length Matrix (RLM), 11 Gray Level Size Zone Matrix (GLSZM) and 33 Intensity-based features (IBF). The treatment outcome was divided based on the last follow up status into three classes: No Evidence of Disease (NED), Alive with Disease (AWD) and Dead of Disease (DOD). The ability for the radiomics features to differentiate between the 3 treatments outcome categories were assessed by One-Way ANOVA test with p-value < 0.05 was to be statistically significant. The results from the analysis were compared with the ones obtained previously for standard Uptake Value (SUV). Results: Based on patients last clinical follow-up; 52 showed NED, 17 AWD and 10 DOD. Radiomics Features were able to classify the patients based on their treatment response. A parallel analysis was done for SUV measurements for comparison. Conclusion: Radiomics features were able to differentiate between the three different classes of treatment outcomes. However, most of the features were only able to differentiate between NED and DOD class. Also, The ability or radiomics features to differentiate types of response were more significant than SUV.« less

Visual saliency in MPEG-4 AVC video stream

NASA Astrophysics Data System (ADS)

Ammar, M.; Mitrea, M.; Hasnaoui, M.; Le Callet, P.

2015-03-01

Visual saliency maps already proved their efficiency in a large variety of image/video communication application fields, covering from selective compression and channel coding to watermarking. Such saliency maps are generally based on different visual characteristics (like color, intensity, orientation, motion,…) computed from the pixel representation of the visual content. This paper resumes and extends our previous work devoted to the definition of a saliency map solely extracted from the MPEG-4 AVC stream syntax elements. The MPEG-4 AVC saliency map thus defined is a fusion of static and dynamic map. The static saliency map is in its turn a combination of intensity, color and orientation features maps. Despite the particular way in which all these elementary maps are computed, the fusion techniques allowing their combination plays a critical role in the final result and makes the object of the proposed study. A total of 48 fusion formulas (6 for combining static features and, for each of them, 8 to combine static to dynamic features) are investigated. The performances of the obtained maps are evaluated on a public database organized at IRCCyN, by computing two objective metrics: the Kullback-Leibler divergence and the area under curve.

Shorter Lumbar Paraspinal Fascia Is Associated With High Intensity Low Back Pain and Disability.

PubMed

Ranger, Tom A; Teichtahl, Andrew J; Cicuttini, Flavia M; Wang, Yuanyuan; Wluka, Anita E; OʼSullivan, Richard; Jones, Graeme; Urquhart, Donna M

2016-04-01

A cross-sectional, community-based study. The aim of this study was to investigate the relationship between structural features of the thoracolumbar fascia and low back pain and disability. The thoracolumbar fascia plays a role in stabilization of the spine by transmitting tension from the spinal and abdominal musculature to the vertebrae. It has been hypothesized that the fascia is associated with low back pain through the development of increased pressure in the paraspinal compartment, which leads to muscle ischemia. Seventy-two participants from a community-based study of musculoskeletal health underwent Magnetic Resonance Imaging from the T12 vertebral body to the sacrum. The length of the paraspinal fascia and cross-sectional area of the paraspinal compartment were quantitatively measured from axial images at the level of the transverse processes and the Chronic Pain Grade Scale was used to assess low back pain intensity and disability. A shorter length of fascia around the parapsinal compartment was significantly associated with high intensity low back pain and/or disability, after adjusting for age, gender, and body mass index [right odds ratio (OR) 1.9, 95% CI 0.99-3.8, P = 0.05; left OR 2.6, 95% CI 1.2 to 5.6, P = 0.01). Further adjustment for the cross-sectional area of the compartment strengthened the associations between fascial length and low back pain/or disability (right OR 8.9, 95% CI 1.9-40.9, P = 0.005; left OR 9.6, 95% CI 1.2-42.9, P = 0.003). This study has demonstrated that a shorter lumbar paraspinal fascia is associated with high intensity low back pain and/or disability among community-based adults. Although cohort studies are needed, these results suggest that structural features of the fascia may play a role in high levels of low back pain and disability. 3.

Fully automated registration of first-pass myocardial perfusion MRI using independent component analysis.

PubMed

Milles, J; van der Geest, R J; Jerosch-Herold, M; Reiber, J H C; Lelieveldt, B P F

2007-01-01

This paper presents a novel method for registration of cardiac perfusion MRI. The presented method successfully corrects for breathing motion without any manual interaction using Independent Component Analysis to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of ICA, and used to compute the displacement caused by breathing for each frame. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Validation experiments showed a reduction of the average LV motion from 1.26+/-0.87 to 0.64+/-0.46 pixels. Time-intensity curves are also improved after registration with an average error reduced from 2.65+/-7.89% to 0.87+/-3.88% between registered data and manual gold standard. We conclude that this fully automatic ICA-based method shows an excellent accuracy, robustness and computation speed, adequate for use in a clinical environment.

Associations between disc space narrowing, anterior osteophytes and disability in chronic mechanical low back pain: a cross sectional study.

PubMed

Perera, Romain Shanil; Dissanayake, Poruwalage Harsha; Senarath, Upul; Wijayaratne, Lalith Sirimevan; Karunanayake, Aranjan Lional; Dissanayake, Vajira Harshadeva Weerabaddana

2017-05-15

Radiographic features of lumbar disc degeneration (LDD) are common findings in patients with chronic mechanical low back pain; however, its role in disability and intensity of pain is debatable. This study aims to investigate the associations of the x-ray features of LDD and lumbar spondylolisthesis with severity of disability and intensity of pain. A cross-sectional study was conducted on 439 patients with chronic mechanical low back pain who attended the rheumatology clinic, National Hospital of Sri Lanka, Colombo, from May 2012 to May 2014. Severity of disability was measured using Modified Oswestry Disability Index and intensity of pain was assessed using numeric rating scale (0-100). X-ray features of LDD (disc space narrowing, anterior osteophytes and overall LDD) and spondylolisthesis were assessed in lateral recumbent lumbar x-rays (L1/L2 to L5/S1) and graded by a consultant radiologist blinded to clinical data. Generalised linear model with linear response was used to assess the associations of x-ray features of LDD with severity of disability and intensity of pain adjusting for age, gender, body mass index and pain radiating into legs. Mean age was 48.99 ± 11.21 and 323 (73.58%) were females. 87 (19.82%) were obese. Mean severity of disability was 30.95 ± 13.67 and mean intensity of pain was 45.50 ± 20.37. 69 (15.72%), 26 (5.92%) and 85 (19.36%) patients had grade 2 disc space narrowing, anterior osteophytes and overall LDD, respectively. 51 (11.62%) patients had lumbar spondylolisthesis. Grade of disc space narrowing and overall LDD were not associated with severity of disability or intensity of pain. The presence of lumbar spondylolisthesis was associated with severity of disability. Female gender and pain radiating into legs were associated with severity of disability and intensity of pain. Advancing age was associated with x-ray features of LDD and lumbar spondylolisthesis. Lumbar spondylolisthesis is associated with severity of disability in patients with chronic mechanical low back pain. Associations of x-ray features of LDD with severity of disability and intensity of pain are inconclusive. Female gender and pain radiating into legs are significant confounders.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Two-year outcome of team-based intensive case management for patients with schizophrenia.

PubMed

Aberg-Wistedt, A; Cressell, T; Lidberg, Y; Liljenberg, B; Osby, U

1995-12-01

Two-year outcomes of patients with schizophrenic disorders who were assigned to an intensive, team-based case management program and patients who received standard psychiatric services were assessed. The case management model featured increased staff contact time with patients, rehabilitation plans based on patients' expressed needs, and patients' attendance at team meetings where their rehabilitation plan was discussed. Forty patients were randomly assigned to either the case management group or the control group that received standard services. Patients' use of emergency and inpatient services, their quality of life, the size of their social networks, and their relatives' burden of care were assessed at assignment to the study groups and at two-year follow-up. Patients in the case management group had significantly fewer emergency visits compared with the two years before the study, and their relatives reported significantly reduced burden of care associated with relationships with psychiatric services over the two-year period. The size of patients' social networks increased for the case management group and decreased for the control group. A team-based intensive case management model is an effective intervention in the rehabilitation of patients with chronic schizophrenia.

Determining local and contextual features describing appearance of difficult to identify mitotic figures

NASA Astrophysics Data System (ADS)

Gandomkar, Ziba; Brennan, Patrick C.; Mello-Thoms, Claudia

2017-03-01

Mitotic count is helpful in determining the aggressiveness of breast cancer. In previous studies, it was shown that the agreement among pathologists for grading mitotic index is fairly modest, as mitoses have a large variety of appearances and they could be mistaken for other similar objects. In this study, we determined local and contextual features that differ significantly between easily identifiable mitoses and challenging ones. The images were obtained from the Mitosis-Atypia 2014 challenge. In total, the dataset contained 453 mitotic figures. Two pathologists annotated each mitotic figure. In case of disagreement, an opinion from a third pathologist was requested. The mitoses were grouped into three categories, those recognized as "a true mitosis" by both pathologists ,those labelled as "a true mitosis" by only one of the first two readers and also the third pathologist, and those annotated as "probably a mitosis" by all readers or the majority of them. After color unmixing, the mitoses were segmented from H channel. Shape-based features along with intensity-based and textural features were extracted from H-channel, blue ratio channel and five different color spaces. Holistic features describing each image were also considered. The Kruskal-Wallis H test was used to identify significantly different features. Multiple comparisons were done using the rank-based version of Tukey-Kramer test. The results indicated that there are local and global features which differ significantly among different groups. In addition, variations between mitoses in different groups were captured in the features from HSL and LCH color space more than other ones.

Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

PubMed Central

Wang, Kun-Ching

2015-01-01

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech. PMID:25594590

Validation of the Repetitive Behavior Scale-Revised in Spanish-Speakers Participants with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Martínez-González, A. E.; Piqueras, J. A.

2018-01-01

Restricted and repetitive behavior (RRB) is one of the two key diagnostic features of autism spectrum disorder (ASD). DSM-5 highlights the importance of severity-based diagnostic modifiers assigned on the basis of intensity of needed supports. Therefore, there is a need for available measures that assess the severity of RRB. The repetitive…

Numerical Representation of Wintertime Near-Surface Inversions in the Arctic with a 2.5-km Version of the Global Environmental Multiscale (GEM) Model

NASA Astrophysics Data System (ADS)

Dehghan, A.; Mariani, Z.; Gascon, G.; Bélair, S.; Milbrandt, J.; Joe, P. I.; Crawford, R.; Melo, S.

2017-12-01

Environment and Climate Change Canada (ECCC) is implementing a 2.5-km resolution version of the Global Environmental Multiscale (GEM) model over the Canadian Arctic. Radiosonde observations were used to evaluate the numerical representation of surface-based temperature inversion which is a major feature in the Arctic region. Arctic surface-based inversions are often created by imbalance between radiative cooling processes at surface and warm air advection above. This can have a significant effect on vertical mixing of pollutants and moisture, and ultimately, on cloud formation. It is therefore important to correctly predict the existence of surface inversions along with their characteristics (i.e., intensity and depth). Previous climatological studies showed that the frequency and intensity of surface-based inversions are larger during colder months in the Arctic. Therefore, surface-based inversions were estimated using radiosonde measurements during winter (December 2015 to February 2016) at Iqaluit (Nunavut, Canada). Results show that the inversion intensity can exceed 10 K with depths as large as 1 km. Preliminary evaluation of GEM outputs reveals that the model tends to underestimate the intensity of near-surface inversions, and in some cases, the model failed to predict an inversion. This study presents the factors contributing to this bias including surface temperature and snow cover.

Identification of features in indexed data and equipment therefore

DOEpatents

Jarman, Kristin H [Richland, WA; Daly, Don Simone [Richland, WA; Anderson, Kevin K [Richland, WA; Wahl, Karen L [Richland, WA

2002-04-02

Embodiments of the present invention provide methods of identifying a feature in an indexed dataset. Such embodiments encompass selecting an initial subset of indices, the initial subset of indices being encompassed by an initial window-of-interest and comprising at least one beginning index and at least one ending index; computing an intensity weighted measure of dispersion for the subset of indices using a subset of responses corresponding to the subset of indices; and comparing the intensity weighted measure of dispersion to a dispersion critical value determined from an expected value of the intensity weighted measure of dispersion under a null hypothesis of no transient feature present. Embodiments of the present invention also encompass equipment configured to perform the methods of the present invention.

View subspaces for indexing and retrieval of 3D models

NASA Astrophysics Data System (ADS)

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

2010-02-01

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

Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma.

PubMed

Thomaz, Ricardo de Lima; Carneiro, Pedro Cunha; Bonin, João Eliton; Macedo, Túlio Augusto Alves; Patrocinio, Ana Claudia; Soares, Alcimar Barbosa

2018-05-01

Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC. We compared our approach to a constrained Mahalanobis function and two other unconstrained functions using Welch's t-test and Gaussian Data Descriptors. The performance of each fitness function was evaluated by cross-validating a one-class SVM. The results show that the proposed multi-objective Mahalanobis fitness function is capable of significantly reducing data dimensionality (96.4%) and improving one-class classification of early HCC (0.84 AUC). Furthermore, the results provide strong evidence that intensity features extracted at the arterial to portal and arterial to equilibrium phases are important for classifying early HCC.

Toxocara nematodes in stray cats from shiraz, southern iran: intensity of infection and molecular identification of the isolates.

PubMed

Mikaeili, Fattaneh; Mirhendi, Hossein; Hosseini, Mostafa; Asgari, Qasem; Kia, Eshrat Beigom

2013-10-01

Toxocara is a common nematode of cats in different parts of Iran. Despite the close association of cats with human, no attempt has been done so far for molecular identification of this nematode in the country. Therefore, current study was performed on identification of some isolates of Toxocara from stray cats in Shiraz, Fars Province, Southern Iran, based on morphological and molecular approaches, and also determination of intensity of infection. This cross-sectional study was carried out on 30 stray cats trapped from different geographical areas of Shiraz in 2011. Adult male and female worms were recovered from digestive tract after dissection of cats. Morphological features using existing keys and PCR-sequencing of ITS-rDNA region and pcox1 mitochondrial l gene were applied for the delineating the species of the parasites. Eight out of 30 cats (26.7%) were found infected with Toxocara nematodes. All the isolates were confirmed as Toxocara cati based on morphological features and the sequence of ribosomal and mitochondrial targets. Intensity of infection ranged from one to a maximum of 39 worms per cat, with a mean of 10.25±12.36, and higher abundance of female nematodes. The most prevalent ascaridoid nematode of stray cats in the study area was T. cati and female nematodes were more abundant than that of males. This issue has important role in spreading of eggs in the environment and impact on human toxocariasis.

A random forest model based classification scheme for neonatal amplitude-integrated EEG.

PubMed

Chen, Weiting; Wang, Yu; Cao, Guitao; Chen, Guoqiang; Gu, Qiufang

2014-01-01

Modern medical advances have greatly increased the survival rate of infants, while they remain in the higher risk group for neurological problems later in life. For the infants with encephalopathy or seizures, identification of the extent of brain injury is clinically challenging. Continuous amplitude-integrated electroencephalography (aEEG) monitoring offers a possibility to directly monitor the brain functional state of the newborns over hours, and has seen an increasing application in neonatal intensive care units (NICUs). This paper presents a novel combined feature set of aEEG and applies random forest (RF) method to classify aEEG tracings. To that end, a series of experiments were conducted on 282 aEEG tracing cases (209 normal and 73 abnormal ones). Basic features, statistic features and segmentation features were extracted from both the tracing as a whole and the segmented recordings, and then form a combined feature set. All the features were sent to a classifier afterwards. The significance of feature, the data segmentation, the optimization of RF parameters, and the problem of imbalanced datasets were examined through experiments. Experiments were also done to evaluate the performance of RF on aEEG signal classifying, compared with several other widely used classifiers including SVM-Linear, SVM-RBF, ANN, Decision Tree (DT), Logistic Regression(LR), ML, and LDA. The combined feature set can better characterize aEEG signals, compared with basic features, statistic features and segmentation features respectively. With the combined feature set, the proposed RF-based aEEG classification system achieved a correct rate of 92.52% and a high F1-score of 95.26%. Among all of the seven classifiers examined in our work, the RF method got the highest correct rate, sensitivity, specificity, and F1-score, which means that RF outperforms all of the other classifiers considered here. The results show that the proposed RF-based aEEG classification system with the combined feature set is efficient and helpful to better detect the brain disorders in newborns.

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

PubMed Central

Liu, Xiaojing; Ser, Zheng; Cluntun, Ahmad A.; Mentch, Samantha J.; Locasale, Jason W.

2014-01-01

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.  Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases. PMID:24894601

Multiple light scattering in metallic ejecta produced under intense shockwave compression.

PubMed

Franzkowiak, J-E; Mercier, P; Prudhomme, G; Berthe, L

2018-04-10

A roughened metallic plate, subjected to intense shock wave compression, gives rise to an expanding ejecta particle cloud. Photonic Doppler velocimetry (PDV), a fiber-based heterodyne velocimeter, is often used to track ejecta velocities in dynamic compression experiments and on nanosecond time scales. Shortly after shock breakout at the metal-vacuum interface, a particular feature observed in many experiments in the velocity spectrograms is what appear to be slow-moving ejecta, below the free-surface velocity. Using Doppler Monte Carlo simulations incorporating the transport of polarization in the ejecta, we show that this feature is likely to be explained by the multiple scattering of light, rather than by possible collisions among particles, slowing down the ejecta. As the cloud expands in a vacuum, the contribution of multiple scattering decreases due to the limited field of view of the pigtailed collimator used to probe the ejecta, showing that the whole geometry of the system must be taken into account in the calculations to interpret and predict PDV measurements.

Study of the Geoeffectiveness and Galactic Cosmic-Ray Response of VarSITI-ISEST Campaign Events in Solar Cycle 24

NASA Astrophysics Data System (ADS)

Aslam, O. P. M.; Badruddin

2017-09-01

We analyze and compare the geomagnetic and galactic cosmic-ray (GCR) response of selected solar events, particularly the campaign events of the group International Study of Earth-affecting Solar Transients (ISEST) of the program Variability of the Sun and Its Terrestrial Impact (VarSITI). These selected events correspond to Solar Cycle 24, and we identified various of their features during their near-Earth passage. We evaluated the hourly data of geomagnetic indices and ground-based neutron monitors and the concurrent data of interplanetary plasma and field parameters. We recognized distinct features of these events and solar wind parameters when the geomagnetic disturbance was at its peak and when the cosmic-ray intensity was most affected. We also discuss the similarities and differences in the geoeffectiveness and GCR response of the solar and interplanetary structures in the light of plasma and field variations and physical mechanism(s), which play a crucial role in influencing the geomagnetic activity and GCR intensity.

Online tracking of outdoor lighting variations for augmented reality with moving cameras.

PubMed

Liu, Yanli; Granier, Xavier

2012-04-01

In augmented reality, one of key tasks to achieve a convincing visual appearance consistency between virtual objects and video scenes is to have a coherent illumination along the whole sequence. As outdoor illumination is largely dependent on the weather, the lighting condition may change from frame to frame. In this paper, we propose a full image-based approach for online tracking of outdoor illumination variations from videos captured with moving cameras. Our key idea is to estimate the relative intensities of sunlight and skylight via a sparse set of planar feature-points extracted from each frame. To address the inevitable feature misalignments, a set of constraints are introduced to select the most reliable ones. Exploiting the spatial and temporal coherence of illumination, the relative intensities of sunlight and skylight are finally estimated by using an optimization process. We validate our technique on a set of real-life videos and show that the results with our estimations are visually coherent along the video sequences.

Adiposity and Fat Metabolism in Lactating and Fasting Northern Elephant Seals12

PubMed Central

Crocker, Daniel E.; Champagne, Cory D.; Fowler, Melinda A.; Houser, Dorian S.

2014-01-01

Several taxa of animals fast completely from food and water during energy-intensive periods such as lactation, breeding, and development. In elephant seals, these behaviors are sustained by high adiposity, high rates of fat mobilization, and reduced oxidation of carbohydrates and proteins. Adiposity and the regulation of lipolysis directly affect lactation energetics, milk composition, and mating success. Long-term fasting induces changes in regulation of lipolysis and lipid metabolism that influence fatty acid (FA) availability and the onset of insulin resistance. Hypoinsulinemia and elevated circulating FAs are also associated with several unique features of carbohydrate metabolism, including elevated plasma glucose, gluconeogenesis, and Cori cycle activity as well as high rates of pyruvate and tricarboxylic acid cycling. Glucose-lactate pools and triacylglycerol-FA cycles may be linked via glyceroneogenesis and this may be an important pathway influencing both fat and carbohydrate metabolism. Together, these features allow a sustained, high intensity, fat-based metabolism without substantial accumulation of ketoacids. PMID:24425723

What Constitutes a Phrase in Sound-Based Music? A Mixed-Methods Investigation of Perception and Acoustics.

PubMed

Olsen, Kirk N; Dean, Roger T; Leung, Yvonne

2016-01-01

Phrasing facilitates the organization of auditory information and is central to speech and music. Not surprisingly, aspects of changing intensity, rhythm, and pitch are key determinants of musical phrases and their boundaries in instrumental note-based music. Different kinds of speech (such as tone- vs. stress-languages) share these features in different proportions and form an instructive comparison. However, little is known about whether or how musical phrasing is perceived in sound-based music, where the basic musical unit from which a piece is created is commonly non-instrumental continuous sounds, rather than instrumental discontinuous notes. This issue forms the target of the present paper. Twenty participants (17 untrained in music) were presented with six stimuli derived from sound-based music, note-based music, and environmental sound. Their task was to indicate each occurrence of a perceived phrase and qualitatively describe key characteristics of the stimulus associated with each phrase response. It was hypothesized that sound-based music does elicit phrase perception, and that this is primarily associated with temporal changes in intensity and timbre, rather than rhythm and pitch. Results supported this hypothesis. Qualitative analysis of participant descriptions showed that for sound-based music, the majority of perceived phrases were associated with intensity or timbral change. For the note-based piano piece, rhythm was the main theme associated with perceived musical phrasing. We modeled the occurrence in time of perceived musical phrases with recurrent event 'hazard' analyses using time-series data representing acoustic predictors associated with intensity, spectral flatness, and rhythmic density. Acoustic intensity and timbre (represented here by spectral flatness) were strong predictors of perceived musical phrasing in sound-based music, and rhythm was only predictive for the piano piece. A further analysis including five additional spectral measures linked to timbre strengthened the models. Overall, results show that even when little of the pitch and rhythm information important for phrasing in note-based music is available, phrasing is still perceived, primarily in response to changes of intensity and timbre. Implications for electroacoustic music composition and music recommender systems are discussed.

What Constitutes a Phrase in Sound-Based Music? A Mixed-Methods Investigation of Perception and Acoustics

PubMed Central

Olsen, Kirk N.; Dean, Roger T.; Leung, Yvonne

2016-01-01

Phrasing facilitates the organization of auditory information and is central to speech and music. Not surprisingly, aspects of changing intensity, rhythm, and pitch are key determinants of musical phrases and their boundaries in instrumental note-based music. Different kinds of speech (such as tone- vs. stress-languages) share these features in different proportions and form an instructive comparison. However, little is known about whether or how musical phrasing is perceived in sound-based music, where the basic musical unit from which a piece is created is commonly non-instrumental continuous sounds, rather than instrumental discontinuous notes. This issue forms the target of the present paper. Twenty participants (17 untrained in music) were presented with six stimuli derived from sound-based music, note-based music, and environmental sound. Their task was to indicate each occurrence of a perceived phrase and qualitatively describe key characteristics of the stimulus associated with each phrase response. It was hypothesized that sound-based music does elicit phrase perception, and that this is primarily associated with temporal changes in intensity and timbre, rather than rhythm and pitch. Results supported this hypothesis. Qualitative analysis of participant descriptions showed that for sound-based music, the majority of perceived phrases were associated with intensity or timbral change. For the note-based piano piece, rhythm was the main theme associated with perceived musical phrasing. We modeled the occurrence in time of perceived musical phrases with recurrent event ‘hazard’ analyses using time-series data representing acoustic predictors associated with intensity, spectral flatness, and rhythmic density. Acoustic intensity and timbre (represented here by spectral flatness) were strong predictors of perceived musical phrasing in sound-based music, and rhythm was only predictive for the piano piece. A further analysis including five additional spectral measures linked to timbre strengthened the models. Overall, results show that even when little of the pitch and rhythm information important for phrasing in note-based music is available, phrasing is still perceived, primarily in response to changes of intensity and timbre. Implications for electroacoustic music composition and music recommender systems are discussed. PMID:27997625

Spinal focal lesion detection in multiple myeloma using multimodal image features

NASA Astrophysics Data System (ADS)

Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

2015-03-01

Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

Rotational distortion correction in endoscopic optical coherence tomography based on speckle decorrelation

PubMed Central

Uribe-Patarroyo, Néstor; Bouma, Brett E.

2015-01-01

We present a new technique for the correction of nonuniform rotation distortion in catheter-based optical coherence tomography (OCT), based on the statistics of speckle between A-lines using intensity-based dynamic light scattering. This technique does not rely on tissue features and can be performed on single frames of data, thereby enabling real-time image correction. We demonstrate its suitability in a gastrointestinal balloon-catheter OCT system, determining the actual rotational speed with high temporal resolution, and present corrected cross-sectional and en face views showing significant enhancement of image quality. PMID:26625040

Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer

NASA Astrophysics Data System (ADS)

Gangeh, Mehrdad J.; Fung, Brandon; Tadayyon, Hadi; Tran, William T.; Czarnota, Gregory J.

2016-03-01

A non-invasive computer-aided-theragnosis (CAT) system was developed for the early assessment of responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer. The CAT system was based on quantitative ultrasound spectroscopy methods comprising several modules including feature extraction, a metric to measure the dissimilarity between "pre-" and "mid-treatment" scans, and a supervised learning algorithm for the classification of patients to responders/non-responders. One major requirement for the successful design of a high-performance CAT system is to accurately measure the changes in parametric maps before treatment onset and during the course of treatment. To this end, a unified framework based on Hilbert-Schmidt independence criterion (HSIC) was used for the design of feature extraction from parametric maps and the dissimilarity measure between the "pre-" and "mid-treatment" scans. For the feature extraction, HSIC was used to design a supervised dictionary learning (SDL) method by maximizing the dependency between the scans taken from "pre-" and "mid-treatment" with "dummy labels" given to the scans. For the dissimilarity measure, an HSIC-based metric was employed to effectively measure the changes in parametric maps as an indication of treatment effectiveness. The HSIC-based feature extraction and dissimilarity measure used a kernel function to nonlinearly transform input vectors into a higher dimensional feature space and computed the population means in the new space, where enhanced group separability was ideally obtained. The results of the classification using the developed CAT system indicated an improvement of performance compared to a CAT system with basic features using histogram of intensity.

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

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-08-01

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

Simple ray tracing of Galileo-observed hectometric attenuation features

NASA Astrophysics Data System (ADS)

Higgins, Charles A.; Thieman, James R.; Fung, Shing F.; Green, James L.; Candey, Robert M.

Observations of persistent structural features within Jovian hectometric (HOM) radio emission have been made with the Galileo spacecraft. Two well-defined sinusoidal-shaped ``band'' features of reduced emission intensity and occurrence probability exist at all Jovian longitudes and nearly cover the entire spectrum of HOM radio emission from ~500 kHz to 3000 kHz. These two sinusoidal lanes have a bandwidth of 200-400 kHz and are 180° out of phase with one another, suggesting that they are a result of HOM radio emission propagation processes from opposite hemispheres. These features become more apparent when presented as intensity or occurrence probability spectrograms added together over multiple Jovian rotations. Enhancements in the HOM intensity and occurrence are seen along the edges of one of the observed sinusoidal lane features which may indicate caustic surfaces due to refraction along the propagation path. We present some simple ray tracing analyses to show that refraction from density enhancements in the Io torus flux tube may explain some of the observations. Using this simple method, we approximate the density enhancements in the Io flux tube to be 100 cm-3.

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.

PubMed

Zheng, Qiang; Warner, Steven; Tasian, Gregory; Fan, Yong

2018-02-12

Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in US images by integrating image intensity information and texture feature maps. We develop a new graph cuts-based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts-based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation. Experiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index = 0.9446, average mean distance = 2.2551, average specificity = 0.9971, average accuracy = 0.9919), better than other methods under comparison (P < .05, paired Wilcoxon rank sum tests). The proposed method achieved promising performance for segmenting kidneys in two-dimensional US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such as progression of chronic kidney disease. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Evaluation of nuclear chromatin using grayscale intensity and thresholded percentage area in liquid-based cervical cytology.

PubMed

Lee, Hyekyung; Han, Myungein; Yoo, Taejo; Jung, Chanho; Son, Hyun-Jin; Cho, Migyung

2018-05-01

Development of computerized image analysis techniques has opened up the possibility for the quantitative analysis of nuclear chromatin in pathology. We hypothesized that the features extracted from digital images could be used to determine specific cytomorphological findings for nuclear chromatin that may be applicable for establishing a medical diagnosis. Three parameters were evaluated from nuclear chromatin images obtained from the liquid-based cervical cytology samples of patients with biopsy-proven high-grade squamous intraepithelial lesion (HGSIL), and compared between non-neoplastic squamous epithelia and dysplastic epithelia groups: (1) standard deviation (SD) of the grayscale intensity; (2) difference between the maximum and minimum grayscale intensity (M-M); and (3) thresholded area percentage. Each parameter was evaluated at the mean, mean-1SD, and mean-2SD thresholding intensity levels. Between the mean and mean-1SD levels, the thresholded nuclear chromatin pattern was most similar to the chromatin granularity of the unthresholded grayscale images. The SD of the gray intensity and the thresholded area percentage differed significantly between the non-neoplastic squamous epithelia and dysplastic epithelia of HGSIL images at all three thresholding intensity levels (mean, mean-1SD, and mean-2SD). However, the M-M significantly differed between the two sample types for only two of the thresholding intensity levels (mean-1SD and mean-2SD). The digital parameters SD and M-M of the grayscale intensity, along with the thresholded area percentage could be useful in automated cytological evaluations. Further studies are needed to identify more valuable parameters for clinical application. © 2018 Wiley Periodicals, Inc.

rFRET: A comprehensive, Matlab-based program for analyzing intensity-based ratiometric microscopic FRET experiments.

PubMed

Nagy, Peter; Szabó, Ágnes; Váradi, Tímea; Kovács, Tamás; Batta, Gyula; Szöllősi, János

2016-04-01

Fluorescence or Förster resonance energy transfer (FRET) remains one of the most widely used methods for assessing protein clustering and conformation. Although it is a method with solid physical foundations, many applications of FRET fall short of providing quantitative results due to inappropriate calibration and controls. This shortcoming is especially valid for microscopy where currently available tools have limited or no capability at all to display parameter distributions or to perform gating. Since users of multiparameter flow cytometry usually apply these tools, the absence of these features in applications developed for microscopic FRET analysis is a significant limitation. Therefore, we developed a graphical user interface-controlled Matlab application for the evaluation of ratiometric, intensity-based microscopic FRET measurements. The program can calculate all the necessary overspill and spectroscopic correction factors and the FRET efficiency and it displays the results on histograms and dot plots. Gating on plots and mask images can be used to limit the calculation to certain parts of the image. It is an important feature of the program that the calculated parameters can be determined by regression methods, maximum likelihood estimation (MLE) and from summed intensities in addition to pixel-by-pixel evaluation. The confidence interval of calculated parameters can be estimated using parameter simulations if the approximate average number of detected photons is known. The program is not only user-friendly, but it provides rich output, it gives the user freedom to choose from different calculation modes and it gives insight into the reliability and distribution of the calculated parameters. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

Segmentation of photospheric magnetic elements corresponding to coronal features to understand the EUV and UV irradiance variability

NASA Astrophysics Data System (ADS)

Zender, J. J.; Kariyappa, R.; Giono, G.; Bergmann, M.; Delouille, V.; Damé, L.; Hochedez, J.-F.; Kumara, S. T.

2017-09-01

Context. The magnetic field plays a dominant role in the solar irradiance variability. Determining the contribution of various magnetic features to this variability is important in the context of heliospheric studies and Sun-Earth connection. Aims: We studied the solar irradiance variability and its association with the underlying magnetic field for a period of five years (January 2011-January 2016). We used observations from the Large Yield Radiometer (LYRA), the Sun Watcher with Active Pixel System detector and Image Processing (SWAP) on board PROBA2, the Atmospheric Imaging Assembly (AIA), and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Methods: The Spatial Possibilistic Clustering Algorithm (SPoCA) is applied to the extreme ultraviolet (EUV) observations obtained from the AIA to segregate coronal features by creating segmentation maps of active regions (ARs), coronal holes (CHs) and the quiet sun (QS). Further, these maps are applied to the full-disk SWAP intensity images and the full-disk (FD) HMI line-of-sight (LOS) magnetograms to isolate the SWAP coronal features and photospheric magnetic counterparts, respectively. We then computed full-disk and feature-wise averages of EUV intensity and line of sight (LOS) magnetic flux density over ARs/CHs/QS/FD. The variability in these quantities is compared with that of LYRA irradiance values. Results: Variations in the quantities resulting from the segmentation, namely the integrated intensity and the total magnetic flux density of ARs/CHs/QS/FD regions, are compared with the LYRA irradiance variations. We find that the EUV intensity over ARs/CHs/QS/FD is well correlated with the underlying magnetic field. In addition, variations in the full-disk integrated intensity and magnetic flux density values are correlated with the LYRA irradiance variations. Conclusions: Using the segmented coronal features observed in the EUV wavelengths as proxies to isolate the underlying magnetic structures is demonstrated in this study. Sophisticated feature identification and segmentation tools are important in providing more insights into the role of various magnetic features in both the short- and long-term changes in the solar irradiance. The movie associated to Fig. 2 is available at http://www.aanda.org

A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising

NASA Astrophysics Data System (ADS)

Cui, Xin; Li, Shipeng; Yan, Xiutian; He, Xinhua

2018-04-01

In order to remove the noise of three-dimensional scattered point cloud and smooth the data without damnify the sharp geometric feature simultaneity, a novel algorithm is proposed in this paper. The feature-preserving weight is added to fuzzy c-means algorithm which invented a curvature weighted fuzzy c-means clustering algorithm. Firstly, the large-scale outliers are removed by the statistics of r radius neighboring points. Then, the algorithm estimates the curvature of the point cloud data by using conicoid parabolic fitting method and calculates the curvature feature value. Finally, the proposed clustering algorithm is adapted to calculate the weighted cluster centers. The cluster centers are regarded as the new points. The experimental results show that this approach is efficient to different scale and intensities of noise in point cloud with a high precision, and perform a feature-preserving nature at the same time. Also it is robust enough to different noise model.

Image segmentation using local shape and gray-level appearance models

NASA Astrophysics Data System (ADS)

Seghers, Dieter; Loeckx, Dirk; Maes, Frederik; Suetens, Paul

2006-03-01

A new generic model-based segmentation scheme is presented, which can be trained from examples akin to the Active Shape Model (ASM) approach in order to acquire knowledge about the shape to be segmented and about the gray-level appearance of the object in the image. Because in the ASM approach the intensity and shape models are typically applied alternately during optimizing as first an optimal target location is selected for each landmark separately based on local gray-level appearance information only to which the shape model is fitted subsequently, the ASM may be misled in case of wrongly selected landmark locations. Instead, the proposed approach optimizes for shape and intensity characteristics simultaneously. Local gray-level appearance information at the landmark points extracted from feature images is used to automatically detect a number of plausible candidate locations for each landmark. The shape information is described by multiple landmark-specific statistical models that capture local dependencies between adjacent landmarks on the shape. The shape and intensity models are combined in a single cost function that is optimized non-iteratively using dynamic programming which allows to find the optimal landmark positions using combined shape and intensity information, without the need for initialization.

Cell Based Therapeutic Approach in Vascular Surgery: Application and Review

PubMed Central

Rocca, Aldo; Tafuri, Domenico; Paccone, Marianna; Giuliani, Antonio; Zamboli, Anna Ginevra Immacolata; Surfaro, Giuseppe; Paccone, Andrea; Compagna, Rita; Amato, Maurizo; Serra, Raffaele; Amato, Bruno

2017-01-01

Abstract Multipotent stem cells - such as mesenchymal stem/stromal cells and stem cells derived from different sources like vascular wall are intensely studied to try to rapidly translate their discovered features from bench to bedside. Vascular wall resident stem cells recruitment, differentiation, survival, proliferation, growth factor production, and signaling pathways transduced were analyzed. We studied biological properties of vascular resident stem cells and explored the relationship from several factors as Matrix Metalloproteinases (MMPs) and regulations of biological, translational and clinical features of these cells. In this review we described a translational and clinical approach to Adult Vascular Wall Resident Multipotent Vascular Stem Cells (VW-SCs) and reported their involvement in alternative clinical approach as cells based therapy in vascular disease like arterial aneurysms or peripheral arterial obstructive disease. PMID:29071303

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

PubMed

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images.

PubMed

Xu, Yingying; Lin, Lanfen; Hu, Hongjie; Wang, Dan; Zhu, Wenchao; Wang, Jian; Han, Xian-Hua; Chen, Yen-Wei

2018-01-01

The bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis. This paper presents a texture-specific BoVW method to represent focal liver lesions (FLLs). Pixels in the region of interest (ROI) are classified into nine texture categories using the rotation-invariant uniform local binary pattern method. The BoVW-based features are calculated for each texture category. In addition, a spatial cone matching (SCM)-based representation strategy is proposed to describe the spatial information of the visual words in the ROI. In a pilot study, eight radiologists with different clinical experience performed diagnoses for 20 cases with and without the top six retrieved results. A total of 132 multiphase computed tomography volumes including five pathological types were collected. The texture-specific BoVW was compared to other BoVW-based methods using the constructed dataset of FLLs. The results show that our proposed model outperforms the other three BoVW methods in discriminating different lesions. The SCM method, which adds spatial information to the orderless BoVW model, impacted the retrieval performance. In the pilot trial, the average diagnosis accuracy of the radiologists was improved from 66 to 80% using the retrieval system. The preliminary results indicate that the texture-specific features and the SCM-based BoVW features can effectively characterize various liver lesions. The retrieval system has the potential to improve the diagnostic accuracy and the confidence of the radiologists.

Distinguish self- and hetero-perceived stress through behavioral imaging and physiological features.

PubMed

Spodenkiewicz, Michel; Aigrain, Jonathan; Bourvis, Nadège; Dubuisson, Séverine; Chetouani, Mohamed; Cohen, David

2018-03-02

Stress reactivity is a complex phenomenon associated to multiple and multimodal expressions. Response to stressors has an obvious survival function and may be seen as an internal regulation to adapt to threat or danger. The intensity of this internal response can be assessed as the self-perception of the stress response. In species with social organization, this response also serves a communicative function, so-called hetero-perception. Our study presents multimodal stress detection assessment - a new methodology combining behavioral imaging and physiological monitoring for analyzing stress from these two perspectives. The system is based on automatic extraction of 39 behavioral (2D+3D video recording) and 62 physiological (Nexus-10 recording) features during a socially evaluated mental arithmetic test. The analysis with machine learning techniques for automatic classification using Support Vector Machine (SVM) show that self-perception and hetero-perception of social stress are both close but different phenomena: self-perception was significantly correlated with hetero-perception but significantly differed from it. Also, assessing stress with SVM through multimodality gave excellent classification results (F1 score values: 0.9±0.012 for hetero-perception and 0.87±0.021 for self-perception). In the best selected feature subsets, we found some common behavioral and physiological features that allow classification of both self- and hetero-perceived stress. However, we also found the contributing features for automatic classifications had opposite distributions: self-perception classification was mainly based on physiological features and hetero-perception was mainly based on behavioral features. Copyright © 2017. Published by Elsevier Inc.

Simple 2.5 GHz time-bin quantum key distribution

NASA Astrophysics Data System (ADS)

Boaron, Alberto; Korzh, Boris; Houlmann, Raphael; Boso, Gianluca; Rusca, Davide; Gray, Stuart; Li, Ming-Jun; Nolan, Daniel; Martin, Anthony; Zbinden, Hugo

2018-04-01

We present a 2.5 GHz quantum key distribution setup with the emphasis on a simple experimental realization. It features a three-state time-bin protocol based on a pulsed diode laser and a single intensity modulator. Implementing an efficient one-decoy scheme and finite-key analysis, we achieve record breaking secret key rates of 1.5 kbps over 200 km of standard optical fibers.

A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Tseng, Tzu-Liang B.; Zheng, Bin; Zhang, Jianying; Qian, Wei

2015-03-01

A novel breast cancer risk analysis approach is proposed for enhancing performance of computerized breast cancer risk analysis using bilateral mammograms. Based on the intensity of breast area, five different sub-regions were acquired from one mammogram, and bilateral features were extracted from every sub-region. Our dataset includes 180 bilateral mammograms from 180 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including sub-region segmentation, bilateral feature extraction, feature selection, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under the curve (AUC) is 0.763 ± 0.021 when applying the multiple sub-region features to our testing dataset. The positive predictive value and the negative predictive value were 0.60 and 0.73, respectively. The study demonstrates that (1) features extracted from multiple sub-regions can improve the performance of our scheme compared to using features from whole breast area only; (2) a classifier using asymmetry bilateral features can effectively predict breast cancer risk; (3) incorporating texture and morphological features with density features can boost the classification accuracy.

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

NASA Astrophysics Data System (ADS)

Du, Hongbo; Al-Jubouri, Hanan; Sellahewa, Harin

2014-05-01

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors' own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.

Unsupervised Word Spotting in Historical Handwritten Document Images using Document-oriented Local Features.

PubMed

Zagoris, Konstantinos; Pratikakis, Ioannis; Gatos, Basilis

2017-05-03

Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that it relies upon document-oriented local features which take into account information around representative keypoints as well a matching process that incorporates spatial context in a local proximity search without using any training data. Experimental results on four historical handwritten datasets for two different scenarios (segmentation-based and segmentation-free) using standard evaluation measures show the improved performance achieved by the proposed methodology.

Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.

PubMed

Fiot, Jean-Baptiste; Cohen, Laurent D; Raniga, Parnesh; Fripp, Jurgen

2013-09-01

Support vector machines (SVM) are machine learning techniques that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities [T1-w, T2-w, proton-density and fluid attenuated inversion recovery(FLAIR)], differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of 0.54  ±  0.12, 0.72  ±  0.06 and 0.82  ±  0.06 respectively for small, moderate and severe lesion loads); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52  ±  0.13, 0.71  ±  0.08 and 0.81  ±  0.07). Furthermore, there was a negligible difference between using 5 × 5 × 5 and 3 × 3 × 3 features (p = 0.93). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification, which outperforms the one based on all features with post-processing. Copyright © 2013 John Wiley & Sons, Ltd.

Ultrafast third-harmonic spectroscopy of single nanoantennas fabricated using helium-ion beam lithography

NASA Astrophysics Data System (ADS)

Kollmann, H.; Esmann, M.; Becker, S. F.; Piao, X.; Huynh, C.; Kautschor, L.-O.; Bösker, G.; Vieker, H.; Beyer, A.; Gölzhäuser, A.; Park, N.; Silies, M.; Lienau, C.

2016-03-01

Metallic nanoantennas are able to spatially localize far-field electromagnetic waves on a few nanometer length scale in the form of surface plasmon excitations 1-3. Standard tools for fabricating bowtie and rod antennas with sub-20 nm feature sizes are Electron Beam Lithography or Ga-based Focused Ion Beam (FIB) Milling. These structures, however, often suffer from surface roughness and hence show only a limited optical polarization contrast and therefore a limited electric field localization. Here, we combine Ga- and He-ion based milling (HIM) for the fabrication of gold bowtie and rod antennas with gap sizes of less than 6 nm combined with a high aspect ratio. Using polarization-sensitive Third-Harmonic (TH) spectroscopy, we compare the nonlinear optical properties of single HIM-antennas with sub-6-nm gaps with those produced by standard Ga-based FIB. We find a pronounced enhancement of the total TH intensity of more than three in comparison to Ga-FIB antennas and a highly improved polarization contrast of the TH intensity of 250:1 for Heion produced antennas 4. These findings combined with Finite-Element Method calculations demonstrate a field enhancement of up to one hundred in the few-nanometer gap of the antenna. This makes He-ion beam milling a highly attractive and promising new tool for the fabrication of plasmonic nanoantennas with few-nanometer feature sizes.

Characterizing tobacco control mass media campaigns in England.

PubMed

Langley, Tessa; Lewis, Sarah; McNeill, Ann; Gilmore, Anna; Szatkowski, Lisa; West, Robert; Sims, Michelle

2013-11-01

To characterize publically funded tobacco control campaigns in England between 2004 and 2010 and to explore if they were in line with recommendations from the literature in terms of their content and intensity. International evidence suggests that campaigns which warn of the negative consequences of smoking and feature testimonials from real-life smokers are most effective, and that four exposures per head per month are required to reduce smoking prevalence. Characterization of tobacco control advertisements using a theoretically based framework designed to describe advertisement themes, informational and emotional content and style. Study of the intensity of advertising and exposure to different types of advertisement using data on population-level exposure to advertisements shown during the study period. England. Television Ratings (TVRs), a standard measure of advertising exposure, were used to calculate exposure to each different campaign type. A total of 89% of advertising was for smoking cessation; half of this advertising warned of the negative consequences of smoking, while half contained how-to-quit messages. Acted scenes featured in 72% of advertising, while only 17% featured real-life testimonials. Only 39% of months had at least four exposures to tobacco control campaigns per head. A theory-driven approach enabled a systematic characterization of tobacco control advertisements in England. Between 2004 and 2010 only a small proportion of tobacco control advertisements utilized the most effective strategies-negative health effects messages and testimonials from real-life smokers. The intensity of campaigns was lower than international recommendations. © 2013 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of The Society for the Study of Addiction.

Innovative method to suppress local geometry distortions for fabrication of interdigitated electrode arrays with nano gaps

NASA Astrophysics Data System (ADS)

Partel, S.; Urban, G.

2016-03-01

In this paper we present a method to optimize the lithography process for the fabrication of interdigitated electrode arrays (IDA) for a lift-off free electrochemical biosensor. The biosensor is based on amperometric method to allow a signal amplification by redox cycling. We already demonstrated a method to fabricate IDAs with nano gaps with conventional mask aligner lithography and two subsequent deposition processes. By decreasing the distance down to the nanometer range the linewidth variation is becoming the most critical factor and can result in a short circuit of the electrodes. Therefore, the light propagation and the resist pattern of the mask aligner lithography process are simulated to optimize the lithography process. To optimize the outer finger structure assistant features (AsFe) were introduced. The AsFe allow an optimization of the intensity distribution at the electrode fingers. Hence, the periodicity is expanded and the outer structure of the IDA is practically a part of the periodic array. The better CD uniformity can be obtained by adding three assistant features which generate an equal intensity distributions for the complete finger pattern. Considering a mask optimization of the outer structures would also be feasible. However, due to the strong impact of the gap between mask and wafer at contact lithography it is not practicable. The better choice is to create the same intensity distribution for all finger structures. With the introduction of the assistant features large areas with electrode gap sizes in the sub 100 nm region are demonstrated.

Formation of 238U16O and 238U18O observed by time-resolved emission spectroscopy subsequent to laser ablation

NASA Astrophysics Data System (ADS)

Weisz, David G.; Crowhurst, Jonathan C.; Siekhaus, Wigbert J.; Rose, Timothy P.; Koroglu, Batikan; Radousky, Harry B.; Zaug, Joseph M.; Armstrong, Michael R.; Isselhardt, Brett H.; Savina, Michael R.; Azer, Magdi; Finko, Mikhail S.; Curreli, Davide

2017-07-01

We have measured vibronic emission spectra of an oxide of uranium formed after laser ablation of the metal in gaseous oxygen. Specifically, we have measured the time-dependent relative intensity of a band located at approximately 593.6 nm in 16O2. This band grew in intensity relative to neighboring atomic features as a function time in an oxygen environment but was relatively invariant with time in argon. In addition, we have measured the spectral shift of this band in an 18O2 atmosphere. Based on this shift, and by comparison with earlier results obtained from free-jet expansion and laser excitation, we can confirm that the oxide in question is UO, consistent with recent reports based on laser ablation in 16O2 only.

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

PubMed

Zhao, Zhehuan; Yang, Zhihao; Luo, Ling; Wang, Lei; Zhang, Yin; Lin, Hongfei; Wang, Jian

2017-12-28

Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated. In this paper, we propose a novel multiple label convolutional neural network (MCNN) based disease NER approach. In this approach, instead of the CRF layer, a multiple label strategy (MLS) first introduced by us, is employed. First, the character-level embedding, word-level embedding and lexicon feature embedding are concatenated. Then several convolutional layers are stacked over the concatenated embedding. Finally, MLS strategy is applied to the output layer to capture the correlation information between neighboring labels. As shown by the experimental results, MCNN can achieve the state-of-the-art performance on both NCBI and CDR corpora. The proposed MCNN based disease NER method achieves the state-of-the-art performance with little feature engineering. And the experimental results show the MLS strategy's effectiveness of capturing the correlation information between labels in the neighborhood.

On Feature Extraction from Large Scale Linear LiDAR Data

NASA Astrophysics Data System (ADS)

Acharjee, Partha Pratim

Airborne light detection and ranging (LiDAR) can generate co-registered elevation and intensity map over large terrain. The co-registered 3D map and intensity information can be used efficiently for different feature extraction application. In this dissertation, we developed two algorithms for feature extraction, and usages of features for practical applications. One of the developed algorithms can map still and flowing waterbody features, and another one can extract building feature and estimate solar potential on rooftops and facades. Remote sensing capabilities, distinguishing characteristics of laser returns from water surface and specific data collection procedures provide LiDAR data an edge in this application domain. Furthermore, water surface mapping solutions must work on extremely large datasets, from a thousand square miles, to hundreds of thousands of square miles. National and state-wide map generation/upgradation and hydro-flattening of LiDAR data for many other applications are two leading needs of water surface mapping. These call for as much automation as possible. Researchers have developed many semi-automated algorithms using multiple semi-automated tools and human interventions. This reported work describes a consolidated algorithm and toolbox developed for large scale, automated water surface mapping. Geometric features such as flatness of water surface, higher elevation change in water-land interface and, optical properties such as dropouts caused by specular reflection, bimodal intensity distributions were some of the linear LiDAR features exploited for water surface mapping. Large-scale data handling capabilities are incorporated by automated and intelligent windowing, by resolving boundary issues and integrating all results to a single output. This whole algorithm is developed as an ArcGIS toolbox using Python libraries. Testing and validation are performed on a large datasets to determine the effectiveness of the toolbox and results are presented. Significant power demand is located in urban areas, where, theoretically, a large amount of building surface area is also available for solar panel installation. Therefore, property owners and power generation companies can benefit from a citywide solar potential map, which can provide available estimated annual solar energy at a given location. An efficient solar potential measurement is a prerequisite for an effective solar energy system in an urban area. In addition, the solar potential calculation from rooftops and building facades could open up a wide variety of options for solar panel installations. However, complex urban scenes make it hard to estimate the solar potential, partly because of shadows cast by the buildings. LiDAR-based 3D city models could possibly be the right technology for solar potential mapping. Although, most of the current LiDAR-based local solar potential assessment algorithms mainly address rooftop potential calculation, whereas building facades can contribute a significant amount of viable surface area for solar panel installation. In this paper, we introduce a new algorithm to calculate solar potential of both rooftop and building facades. Solar potential received by the rooftops and facades over the year are also investigated in the test area.

A probabilistic tornado wind hazard model for the continental United States

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

Hossain, Q; Kimball, J; Mensing, R

A probabilistic tornado wind hazard model for the continental United States (CONUS) is described. The model incorporates both aleatory (random) and epistemic uncertainties associated with quantifying the tornado wind hazard parameters. The temporal occurrences of tornadoes within the continental United States (CONUS) is assumed to be a Poisson process. A spatial distribution of tornado touchdown locations is developed empirically based on the observed historical events within the CONUS. The hazard model is an aerial probability model that takes into consideration the size and orientation of the facility, the length and width of the tornado damage area (idealized as a rectanglemore » and dependent on the tornado intensity scale), wind speed variation within the damage area, tornado intensity classification errors (i.e.,errors in assigning a Fujita intensity scale based on surveyed damage), and the tornado path direction. Epistemic uncertainties in describing the distributions of the aleatory variables are accounted for by using more than one distribution model to describe aleatory variations. The epistemic uncertainties are based on inputs from a panel of experts. A computer program, TORNADO, has been developed incorporating this model; features of this program are also presented.« less

Controlled generation of high-intensity optical rogue waves by induced modulation instability

PubMed Central

Zhao, Saili; Yang, Hua; Chen, Nengsong; Zhao, Chujun

2017-01-01

Optical rogue waves are featured as the generation of high amplitude events at low probability in optical systems. Moreover, the formation of optical rogue waves is unpredictable and transient in photonic crystal fibers. In this paper, we put forward a method to generate high-intensity optical rogue waves in a more controlled way based on induced modulation instability, which can suppress the noise effect and hence play a leading role in the process of pulse evolution. Our numerical simulations indicate that the generation of rogue wave can be controlled when seeding at the optimal modulation frequency and the intensity of rogue wave can be enhanced with appropriate modulation depth. Further, high-intensity rogue wave can also be ejected in the fiber with a shorter propagation length by regulating the modulation depth. These results all provide a better understanding of optical rogue wave, which can contribute to the generation of tunable long-wavelength spectral components and selective excitation of mid-infrared supercontinuum. PMID:28051149

Controlled generation of high-intensity optical rogue waves by induced modulation instability.

PubMed

Zhao, Saili; Yang, Hua; Chen, Nengsong; Zhao, Chujun

2017-01-04

Optical rogue waves are featured as the generation of high amplitude events at low probability in optical systems. Moreover, the formation of optical rogue waves is unpredictable and transient in photonic crystal fibers. In this paper, we put forward a method to generate high-intensity optical rogue waves in a more controlled way based on induced modulation instability, which can suppress the noise effect and hence play a leading role in the process of pulse evolution. Our numerical simulations indicate that the generation of rogue wave can be controlled when seeding at the optimal modulation frequency and the intensity of rogue wave can be enhanced with appropriate modulation depth. Further, high-intensity rogue wave can also be ejected in the fiber with a shorter propagation length by regulating the modulation depth. These results all provide a better understanding of optical rogue wave, which can contribute to the generation of tunable long-wavelength spectral components and selective excitation of mid-infrared supercontinuum.

Independent processing of changes in auditory single features and feature conjunctions in humans as indexed by the mismatch negativity.

PubMed

Takegata, R; Paavilainen, P; Näätänen, R; Winkler, I

1999-05-07

The mismatch negativity (MMN), an event-related potential component of the EEG, is elicited by violations of auditory regularities In the present study, the stimulus blocks contained two types of standard tones, differing from each other in frequency and intensity. MMNs were recorded to three different types of deviant stimuli: (a) feature deviants, differing from standards in their perceived locus of origin; (b) conjunction deviants, having the frequency of one of the standards and the intensity of the other; (c) double deviants, differing from standards in both (a) and (b). The MMN to double deviants was similar to the sum of the MMNs to feature and conjunction deviants. This result indicates that changes in simple stimulus features and conjunction of features are processed independently by the automatic sound change detection system indexed by MMN.

Speech-feature discrimination in children with Asperger syndrome as determined with the multi-feature mismatch negativity paradigm.

PubMed

Kujala, T; Kuuluvainen, S; Saalasti, S; Jansson-Verkasalo, E; von Wendt, L; Lepistö, T

2010-09-01

Asperger syndrome, belonging to the autistic spectrum of disorders, involves deficits in social interaction and prosodic use of language but normal development of formal language abilities. Auditory processing involves both hyper- and hypoactive reactivity to acoustic changes. Responses composed of mismatch negativity (MMN) and obligatory components were recorded for five types of deviations in syllables (vowel, vowel duration, consonant, syllable frequency, syllable intensity) with the multi-feature paradigm from 8-12-year old children with Asperger syndrome. Children with Asperger syndrome had larger MMNs for intensity and smaller MMNs for frequency changes than typically developing children, whereas no MMN group differences were found for the other deviant stimuli. Furthermore, children with Asperger syndrome performed more poorly than controls in Comprehension of Instructions subtest of a language test battery. Cortical speech-sound discrimination is aberrant in children with Asperger syndrome. This is evident both as hypersensitive and depressed neural reactions to speech-sound changes, and is associated with features (frequency, intensity) which are relevant for prosodic processing. The multi-feature MMN paradigm, which includes variation and thereby resembles natural speech hearing circumstances, suggests abnormal pattern of speech discrimination in Asperger syndrome, including both hypo- and hypersensitive responses for speech features. 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection

PubMed Central

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-01-01

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices. PMID:25177107

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

PubMed

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-11-01

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

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

Galavis, P; Friedman, K; Chandarana, H

Purpose: Radiomics involves the extraction of texture features from different imaging modalities with the purpose of developing models to predict patient treatment outcomes. The purpose of this study is to investigate texture feature reproducibility across [18F]FDG PET/CT and [18F]FDG PET/MR imaging in patients with primary malignancies. Methods: Twenty five prospective patients with solid tumors underwent clinical [18F]FDG PET/CT scan followed by [18F]FDG PET/MR scans. In all patients the lesions were identified using nuclear medicine reports. The images were co-registered and segmented using an in-house auto-segmentation method. Fifty features, based on the intensity histogram, second and high order matrices, were extractedmore » from the segmented regions from both image data sets. One-way random-effects ANOVA model of the intra-class correlation coefficient (ICC) was used to establish texture feature correlations between both data sets. Results: Fifty features were classified based on their ICC values, which were found in the range from 0.1 to 0.86, in three categories: high, intermediate, and low. Ten features extracted from second and high-order matrices showed large ICC ≥ 0.70. Seventeen features presented intermediate 0.5 ≤ ICC ≤ 0.65 and the remaining twenty three presented low ICC ≤ 0.45. Conclusion: Features with large ICC values could be reliable candidates for quantification as they lead to similar results from both imaging modalities. Features with small ICC indicates a lack of correlation. Therefore, the use of these features as a quantitative measure will lead to different assessments of the same lesion depending on the imaging modality from where they are extracted. This study shows the importance of the need for further investigation and standardization of features across multiple imaging modalities.« less

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.

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.

The Monitoring Case of Ground-Based Synthetic Aperture Radar with Frequency Modulated Continuous Wave System

NASA Astrophysics Data System (ADS)

Zhang, H. Y.; Zhai, Q. P.; Chen, L.; Liu, Y. J.; Zhou, K. Q.; Wang, Y. S.; Dou, Y. D.

2017-09-01

The features of the landslide geological disaster are wide distribution, variety, high frequency, high intensity, destructive and so on. It has become a natural disaster with harmful and wide range of influence. The technology of ground-based synthetic aperture radar is a novel deformation monitoring technology developed in recent years. The features of the technology are large monitoring area, high accuracy, long distance without contact and so on. In this paper, fast ground-based synthetic aperture radar (Fast-GBSAR) based on frequency modulated continuous wave (FMCW) system is used to collect the data of Ma Liuzui landslide in Chongqing. The device can reduce the atmospheric errors caused by rapidly changing environment. The landslide deformation can be monitored in severe weather conditions (for example, fog) by Fast-GBSAR with acquisition speed up to 5 seconds per time. The data of Ma Liuzui landslide in Chongqing are analyzed in this paper. The result verifies that the device can monitor landslide deformation under severe weather conditions.

Growth plate injury at the base of the coracoid: MRI features.

PubMed

Alaia, Erin FitzGerald; Rosenberg, Zehava Sadka; Rossi, Ignacio; Zember, Jonathan; Roedl, Johannes B; Pinkney, Lynne; Steinbach, Lynne S

2017-11-01

To assess the MRI features of growth plate injury at the base of the coracoid process. Subjects were identified through retrospective search of our department imaging database and teaching files and the teaching files of two outside academic institutions. The coracoid base growth plate was examined with attention to widening, irregularity, abnormal signal intensity of the growth plate, and the presence of adjacent soft tissue edema. The apposing coracoid and scapular bony surfaces were examined for signal intensity and morphology. Shoulder MRIs in eight patients with coracoid base growth plate disturbances were retrospectively reviewed (7 males, 1 female, mean age 15 years). Growth plate injury manifested as widening, irregularity and increased signal, apposing bony marrow edema and hypertrophy, and surrounding soft tissue edema. Five subjects were athletes (football, archery, basketball, swimming, rugby), two had a history of neuromuscular disorders, and one subject presented after a fall. Clinical indications included: rule out labral tear (n = 3), rule out rotator cuff tear or fracture after fall (n = 1), nonspecific pain (n = 1), shoulder subluxation, rule out glenoid pathology (n = 1, patient with underlying neuromuscular disorder), muscular dystrophy with shoulder pain (n = 1), and impingement (n = 1). Coracoid growth plate injury was not suspected clinically in any of the patients. Awareness of the imaging appearance of coracoid base growth plate injury can aid in a more accurate diagnosis of shoulder MRI studies in young pediatric athletes. While uncommon, coracoid growth plate injury should be considered when assessing children with shoulder symptomatology.

[Emission Characteristics of Vehicle Exhaust in Beijing Based on Actual Traffic Flow Information].

PubMed

Fan, Shou-bin; Tian, Ling-di; Zhang, Dong-xu; Qu, Song

2015-08-01

The basic data of traffic volume, vehicle type constitute and speed on road networks in Beijing was obtained fly modei simulation and field survey. Based on actual traffic flow information and. emission factors data with temporal and spatial distribution features, emission inventory of motor vehicle exhaust in Beijing was built on the ArcGIS platform, meanwhile, the actual road emission characteristics and spatial distribution of the pollutant emissions were analyzed. The results showed that the proportion of passenger car was higher than 89% on each type of road in the urban, and the proportion of passenger car was the highest in suburban roads as well while the pickup truck, medium truck, heavy truck, motorbus, tractor and motorcycle also occupied a certain proportion. There was a positive correlation between the pollutant emission intensity and traffic volume, and the emission intensity was generally higher in daytime than nighttime, but the diurnal variation trend of PM emission was not clear for suburban roads and the emission intensity was higher in nighttime than daytime for highway. The emission intensities in urban area, south, southeast and northeast areas near urban were higher than those in the western and northern mountainous areas with lower density of road network. The ring roads in urban and highways in suburban had higher emission intensity because of the heavy traffic volume.

Temporal trends in patient characteristics and survival of intensive care admissions with sepsis: a multicenter analysis*.

PubMed

Dreiher, Jacob; Almog, Yaniv; Sprung, Charles L; Codish, Shlomi; Klein, Moti; Einav, Sharon; Bar-Lavie, Yaron; Singer, Pierre P; Nimrod, Adi; Sachs, Jeffrey; Talmor, Daniel; Friger, Michael; Greenberg, Dan; Olsfanger, David; Hersch, Moshe; Novack, Victor

2012-03-01

To estimate in-hospital, 1-yr, and long-term mortality and to assess time trends in incidence and outcomes of sepsis admissions in the intensive care unit. A population-based, multicenter, retrospective cohort study. Patients hospitalized with sepsis in the intensive care unit in seven general hospitals in Israel during 2002-2008. None. Survival data were collected and analyzed according to demographic and background clinical characteristics, as well as features of the sepsis episode, using Kaplan-Meier approach for long-term survival. A total of 5,155 patients were included in the cohort (median age: 70, 56.3% males; median Charlson comorbidity index: 4). The mean number of intensive care unit admissions per month increased over time, while no change in in-hospital mortality was observed. The proportion of patients surviving to hospital discharge was 43.9%. The 1-, 2-, 5-, and 8-yr survival rates were 33.0%, 29.8%, 23.3%, and 19.8%, respectively. Mortality was higher in older patients, patients with a higher Charlson comorbidity index, and those with multiorgan failure, and similar in males and females. One-year age-standardized mortality ratio was 21-fold higher than expected, based on the general population rates. Mortality following intensive care unit sepsis admission remains high and is correlated with underlying patients' characteristics, including age, comorbidities, and the number of failing organ systems.

A robust and hierarchical approach for the automatic co-registration of intensity and visible images

NASA Astrophysics Data System (ADS)

González-Aguilera, Diego; Rodríguez-Gonzálvez, Pablo; Hernández-López, David; Luis Lerma, José

2012-09-01

This paper presents a new robust approach to integrate intensity and visible images which have been acquired with a terrestrial laser scanner and a calibrated digital camera, respectively. In particular, an automatic and hierarchical method for the co-registration of both sensors is developed. The approach integrates several existing solutions to improve the performance of the co-registration between range-based and visible images: the Affine Scale-Invariant Feature Transform (A-SIFT), the epipolar geometry, the collinearity equations, the Groebner basis solution and the RANdom SAmple Consensus (RANSAC), integrating a voting scheme. The approach presented herein improves the existing co-registration approaches in automation, robustness, reliability and accuracy.

The hydrogen coverage of interstellar PAHs

NASA Technical Reports Server (NTRS)

Barker, J. R.; Cohen, M.; Tielens, Alexander G. G. M.; Allamandola, Louis J.; Barker, J. R.; Barker, J. R.

1986-01-01

The rate at which the CH bond in interstellar Polycyclic Aromatic Hydrocarbons (PAHs) rupture due to the absorption of a UV photon has been calculated. The results show that small PAHs (less than or equal to 25 carbon atoms) are expected to be partially dehydrogenated in regions with intense UV fields, while large PAHs (greater than or equal to 25 atoms) are expected to be completely hydrogenated in those regions. Because estimate of the carbon content of interstellar PAHs lie in the range of 20 to 25 carbon atoms, dehydrogenation is probably not very important. Because of the absence of other emission features besides the 11.3 micrometer feature in ground-based 8 to 13 micrometer spectra, it has been suggested that interstellar PAHs are partially dehydrogenated. However, IRAS 8 to 22 micrometer spectra of most sources that show strong 7.7 and 11.2 micrometer emission features also show a plateau of emission extending from about 11.3 to 14 micrometer. Like the 11.3 micrometer feature, this new feature is attributed to the CH out of plane bending mode in PAHs. This new feature shows that interstellar PAHs are not as dehydrogenated as estimated from ground-based 8 to 13 micrometer spectra. It also constrains the molecular structure of interstellar PAHs. In particular, it seems that very condensed PAHs, such as coronene and circumcoronene, dominate the interstellar PAH mixture as expected from stability arguments.

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

PubMed

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

2012-04-01

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

Erosion of Coastal Foredunes: A Review on the Effect of Dune Vegetation

DTIC Science & Technology

2017-02-01

intensity, sustainable nature-based coastal protection measures are of growing interest. One of these considered features is coastal dunes, which... protection by sand banks, beaches, and dunes. Coastal Engineering 87:136–146. Hesp, P. 1991. Ecological processes and plant adaptations on coastal dunes...ERDC/CHL CHETN-I-94 February 2017 Approved for public release; distribution is unlimited. Erosion of Coastal Foredunes: A Review on the Effect

Northeast Artificial Intelligence Consortium Annual Report for 1987. Volume 4. Research in Automated Photointerpretation

DTIC Science & Technology

1989-03-01

KOWLEDGE INFERENCE IMAGE DAAAEENGINE DATABASE Automated Photointerpretation Testbed. 4.1.7 Fig. .1.1-2 An Initial Segmentation of an Image / zx...MRF) theory provide a powerful alternative texture model and have resulted in intensive research activity in MRF model- based texture analysis...interpretation process. 5. Additional, and perhaps more powerful , features have to be incorporated into the image segmentation procedure. 6. Object detection

Classification of normal and malignant human gastric mucosa tissue with confocal Raman microspectroscopy and wavelet analysis

NASA Astrophysics Data System (ADS)

Hu, Yaogai; Shen, Aiguo; Jiang, Tao; Ai, Yong; Hu, Jiming

2008-02-01

Thirty-two samples from the human gastric mucosa tissue, including 13 normal and 19 malignant tissue samples were measured by confocal Raman microspectroscopy. The low signal-to-background ratio spectra from human gastric mucosa tissues were obtained by this technique without any sample preparation. Raman spectral interferences include a broad featureless sloping background due to fluorescence and noise. They mask most Raman spectral feature and lead to problems with precision and quantitation of the original spectral information. A preprocessed algorithm based on wavelet analysis was used to reduce noise and eliminate background/baseline of Raman spectra. Comparing preprocessed spectra of malignant gastric mucosa tissues with those of counterpart normal ones, there were obvious spectral changes, including intensity increase at ˜1156 cm -1 and intensity decrease at ˜1587 cm -1. The quantitative criterion based upon the intensity ratio of the ˜1156 and ˜1587 cm -1 was extracted for classification of the normal and malignant gastric mucosa tissue samples. This could result in a new diagnostic method, which would assist the early diagnosis of gastric cancer.

Mechanisms of resonant low frequency Raman scattering from metallic nanoparticle Lamb modes

NASA Astrophysics Data System (ADS)

Girard, A.; Lermé, J.; Gehan, H.; Margueritat, J.; Mermet, A.

2017-05-01

The low frequency Raman scattering from gold nanoparticle bimodal assemblies with controlled size distributions has been studied. Special care has been paid to determining the size dependence of the Raman intensity corresponding to the quadrupolar Lamb mode. Existing models based on a microscopic description of the scattering mechanism in small particles (bond polarizability, dipole induced dipole models) predict, for any Raman-active Lamb modes, an inelastic intensity scaling as the volume of the nanoparticle. Surprisingly experimental intensity ratios are found to be anomalously much greater than theoretical ones, calling into question this scaling law. To explain these discrepancies, a simple mechanism of Raman scattering, based on the density fluctuations in the nanoparticles induced by the Lamb modes, is introduced. This modeling, in which the nanoparticle is described as an elastic isotropic continuous medium—as in Lamb theory, successfully explains the major features exhibited by low frequency Raman modes. Moreover this model provides a unified picture for any material, suitable for handling both small and large size ranges, as well as non-resonant and resonant excitation conditions in the case of metallic species.

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

Makarov, Nikolay S.; Lin, Qianglu; Pietryga, Jeffrey M.

One source of efficiency losses in photovoltaic cells is their transparency toward solar photons with energies below the band gap of the absorbing layer. This loss can be reduced using a process of up-conversion whereby two or more sub-band-gap photons generate a single above-gap exciton. Traditional approaches to up-conversion, such as nonlinear two-photon absorption (2PA) or triplet fusion, suffer from low efficiency at solar light intensities, a narrow absorption bandwidth, nonoptimal absorption energies, and difficulties for implementing in practical devices. We show that these deficiencies can be alleviated using the effect of Auger up-conversion in thick-shell PbSe/CdSe quantum dots. Thismore » process relies on Auger recombination whereby two low-energy, core-based excitons are converted into a single higher-energy, shell-based exciton. When compared to their monocomponent counterparts, the tailored PbSe/CdSe heterostructures feature enhanced absorption cross-sections, a higher efficiency of the “productive” Auger pathway involving re-excitation of a hole, and longer lifetimes of both core- and shell-localized excitons. These features lead to effective up-conversion cross-sections that are more than 6 orders of magnitude higher than for standard nonlinear 2PA, which allows for efficient up-conversion of continuous wave infrared light at intensities as low as a few watts per square centimeter.« less

A comparative work on the magnetic field-dependent properties of plate-like and spherical iron particle-based magnetorheological grease

PubMed Central

Ubaidillah; Imaduddin, F.; Choi, Seung-Bok; Yazid, I. I. M.

2018-01-01

In this study, a new magnetorheological (MR) grease was made featuring plate-like carbonyl iron (CI) particles, and its magnetic field-dependent rheological properties were experimentally characterized. The plate-like CI particles were prepared through high-energy ball milling of spherical CI particles. Then, three different ratios of the CI particles in the MR grease, varying from 30 to 70 wt% were mixed by dispersing the plate-like CI particles into the grease medium with a mechanical stirrer. The magnetic field-dependent rheological properties of the plate-like CI particle-based MR grease were then investigated using a rheometer by changing the magnetic field intensity from 0 to 0.7 T at room temperature. The measurement was undertaken at two different modes, namely, a continuous shear mode and oscillation mode. It was shown that both the apparent viscosity and storage modulus of the MR grease were heavily dependent on the magnetic field intensity as well as the CI particle fraction. In addition, the differences in the yield stress and the MR effect between the proposed MR grease featuring the plate-like CI particles and the existing MR grease with the spherical CI particles were investigated and discussed in detail. PMID:29630595

A comparative work on the magnetic field-dependent properties of plate-like and spherical iron particle-based magnetorheological grease.

PubMed

Mohamad, N; Ubaidillah; Mazlan, S A; Imaduddin, F; Choi, Seung-Bok; Yazid, I I M

2018-01-01

In this study, a new magnetorheological (MR) grease was made featuring plate-like carbonyl iron (CI) particles, and its magnetic field-dependent rheological properties were experimentally characterized. The plate-like CI particles were prepared through high-energy ball milling of spherical CI particles. Then, three different ratios of the CI particles in the MR grease, varying from 30 to 70 wt% were mixed by dispersing the plate-like CI particles into the grease medium with a mechanical stirrer. The magnetic field-dependent rheological properties of the plate-like CI particle-based MR grease were then investigated using a rheometer by changing the magnetic field intensity from 0 to 0.7 T at room temperature. The measurement was undertaken at two different modes, namely, a continuous shear mode and oscillation mode. It was shown that both the apparent viscosity and storage modulus of the MR grease were heavily dependent on the magnetic field intensity as well as the CI particle fraction. In addition, the differences in the yield stress and the MR effect between the proposed MR grease featuring the plate-like CI particles and the existing MR grease with the spherical CI particles were investigated and discussed in detail.

Archeomagnetic intensity spikes: global or regional geomagnetic field features?

NASA Astrophysics Data System (ADS)

Korte, Monika; Constable, Catherine G.

2018-03-01

Variations of the geomagnetic field prior to direct observations are inferred from archeo- and paleomagnetic experiments. Seemingly unusual variations not seen in the present day and historical field are of particular interest to constrain the full range of core dynamics. Recently, archeomagnetic intensity spikes, characterised by very high field values that appear to be associated with rapid secular variation rates, have been reported from several parts of the world. They were first noted in data from the Levant at around 900 BCE. A recent re-assessment of previous and new Levantine data, involving a rigorous quality assessment, interprets the observations as an extreme local geomagnetic high with at least two intensity spikes between the 11^{th} and 8^{th} centuries BCE. Subsequent reports of similar features from Asia, the Canary Islands and Texas raise the question of whether such features might be common occurrences, or whether they might even be part of a global magnetic field feature. Here we use spherical harmonic modelling to test two hypotheses: firstly, whether the Levantine and other potential spikes might be associated with higher dipole field intensity than shown by existing global field models around 1000 BCE, and secondly, whether the observations from different parts of the world are compatible with a westward drifting intense flux patch. Our results suggest that the spikes originate from intense flux patches growing and decaying mostly in situ, combined with stronger and more variable dipole moment than shown by previous global field models. Axial dipole variations no more than 60% higher than observed in the present field, probably within the range of normal geodynamo behaviour, seem sufficient to explain the observations.

Automatic classification of endoscopic images for premalignant conditions of the esophagus

NASA Astrophysics Data System (ADS)

Boschetto, Davide; Gambaretto, Gloria; Grisan, Enrico

2016-03-01

Barrett's esophagus (BE) is a precancerous complication of gastroesophageal reflux disease in which normal stratified squamous epithelium lining the esophagus is replaced by intestinal metaplastic columnar epithelium. Repeated endoscopies and multiple biopsies are often necessary to establish the presence of intestinal metaplasia. Narrow Band Imaging (NBI) is an imaging technique commonly used with endoscopies that enhances the contrast of vascular pattern on the mucosa. We present a computer-based method for the automatic normal/metaplastic classification of endoscopic NBI images. Superpixel segmentation is used to identify and cluster pixels belonging to uniform regions. From each uniform clustered region of pixels, eight features maximizing differences among normal and metaplastic epithelium are extracted for the classification step. For each superpixel, the three mean intensities of each color channel are firstly selected as features. Three added features are the mean intensities for each superpixel after separately applying to the red-channel image three different morphological filters (top-hat filtering, entropy filtering and range filtering). The last two features require the computation of the Grey-Level Co-Occurrence Matrix (GLCM), and are reflective of the contrast and the homogeneity of each superpixel. The classification step is performed using an ensemble of 50 classification trees, with a 10-fold cross-validation scheme by training the classifier at each step on a random 70% of the images and testing on the remaining 30% of the dataset. Sensitivity and Specificity are respectively of 79.2% and 87.3%, with an overall accuracy of 83.9%.

Insight into resolution enhancement in generalized two-dimensional correlation spectroscopy.

PubMed

Ma, Lu; Sikirzhytski, Vitali; Hong, Zhenmin; Lednev, Igor K; Asher, Sanford A

2013-03-01

Generalized two-dimensional correlation spectroscopy (2D-COS) can be used to enhance spectral resolution in order to help differentiate highly overlapped spectral bands. Despite the numerous extensive 2D-COS investigations, the origin of the 2D spectral resolution enhancement mechanism(s) is not completely understood. In the work here, we studied the 2D-COS of simulated spectra in order to develop new insights into the dependence of 2D-COS spectral features on the overlapping band separations, their intensities and bandwidths, and their band intensity change rates. We found that the features in the 2D-COS maps that are derived from overlapping bands were determined by the spectral normalized half-intensities and the total intensity changes of the correlated bands. We identified the conditions required to resolve overlapping bands. In particular, 2D-COS peak resolution requires that the normalized half-intensities of a correlating band have amplitudes between the maxima and minima of the normalized half-intensities of the overlapping bands.

Insight into Resolution Enhancement in Generalized Two-Dimensional Correlation Spectroscopy

PubMed Central

Ma, Lu; Sikirzhytski, Vitali; Hong, Zhenmin; Lednev, Igor K.; Asher, Sanford A.

2014-01-01

Generalized two-dimensional correlation spectroscopy (2D COS) can be used to enhance spectral resolution in order to help differentiate highly overlapped spectral bands. Despite the numerous extensive 2D COS investigations, the origin of the 2D spectral resolution enhancement mechanism(s) are not completely understood. In the work here we studied the 2D COS of simulated spectra in order to develop new insights into the dependence of the 2D COS spectral features on the overlapping band separations, their intensities and bandwidths, and their band intensity change rates. We find that the features in the 2D COS maps that derive from overlapping bands are determined by the spectral normalized half-intensities and the total intensity changes of the correlated bands. We identify the conditions required to resolve overlapping bands. In particular, 2D COS peak resolution requires that the normalized half-intensities of a correlating band have amplitudes between the maxima and minima of the normalized half-intensities of the overlapping bands. PMID:23452492

Predicting human olfactory perception from chemical features of odor molecules.

PubMed

Keller, Andreas; Gerkin, Richard C; Guan, Yuanfang; Dhurandhar, Amit; Turu, Gabor; Szalai, Bence; Mainland, Joel D; Ihara, Yusuke; Yu, Chung Wen; Wolfinger, Russ; Vens, Celine; Schietgat, Leander; De Grave, Kurt; Norel, Raquel; Stolovitzky, Gustavo; Cecchi, Guillermo A; Vosshall, Leslie B; Meyer, Pablo

2017-02-24

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule. Copyright © 2017, American Association for the Advancement of Science.

Optical Flow Estimation for Flame Detection in Videos

PubMed Central

Mueller, Martin; Karasev, Peter; Kolesov, Ivan; Tannenbaum, Allen

2014-01-01

Computational vision-based flame detection has drawn significant attention in the past decade with camera surveillance systems becoming ubiquitous. Whereas many discriminating features, such as color, shape, texture, etc., have been employed in the literature, this paper proposes a set of motion features based on motion estimators. The key idea consists of exploiting the difference between the turbulent, fast, fire motion, and the structured, rigid motion of other objects. Since classical optical flow methods do not model the characteristics of fire motion (e.g., non-smoothness of motion, non-constancy of intensity), two optical flow methods are specifically designed for the fire detection task: optimal mass transport models fire with dynamic texture, while a data-driven optical flow scheme models saturated flames. Then, characteristic features related to the flow magnitudes and directions are computed from the flow fields to discriminate between fire and non-fire motion. The proposed features are tested on a large video database to demonstrate their practical usefulness. Moreover, a novel evaluation method is proposed by fire simulations that allow for a controlled environment to analyze parameter influences, such as flame saturation, spatial resolution, frame rate, and random noise. PMID:23613042

Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain.

PubMed

Cheng, J C; Rogachov, A; Hemington, K S; Kucyi, A; Bosma, R L; Lindquist, M A; Inman, R D; Davis, K D

2018-04-26

Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients' pain assessed at various time-scales (e.g., short-term state versus long-term trait). Patients experience pain "traits" indicative of their general condition, but also pain "states" that vary day to day. Here, we used network-based multivariate machine learning to determine how patterns in dynamic and static brain communication are related to different characteristics and timescales of chronic pain. Our models were based on resting state dynamic and static functional connectivity (dFC, sFC) in patients with chronic neuropathic pain (NP) or non-NP. The most prominent networks in the models were the default mode, salience, and executive control networks. We also found that cross-network measures of dFC rather than sFC were better associated with patients' pain, but only in those with NP features. These associations were also more highly and widely associated with measures of trait rather than state pain. Furthermore, greater dynamic connectivity with executive control networks was associated with milder neuropathic pain, but greater dynamic connectivity with limbic networks was associated greater neuropathic pain. Compared with healthy individuals, the dFC features most highly related to trait neuropathic pain were also more abnormal in patients with greater pain. Our findings indicate that dFC reflects patients' overall pain condition (i.e., trait pain), not just their current state, and is impacted by complexities in pain features beyond intensity.

Contrasting Tropical Rainfall Regimes Using TRMM and Ground-Based Polarimetric Radar

NASA Astrophysics Data System (ADS)

Rutledge, S. A.; Cifelli, R.; Lang, T.; Nesbitt, S.

2009-04-01

The NASA TRMM satellite has provided unprecedented data for over 11 years. TRMM precipitation products have advanced our understanding of tropical precipitation considerably. Field programs in the tropics, specifically TRMM-LBA (January-February 1999 in Brazil; a TRMM ground validation experiment) and NAME (North American Monsoon Experiment, summer 2004 along the west coast of Mexico) have provided opportunities to investigate the characteristics of precipitation using S-band polarimetric radar data. Both of these locales feature heavy, monsoon-like precipitation. However, there is significant variability in precipitation in these regions. In Brazil, two distinct rainfall regimes were observed. During "easterly" phase periods, precipitation was continental like, featuring deep, intense convection. During "westerly" periods, precipitation was more oceanic like, featuring weaker convection embedded in widespread stratiform precipitation. In NAME, precipitation variability was forced more by terrain, opposed to synoptic conditions, as was the case in Brazil. The National Center for Atmospheric Research S-pol radar was used to diagnose precipitation characteristics. Larger drops, larger ice mass aloft, and larger rain contents were found in the TRMM-LBA easterly phases compared to westerly events. For NAME, larger drops, larger ice mass aloft, and larger rain contents were found for coastal plain convection compared to convection over the higher terrain of the Sierra Madre Occidental or adjacent coastal waters. The effects of these differences on TRMM Precipitation Radar based rainfall estimates are investigated. These microphysical differences suggest the use of different Z-R estimators as a function of regime and elevation. It appears that the TRMM attenuation correction is inadequate for intense convection observed in these two regions.

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.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Azadirachtin poisoning: a case report.

PubMed

Iyyadurai, Ramya; Surekha, V; Sathyendra, Sowmya; Paul Wilson, Benny; Gopinath, K G

2010-10-01

The use of neem-based products is widespread in the Indian Subcontinent. Neem-based pesticides obtained from neem kernels are considered natural and safe. The toxic effects of ingestion and overdose of this pesticide in adults have not been described in this literature. We report the case of a 35-year-old lady who had consumed Azadirachtin in an attempt of deliberate self-harm. The patient had features of neurotoxicity because of Azadirachtin requiring intensive medical care with mechanical ventilation. The patient survived the overdose with no long-lasting side effects of the toxin.

Investigation of evanescent coupling between tapered fiber and a multimode slab waveguide.

PubMed

Dong, Shaofei; Ding, Hui; Liu, Yiying; Qi, Xiaofeng

2012-04-01

A tapered fiber-slab waveguide coupler (TFSC) is proposed in this paper. Both the numerical analysis based on the beam propagation method and experiments are used for investigating the dependencies of TFSC transmission features on their geometric parameters. From the simulations and experimental results, the rules for fabricating a TFSC with low transmission loss and sharp resonant spectra by optimizing the configuration parameters are presented. The conclusions derived from our work may provide helpful references for optimally designing and fabricating TFSC-based devices, such as sensors, wavelength filters, and intensity modulators.

Toward a new modeling of international economics: An attempt to reformulate an international trade model based on real option theory

NASA Astrophysics Data System (ADS)

Fujita, Yasunori

2007-09-01

Reformulation of economics by physics has been carried out intensively to reveal many features of the asset market, which were missed in the classical economic theories. The present paper attempts to shed new light on this field. That is, this paper aims at reformulating the international trade model by making use of the real option theory. Based on such a stochastic dynamic model, we examine how the fluctuation of the foreign exchange rate makes effect on the welfare of the exporting country.

Fire detection system using random forest classification for image sequences of complex background

NASA Astrophysics Data System (ADS)

Kim, Onecue; Kang, Dong-Joong

2013-06-01

We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.

Camouflaging in a Complex Environment—Octopuses Use Specific Features of Their Surroundings for Background Matching

PubMed Central

Josef, Noam; Amodio, Piero; Fiorito, Graziano; Shashar, Nadav

2012-01-01

Living under intense predation pressure, octopuses evolved an effective and impressive camouflaging ability that exploits features of their surroundings to enable them to “blend in.” To achieve such background matching, an animal may use general resemblance and reproduce characteristics of its entire surroundings, or it may imitate a specific object in its immediate environment. Using image analysis algorithms, we examined correlations between octopuses and their backgrounds. Field experiments show that when camouflaging, Octopus cyanea and O. vulgaris base their body patterns on selected features of nearby objects rather than attempting to match a large field of view. Such an approach enables the octopus to camouflage in partly occluded environments and to solve the problem of differences in appearance as a function of the viewing inclination of the observer. PMID:22649542

Robust human detection, tracking, and recognition in crowded urban areas

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2014-06-01

In this paper, we present algorithms we recently developed to support an automated security surveillance system for very crowded urban areas. In our approach for human detection, the color features are obtained by taking the difference of R, G, B spectrum and converting R, G, B to HSV (Hue, Saturation, Value) space. Morphological patch filtering and regional minimum and maximum segmentation on the extracted features are applied for target detection. The human tracking process approach includes: 1) Color and intensity feature matching track candidate selection; 2) Separate three parallel trackers for color, bright (above mean intensity), and dim (below mean intensity) detections, respectively; 3) Adaptive track gate size selection for reducing false tracking probability; and 4) Forward position prediction based on previous moving speed and direction for continuing tracking even when detections are missed from frame to frame. The Human target recognition is improved with a Super-Resolution Image Enhancement (SRIE) process. This process can improve target resolution by 3-5 times and can simultaneously process many targets that are tracked. Our approach can project tracks from one camera to another camera with a different perspective viewing angle to obtain additional biometric features from different perspective angles, and to continue tracking the same person from the 2nd camera even though the person moved out of the Field of View (FOV) of the 1st camera with `Tracking Relay'. Finally, the multiple cameras at different view poses have been geo-rectified to nadir view plane and geo-registered with Google- Earth (or other GIS) to obtain accurate positions (latitude, longitude, and altitude) of the tracked human for pin-point targeting and for a large area total human motion activity top-view. Preliminary tests of our algorithms indicate than high probability of detection can be achieved for both moving and stationary humans. Our algorithms can simultaneously track more than 100 human targets with averaged tracking period (time length) longer than the performance of the current state-of-the-art.

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.

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

NASA Technical Reports Server (NTRS)

Maskey, Manil; Cecil, Dan; Ramachandran, Rahul; Miller, Jeffrey J.

2018-01-01

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed.

A Comparison of Natural and Urban Characteristics and the Development of Urban Intensity Indices Across Six Geographic Settings

USGS Publications Warehouse

Falcone, James A.; Stewart, Jana; Sobieszczyk, Steven; Dupree, Jean; McMahon, Gerard; Buell, Gary

2007-01-01

As part of the U.S. Geological Survey National Water-Quality Assessment Program, the effects of urbanization on stream ecosystems have been intensively investigated in six metropolitan areas in the United States. Approximately 30 watersheds in each area, ranging in size from 4 to 560 square kilometers (median is 50 square kilometers), and spanning a development gradient from very low to very high urbanization, were examined near Atlanta, Georgia; Raleigh, North Carolina; Denver, Colorado; Dallas-Fort Worth, Texas; Portland, Oregon; and Milwaukee-Green Bay, Wisconsin. These six studies are a continuation of three previous studies in Boston, Massachusetts; Birmingham, Alabama; and Salt Lake City, Utah. In each study, geographic information system data for approximately 300 variables were assembled to (a) characterize the environmental settings of the areas and (b) establish a consistent multimetric urban intensity index based on locally important land-cover, infrastructure, and socioeconomic variables. This paper describes the key features of urbanization and the urban intensity index for the study watersheds within each area, how they differ across study areas, and the relation between the environmental setting and the characteristics of urbanization. A number of features of urbanization were identified that correlated very strongly to population density in every study area. Of these, road density had the least variability across diverse geographic settings and most closely matched the multimetric nature of the urban intensity index. A common urban intensity index was derived that ranks watersheds across all six study areas. Differences in local natural settings and urban geography were challenging in (a) identifying consistent urban gradients in individual study areas and (b) creating a common urban intensity index that matched the site scores of the local urban intensity index in all areas. It is intended that the descriptions of the similarities and differences in urbanization and environmental settings across these study areas will provide a foundation for understanding and interpreting the effects of urbanization on stream ecosystems in the studies being conducted as part of the National Water-Quality Assessment Program.

Two decades (1993-2012) of adult intensive care unit design: a comparative study of the physical design features of the best practice examples.

PubMed

Rashid, Mahbub

2014-01-01

In 2006, Critical Care Nursing Quarterly published a study of the physical design features of a set of best practice example adult intensive care units (ICUs). These adult ICUs were awarded between 1993 and 2003 by the Society of Critical Care Medicine (SCCM), the American Association of Critical-Care Nurses, and the American Institute of Architects/Academy of Architecture for Health for their efforts to promote the critical care unit environment through design. Since 2003, several more adult ICUs were awarded by the same organizations for similar efforts. This study includes these newer ICUs along with those of the previous study to cover a period of 2 decades from 1993 to 2012. Like the 2006 study, this study conducts a systematic content analysis of the materials submitted by the award-winning adult ICUs. On the basis of the analysis, the study compares the 1993-2002 and 2003-2012 adult ICUs in relation to construction type, unit specialty, unit layout, unit size, patient room size and design, support and service area layout, and family space design. The study also compares its findings with the 2010 Guidelines for Design and Construction of Health Care Facilities of the Facility Guidelines Institute and the 2012 Guidelines for Intensive Care Unit Design of the SCCM. The study indicates that the award-winning ICUs of both decades used several design features that were associated with positive outcomes in research studies. The study also indicates that the award-winning ICUs of the second decade used more evidence-based design features than those of the first decades. In most cases, these ICUs exceeded the requirements of the Facility Guidelines Institute Guidelines to meet those of the SCCM Guidelines. Yet, the award-winning ICUs of both decades also used several features that had very little or no supporting research evidence. Since they all were able to create an optimal critical care environment for which they were awarded, having knowledge of the physical design of these award-winning ICUs may help design better ICUs.

SU-F-J-94: Development of a Plug-in Based Image Analysis Tool for Integration Into Treatment Planning

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

Owen, D; Anderson, C; Mayo, C

Purpose: To extend the functionality of a commercial treatment planning system (TPS) to support (i) direct use of quantitative image-based metrics within treatment plan optimization and (ii) evaluation of dose-functional volume relationships to assist in functional image adaptive radiotherapy. Methods: A script was written that interfaces with a commercial TPS via an Application Programming Interface (API). The script executes a program that performs dose-functional volume analyses. Written in C#, the script reads the dose grid and correlates it with image data on a voxel-by-voxel basis through API extensions that can access registration transforms. A user interface was designed through WinFormsmore » to input parameters and display results. To test the performance of this program, image- and dose-based metrics computed from perfusion SPECT images aligned to the treatment planning CT were generated, validated, and compared. Results: The integration of image analysis information was successfully implemented as a plug-in to a commercial TPS. Perfusion SPECT images were used to validate the calculation and display of image-based metrics as well as dose-intensity metrics and histograms for defined structures on the treatment planning CT. Various biological dose correction models, custom image-based metrics, dose-intensity computations, and dose-intensity histograms were applied to analyze the image-dose profile. Conclusion: It is possible to add image analysis features to commercial TPSs through custom scripting applications. A tool was developed to enable the evaluation of image-intensity-based metrics in the context of functional targeting and avoidance. In addition to providing dose-intensity metrics and histograms that can be easily extracted from a plan database and correlated with outcomes, the system can also be extended to a plug-in optimization system, which can directly use the computed metrics for optimization of post-treatment tumor or normal tissue response models. Supported by NIH - P01 - CA059827.« less

An object-based visual attention model for robotic applications.

PubMed

Yu, Yuanlong; Mann, George K I; Gosine, Raymond G

2010-10-01

By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.

Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading.

PubMed

Xie, Tian; Chen, Xiao; Fang, Jingqin; Kang, Houyi; Xue, Wei; Tong, Haipeng; Cao, Peng; Wang, Sumei; Yang, Yizeng; Zhang, Weiguo

2018-04-01

Presurgical glioma grading by dynamic contrast-enhanced MRI (DCE-MRI) has unresolved issues. The aim of this study was to investigate the ability of textural features derived from pharmacokinetic model-based or model-free parameter maps of DCE-MRI in discriminating between different grades of gliomas, and their correlation with pathological index. Retrospective. Forty-two adults with brain gliomas. 3.0T, including conventional anatomic sequences and DCE-MRI sequences (variable flip angle T1-weighted imaging and three-dimensional gradient echo volumetric imaging). Regions of interest on the cross-sectional images with maximal tumor lesion. Five commonly used textural features, including Energy, Entropy, Inertia, Correlation, and Inverse Difference Moment (IDM), were generated. All textural features of model-free parameters (initial area under curve [IAUC], maximal signal intensity [Max SI], maximal up-slope [Max Slope]) could effectively differentiate between grade II (n = 15), grade III (n = 13), and grade IV (n = 14) gliomas (P < 0.05). Two textural features, Entropy and IDM, of four DCE-MRI parameters, including Max SI, Max Slope (model-free parameters), vp (Extended Tofts), and vp (Patlak) could differentiate grade III and IV gliomas (P < 0.01) in four measurements. Both Entropy and IDM of Patlak-based K trans and vp could differentiate grade II (n = 15) from III (n = 13) gliomas (P < 0.01) in four measurements. No textural features of any DCE-MRI parameter maps could discriminate between subtypes of grade II and III gliomas (P < 0.05). Both Entropy and IDM of Extended Tofts- and Patlak-based vp showed highest area under curve in discriminating between grade III and IV gliomas. However, intraclass correlation coefficient (ICC) of these features revealed relatively lower inter-observer agreement. No significant correlation was found between microvascular density and textural features, compared with a moderate correlation found between cellular proliferation index and those features. Textural features of DCE-MRI parameter maps displayed a good ability in glioma grading. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111. © 2017 International Society for Magnetic Resonance in Medicine.

Nitrogen airglow sources - Comparison of Triton, Titan, and earth

NASA Technical Reports Server (NTRS)

Strobel, Darrell F.; Meier, R. R.; Summers, Michael E.; Strickland, Douglas J.

1991-01-01

The individual contributions of direct solar excitation, photoelectron excitation, and magnetospheric electron excitation of Triton and Titan airglow observed by the Voyager Ultraviolet Spectrometer (UVS) are quantified. The principal spectral features of Triton's airglow are shown to be consistent with precipitation of magnetospheric electrons with power dissipation about 500 million W. Solar excitation rates of the dominant N2 and N(+) emission features are factors of 2-7 weaker than magnetospheric electron excitation. On Titan, the calculated disk center and bright limb N(+) 1085 A intensities due to solar excitation agree with observed values, while the 970 A feature is mostly N21 c5 band emission. The calculated LBH intensity by photoelectrons suggests that magnetospheric electrons play a minor role in Titan's UV airglow. On earth, solar/photoelectron excitation explains the observed N(+) 1085 A and LBH intensites and accounts for only 40 percent of the N(+) 916 A intensity.

The application of inelastic neutron scattering to explore the significance of a magnetic transition in an iron based Fischer-Tropsch catalyst that is active for the hydrogenation of CO

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

Warringham, Robbie; McFarlane, Andrew R.; Lennon, David, E-mail: David.Lennon@Glasgow.ac.uk

2015-11-07

An iron based Fischer-Tropsch synthesis catalyst is evaluated using CO hydrogenation at ambient pressure as a test reaction and is characterised by a combination of inelastic neutron scattering (INS), powder X-ray diffraction, temperature-programmed oxidation, Raman scattering, and transmission electron microscopy. The INS spectrum of the as-prepared bulk iron oxide pre-catalyst (hematite, α-Fe{sub 2}O{sub 3}) is distinguished by a relatively intense band at 810 cm{sup −1}, which has previously been tentatively assigned as a magnon (spinon) feature. An analysis of the neutron scattering intensity of this band as a function of momentum transfer unambiguously confirms this assignment. Post-reaction, the spinon featuremore » disappears and the INS spectrum is characterised by the presence of a hydrocarbonaceous overlayer. A role for the application of INS in magnetic characterisation of iron based FTS catalysts is briefly considered.« less

Theoretical approach to resonant inelastic x-ray scattering in iron-based superconductors at the energy scale of the superconducting gap

PubMed Central

Marra, Pasquale; van den Brink, Jeroen; Sykora, Steffen

2016-01-01

We develop a phenomenological theory to predict the characteristic features of the momentum-dependent scattering amplitude in resonant inelastic x-ray scattering (RIXS) at the energy scale of the superconducting gap in iron-based super-conductors. Taking into account all relevant orbital states as well as their specific content along the Fermi surface we evaluate the charge and spin dynamical structure factors for the compounds LaOFeAs and LiFeAs, based on tight-binding models which are fully consistent with recent angle-resolved photoemission spectroscopy (ARPES) data. We find a characteristic intensity redistribution between charge and spin dynamical structure factors which discriminates between sign-reversing and sign-preserving quasiparticle excitations. Consequently, our results show that RIXS spectra can distinguish between s± and s++ wave gap functions in the singlet pairing case. In addition, we find that an analogous intensity redistribution at small momenta can reveal the presence of a chiral p-wave triplet pairing. PMID:27151253

Formation of 238U 16O and 238U 18O observed by time-resolved emission spectroscopy subsequent to laser ablation

DOE PAGES

Weisz, David G.; Crowhurst, Jonathan C.; Siekhaus, Wigbert J.; ...

2017-07-17

Here, we have measured vibronic emission spectra of an oxide of uranium formed after laser ablation of the metal in gaseous oxygen. Specifically, we have measured the time-dependent relative intensity of a band located at approximately 593.6 nm in 16O 2. This band grew in intensity relative to neighboring atomic features as a function time in an oxygen environment but was relatively invariant with time in argon. In addition, we have measured the spectral shift of this band in an 18O 2 atmosphere. Based on this shift, and by comparison with earlier results obtained from free-jet expansion and laser excitation,more » we can confirm that the oxide in question is UO, consistent with recent reports based on laser ablation in 16O 2 only.« less

Formation of 238U 16O and 238U 18O observed by time-resolved emission spectroscopy subsequent to laser ablation

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

Weisz, David G.; Crowhurst, Jonathan C.; Siekhaus, Wigbert J.

Here, we have measured vibronic emission spectra of an oxide of uranium formed after laser ablation of the metal in gaseous oxygen. Specifically, we have measured the time-dependent relative intensity of a band located at approximately 593.6 nm in 16O 2. This band grew in intensity relative to neighboring atomic features as a function time in an oxygen environment but was relatively invariant with time in argon. In addition, we have measured the spectral shift of this band in an 18O 2 atmosphere. Based on this shift, and by comparison with earlier results obtained from free-jet expansion and laser excitation,more » we can confirm that the oxide in question is UO, consistent with recent reports based on laser ablation in 16O 2 only.« less

ProteoCloud: a full-featured open source proteomics cloud computing pipeline.

PubMed

Muth, Thilo; Peters, Julian; Blackburn, Jonathan; Rapp, Erdmann; Martens, Lennart

2013-08-02

We here present the ProteoCloud pipeline, a freely available, full-featured cloud-based platform to perform computationally intensive, exhaustive searches in a cloud environment using five different peptide identification algorithms. ProteoCloud is entirely open source, and is built around an easy to use and cross-platform software client with a rich graphical user interface. This client allows full control of the number of cloud instances to initiate and of the spectra to assign for identification. It also enables the user to track progress, and to visualize and interpret the results in detail. Source code, binaries and documentation are all available at http://proteocloud.googlecode.com. Copyright © 2012 Elsevier B.V. All rights reserved.

Multi-focus image fusion using a guided-filter-based difference image.

PubMed

Yan, Xiang; Qin, Hanlin; Li, Jia; Zhou, Huixin; Yang, Tingwu

2016-03-20

The aim of multi-focus image fusion technology is to integrate different partially focused images into one all-focused image. To realize this goal, a new multi-focus image fusion method based on a guided filter is proposed and an efficient salient feature extraction method is presented in this paper. Furthermore, feature extraction is primarily the main objective of the present work. Based on salient feature extraction, the guided filter is first used to acquire the smoothing image containing the most sharpness regions. To obtain the initial fusion map, we compose a mixed focus measure by combining the variance of image intensities and the energy of the image gradient together. Then, the initial fusion map is further processed by a morphological filter to obtain a good reprocessed fusion map. Lastly, the final fusion map is determined via the reprocessed fusion map and is optimized by a guided filter. Experimental results demonstrate that the proposed method does markedly improve the fusion performance compared to previous fusion methods and can be competitive with or even outperform state-of-the-art fusion methods in terms of both subjective visual effects and objective quality metrics.

How we developed and piloted an electronic key features examination for the internal medicine clerkship based on a US national curriculum.

PubMed

Bronander, Kirk A; Lang, Valerie J; Nixon, L James; Harrell, Heather E; Kovach, Regina; Hingle, Susan; Berman, Norman

2015-01-01

Key features examinations (KFEs) have been used to assess clinical decision making in medical education, yet there are no reports of an online KFE-based on a national curriculum for the internal medicine clerkship. What we did: The authors developed and pilot tested an electronic KFE based on the US Clerkship Directors in Internal Medicine core curriculum. Teams, with expert oversight and peer review, developed key features (KFs) and cases. The exam was pilot tested at eight medical schools with 162 third and fourth year medical students, of whom 96 (59.3%) responded to a survey. While most students reported that the exam was more difficult than a multiple choice question exam, 61 (83.3%) students agreed that it reflected problems seen in clinical practice and 51 (69.9%) students reported that it more accurately assessed the ability to make clinical decisions. The development of an electronic KFs exam is a time-intensive process. A team approach offers built-in peer review and accountability. Students, although not familiar with this format in the US, recognized it as authentically assessing clinical decision-making for problems commonly seen in the clerkship.

Illumination robust face recognition using spatial adaptive shadow compensation based on face intensity prior

NASA Astrophysics Data System (ADS)

Hsieh, Cheng-Ta; Huang, Kae-Horng; Lee, Chang-Hsing; Han, Chin-Chuan; Fan, Kuo-Chin

2017-12-01

Robust face recognition under illumination variations is an important and challenging task in a face recognition system, particularly for face recognition in the wild. In this paper, a face image preprocessing approach, called spatial adaptive shadow compensation (SASC), is proposed to eliminate shadows in the face image due to different lighting directions. First, spatial adaptive histogram equalization (SAHE), which uses face intensity prior model, is proposed to enhance the contrast of each local face region without generating visible noises in smooth face areas. Adaptive shadow compensation (ASC), which performs shadow compensation in each local image block, is then used to produce a wellcompensated face image appropriate for face feature extraction and recognition. Finally, null-space linear discriminant analysis (NLDA) is employed to extract discriminant features from SASC compensated images. Experiments performed on the Yale B, Yale B extended, and CMU PIE face databases have shown that the proposed SASC always yields the best face recognition accuracy. That is, SASC is more robust to face recognition under illumination variations than other shadow compensation approaches.

Understanding Zeeman EIT Noise Correlation Spectra in Buffered Rb Vapor

NASA Astrophysics Data System (ADS)

O'Leary, Shannon; Zheng, Aojie; Crescimanno, Michael

2014-05-01

Noise correlation spectroscopy on systems manifesting Electromagnetically Induced Transparency (EIT) holds promise as a simple, robust method for performing high-resolution spectroscopy used in applications such as EIT-based atomic magnetometry and clocks. During laser light's propagation through a resonant medium, interaction with the medium converts laser phase noise into intensity noise. While this noise conversion can diminish the precision of EIT applications, noise correlation techniques transform the noise into a useful spectroscopic tool that can improve the application's precision. Using a single diode laser with large phase noise, we examine laser intensity noise and noise correlations from Zeeman EIT in a buffered Rb vapor. Of particular interest is a narrow noise correlation feature, resonant with EIT, that has been shown in earlier work to be power-broadening resistant at low powers. We report here on our recent experimental work and complementary theoretical modeling on EIT noise spectra, including a study of power broadening of the narrow noise correlation feature. Understanding the nature of the noise correlation spectrum is essential for optimizing EIT-noise applications.

An overview of UK C130 observations from the FIRE marine stratocumulus IFO

NASA Technical Reports Server (NTRS)

Nicholls, S.; Leighton, J.; Barlow, R.

1990-01-01

An overview of the characteristics of the ten flights made by the C130 during the Intensive Field Observations (IFO) are presented. An indication of data quality is given. Comparisons of the carbon dioxide radiation thermometer and the Rosemount suggest there may be potentially serious wetting problems with the latter, especially close to cloud base. The main features of the cloud layers sampled by the C130 are summarized, including microphysical parameters. Many of the results shown are derived from a first order analysis of turbulence quantities. The gross vertical variation of heat, vapor, and liquid water fluxes from case to case, together with the observed vertical velocity variance, a good indicator of convective activity, are discussed. Preliminary results suggest that the different cases can be usefully categorized using these data. The relationship between these and the measured boundary conditions is mentioned. Similarities and differences between the various cases are highlighted, and interpreted in terms of the researchers' current understanding of stratocumulus. Attention is given to particular features of the various flights which may deserve more intensive study.

Effects of counter-rotating-wave terms of the driving field on the spectrum of resonance fluorescence

NASA Astrophysics Data System (ADS)

Yan, Yiying; Lü, Zhiguo; Zheng, Hang

2013-11-01

We investigate the fluorescence spectrum of a two-level system driven by a monochromatic classical field by the Born-Markovian master equation based on a unitary transformation. The main purpose is to understand the effects of counter-rotating-wave terms of the driving on spectral features of the fluorescence. We have derived an analytical expression for the fluorescence spectrum, which is different from Mollow's theory, while Mollow's result on resonance is the limiting case of ours in moderately weak driving regimes. Our results demonstrate precisely that the counter-rotating-wave terms of the driving play an important role in the fluorescence spectrum for intense driving: (i) the counter-rotating coupling suppresses the red sideband in the Mollow triplet and it enhances the blue one in explicitly contrast to the well-known equal intensity of the sideband in Mollow's theory, (ii) the higher-order Mollow triplets appear as a characteristic spectral feature arising from counter-rotating-wave terms of the driving, and (iii) a significant frequency shift of the sidebands is observed, which depends on both the detuning and driving strength.

Stronger Neural Modulation by Visual Motion Intensity in Autism Spectrum Disorders

PubMed Central

Peiker, Ina; Schneider, Till R.; Milne, Elizabeth; Schöttle, Daniel; Vogeley, Kai; Münchau, Alexander; Schunke, Odette; Siegel, Markus; Engel, Andreas K.; David, Nicole

2015-01-01

Theories of autism spectrum disorders (ASD) have focused on altered perceptual integration of sensory features as a possible core deficit. Yet, there is little understanding of the neuronal processing of elementary sensory features in ASD. For typically developed individuals, we previously established a direct link between frequency-specific neural activity and the intensity of a specific sensory feature: Gamma-band activity in the visual cortex increased approximately linearly with the strength of visual motion. Using magnetoencephalography (MEG), we investigated whether in individuals with ASD neural activity reflect the coherence, and thus intensity, of visual motion in a similar fashion. Thirteen adult participants with ASD and 14 control participants performed a motion direction discrimination task with increasing levels of motion coherence. A polynomial regression analysis revealed that gamma-band power increased significantly stronger with motion coherence in ASD compared to controls, suggesting excessive visual activation with increasing stimulus intensity originating from motion-responsive visual areas V3, V6 and hMT/V5. Enhanced neural responses with increasing stimulus intensity suggest an enhanced response gain in ASD. Response gain is controlled by excitatory-inhibitory interactions, which also drive high-frequency oscillations in the gamma-band. Thus, our data suggest that a disturbed excitatory-inhibitory balance underlies enhanced neural responses to coherent motion in ASD. PMID:26147342

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

NASA Astrophysics Data System (ADS)

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-01

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets—consisting of 20 and 18 volumes, respectively—provided by the Internet Brain Segmentation Repository.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction.

PubMed

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-07

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets-consisting of 20 and 18 volumes, respectively-provided by the Internet Brain Segmentation Repository.

Automated determination of wakefulness and sleep in rats based on non-invasively acquired measures of movement and respiratory activity

PubMed Central

Zeng, Tao; Mott, Christopher; Mollicone, Daniel; Sanford, Larry D.

2012-01-01

The current standard for monitoring sleep in rats requires labor intensive surgical procedures and the implantation of chronic electrodes which have the potential to impact behavior and sleep. With the goal of developing a non-invasive method to determine sleep and wakefulness, we constructed a non-contact monitoring system to measure movement and respiratory activity using signals acquired with pulse Doppler radar and from digitized video analysis. A set of 23 frequency and time-domain features were derived from these signals and were calculated in 10 s epochs. Based on these features, a classification method for automated scoring of wakefulness, non-rapid eye movement sleep (NREM) and REM in rats was developed using a support vector machine (SVM). We then assessed the utility of the automated scoring system in discriminating wakefulness and sleep by comparing the results to standard scoring of wakefulness and sleep based on concurrently recorded EEG and EMG. Agreement between SVM automated scoring based on selected features and visual scores based on EEG and EMG were approximately 91% for wakefulness, 84% for NREM and 70% for REM. The results indicate that automated scoring based on non-invasively acquired movement and respiratory activity will be useful for studies requiring discrimination of wakefulness and sleep. However, additional information or signals will be needed to improve discrimination of NREM and REM episodes within sleep. PMID:22178621

Systematic review: internet-based program for youth smoking prevention and cessation.

PubMed

Park, Eunhee; Drake, Emily

2015-01-01

To review the characteristics and effects Internet-based youth smoking prevention and cessation programs. Systematic review of published articles in peer-reviewed journals in the past 10 years, focused on Internet-based youth smoking prevention and cessation programs. Twelve articles were selected based on the following criteria: studies reporting the outcomes of Internet-based smoking cessation or prevention intervention programs for adolescents who are younger than 24 years. The components of youth Internet-based smoking intervention programs are analyzed based on study features (i.e., sample, design, theoretical basis, analysis, outcome measures) and program characteristics (i.e., focus, setting, frequency, duration, intensity, and different components) that make the programs effective. The most common components of effective Internet-based programs are identified as the following: the use of multimedia, tailored approaches, personalized feedback, and interactive features. The characteristics and effects of the programs vary, but most programs show positive results in youth smoking prevention and cessation in spite of the studies' limitations. The evidence from this review provides useful information of recent efforts related to Internet-based youth smoking prevention and cessation programs, which can have significant clinical implications in developing future innovative youth smoking prevention and intervention programs. © 2014 Sigma Theta Tau International.

Spectropathology-corroborated multimodal quantitative imaging biomarkers for neuroretinal degeneration in diabetic retinopathy

PubMed Central

Guha Mazumder, Arpan; Chatterjee, Swarnadip; Chatterjee, Saunak; Gonzalez, Juan Jose; Bag, Swarnendu; Ghosh, Sambuddha; Mukherjee, Anirban; Chatterjee, Jyotirmoy

2017-01-01

Introduction Image-based early detection for diabetic retinopathy (DR) needs value addition due to lack of well-defined disease-specific quantitative imaging biomarkers (QIBs) for neuroretinal degeneration and spectropathological information at the systemic level. Retinal neurodegeneration is an early event in the pathogenesis of DR. Therefore, development of an integrated assessment method for detecting neuroretinal degeneration using spectropathology and QIBs is necessary for the early diagnosis of DR. Methods The present work explored the efficacy of intensity and textural features extracted from optical coherence tomography (OCT) images after selecting a specific subset of features for the precise classification of retinal layers using variants of support vector machine (SVM). Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) spectroscopy were also performed to confirm the spectropathological attributes of serum for further value addition to the OCT, fundoscopy, and fluorescein angiography (FA) findings. The serum metabolomic findings were also incorporated for characterizing retinal layer thickness alterations and vascular asymmetries. Results Results suggested that OCT features could differentiate the retinal lesions indicating retinal neurodegeneration with high sensitivity and specificity. OCT, fundoscopy, and FA provided geometrical as well as optical features. NMR revealed elevated levels of ribitol, glycerophosphocholine, and uridine diphosphate N-acetyl glucosamine, while the FTIR of serum samples confirmed the higher expressions of lipids and β-sheet-containing proteins responsible for neoangiogenesis, vascular fragility, vascular asymmetry, and subsequent neuroretinal degeneration in DR. Conclusion Our data indicated that disease-specific spectropathological alterations could be the major phenomena behind the vascular attenuations observed through fundoscopy and FA, as well as the variations in the intensity and textural features observed in OCT images. Finally, we propose a model that uses spectropathology corroborated with specific QIBs for detecting neuroretinal degeneration in early diagnosis of DR. PMID:29200821

Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics.

PubMed

Shi, Y; Qi, F; Xue, Z; Chen, L; Ito, K; Matsuo, H; Shen, D

2008-04-01

This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. There are two novelties in the proposed deformable model. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel. Second, the deformable contour is constrained by both population-based and patient-specific shape statistics, and it yields more robust and accurate segmentation of lung fields for serial chest radiographs. In particular, for segmenting the initial time-point images, the population-based shape statistics is used to constrain the deformable contour; as more subsequent images of the same patient are acquired, the patient-specific shape statistics online collected from the previous segmentation results gradually takes more roles. Thus, this patient-specific shape statistics is updated each time when a new segmentation result is obtained, and it is further used to refine the segmentation results of all the available time-point images. Experimental results show that the proposed method is more robust and accurate than other active shape models in segmenting the lung fields from serial chest radiographs.

THE ROLE OF THE HIPPOCAMPUS IN OBJECT DISCRIMINATION BASED ON VISUAL FEATURES.

PubMed

Levcik, David; Nekovarova, Tereza; Antosova, Eliska; Stuchlik, Ales; Klement, Daniel

2018-06-07

The role of rodent hippocampus has been intensively studied in different cognitive tasks. However, its role in discrimination of objects remains controversial due to conflicting findings. We tested whether the number and type of features available for the identification of objects might affect the strategy (hippocampal-independent vs. hippocampal-dependent) that rats adopt to solve object discrimination tasks. We trained rats to discriminate 2D visual objects presented on a computer screen. The objects were defined either by their shape only or by multiple-features (a combination of filling pattern and brightness in addition to the shape). Our data showed that objects displayed as simple geometric shapes are not discriminated by trained rats after their hippocampi had been bilaterally inactivated by the GABA A -agonist muscimol. On the other hand, objects containing a specific combination of non-geometric features in addition to the shape are discriminated even without the hippocampus. Our results suggest that the involvement of the hippocampus in visual object discrimination depends on the abundance of object's features. Copyright © 2018. Published by Elsevier Inc.

Detection of hypertensive retinopathy using vessel measurements and textural features.

PubMed

Agurto, Carla; Joshi, Vinayak; Nemeth, Sheila; Soliz, Peter; Barriga, Simon

2014-01-01

Features that indicate hypertensive retinopathy have been well described in the medical literature. This paper presents a new system to automatically classify subjects with hypertensive retinopathy (HR) using digital color fundus images. Our method consists of the following steps: 1) normalization and enhancement of the image; 2) determination of regions of interest based on automatic location of the optic disc; 3) segmentation of the retinal vasculature and measurement of vessel width and tortuosity; 4) extraction of color features; 5) classification of vessel segments as arteries or veins; 6) calculation of artery-vein ratios using the six widest (major) vessels for each category; 7) calculation of mean red intensity and saturation values for all arteries; 8) calculation of amplitude-modulation frequency-modulation (AM-FM) features for entire image; and 9) classification of features into HR and non-HR using linear regression. This approach was tested on 74 digital color fundus photographs taken with TOPCON and CANON retinal cameras using leave-one out cross validation. An area under the ROC curve (AUC) of 0.84 was achieved with sensitivity and specificity of 90% and 67%, respectively.

Automatic Matching of Large Scale Images and Terrestrial LIDAR Based on App Synergy of Mobile Phone

NASA Astrophysics Data System (ADS)

Xia, G.; Hu, C.

2018-04-01

The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.

Light intensity dependence of open-circuit voltage and short-circuit current of polymer/fullerene solar cells

NASA Astrophysics Data System (ADS)

Koster, L. Jan A.; Mihailetchi, Valentin D.; Ramaker, Robert; Xie, Hangxing; Blom, Paul W. M.

2006-04-01

The open-circuit voltage (Voc) of polymer/fullerene bulk heterojunction solar cells is investigated as a function of light intensity for different temperatures. The observed photogenerated current and V oc are at variance with classical p-n junctionbased models. The influence of light intensity and recombination strength on V oc is consistently explained by a model based on the notion that the quasi-Fermi levels are constant throughout the device, including both drift and diffusion of charge carriers. The light intensity dependence of the short-circuit current density (J sc) is also addressed. A typical feature of polymer/fullerene based solar cells is that Jsc does not scale exactly linearly with light intensity (I). Instead, a power law relationship is found given by Jsc~ Iα, where α ranges from 0.9 to 1. In a number of reports this deviation from unity is attributed to the occurrence of bimolecular recombination. We demonstrate that the dependence of the photocurrent in bulk heterojunction solar cells is governed by the build-up of space charge in the device. The occurrence of space-charge stems from the difference in charge carrier mobility of electrons and holes. In blends of poly(3-hexylthiophene) and 6,6- phenyl C61-butyric acid methyl ester this mobility difference can be tuned in between one and three orders of magnitude, depending on the annealing conditions. This allows us to experimentally verify the relation between space charge build-up and intensity dependence of Jsc. Model calculations confirm that bimolecular recombination leads only to a typical loss of 1% of all free charge carriers at Jsc for these devices. Therefore, bimolecular recombination plays only a minor role as compared to the effect of space charge in the intensity dependence of J sc.

Combining functional weed ecology and crop stable isotope ratios to identify cultivation intensity: a comparison of cereal production regimes in Haute Provence, France and Asturias, Spain.

PubMed

Bogaard, Amy; Hodgson, John; Nitsch, Erika; Jones, Glynis; Styring, Amy; Diffey, Charlotte; Pouncett, John; Herbig, Christoph; Charles, Michael; Ertuğ, Füsun; Tugay, Osman; Filipovic, Dragana; Fraser, Rebecca

This investigation combines two independent methods of identifying crop growing conditions and husbandry practices-functional weed ecology and crop stable carbon and nitrogen isotope analysis-in order to assess their potential for inferring the intensity of past cereal production systems using archaeobotanical assemblages. Present-day organic cereal farming in Haute Provence, France features crop varieties adapted to low-nutrient soils managed through crop rotation, with little to no manuring. Weed quadrat survey of 60 crop field transects in this region revealed that floristic variation primarily reflects geographical differences. Functional ecological weed data clearly distinguish the Provence fields from those surveyed in a previous study of intensively managed spelt wheat in Asturias, north-western Spain: as expected, weed ecological data reflect higher soil fertility and disturbance in Asturias. Similarly, crop stable nitrogen isotope values distinguish between intensive manuring in Asturias and long-term cultivation with minimal manuring in Haute Provence. The new model of cereal cultivation intensity based on weed ecology and crop isotope values in Haute Provence and Asturias was tested through application to two other present-day regimes, successfully identifying a high-intensity regime in the Sighisoara region, Romania, and low-intensity production in Kastamonu, Turkey. Application of this new model to Neolithic archaeobotanical assemblages in central Europe suggests that early farming tended to be intensive, and likely incorporated manuring, but also exhibited considerable variation, providing a finer grained understanding of cultivation intensity than previously available.

White perovskite based lighting devices.

PubMed

Bidikoudi, M; Fresta, E; Costa, R D

2018-06-28

Hybrid organic-inorganic and all-inorganic metal halide perovskites have been one of the most intensively studied materials during the last few years. In particular, research focusing on understanding how to tune the photoluminescence features and to apply perovskites to optoelectronic applications has led to a myriad of new materials featuring high photoluminescence quantum yields covering the whole visible range, as well as devices with remarkable performances. Having already established their successful incorporation in highly efficient solar cells, the next step is to tackle the challenges in solid-state lighting (SSL) devices. Here, the most prominent is the preparation of white-emitting devices. Herein, we have provided a comprehensive view of the route towards perovskite white lighting devices, including thin film light-emitting diodes (PeLEDs) and hybrid LEDs (HLEDs), using perovskite based color down-converting coatings. While synthesis and photoluminescence features are briefly discussed, we focus on highlighting the major achievements and limitations in white devices. Overall, we expect that this review will provide the reader a general overview of the current state of perovskite white SSL, paving the way towards new breakthroughs in the near future.

Multiple Representations-Based Face Sketch-Photo Synthesis.

PubMed

Peng, Chunlei; Gao, Xinbo; Wang, Nannan; Tao, Dacheng; Li, Xuelong; Li, Jie

2016-11-01

Face sketch-photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch-photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch-photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science.

18F-FDG PET brain images as features for Alzheimer classification

NASA Astrophysics Data System (ADS)

Azmi, M. H.; Saripan, M. I.; Nordin, A. J.; Ahmad Saad, F. F.; Abdul Aziz, S. A.; Wan Adnan, W. A.

2017-08-01

2-Deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) Positron Emission Tomography (PET) imaging offers meaningful information for various types of diseases diagnosis. In Alzheimer's disease (AD), the hypometabolism of glucose which observed on the low intensity voxel in PET image may relate to the onset of the disease. The importance of early detection of AD is inevitable because the resultant brain damage is irreversible. Several statistical analysis and machine learning algorithm have been proposed to investigate the rate and the pattern of the hypometabolism. This study focus on the same aim with further investigation was performed on several hypometabolism pattern. Some pre-processing steps were implemented to standardize the data in order to minimize the effect of resolution and anatomical differences. The features used are the mean voxel intensity within the AD pattern mask, which derived from several z-score and FDR threshold values. The global mean voxel (GMV) and slice-based mean voxel (SbMV) intensity were observed and used as input to the neural network. Several neural network architectures were tested and compared to the nearest neighbour method. The highest accuracy equals to 0.9 and recorded at z-score ≤-1.3 with 1 node neural network architecture (sensitivity=0.81 and specificity=0.95) and at z-score ≤-0.7 with 10 nodes neural network (sensitivity=0.83 and specificity=0.94).

WE-E-17A-02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG-PET Images

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

Li, R; Aguilera, T; Shultz, D

2014-06-15

Purpose: This study aims to develop predictive models of patient outcome by extracting advanced imaging features (i.e., Radiomics) from FDG-PET images. Methods: We acquired pre-treatment PET scans for 51 stage I NSCLC patients treated with SABR. We calculated 139 quantitative features from each patient PET image, including 5 morphological features, 8 statistical features, 27 texture features, and 100 features from the intensity-volume histogram. Based on the imaging features, we aim to distinguish between 2 risk groups of patients: those with regional failure or distant metastasis versus those without. We investigated 3 pattern classification algorithms: linear discriminant analysis (LDA), naive Bayesmore » (NB), and logistic regression (LR). To avoid the curse of dimensionality, we performed feature selection by first removing redundant features and then applying sequential forward selection using the wrapper approach. To evaluate the predictive performance, we performed 10-fold cross validation with 1000 random splits of the data and calculated the area under the ROC curve (AUC). Results: Feature selection identified 2 texture features (homogeneity and/or wavelet decompositions) for NB and LR, while for LDA SUVmax and one texture feature (correlation) were identified. All 3 classifiers achieved statistically significant improvements over conventional PET imaging metrics such as tumor volume (AUC = 0.668) and SUVmax (AUC = 0.737). Overall, NB achieved the best predictive performance (AUC = 0.806). This also compares favorably with MTV using the best threshold at an SUV of 11.6 (AUC = 0.746). At a sensitivity of 80%, NB achieved 69% specificity, while SUVmax and tumor volume only had 36% and 47% specificity. Conclusion: Through a systematic analysis of advanced PET imaging features, we are able to build models with improved predictive value over conventional imaging metrics. If validated in a large independent cohort, the proposed techniques could potentially aid in identifying patients who might benefit from adjuvant therapy.« less

Spatial-Temporal [{sup 18}F]FDG-PET Features for Predicting Pathologic Response of Esophageal Cancer to Neoadjuvant Chemoradiation Therapy

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

Tan, Shan; Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan; Kligerman, Seth

2013-04-01

Purpose: To extract and study comprehensive spatial-temporal {sup 18}F-labeled fluorodeoxyglucose ([{sup 18}F]FDG) positron emission tomography (PET) features for the prediction of pathologic tumor response to neoadjuvant chemoradiation therapy (CRT) in esophageal cancer. Methods and Materials: Twenty patients with esophageal cancer were treated with trimodal therapy (CRT plus surgery) and underwent [{sup 18}F]FDG-PET/CT scans both before (pre-CRT) and after (post-CRT) CRT. The 2 scans were rigidly registered. A tumor volume was semiautomatically delineated using a threshold standardized uptake value (SUV) of ≥2.5, followed by manual editing. Comprehensive features were extracted to characterize SUV intensity distribution, spatial patterns (texture), tumor geometry, andmore » associated changes resulting from CRT. The usefulness of each feature in predicting pathologic tumor response to CRT was evaluated using the area under the receiver operating characteristic curve (AUC) value. Results: The best traditional response measure was decline in maximum SUV (SUV{sub max}; AUC, 0.76). Two new intensity features, decline in mean SUV (SUV{sub mean}) and skewness, and 3 texture features (inertia, correlation, and cluster prominence) were found to be significant predictors with AUC values ≥0.76. According to these features, a tumor was more likely to be a responder when the SUV{sub mean} decline was larger, when there were relatively fewer voxels with higher SUV values pre-CRT, or when [{sup 18}F]FDG uptake post-CRT was relatively homogeneous. All of the most accurate predictive features were extracted from the entire tumor rather than from the most active part of the tumor. For SUV intensity features and tumor size features, changes were more predictive than pre- or post-CRT assessment alone. Conclusion: Spatial-temporal [{sup 18}F]FDG-PET features were found to be useful predictors of pathologic tumor response to neoadjuvant CRT in esophageal cancer.« less

Displacement of polarons by vibrational modes in doped conjugated polymers

NASA Astrophysics Data System (ADS)

Anderson, M.; Ramanan, C.; Fontanesi, C.; Frick, A.; Surana, S.; Cheyns, D.; Furno, M.; Keller, T.; Allard, S.; Scherf, U.; Beljonne, D.; D'Avino, G.; von Hauff, E.; Da Como, E.

2017-10-01

Organic pi-conjugated polymers are deemed to be soft materials with strong electron-phonon coupling, which results in the formation of polarons, i.e., charge carriers dressed by self-localized distortion of the nuclei. Universal signatures for polarons are optical resonances below the band gap and intense vibrational modes (IVMs), both found in the infrared (IR) spectral region. Here, we study p -doped conjugated homo- and copolymers by combining first-principles modelling and optical spectroscopy from the far-IR to the visible. Polaronic IVMs are found to feature absorption intensities comparable to purely electronic transitions and, most remarkably, show only loose resemblance to the Raman or IR-active modes of the neutral polymer. The IVM frequency is dramatically scaled down (up to 50%) compared to the backbone carbon-stretching modes in the pristine polymers. The very large intensity of IVMs is associated with displacement of the excess positive charge along the backbone driven by specific vibrational modes. We propose a quantitative picture for the identification of these polaron shifting modes that solely based on structural information, directly correlates with their IR intensity. This finding finally discloses the elusive microscopic mechanism behind the huge IR intensity of IVMs in doped polymeric semiconductors.

Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM).

PubMed

Gao, Hao; Yu, Hengyong; Osher, Stanley; Wang, Ge

2011-11-01

We propose a compressive sensing approach for multi-energy computed tomography (CT), namely the prior rank, intensity and sparsity model (PRISM). To further compress the multi-energy image for allowing the reconstruction with fewer CT data and less radiation dose, the PRISM models a multi-energy image as the superposition of a low-rank matrix and a sparse matrix (with row dimension in space and column dimension in energy), where the low-rank matrix corresponds to the stationary background over energy that has a low matrix rank, and the sparse matrix represents the rest of distinct spectral features that are often sparse. Distinct from previous methods, the PRISM utilizes the generalized rank, e.g., the matrix rank of tight-frame transform of a multi-energy image, which offers a way to characterize the multi-level and multi-filtered image coherence across the energy spectrum. Besides, the energy-dependent intensity information can be incorporated into the PRISM in terms of the spectral curves for base materials, with which the restoration of the multi-energy image becomes the reconstruction of the energy-independent material composition matrix. In other words, the PRISM utilizes prior knowledge on the generalized rank and sparsity of a multi-energy image, and intensity/spectral characteristics of base materials. Furthermore, we develop an accurate and fast split Bregman method for the PRISM and demonstrate the superior performance of the PRISM relative to several competing methods in simulations.

Targeted Feature Detection for Data-Dependent Shotgun Proteomics

PubMed Central

2017-01-01

Label-free quantification of shotgun LC–MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification (“FFId”), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between “internal” and “external” (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the “uncertain” feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS (www.openms.org). PMID:28673088

Targeted Feature Detection for Data-Dependent Shotgun Proteomics.

PubMed

Weisser, Hendrik; Choudhary, Jyoti S

2017-08-04

Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS ( www.openms.org ).

Prompt and delayed Coulomb explosion of doubly ionized hydrogen chloride molecules in intense femtosecond laser fields

NASA Astrophysics Data System (ADS)

Ma, Junyang; Li, Hui; Lin, Kang; Song, Qiying; Ji, Qinying; Zhang, Wenbin; Li, Hanxiao; Sun, Fenghao; Qiang, Junjie; Lu, Peifen; Gong, Xiaochun; Zeng, Heping; Wu, Jian

2018-06-01

We experimentally investigate the dissociative double ionization of hydrogen chloride (HCl) molecules in intense femtosecond laser pulses. In addition to the prompt dissociation channels which occur on femtosecond timescales, long-lived hydrogen chloride dications which Coulomb-explode in flight towards the detector are clearly identified in the photoion-photoion coincidence spectrum. Different pathways leading to these prompt and delayed dissociation channels involving various bound and repulsive states of the HCl dication are discussed based on the observed kinetic energy release and momentum distributions. Our results indicate that the specific features of the HCl dication potential energy curves are responsible for the generation of the delayed fragmentation channels, which are expected to be general processes for the hydrogen halides.

Automatic Feature Selection and Weighting for the Formation of Homogeneous Groups for Regional Intensity-Duration-Frequency (IDF) Curve Estimation

NASA Astrophysics Data System (ADS)

Yang, Z.; Burn, D. H.

2017-12-01

Extreme rainfall events can have devastating impacts on society. To quantify the associated risk, the IDF curve has been used to provide the essential rainfall-related information for urban planning. However, the recent changes in the rainfall climatology caused by climate change and urbanization have made the estimates provided by the traditional regional IDF approach increasingly inaccurate. This inaccuracy is mainly caused by two problems: 1) The ineffective choice of similarity indicators for the formation of a homogeneous group at different regions; and 2) An inadequate number of stations in the pooling group that does not adequately reflect the optimal balance between group size and group homogeneity or achieve the lowest uncertainty in the rainfall quantiles estimates. For the first issue, to consider the temporal difference among different meteorological and topographic indicators, a three-layer design is proposed based on three stages in the extreme rainfall formation: cloud formation, rainfall generation and change of rainfall intensity above urban surface. During the process, the impacts from climate change and urbanization are considered through the inclusion of potential relevant features at each layer. Then to consider spatial difference of similarity indicators for the homogeneous group formation at various regions, an automatic feature selection and weighting algorithm, specifically the hybrid searching algorithm of Tabu search, Lagrange Multiplier and Fuzzy C-means Clustering, is used to select the optimal combination of features for the potential optimal homogenous groups formation at a specific region. For the second issue, to compare the uncertainty of rainfall quantile estimates among potential groups, the two sample Kolmogorov-Smirnov test-based sample ranking process is used. During the process, linear programming is used to rank these groups based on the confidence intervals of the quantile estimates. The proposed methodology fills the gap of including the urbanization impacts during the pooling group formation, and challenges the traditional assumption that the same set of similarity indicators can be equally effective in generating the optimal homogeneous group for regions with different geographic and meteorological characteristics.

Adaptive Morphological Feature-Based Object Classifier for a Color Imaging System

NASA Technical Reports Server (NTRS)

McDowell, Mark; Gray, Elizabeth

2009-01-01

Utilizing a Compact Color Microscope Imaging System (CCMIS), a unique algorithm has been developed that combines human intelligence along with machine vision techniques to produce an autonomous microscope tool for biomedical, industrial, and space applications. This technique is based on an adaptive, morphological, feature-based mapping function comprising 24 mutually inclusive feature metrics that are used to determine the metrics for complex cell/objects derived from color image analysis. Some of the features include: Area (total numbers of non-background pixels inside and including the perimeter), Bounding Box (smallest rectangle that bounds and object), centerX (x-coordinate of intensity-weighted, center-of-mass of an entire object or multi-object blob), centerY (y-coordinate of intensity-weighted, center-of-mass, of an entire object or multi-object blob), Circumference (a measure of circumference that takes into account whether neighboring pixels are diagonal, which is a longer distance than horizontally or vertically joined pixels), . Elongation (measure of particle elongation given as a number between 0 and 1. If equal to 1, the particle bounding box is square. As the elongation decreases from 1, the particle becomes more elongated), . Ext_vector (extremal vector), . Major Axis (the length of a major axis of a smallest ellipse encompassing an object), . Minor Axis (the length of a minor axis of a smallest ellipse encompassing an object), . Partial (indicates if the particle extends beyond the field of view), . Perimeter Points (points that make up a particle perimeter), . Roundness [(4(pi) x area)/perimeter(squared)) the result is a measure of object roundness, or compactness, given as a value between 0 and 1. The greater the ratio, the rounder the object.], . Thin in center (determines if an object becomes thin in the center, (figure-eight-shaped), . Theta (orientation of the major axis), . Smoothness and color metrics for each component (red, green, blue) the minimum, maximum, average, and standard deviation within the particle are tracked. These metrics can be used for autonomous analysis of color images from a microscope, video camera, or digital, still image. It can also automatically identify tumor morphology of stained images and has been used to detect stained cell phenomena (see figure).

Material and shape perception based on two types of intensity gradient information

PubMed Central

Nishida, Shin'ya

2018-01-01

Visual estimation of the material and shape of an object from a single image includes a hard ill-posed computational problem. However, in our daily life we feel we can estimate both reasonably well. The neural computation underlying this ability remains poorly understood. Here we propose that the human visual system uses different aspects of object images to separately estimate the contributions of the material and shape. Specifically, material perception relies mainly on the intensity gradient magnitude information, while shape perception relies mainly on the intensity gradient order information. A clue to this hypothesis was provided by the observation that luminance-histogram manipulation, which changes luminance gradient magnitudes but not the luminance-order map, effectively alters the material appearance but not the shape of an object. In agreement with this observation, we found that the simulated physical material changes do not significantly affect the intensity order information. A series of psychophysical experiments further indicate that human surface shape perception is robust against intensity manipulations provided they do not disturb the intensity order information. In addition, we show that the two types of gradient information can be utilized for the discrimination of albedo changes from highlights. These findings suggest that the visual system relies on these diagnostic image features to estimate physical properties in a distal world. PMID:29702644

Hybrid registration of PET/CT in thoracic region with pre-filtering PET sinogram

NASA Astrophysics Data System (ADS)

Mokri, S. S.; Saripan, M. I.; Marhaban, M. H.; Nordin, A. J.; Hashim, S.

2015-11-01

The integration of physiological (PET) and anatomical (CT) images in cancer delineation requires an accurate spatial registration technique. Although hybrid PET/CT scanner is used to co-register these images, significant misregistrations exist due to patient and respiratory/cardiac motions. This paper proposes a hybrid feature-intensity based registration technique for hybrid PET/CT scanner. First, simulated PET sinogram was filtered with a 3D hybrid mean-median before reconstructing the image. The features were then derived from the segmented structures (lung, heart and tumor) from both images. The registration was performed based on modified multi-modality demon registration with multiresolution scheme. Apart from visual observations improvements, the proposed registration technique increased the normalized mutual information index (NMI) between the PET/CT images after registration. All nine tested datasets show marked improvements in mutual information (MI) index than free form deformation (FFD) registration technique with the highest MI increase is 25%.

[Affine transformation-based automatic registration for peripheral digital subtraction angiography (DSA)].

PubMed

Kong, Gang; Dai, Dao-Qing; Zou, Lu-Min

2008-07-01

In order to remove the artifacts of peripheral digital subtraction angiography (DSA), an affine transformation-based automatic image registration algorithm is introduced here. The whole process is described as follows: First, rectangle feature templates are constructed with their centers of the extracted Harris corners in the mask, and motion vectors of the central feature points are estimated using template matching technology with the similarity measure of maximum histogram energy. And then the optimal parameters of the affine transformation are calculated with the matrix singular value decomposition (SVD) method. Finally, bilinear intensity interpolation is taken to the mask according to the specific affine transformation. More than 30 peripheral DSA registrations are performed with the presented algorithm, and as the result, moving artifacts of the images are removed with sub-pixel precision, and the time consumption is less enough to satisfy the clinical requirements. Experimental results show the efficiency and robustness of the algorithm.

Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images.

PubMed

Dexter, Alex; Race, Alan M; Steven, Rory T; Barnes, Jennifer R; Hulme, Heather; Goodwin, Richard J A; Styles, Iain B; Bunch, Josephine

2017-11-07

Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach that uses a graph-based algorithm with a two-phase sampling method that overcomes this limitation. We demonstrate the algorithm on a range of sample types and show that it can segment anatomical features that are not identified using commonly employed algorithms in MSI, and we validate our results on synthetic MSI data. We show that the algorithm is robust to fluctuations in data quality by successfully clustering data with a designed-in variance using data acquired with varying laser fluence. Finally, we show that this method is capable of generating accurate segmentations of large MSI data sets acquired on the newest generation of MSI instruments and evaluate these results by comparison with histopathology.

The Computer Aided Aircraft-design Package (CAAP)

NASA Technical Reports Server (NTRS)

Yalif, Guy U.

1994-01-01

The preliminary design of an aircraft is a complex, labor-intensive, and creative process. Since the 1970's, many computer programs have been written to help automate preliminary airplane design. Time and resource analyses have identified, 'a substantial decrease in project duration with the introduction of an automated design capability'. Proof-of-concept studies have been completed which establish 'a foundation for a computer-based airframe design capability', Unfortunately, today's design codes exist in many different languages on many, often expensive, hardware platforms. Through the use of a module-based system architecture, the Computer aided Aircraft-design Package (CAAP) will eventually bring together many of the most useful features of existing programs. Through the use of an expert system, it will add an additional feature that could be described as indispensable to entry level engineers and students: the incorporation of 'expert' knowledge into the automated design process.

Tier II Interventions within the Framework of School-Wide Positive Behavior Support: Essential Features for Design, Implementation, and Maintenance.

PubMed

Anderson, Cynthia M; Borgmeier, Chris

2010-01-01

To meet the complex social behavioral and academic needs of all students, schools benefit from having available multiple evidence-based interventions of varying intensity. School-wide positive behavior support provides a framework within which a continuum of evidence-based interventions can be implemented in a school. This framework includes three levels or tiers of intervention; Tier I (primary or universal), Tier II (secondary or targeted), and Tier III (tertiary or individualized) supports. In this paper we review the logic behind school-wide positive behavior support and then focus on Tier II interventions, as this level of support has received the least attention in the literature. We delineate the key features of Tier II interventions as implemented within school-wide positive behavior support, provide guidelines for matching Tier II interventions to school and student needs, and describe how schools plan for implementation and maintenance of selected interventions.

Efficient Generation of Dancing Animation Synchronizing with Music Based on Meta Motion Graphs

NASA Astrophysics Data System (ADS)

Xu, Jianfeng; Takagi, Koichi; Sakazawa, Shigeyuki

This paper presents a system for automatic generation of dancing animation that is synchronized with a piece of music by re-using motion capture data. Basically, the dancing motion is synthesized according to the rhythm and intensity features of music. For this purpose, we propose a novel meta motion graph structure to embed the necessary features including both rhythm and intensity, which is constructed on the motion capture database beforehand. In this paper, we consider two scenarios for non-streaming music and streaming music, where global search and local search are required respectively. In the case of the former, once a piece of music is input, the efficient dynamic programming algorithm can be employed to globally search a best path in the meta motion graph, where an objective function is properly designed by measuring the quality of beat synchronization, intensity matching, and motion smoothness. In the case of the latter, the input music is stored in a buffer in a streaming mode, then an efficient search method is presented for a certain amount of music data (called a segment) in the buffer with the same objective function, resulting in a segment-based search approach. For streaming applications, we define an additional property in the above meta motion graph to deal with the unpredictable future music, which guarantees that there is some motion to match the unknown remaining music. A user study with totally 60 subjects demonstrates that our system outperforms the stat-of-the-art techniques in both scenarios. Furthermore, our system improves the synthesis speed greatly (maximal speedup is more than 500 times), which is essential for mobile applications. We have implemented our system on commercially available smart phones and confirmed that it works well on these mobile phones.

High performance organic integrated device with ultraviolet photodetective and electroluminescent properties consisting of a charge-transfer-featured naphthalimide derivative

NASA Astrophysics Data System (ADS)

Wang, Hanyu; Zhou, Jie; Wang, Xu; Lu, Zhiyun; Yu, Junsheng

2014-08-01

A high performance organic integrated device (OID) with ultraviolet photodetective and electroluminescent (EL) properties was fabricated by using a charge-transfer-featured naphthalimide derivative of 6-{3,5-bis-[9-(4-t-butylphenyl)-9H-carbazol-3-yl]-phenoxy}-2-(4-t-butylphenyl)-benzo[de]isoquinoline-1,3-dione (CzPhONI) as the active layer. The results showed that the OID had a high detectivity of 1.5 × 1011 Jones at -3 V under the UV-350 nm illumination with an intensity of 0.6 mW/cm2, and yielded an exciplex EL light emission with a maximum brightness of 1437 cd/m2. Based on the energy band diagram, both the charge transfer feature of CzPhONI and matched energy level alignment were responsible for the dual ultraviolet photodetective and EL functions of OID.

Optical imaging of tumor microenvironment

PubMed Central

Wu, Yihan; Zhang, Wenjie; Li, Jinbo; Zhang, Yan

2013-01-01

Tumor microenvironment plays important roles in tumor development and metastasis. Features of the tumor microenvironment that are significantly different from normal tissues include acidity, hypoxia, overexpressed proteases and so on. Therefore, these features can serve as not only biomarkers for tumor diagnosis but also theraputic targets for tumor treatment. Imaging modalities such as optical, positron emission tomography (PET) and magnetic resonance imaging (MRI) have been intensively applied to investigate tumor microenvironment. Various imaging probes targeting pH, hypoxia and proteases in tumor microenvironment were thus well developed. In this review, we will focus on recent examples on fluorescent probes for optical imaging of tumor microenvironment. Construction of these fluorescent probes were based on characteristic feature of pH, hypoxia and proteases in tumor microenvironment. Strategies for development of these fluorescent probes and applications of these probes in optical imaging of tumor cells or tissues will be discussed in this review paper. PMID:23342297

Imperfection and radiation damage in protein crystals studied with coherent radiation

PubMed Central

Nave, Colin; Sutton, Geoff; Evans, Gwyndaf; Owen, Robin; Rau, Christoph; Robinson, Ian; Stuart, David Ian

2016-01-01

Fringes and speckles occur within diffraction spots when a crystal is illuminated with coherent radiation during X-ray diffraction. The additional information in these features provides insight into the imperfections in the crystal at the sub-micrometre scale. In addition, these features can provide more accurate intensity measurements (e.g. by model-based profile fitting), detwinning (by distinguishing the various components), phasing (by exploiting sampling of the molecular transform) and refinement (by distinguishing regions with different unit-cell parameters). In order to exploit these potential benefits, the features due to coherent diffraction have to be recorded and any change due to radiation damage properly modelled. Initial results from recording coherent diffraction at cryotemperatures from polyhedrin crystals of approximately 2 µm in size are described. These measurements allowed information about the type of crystal imperfections to be obtained at the sub-micrometre level, together with the changes due to radiation damage. PMID:26698068

Extraction and labeling high-resolution images from PDF documents

NASA Astrophysics Data System (ADS)

Chachra, Suchet K.; Xue, Zhiyun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Accuracy of content-based image retrieval is affected by image resolution among other factors. Higher resolution images enable extraction of image features that more accurately represent the image content. In order to improve the relevance of search results for our biomedical image search engine, Open-I, we have developed techniques to extract and label high-resolution versions of figures from biomedical articles supplied in the PDF format. Open-I uses the open-access subset of biomedical articles from the PubMed Central repository hosted by the National Library of Medicine. Articles are available in XML and in publisher supplied PDF formats. As these PDF documents contain little or no meta-data to identify the embedded images, the task includes labeling images according to their figure number in the article after they have been successfully extracted. For this purpose we use the labeled small size images provided with the XML web version of the article. This paper describes the image extraction process and two alternative approaches to perform image labeling that measure the similarity between two images based upon the image intensity projection on the coordinate axes and similarity based upon the normalized cross-correlation between the intensities of two images. Using image identification based on image intensity projection, we were able to achieve a precision of 92.84% and a recall of 82.18% in labeling of the extracted images.

Image Quality Assessment of High-Resolution Satellite Images with Mtf-Based Fuzzy Comprehensive Evaluation Method

NASA Astrophysics Data System (ADS)

Wu, Z.; Luo, Z.; Zhang, Y.; Guo, F.; He, L.

2018-04-01

A Modulation Transfer Function (MTF)-based fuzzy comprehensive evaluation method was proposed in this paper for the purpose of evaluating high-resolution satellite image quality. To establish the factor set, two MTF features and seven radiant features were extracted from the knife-edge region of image patch, which included Nyquist, MTF0.5, entropy, peak signal to noise ratio (PSNR), average difference, edge intensity, average gradient, contrast and ground spatial distance (GSD). After analyzing the statistical distribution of above features, a fuzzy evaluation threshold table and fuzzy evaluation membership functions was established. The experiments for comprehensive quality assessment of different natural and artificial objects was done with GF2 image patches. The results showed that the calibration field image has the highest quality scores. The water image has closest image quality to the calibration field, quality of building image is a little poor than water image, but much higher than farmland image. In order to test the influence of different features on quality evaluation, the experiment with different weights were tested on GF2 and SPOT7 images. The results showed that different weights correspond different evaluating effectiveness. In the case of setting up the weights of edge features and GSD, the image quality of GF2 is better than SPOT7. However, when setting MTF and PSNR as main factor, the image quality of SPOT7 is better than GF2.

Complementary codes for odor identity and intensity in olfactory cortex

PubMed Central

Bolding, Kevin A; Franks, Kevin M

2017-01-01

The ability to represent both stimulus identity and intensity is fundamental for perception. Using large-scale population recordings in awake mice, we find distinct coding strategies facilitate non-interfering representations of odor identity and intensity in piriform cortex. Simply knowing which neurons were activated is sufficient to accurately represent odor identity, with no additional information about identity provided by spike time or spike count. Decoding analyses indicate that cortical odor representations are not sparse. Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations. Cortical inhibition transforms olfactory bulb output to sharpen these dynamics. Our data therefore reveal complementary coding strategies that can selectively represent distinct features of a stimulus. DOI: http://dx.doi.org/10.7554/eLife.22630.001 PMID:28379135

Preliminary Results on Irradiance Measurements from Lyra and Swap

NASA Astrophysics Data System (ADS)

Kumara, S. T.; Kariyappa, R.; Dominique, M.; Berghmans, D.; Damé, L.; Hochedez, J. F.; Doddamani, V. H.; Chitta, Lakshmi Pradeep

The first and preliminary results of the photometry of Large Yield Radiometer (LYRA) and Sun Watcher using Active Pixel system detector and Image Processing (SWAP) onboard PROBA2 are presented in this paper. To study the day-to-day variations of LYRA irradiance, we have compared the LYRA irradiance values (observed Sun as a star) measured in Aluminum filter channel (171Å-500Å) with spatially resolved full-disk integrated intensity values measured with SWAP (174Å) and Ca II K 1 Å index values (ground-based observations from NSO/Sac Peak) for the period from 01 April 2010 to 15 Mar 2011. We found that there is a good correlation between these parameters. This indicates that the spatial resolution of SWAP complements the high temporal resolution of LYRA. Hence SWAP can be considered as an additional radiometric channel. Also the K emission index is the integrated intensity (or flux) over a 1 Å band centered on the K line and is proportional to the total emission from the chromosphere; this comparison clearly explains that the LYRA irradiance variations are due to the various magnetic features, which are contributing significantly. In addition to this we have made an attempt to segregate coronal features from full-disk SWAP images. This will help to understand and determine the actual contribution of the individual coronal feature to LYRA irradiance variations.

Low rate of self-awareness and medical recognition of migraine in Germany.

PubMed

Radtke, A; Neuhauser, H

2012-10-01

The study's objective was to assess self-awareness and medical recognition of migraine and their determinants in Germany. We conducted a nationally representative study of the general population of Germany (N = 7341, aged ≥18 years) by means of computer-assisted telephone interviews. Migraine was diagnosed based on the International Classification of Headache Disorders, second edition (ICDH-II). Twelve-month prevalence of ICHD-II-migraine was 10.6% (women 15.6%, men 5.3%). Seventy percent of ICDH-II-migraineurs recognised their headaches as migraine (moderate agreement between ICDH-II and self-diagnosis, κ = 0.46). Only 42% of migraineurs consulted a physician in the previous 12 months. Of those, 63% reported a medical diagnosis of migraine (moderate agreement, κ = 0.40). Women were more likely to be self-aware (odds ratio [OR] 1.81, 95% confidence interval [CI] 1.27-2.60), but the difference was no longer significant when adjusting for migraine features. Physician recognition was more likely in patients with higher educational level (high vs. low education OR 3.90, 95% CI 1.43-10.61 after adjusting for migrainous features). Best predictors for self-awareness and medical recognition of migraine were typical migraine accompaniments and greater headache intensity. Self-awareness and physician recognition of migraine are low in Germany. Presence of typical migraine features and greater headache intensity facilitate medical recognition and awareness of migraine, especially in females.

Lung Texture in Serial Thoracic Computed Tomography Scans: Correlation of Radiomics-based Features With Radiation Therapy Dose and Radiation Pneumonitis Development

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

Cunliffe, Alexandra; Armato, Samuel G.; Castillo, Richard

2015-04-01

Purpose: To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP). Methods and Materials: A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identifiedmore » in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction. Results: For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84). Conclusions: A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.« less

Disrupted reward and cognitive control networks contribute to anhedonia in depression.

PubMed

Gong, Liang; He, Cancan; Zhang, Haisan; Zhang, Hongxing; Zhang, Zhijun; Xie, Chunming

2018-08-01

Neuroimaging studies have identified that anhedonia, a core feature of major depressive disorder (MDD), is associated with dysfunction in reward and cognitive control processing. However, it is still not clear how the reward network (β-network) and the cognitive control network (δ-network) are linked to biased anhedonia in MDD patients. Sixty-eight MDD patients and 64 cognitively normal (CN) subjects underwent a resting-state functional magnetic resonance imaging scan. A 2*2 ANCOVA analysis was used to explore the differences in the nucleus accumbens-based, voxelwise functional connectivity (FC) between the groups. Then, the β- and δ-networks were constructed, and the FC intensities were compared within and between theβ- and δ-networks across all subjects. Multiple linear regression analyses were also employed to investigate the relationships between the neural features of the β- and δ-networks and anhedonia in MDD patients. Compared to the CN subjects, the MDD patients showed synergistic functional decoupling in both the β- and δ-networks, as well as decreased FC intensities in the intra- and inter- β- and δ-networks. In addition, the FC in both the β- and δ-networks was significantly correlated with anhedonia severity in the MDD patients. Importantly, the integrated neural features of the β- and δ-networks could more precisely predict anhedonic symptoms. These findings initially demonstrated that the imbalance between β- and δ-network activity successfully predicted anhedonia severity and suggested that the neural features of both the β- and δ-networks could represent a fundamental mechanism that underlies anhedonia in MDD patients. Copyright © 2018 Elsevier Ltd. All rights reserved.

Magnetic resonance imaging features of esthesioneuroblastoma in three dogs and one cat.

PubMed

Söffler, Charlotte; Hartmann, Antje; Gorgas, Daniela; Ludewig, Eberhard; von Pückler, Kerstin; Kramer, Martin; Schmidt, Martin J

2016-10-12

Esthesioneuroblastoma is a rare malignant intranasal tumor that originates from the olfactory neuroepithelium of the upper nasal cavity, and can destroy the cribriform plate and expand into the neurocranium. Descriptions of the magnetic resonance features of esthesioneuroblastomas in animals are scarce. The objectives of this study were to report the magnetic resonance imaging features of esthesioneuroblastomas in order to determine distinct imaging characteristics that may help distinguish it from other intracranial tumor types. Magnetic resonance images of four patients with confirmed esthesioneuroblastomas were reviewed and compared with previously reported cases. The esthesioneuroblastomas appeared as oval-shaped, solitary lesions in the caudal nasal cavity that caused osteolysis of the cribriform plate and extended into the brain in all cases. Signal intensity was variable. Contrast enhancement was mild and varied from homogeneous to heterogeneous. A peripheral cystic component was found in two patients and was reported in only one previous case. Mass effect and white matter edema were marked to severe. Osteolysis of facial bones and extension into the facial soft tissues or retrobulbar space were not present in any of the cases, although this has been reported in the literature. A definitive diagnosis of esthesioneuroblastoma based on signal intensity or contrast behavior was not possible. Nevertheless, the presence of a mass in the caudal nasal cavity with extension into the neurocranium seems to be a feature highly suspicious of esthesioneuroblastoma. In contrast to other extra-cranial lesions, the extra-cranial mass was relatively small and destruction of facial bones seems to be rare.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

NASA Astrophysics Data System (ADS)

Xie, Yaoqin; Xing, Lei; Gu, Jia; Liu, Wu

2013-06-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow.

National-scale cropland mapping based on spectral-temporal features and outdated land cover information.

PubMed

Waldner, François; Hansen, Matthew C; Potapov, Peter V; Löw, Fabian; Newby, Terence; Ferreira, Stefanus; Defourny, Pierre

2017-01-01

The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral-temporal features that target salient cropland characteristics. Second, we reduce the spatial variability of the class signatures by stratifying the country and by classifying each stratum independently. Finally, we remove speckle with a weighted majority filter accounting for per-pixel classification confidence. Capitalizing on a wall-to-wall validation data set, the method was tested in South Africa using a 16-year old land cover map and multi-sensor Landsat time series. The overall accuracy of the resulting cropland map reached 92%. A spatially explicit validation revealed large variations across the country and suggests that intensive grain-growing areas were better characterized than smallholder farming systems. Informative features in the classification process vary from one stratum to another but features targeting the minimum of vegetation as well as short-wave infrared features were consistently important throughout the country. Overall, the approach showed potential for routinely delivering consistent cropland maps over large areas as required for operational crop monitoring.

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

PubMed Central

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

2018-01-01

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

National-scale cropland mapping based on spectral-temporal features and outdated land cover information

PubMed Central

Hansen, Matthew C.; Potapov, Peter V.; Löw, Fabian; Newby, Terence; Ferreira, Stefanus; Defourny, Pierre

2017-01-01

The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral-temporal features that target salient cropland characteristics. Second, we reduce the spatial variability of the class signatures by stratifying the country and by classifying each stratum independently. Finally, we remove speckle with a weighted majority filter accounting for per-pixel classification confidence. Capitalizing on a wall-to-wall validation data set, the method was tested in South Africa using a 16-year old land cover map and multi-sensor Landsat time series. The overall accuracy of the resulting cropland map reached 92%. A spatially explicit validation revealed large variations across the country and suggests that intensive grain-growing areas were better characterized than smallholder farming systems. Informative features in the classification process vary from one stratum to another but features targeting the minimum of vegetation as well as short-wave infrared features were consistently important throughout the country. Overall, the approach showed potential for routinely delivering consistent cropland maps over large areas as required for operational crop monitoring. PMID:28817618

A media-based assessment of damage and ground motions from the January 26th, 2001 M 7.6 Bhuj, India earthquake

USGS Publications Warehouse

Hough, S.E.; Martin, S.; Bilham, R.; Atkinson, G.M.

2003-01-01

We compiled available news and internet accounts of damage and other effects from the 26th January, 2001, Bhuj earthquake, and interpreted them to obtain modified Mercalli intensities at over 200 locations throughout the Indian subcontinent. These values are used to map the intensity distribution using a simple mathematical interpolation method. The maps reveal several interesting features. Within the Kachchh region, the most heavily damaged villages are concentrated towards the western edge of the inferred fault, consistent with western directivity. Significant sediment-induced amplification is also suggested at a number of locations around the Gulf of Kachchh to the south of the epicenter. Away from the Kachchh region intensities were clearly amplified significantly in areas that are along rivers, within deltas, or on coastal alluvium such as mud flats and salt pans. In addition we use fault rupture parameters inferred from teleseismic data to predict shaking intensity at distances of 0-1000 km. We then convert the predicted hard rock ground motion parameters to MMI using a relationship (derived from internet-based intensity surveys) that assigns MMI based on the average effects in a region. The predicted MMIs are typically lower by 1-2 units than those estimated from news accounts. This discrepancy is generally consistent with the expected effect of sediment response, but it could also reflect other factors such as a tendency for media accounts to focus on the most dramatic damage, rather than the average effects. Our modeling results also suggest, however, that the Bhuj earthquake generated more high-frequency shaking than is expected for earthquakes of similar magnitude in California, and may therefore have been especially damaging.

Determination of dosimetric and kinetic features of gamma irradiated solid calcium ascorbate dihydrate using ESR spectroscopy

NASA Astrophysics Data System (ADS)

Tuner, H.

2013-01-01

Effects of gamma radiation on solid calcium ascorbate dihydrate were studied using electron spin resonance (ESR) spectroscopy. Irradiated samples were found to present two specific ESR lines with shoulder at low and high magnetic field sides. Structural and kinetic features of the radicalic species responsible for experimental ESR spectrum were explored through the variations of the signal intensities with applied microwave power, variable temperature, high-temperature annealing and room temperature storage time studies. Dosimetric potential of the sample was also determined using spectrum area and measured signal intensity measurements. It was concluded that three radicals with different spectroscopic and kinetic features were produced upon gamma irradiation.

Fabrication of two-dimensional visible wavelength nanoscale plasmonic structures using hydrogen silsesquioxane based resist

NASA Astrophysics Data System (ADS)

Smith, Kyle Z.; Gadde, Akshitha; Kadiyala, Anand; Dawson, Jeremy M.

2016-03-01

In recent years, the global market for biosensors has continued to increase in combination with their expanding use in areas such as biodefense/detection, home diagnostics, biometric identification, etc. A constant necessity for inexpensive, portable bio-sensing methods, while still remaining simple to understand and operate, is the motivation behind novel concepts and designs. Labeled visible spectrum bio-sensing systems provide instant feedback that is both simple and easy to work with, but are limited by the light intensity thresholds required by the imaging systems. In comparison, label-free bio-sensing systems and other detection modalities like electrochemical, frequency resonance, thermal change, etc., can require additional technical processing steps to convey the final result, increasing the system's complexity and possibly the time required for analysis. Further decrease in the detection limit can be achieved through the addition of plasmonic structures into labeled bio-sensing systems. Nano-structures that operate in the visible spectrum have feature sizes typically in the order of the operating wavelength, calling for high aspect ratio nanoscale fabrication capabilities. In order to achieve these dimensions, electron beam lithography (EBL) is used due to its accurate feature production. Hydrogen silsesquioxane (HSQ) based electron beam resist is chosen for one of its benefits, which is after exposure to oxygen plasma, the patterned resist cures into silicon dioxide (SiO2). These cured features in conjunction with nanoscale gold particles help in producing a high electric field through dipole generation. In this work, a detailed process flow of the fabrication of square lattice of plasmonic structures comprising of gold coated silicon dioxide pillars designed to operate at 560 nm wavelength and produce an intensity increase of roughly 100 percent will be presented.

Computer-aided Assessment of Regional Abdominal Fat with Food Residue Removal in CT

PubMed Central

Makrogiannis, Sokratis; Caturegli, Giorgio; Davatzikos, Christos; Ferrucci, Luigi

2014-01-01

Rationale and Objectives Separate quantification of abdominal subcutaneous and visceral fat regions is essential to understand the role of regional adiposity as risk factor in epidemiological studies. Fat quantification is often based on computed tomography (CT) because fat density is distinct from other tissue densities in the abdomen. However, the presence of intestinal food residues with densities similar to fat may reduce fat quantification accuracy. We introduce an abdominal fat quantification method in CT with interest in food residue removal. Materials and Methods Total fat was identified in the feature space of Hounsfield units and divided into subcutaneous and visceral components using model-based segmentation. Regions of food residues were identified and removed from visceral fat using a machine learning method integrating intensity, texture, and spatial information. Cost-weighting and bagging techniques were investigated to address class imbalance. Results We validated our automated food residue removal technique against semimanual quantifications. Our feature selection experiments indicated that joint intensity and texture features produce the highest classification accuracy at 95%. We explored generalization capability using k-fold cross-validation and receiver operating characteristic (ROC) analysis with variable k. Losses in accuracy and area under ROC curve between maximum and minimum k were limited to 0.1% and 0.3%. We validated tissue segmentation against reference semimanual delineations. The Dice similarity scores were as high as 93.1 for subcutaneous fat and 85.6 for visceral fat. Conclusions Computer-aided regional abdominal fat quantification is a reliable computational tool for large-scale epidemiological studies. Our proposed intestinal food residue reduction scheme is an original contribution of this work. Validation experiments indicate very good accuracy and generalization capability. PMID:24119354

Computer-aided assessment of regional abdominal fat with food residue removal in CT.

PubMed

Makrogiannis, Sokratis; Caturegli, Giorgio; Davatzikos, Christos; Ferrucci, Luigi

2013-11-01

Separate quantification of abdominal subcutaneous and visceral fat regions is essential to understand the role of regional adiposity as risk factor in epidemiological studies. Fat quantification is often based on computed tomography (CT) because fat density is distinct from other tissue densities in the abdomen. However, the presence of intestinal food residues with densities similar to fat may reduce fat quantification accuracy. We introduce an abdominal fat quantification method in CT with interest in food residue removal. Total fat was identified in the feature space of Hounsfield units and divided into subcutaneous and visceral components using model-based segmentation. Regions of food residues were identified and removed from visceral fat using a machine learning method integrating intensity, texture, and spatial information. Cost-weighting and bagging techniques were investigated to address class imbalance. We validated our automated food residue removal technique against semimanual quantifications. Our feature selection experiments indicated that joint intensity and texture features produce the highest classification accuracy at 95%. We explored generalization capability using k-fold cross-validation and receiver operating characteristic (ROC) analysis with variable k. Losses in accuracy and area under ROC curve between maximum and minimum k were limited to 0.1% and 0.3%. We validated tissue segmentation against reference semimanual delineations. The Dice similarity scores were as high as 93.1 for subcutaneous fat and 85.6 for visceral fat. Computer-aided regional abdominal fat quantification is a reliable computational tool for large-scale epidemiological studies. Our proposed intestinal food residue reduction scheme is an original contribution of this work. Validation experiments indicate very good accuracy and generalization capability. Published by Elsevier Inc.

Development and Initial Review of the Mark II Navy 44 Sail Training Craft

DTIC Science & Technology

2009-03-01

intensive training environment) • Offshore capability for trips to Bermuda with a semi-skilled crew of ten • Favorable treatment under existing rating... triangle and headsails • Existing systems by type, to be updated as directed Features to be improved: • Construction materials, scantlings and...consistent with the MK I’s. The mast height, standing rigging and fore triangle base had to be identical between the MK I and MK II. However, PYD wanted

Oxidation of ultrathin GaSe

DOE PAGES

Thomas Edwin Beechem; McDonald, Anthony E.; Ohta, Taisuke; ...

2015-10-26

Oxidation of exfoliated gallium selenide (GaSe) is investigated through Raman, photoluminescence, Auger, and X-ray photoelectron spectroscopies. Photoluminescence and Raman intensity reductions associated with spectral features of GaSe are shown to coincide with the emergence of signatures emanating from the by-products of the oxidation reaction, namely, Ga 2Se 3 and amorphous Se. Furthermore, photoinduced oxidation is initiated over a portion of a flake highlighting the potential for laser based patterning of two-dimensional heterostructures via selective oxidation.

Efficient thermal noise removal of Sentinel-1 image and its impacts on sea ice applications

NASA Astrophysics Data System (ADS)

Park, Jeong-Won; Korosov, Anton; Babiker, Mohamed

2017-04-01

Wide swath SAR observation from several spaceborne SAR missions played an important role in studying sea ice in the polar region. Sentinel 1A and 1B are producing dual-polarization observation data with the highest temporal resolution ever. For a proper use of dense time-series, radiometric properties must be qualified. Thermal noise is often neglected in many sea ice applications, but is impacting seriously the utility of dual-polarization SAR data. Sentinel-1 TOPSAR image intensity is disturbed by additive thermal noise particularly in cross-polarization channel. Although ESA provides calibrated noise vectors for noise power subtraction, residual noise contribution is significant considering relatively narrow backscattering distribution of cross-polarization channel. In this study, we investigate the noise characteristics and propose an efficient method for noise reduction based on three types of correction: azimuth de-scalloping, noise scaling, and inter-swath power balancing. The core idea is to find optimum correction coefficients resulting in the most noise-uncorrelated gentle backscatter profile over homogeneous region and to combine them with scalloping gain for reconstruction of complete two-dimensional noise field. Denoising is accomplished by subtracting the reconstructed noise field from the original image. The resulting correction coefficients determined by extensive experiments showed different noise characteristics for different Instrument Processing Facility (IPF) versions of Level 1 product generation. Even after thermal noise subtraction, the image still suffers from residual noise, which distorts local statistics. Since this residual noise depends on local signal-to-noise ratio, it can be compensated by variance normalization with coefficients determined from an empirical model. Denoising improved not only visual interpretability but also performances in SAR intensity-based sea ice applications. Results from two applications showed the effectiveness of the proposed method: feature tracking based sea ice drift and texture analysis based sea ice classification. For feature tracking, large spatial asymmetry of keypoint distribution caused by higher noise level in the nearest subswath was decresed so that the matched features to be selected evenly in space. For texture analysis, inter-subswath texture differences caused by different noise equivalent sigma zero were normalized so that the texture features estimated in any subswath have similar value with those in other subswaths.

Ion Mobility-Mass Spectrometry Analysis of Serum N-linked Glycans from Esophageal Adenocarcinoma Phenotypes

PubMed Central

Gaye, M. M.; Valentine, S. J.; Hu, Y.; Mirjankar, N.; Hammoud, Z. T.; Mechref, Y.; Lavine, B. K.; Clemmer, D. E.

2012-01-01

Three disease phenotypes, Barrett’s esophagus (BE), high-grade dysplasia (HGD), esophageal adenocarcinoma (EAC), and a set of normal control (NC) serum samples are examined using a combination of ion mobility spectrometry (IMS), mass spectrometry (MS) and principal component analysis (PCA) techniques. Samples from a total of 136 individuals were examined, including: 7 characterized as BE, 12 as HGD, 56 as EAC and 61 as NC. In typical datasets it was possible to assign ~20 to 30 glycan ions based on MS measurements. Ion mobility distributions for these ions show multiple features. In some cases, such as the [S1H5N4+3Na]3+ and [S1F1H5N4+3Na]3+ glycan ions, the ratio of intensities of high-mobility features to low-mobility features vary significantly for different groups. The degree to which such variations in mobility profiles can be used to distinguish phenotypes is evaluated for eleven N-linked glycan ions. An outlier analysis on each sample class followed by an unsupervised PCA using a genetic algorithm for pattern recognition reveals that EAC samples are separated from NC samples based on 46 features originating from the 11-glycan composite IMS distribution. PMID:23126309

Interactive Dose Shaping - efficient strategies for CPU-based real-time treatment planning

NASA Astrophysics Data System (ADS)

Ziegenhein, P.; Kamerling, C. P.; Oelfke, U.

2014-03-01

Conventional intensity modulated radiation therapy (IMRT) treatment planning is based on the traditional concept of iterative optimization using an objective function specified by dose volume histogram constraints for pre-segmented VOIs. This indirect approach suffers from unavoidable shortcomings: i) The control of local dose features is limited to segmented VOIs. ii) Any objective function is a mathematical measure of the plan quality, i.e., is not able to define the clinically optimal treatment plan. iii) Adapting an existing plan to changed patient anatomy as detected by IGRT procedures is difficult. To overcome these shortcomings, we introduce the method of Interactive Dose Shaping (IDS) as a new paradigm for IMRT treatment planning. IDS allows for a direct and interactive manipulation of local dose features in real-time. The key element driving the IDS process is a two-step Dose Modification and Recovery (DMR) strategy: A local dose modification is initiated by the user which translates into modified fluence patterns. This also affects existing desired dose features elsewhere which is compensated by a heuristic recovery process. The IDS paradigm was implemented together with a CPU-based ultra-fast dose calculation and a 3D GUI for dose manipulation and visualization. A local dose feature can be implemented via the DMR strategy within 1-2 seconds. By imposing a series of local dose features, equal plan qualities could be achieved compared to conventional planning for prostate and head and neck cases within 1-2 minutes. The idea of Interactive Dose Shaping for treatment planning has been introduced and first applications of this concept have been realized.

Obstacle Classification and 3D Measurement in Unstructured Environments Based on ToF Cameras

PubMed Central

Yu, Hongshan; Zhu, Jiang; Wang, Yaonan; Jia, Wenyan; Sun, Mingui; Tang, Yandong

2014-01-01

Inspired by the human 3D visual perception system, we present an obstacle detection and classification method based on the use of Time-of-Flight (ToF) cameras for robotic navigation in unstructured environments. The ToF camera provides 3D sensing by capturing an image along with per-pixel 3D space information. Based on this valuable feature and human knowledge of navigation, the proposed method first removes irrelevant regions which do not affect robot's movement from the scene. In the second step, regions of interest are detected and clustered as possible obstacles using both 3D information and intensity image obtained by the ToF camera. Consequently, a multiple relevance vector machine (RVM) classifier is designed to classify obstacles into four possible classes based on the terrain traversability and geometrical features of the obstacles. Finally, experimental results in various unstructured environments are presented to verify the robustness and performance of the proposed approach. We have found that, compared with the existing obstacle recognition methods, the new approach is more accurate and efficient. PMID:24945679

The underlying emotion and the dream relating dream imagery to the dreamer's underlying emotion can help elucidate the nature of dreaming.

PubMed

Hartmann, Ernest

2010-01-01

There is a widespread consensus that emotion is important in dreams, deriving from both biological and psychological studies. However, the emphasis on examining emotions explicitly mentioned in dreams is misplaced. The dream is basically made of imagery. The focus of our group has been on relating the dream imagery to the dreamer's underlying emotion. What is most important is the underlying emotion--the emotion of the dreamer, not the emotion in the dream. This chapter discusses many studies relating the dream-especially the central image of the dream--to the dreamer's underlying emotion. Focusing on the underlying emotion leads to a coherent and testable view of the nature of dreaming. It also helps to clarify some important puzzling features of the literature on dreams, such as why the clinical literature is different in so many ways from the experimental literature, especially the laboratory-based experimental literature. Based on central image intensity and the associated underlying emotion, we can identify a hierarchy of dreams, from the highest-intensity, "big dreams," to the lowest-intensity dreams from laboratory awakenings. Copyright © 2010 Elsevier Inc. All rights reserved.

The essential features and modes of bacterial polar growth.

PubMed

Cameron, Todd A; Zupan, John R; Zambryski, Patricia C

2015-06-01

Polar growth represents a surprising departure from the canonical dispersed cell growth model. However, we know relatively little of the underlying mechanisms governing polar growth or the requisite suite of factors that direct polar growth. Underscoring how classic doctrine can be turned on its head, the peptidoglycan layer of polar-growing bacteria features unusual crosslinks and in some species the quintessential cell division proteins FtsA and FtsZ are recruited to the growing poles. Remarkably, numerous medically important pathogens utilize polar growth, accentuating the need for intensive research in this area. Here we review models of polar growth in bacteria based on recent research in the Actinomycetales and Rhizobiales, with emphasis on Mycobacterium and Agrobacterium species. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Narrow Cold-Frontal Rainband of 22/23 November 2013

NASA Technical Reports Server (NTRS)

Kidd, Christopher

2015-01-01

The recent paper in Weather by Young (2014) provided a detailed analysis of an intensive cold front as it passed over the UK on 2223 November 2013. This was an extremely good example of linear convection, as it is described in the paper, or a narrow cold-frontal rainband (NCFR; Hobbs and Biswas, 1979). These features are associated with a low-level jet that lies ahead and parallel to the surface cold front, generating a band of intense but relatively shallow convection (see Koch and Kocin, 1991). Although the structure associated with these systems is not uncommon (e.g. Gatzen, 2011), this case was notable for the (linear) length and the longevity of the feature. Critically, fine-scale radar observations using the 1km, 5min UK composite radar product, produced by the UK Met Office and supplied by the British Atmospheric Data Centre, enabled the timing and progression of the most intense band of this feature tobe examined (see Figure 1).

On the vibronic level structure in the NO3 radical. Part III. Observation of intensity borrowing via ground state mixing.

PubMed

Stanton, John F; Okumura, Mitchio

2009-06-21

The A(2)E''<-- X(2)A'(2) absorption spectrum exhibits vibronically allowed transitions from the ground state of NO(3) to upper state levels having a''(1) and e' vibronic symmetries. This paper explores the coupling mechanisms that lend intensities to these features. While transitions to e' vibronic levels borrow intensity from the very strong B(2)E'<-- X(2)A'(2) electronic transition, those to a''(1) levels involve only negligible upper-state borrowing effects. Rather, it is the vibronic mixing of the ground vibronic level of NO(3) with vibrational levels in the B(2)E' electronic state that permit the a''(1) levels to be seen in the spectrum. These ideas are supported by vibronic coupling calculations. The fact that the intensities of features corresponding to the two different vibronic symmetries are comparable is thus accidental.

Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

NASA Astrophysics Data System (ADS)

Das, Bipul; Saha, Punam K.; Wolf, Ronald; Song, Hee Kwon; Wright, Alexander C.; Wehrli, Felix W.

2005-04-01

Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD- weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond exceptionally well with manual segmentation completed by radiology experts. Reproducibility of the proposed method is estimated for both intra- and inter-operator studies.

Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

PubMed Central

2011-01-01

Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. Conclusions We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. PMID:22023778

Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

PubMed

Kennedy, Curtis E; Turley, James P

2011-10-24

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting.

Assessing Landscape Scale Wildfire Exposure for Highly Valued Resources in a Mediterranean Area

NASA Astrophysics Data System (ADS)

Alcasena, Fermín J.; Salis, Michele; Ager, Alan A.; Arca, Bachisio; Molina, Domingo; Spano, Donatella

2015-05-01

We used a fire simulation modeling approach to assess landscape scale wildfire exposure for highly valued resources and assets (HVR) on a fire-prone area of 680 km2 located in central Sardinia, Italy. The study area was affected by several wildfires in the last half century: some large and intense fire events threatened wildland urban interfaces as well as other socioeconomic and cultural values. Historical wildfire and weather data were used to inform wildfire simulations, which were based on the minimum travel time algorithm as implemented in FlamMap. We simulated 90,000 fires that replicated recent large fire events in the area spreading under severe weather conditions to generate detailed maps of wildfire likelihood and intensity. Then, we linked fire modeling outputs to a geospatial risk assessment framework focusing on buffer areas around HVR. The results highlighted a large variation in burn probability and fire intensity in the vicinity of HVRs, and allowed us to identify the areas most exposed to wildfires and thus to a higher potential damage. Fire intensity in the HVR buffers was mainly related to fuel types, while wind direction, topographic features, and historically based ignition pattern were the key factors affecting fire likelihood. The methodology presented in this work can have numerous applications, in the study area and elsewhere, particularly to address and inform fire risk management, landscape planning and people safety on the vicinity of HVRs.

TU-F-CAMPUS-J-04: Impact of Voxel Anisotropy On Statistic Texture Features of Oncologic PET: A Simulation Study

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

Yang, F; Byrd, D; Bowen, S

2015-06-15

Purpose: Texture metrics extracted from oncologic PET have been investigated with respect to their usefulness as definitive indicants for prognosis in a variety of cancer. Metric calculation is often based on cubic voxels. Most commonly used PET scanners, however, produce rectangular voxels, which may change texture metrics. The objective of this study was to examine the variability of PET texture feature metrics resulting from voxel anisotropy. Methods: Sinograms of NEMA NU-2 phantom for 18F-FDG were simulated using the ASIM simulation tool. The obtained projection data was reconstructed (3D-OSEM) on grids of cubic and rectangular voxels, producing PET images of resolutionmore » of 2.73x2.73x3.27mm3 and 3.27x3.27x3.27mm3, respectively. An interpolated dataset obtained from resampling the rectangular voxel data for isotropic voxel dimension (3.27mm) was also considered. For each image dataset, 28 texture parameters based on grey-level co-occurrence matrices (GLCOM), intensity histograms (GLIH), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated within lesions of diameter of 33, 28, 22, and 17mm. Results: In reference to the isotopic image data, texture features appearing on the rectangular voxel data varied with a range of -34-10% for GLCOM based, -31-39% for GLIH based, -80 -161% for GLNDM based, and −6–45% for GLZSM based while varied with a range of -35-23% for GLCOM based, -27-35% for GLIH based, -65-86% for GLNDM based, and -22 -18% for GLZSM based for the interpolated image data. For the anisotropic data, GLNDM-cplx exhibited the largest extent of variation (161%) while GLZSM-zp showed the least (<1%). As to the interpolated data, GLNDM-busy varied the most (86%) while GLIH-engy varied the least (<1%). Conclusion: Variability of texture appearance on oncologic PET with respect to voxel representation is substantial and feature-dependent. It necessitates consideration of standardized voxel representation for inter-institution studies attempting to validate prognostic values of PET texture features in cancer treatment.« less

Overt attention in natural scenes: objects dominate features.

PubMed

Stoll, Josef; Thrun, Michael; Nuthmann, Antje; Einhäuser, Wolfgang

2015-02-01

Whether overt attention in natural scenes is guided by object content or by low-level stimulus features has become a matter of intense debate. Experimental evidence seemed to indicate that once object locations in a scene are known, salience models provide little extra explanatory power. This approach has recently been criticized for using inadequate models of early salience; and indeed, state-of-the-art salience models outperform trivial object-based models that assume a uniform distribution of fixations on objects. Here we propose to use object-based models that take a preferred viewing location (PVL) close to the centre of objects into account. In experiment 1, we demonstrate that, when including this comparably subtle modification, object-based models again are at par with state-of-the-art salience models in predicting fixations in natural scenes. One possible interpretation of these results is that objects rather than early salience dominate attentional guidance. In this view, early-salience models predict fixations through the correlation of their features with object locations. To test this hypothesis directly, in two additional experiments we reduced low-level salience in image areas of high object content. For these modified stimuli, the object-based model predicted fixations significantly better than early salience. This finding held in an object-naming task (experiment 2) and a free-viewing task (experiment 3). These results provide further evidence for object-based fixation selection--and by inference object-based attentional guidance--in natural scenes. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Cyclic development of igneous features and their relationship to high-temperature hydrothermal features in the Henderson porphyry molybdenum deposit, Colorado

USGS Publications Warehouse

Carten, R.B.; Geraghty, E.P.; Walker, B.M.

1988-01-01

The Henderson porphyry molybdenum deposit was formed by the superposition of coupled alteration and mineralization events, of varying intensity and size, that were associated with each of at least 11 intrusions. Deposition of molybdenite was accompanied by time-equivalent silicic and potassic alteration. High-temperature alteration and mineralization are spatially and temporally linked to the crystallization of compositionally zoned magma in the apex of stocks. Differences in hydrothermal features associated with each intrusion (e.g., mass of ore, orientation and type of veins, density of veins, and intensity of alteration) correlate with differences in primary igneous features (e.g., composition, texture, morphology, and size). The systematic relations between hydrothermal and magmatic features suggest that primary magma compositions, including volatile contents, largely control the geometry, volume, level of emplacement, and mechanisms of crystallization of stocks. These elements in turn govern the orientations and densities of fractures, which ultimately determine the distribution patterns of hydrothermal alteration and mineralization. -from Authors

Continuous intensity map optimization (CIMO): A novel approach to leaf sequencing in step and shoot IMRT

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

Cao Daliang; Earl, Matthew A.; Luan, Shuang

2006-04-15

A new leaf-sequencing approach has been developed that is designed to reduce the number of required beam segments for step-and-shoot intensity modulated radiation therapy (IMRT). This approach to leaf sequencing is called continuous-intensity-map-optimization (CIMO). Using a simulated annealing algorithm, CIMO seeks to minimize differences between the optimized and sequenced intensity maps. Two distinguishing features of the CIMO algorithm are (1) CIMO does not require that each optimized intensity map be clustered into discrete levels and (2) CIMO is not rule-based but rather simultaneously optimizes both the aperture shapes and weights. To test the CIMO algorithm, ten IMRT patient cases weremore » selected (four head-and-neck, two pancreas, two prostate, one brain, and one pelvis). For each case, the optimized intensity maps were extracted from the Pinnacle{sup 3} treatment planning system. The CIMO algorithm was applied, and the optimized aperture shapes and weights were loaded back into Pinnacle. A final dose calculation was performed using Pinnacle's convolution/superposition based dose calculation. On average, the CIMO algorithm provided a 54% reduction in the number of beam segments as compared with Pinnacle's leaf sequencer. The plans sequenced using the CIMO algorithm also provided improved target dose uniformity and a reduced discrepancy between the optimized and sequenced intensity maps. For ten clinical intensity maps, comparisons were performed between the CIMO algorithm and the power-of-two reduction algorithm of Xia and Verhey [Med. Phys. 25(8), 1424-1434 (1998)]. When the constraints of a Varian Millennium multileaf collimator were applied, the CIMO algorithm resulted in a 26% reduction in the number of segments. For an Elekta multileaf collimator, the CIMO algorithm resulted in a 67% reduction in the number of segments. An average leaf sequencing time of less than one minute per beam was observed.« less

Efficient green luminescence of terbium oxalate crystals: A case study with Judd-Ofelt theory and single crystal structure analysis and the effect of dehydration on luminescence

NASA Astrophysics Data System (ADS)

Alexander, Dinu; Joy, Monu; Thomas, Kukku; Sisira, S.; Biju, P. R.; Unnikrishnan, N. V.; Sudarsanakumar, C.; Ittyachen, M. A.; Joseph, Cyriac

2018-06-01

Design and synthesis of Lanthanide based metal organic framework is a frontier area of research owing to their structural diversity enabling specific applications. The luminescence properties of rare earths, tuned by the structural features of Ln-MOFs are investigated extensively. Rare earth oxalates which can be synthesized in a facile method, ensuring the structural features of MOFs with excellent photoluminescence characteristics deserves much attention. This work is the first time report on the single crystal structure and Judd-Ofelt (JO) theoretical analysis - their correlation with the intense and sharp green luminescence of Terbium oxalate crystals. The intense green luminescence observed for Terbium oxalate crystals for a wide range of excitation from DUV to visible region despite the luminescence limiting factors are discussed. The absence of concentration quenching and lifting up of forbidden nature of f-f transitions, allowing direct excitation of Terbium ions is analysed with the help of JO theory and single crystal structure analysis. The JO analysis predicted the asymmetry of Terbium sites, allowing the electric dipole transitions and from the JO intensity parameters, promising spectroscopic parameters - emission cross section, branching ratio, gain band width and gain coefficient of the material were calculated. The single crystal structure analysis revealed the asymmetry of Tb sites and structure of Terbium oxalate is formed by the hydrogen bonded stacking of overlapped six Terbium membered rings connected by the oxalate ligands. The molecularly thick layers thus formed on the crystal surface are imaged by the atomic force microscopy. The presence of water channels in the structure and the effect of lattice water molecules on the luminescence intensity are also investigated.

Humans (Homo sapiens) judge the emotional content of piglet (Sus scrofa domestica) calls based on simple acoustic parameters, not personality, empathy, nor attitude toward animals.

PubMed

Maruščáková, Iva L; Linhart, Pavel; Ratcliffe, Victoria F; Tallet, Céline; Reby, David; Špinka, Marek

2015-05-01

The vocal expression of emotion is likely driven by shared physiological principles among species. However, which acoustic features promote decoding of emotional state and how the decoding is affected by their listener's psychology remain poorly understood. Here we tested how acoustic features of piglet vocalizations interact with psychological profiles of human listeners to affect judgments of emotional content of heterospecific vocalizations. We played back 48 piglet call sequences recorded in four different contexts (castration, isolation, reunion, nursing) to 60 listeners. Listeners judged the emotional intensity and valence of the recordings and were further asked to attribute a context of emission from four proposed contexts. Furthermore, listeners completed a series of questionnaires assessing their personality (NEO-FFI personality inventory), empathy [Interpersonal Reactivity Index (IRI)] and attitudes to animals (Animal Attitudes Scale). None of the listeners' psychological traits affected the judgments. On the contrary, acoustic properties of recordings had a substantial effect on ratings. Recordings were rated as more intense with increasing pitch (mean fundamental frequency) and increasing proportion of vocalized sound within each stimulus recording and more negative with increasing pitch and increasing duration of the calls within the recording. More complex acoustic properties (jitter, harmonic-to-noise ratio, and presence of subharmonics) did not seem to affect the judgments. The probability of correct context recognition correlated positively with the assessed emotion intensity for castration and reunion calls, and negatively for nursing calls. In conclusion, listeners judged emotions from pig calls using simple acoustic properties and the perceived emotional intensity might guide the identification of the context. (c) 2015 APA, all rights reserved).

Processing LiDAR Data to Predict Natural Hazards

NASA Technical Reports Server (NTRS)

Fairweather, Ian; Crabtree, Robert; Hager, Stacey

2008-01-01

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Target 3-D reconstruction of streak tube imaging lidar based on Gaussian fitting

NASA Astrophysics Data System (ADS)

Yuan, Qingyu; Niu, Lihong; Hu, Cuichun; Wu, Lei; Yang, Hongru; Yu, Bing

2018-02-01

Streak images obtained by the streak tube imaging lidar (STIL) contain the distance-azimuth-intensity information of a scanned target, and a 3-D reconstruction of the target can be carried out through extracting the characteristic data of multiple streak images. Significant errors will be caused in the reconstruction result by the peak detection method due to noise and other factors. So as to get a more precise 3-D reconstruction, a peak detection method based on Gaussian fitting of trust region is proposed in this work. Gaussian modeling is performed on the returned wave of single time channel of each frame, then the modeling result which can effectively reduce the noise interference and possesses a unique peak could be taken as the new returned waveform, lastly extracting its feature data through peak detection. The experimental data of aerial target is for verifying this method. This work shows that the peak detection method based on Gaussian fitting reduces the extraction error of the feature data to less than 10%; utilizing this method to extract the feature data and reconstruct the target make it possible to realize the spatial resolution with a minimum 30 cm in the depth direction, and improve the 3-D imaging accuracy of the STIL concurrently.

Illumination Modulation for Improved Propagation-Based Phase Imaging

NASA Astrophysics Data System (ADS)

Chakraborty, Tonmoy

Propagation-based phase imaging enables the quantitative reconstruction of a light beam's phase from measurements of its intensity. Because the intensity depends on the time-averaged square of the field the relationship between intensity and phase is, in general, nonlinear. The transport of intensity equation (TIE), is a linear equation relating phase and propagated intensity that arises from restricting the propagation distance to be small. However, the TIE limits the spatial frequencies that can be reliably reconstructed to those below some cutoff, which limits the accuracy of reconstruction of fine features in phase. On the other hand, the low frequency components suffer from poor signal to noise ratio (SNR) unless the propagation distance is sufficiently large, which leads to low frequency artifacts that obscure the reconstruction. In this research, I will consider the use of incoherent primary sources of illumination, in a Kohler illumination setup, to enhance the low-frequency performance of the TIE. The necessary steps required to design and build a table-top imaging setup which is capable of capturing intensity at any defocused position while modulating the source will be explained. In addition, it will be shown how by employing such illumination, the steps required for computationally recovering the phase, i.e. Fourier transforms and frequency-domain filtering, may be performed in the optical system. While these methods can address the low-frequency performance of the TIE, they do not extend its high-frequency cutoff. To avoid this cutoff, for objects with slowly varying phase, the contrast transfer function (CTF) model, an alternative to the TIE, can be used to recover phase. By allowing the combination of longer propagation distances and incoherent sources, it will be shown how CTF can improve performance at both high and low frequencies.

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational ℓ1-Norm Regularization in the Derivative Domain

NASA Astrophysics Data System (ADS)

Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

2014-05-01

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall), and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients (called ℓ1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a data base of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Gamma-ray vortices from nonlinear inverse Thomson scattering of circularly polarized light.

PubMed

Taira, Yoshitaka; Hayakawa, Takehito; Katoh, Masahiro

2017-07-10

Inverse Thomson scattering is a well-known radiation process that produces high-energy photons both in nature and in the laboratory. Nonlinear inverse Thomson scattering occurring inside an intense light field is a process which generates higher harmonic photons. In this paper, we theoretically show that the higher harmonic gamma-ray produced by nonlinear inverse Thomson scattering of circularly polarized light is a gamma-ray vortex, which means that it possesses a helical wave front and carries orbital angular momentum. Our work explains a recent experimental result regarding nonlinear inverse Thomson scattering that clearly shows an annular intensity distribution as a remarkable feature of a vortex beam. Our work implies that gamma-ray vortices should be produced in various situations in astrophysics in which high-energy electrons and intense circularly polarized light fields coexist. Nonlinear inverse Thomson scattering is a promising radiation process for realizing a gamma-ray vortex source based on currently available laser and accelerator technologies, which would be an indispensable tool for exploring gamma-ray vortex science.

Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors.

PubMed

Okada, Toshiyuki; Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki; Sato, Yoshinobu

2013-01-01

The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using intensity information in manually traced training data. The features of the framework are as follows: (1) the organ correlation graph (OCG) is introduced, which encodes the spatial correlations among organs inherent in human anatomy; (2) the patient-specific organ shape and location priors obtained using OCG enable the estimation of intensity priors from only target data and optionally a number of untraced CT data of the same imaging condition as the target data. The proposed methods were evaluated through segmentation of eight abdominal organs (liver, spleen, left and right kidney, pancreas, gallbladder, aorta, and inferior vena cava) from 86 CT data obtained by four imaging conditions at two hospitals. The performance was comparable to the state-of-the-art method using intensity priors constructed from manually traced data.

Pacific-North American teleconnection and North Pacific Oscillation: historical simulation and future projection in CMIP5 models

NASA Astrophysics Data System (ADS)

Chen, Zheng; Gan, Bolan; Wu, Lixin; Jia, Fan

2017-09-01

Based on reanalysis datasets and as many as 35 CMIP5 models, this study evaluates the capability of climate models to simulate the spatiotemporal features of Pacific-North American teleconnection (PNA) and North Pacific Oscillation (NPO) in the twentieth century wintertime, and further investigates their responses to greenhouse warming in the twenty-first century. Analysis reveals that while the majority (80%) of models reasonably simulate either the geographical distribution or the amplitude of PNA/NPO pattern, only half of models can well capture both features in space. As for the temporal features, variabilities of PNA and NPO in most models are biased toward higher amplitude. Additionally, most models simulate the interannual variabilities of PNA and NPO, qualitatively consistent with the observation, whereas models generally lack the capability to reproduce the decadal (20-25 years) variability of PNA. As the climate warms under the strongest future warming scenario, the PNA intensity is found to be strengthened, whereas there is no consensus on the direction of change in the NPO intensity among models. The intensification of positive PNA is primarily manifested in the large deepening of the North Pacific trough, which is robust as it is 2.3 times the unforced internal variability. By focusing on the tropical Pacific Ocean, we find that the multidecadal evolution of the North Pacific trough intensity (dominating the PNA intensity evolution) is closely related to that of the analogous trough in the PNA-like teleconnection forced by sea surface temperature anomalies (SSTa) in the tropical central Pacific (CP) rather than the tropical eastern Pacific (EP). Such association is also found to act under greenhouse warming: that is, the strengthening of the PNA-like teleconnection induced by the CP SSTa rather than the EP SSTa is a driving force for the intensification of PNA. This is in part owing to the robust enhancement of the tropical precipitation response to the CP SST variation. Indeed, further inspection suggests that models with stronger intensification of the CP SST variability and its related tropical precipitation response tend to have larger deepening magnitude of the North Pacific trough associated with the PNA variability.

Pacific-North American teleconnection and North Pacific Oscillation: historical simulation and future projection in CMIP5 models

NASA Astrophysics Data System (ADS)

Chen, Zheng; Gan, Bolan; Wu, Lixin; Jia, Fan

2018-06-01

Based on reanalysis datasets and as many as 35 CMIP5 models, this study evaluates the capability of climate models to simulate the spatiotemporal features of Pacific-North American teleconnection (PNA) and North Pacific Oscillation (NPO) in the twentieth century wintertime, and further investigates their responses to greenhouse warming in the twenty-first century. Analysis reveals that while the majority (80%) of models reasonably simulate either the geographical distribution or the amplitude of PNA/NPO pattern, only half of models can well capture both features in space. As for the temporal features, variabilities of PNA and NPO in most models are biased toward higher amplitude. Additionally, most models simulate the interannual variabilities of PNA and NPO, qualitatively consistent with the observation, whereas models generally lack the capability to reproduce the decadal (20-25 years) variability of PNA. As the climate warms under the strongest future warming scenario, the PNA intensity is found to be strengthened, whereas there is no consensus on the direction of change in the NPO intensity among models. The intensification of positive PNA is primarily manifested in the large deepening of the North Pacific trough, which is robust as it is 2.3 times the unforced internal variability. By focusing on the tropical Pacific Ocean, we find that the multidecadal evolution of the North Pacific trough intensity (dominating the PNA intensity evolution) is closely related to that of the analogous trough in the PNA-like teleconnection forced by sea surface temperature anomalies (SSTa) in the tropical central Pacific (CP) rather than the tropical eastern Pacific (EP). Such association is also found to act under greenhouse warming: that is, the strengthening of the PNA-like teleconnection induced by the CP SSTa rather than the EP SSTa is a driving force for the intensification of PNA. This is in part owing to the robust enhancement of the tropical precipitation response to the CP SST variation. Indeed, further inspection suggests that models with stronger intensification of the CP SST variability and its related tropical precipitation response tend to have larger deepening magnitude of the North Pacific trough associated with the PNA variability.

The issue of applying marketing on the pharmaceutical market in Serbia.

PubMed

Dickov, V; Dickov, A; Martinović-Mitrović, S

2011-03-01

The issue of applying marketing on the pharmaceutical market has the features of subject-based approach, with the intention to appreciate the specific nature of the products, as well as the special characteristics of the complexly formed demand. The relevance of the issue is related to the above-average performance of the pharmaceutical industry, its role in the generation of humanity's demographic transition, and specific development routes of marketing as a scientific and practical discipline. The sensitive nature of a pharmaceutical product on the one hand generates the intense legislation on this market, whereas on the other, the circumstances of its use generate a specific environment in which the production/consumption of the products of pharmaceutical industry is intensively reflected as a specific medical, cultural, economic and even political phenomenon.

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

PubMed

Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

2015-11-01

Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multi-fractal texture features for brain tumor and edema segmentation

NASA Astrophysics Data System (ADS)

Reza, S.; Iftekharuddin, K. M.

2014-03-01

In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.

Intensity ratio curve analysis of small renal masses on T2-weighted magnetic resonance imaging: Differentiation of fat-poor angiomyolipoma from renal cell carcinoma.

PubMed

Moriyama, Shingo; Yoshida, Soichiro; Tanaka, Hajime; Tanaka, Hiroshi; Yokoyama, Minato; Ishioka, Junichiro; Matsuoka, Yoh; Saito, Kazutaka; Kihara, Kazunori; Fujii, Yasuhisa

2018-03-25

To assess the diagnostic ability of a pixel intensity-based analysis in evaluating the magnetic resonance imaging characteristics of small renal masses, especially in differentiating fat-poor angiomyolipoma from renal cell carcinoma. T2-weighted images from 121 solid small renal masses (<4 cm) without visible fat (14 fat-poor angiomyolipomas, 92 clear cell renal cell carcinomas, six chromophobe renal cell carcinomas and nine papillary renal cell carcinomas) were retrospectively evaluated. An intensity ratio curve was plotted using intensity ratios, which were ratios of signal intensities of tumor pixels (each pixel along a linear region of interest drawn across the renal tumor on T2-weighted image) to the signal intensity of a normal renal cortex. The diagnostic ability of the intensity ratio curve analysis was evaluated. The tumors were classified into three types: intensity ratio fat-poor angiomyolipoma (n = 19) with no pseudocapsule, iso-low intensity and no heterogeneity; intensity ratio clear cell renal cell carcinoma (n = 76) with a pseudocapsule, iso-high intensity and heterogeneity; and other type of intensity ratio (n = 26), including tumors that did not fall into the above two categories. The sensitivity/specificity/accuracy of the intensity ratio curve analysis in diagnosing fat-poor angiomyolipoma was 93%/94%/94%, respectively. When the intensity ratio curve analysis was applied only to the tumor with undetermined radiological diagnosis, the sensitivity for diagnosing fat-poor angiomyolipoma compared with subjective reading alone significantly improved (93% vs 50%; P = 0.014). Our novel semiquantitative model for combined assessment of key features of fat-poor angiomyolipoma, including low intensity, homogeneity and absence of a pseudocapsule on T2-weighted image, might make diagnosis of fat-poor angiomyolipoma more accurate. © 2018 The Japanese Urological Association.

Aluminum nanostructures with strong visible-range SERS activity for versatile micropatterning of molecular security labels.

PubMed

Lay, Chee Leng; Koh, Charlynn Sher Lin; Wang, Jing; Lee, Yih Hong; Jiang, Ruibin; Yang, Yijie; Yang, Zhe; Phang, In Yee; Ling, Xing Yi

2018-01-03

The application of aluminum (Al)-based nanostructures for visible-range plasmonics, especially for surface-enhanced Raman scattering (SERS), currently suffers from inconsistent local electromagnetic field distributions and/or inhomogeneous distribution of probe molecules. Herein, we lithographically fabricate structurally uniform Al nanostructures which enable homogeneous adsorption of various probe molecules. Individual Al nanostructures exhibit strong local electromagnetic field enhancements, in turn leading to intense SERS activity. The average SERS enhancement factor (EF) for individual nanostructures exceeds 10 4 for non-resonant probe molecules in the visible spectrum. These Al nanostructures also retain more than 70% of their original SERS intensities after one-month storage, displaying superb stability under ambient conditions. We further achieve tunable polarization-dependent SERS responses using anisotropic Al nanostructures, facilitating the design of sophisticated SERS-based security labels. Our micron-sized security label comprises two-tier security features, including a machine-readable hybrid quick-response (QR) code overlaid with a set of ciphertexts. Our work demonstrates the versatility of Al-based structures in low-cost modern chemical nano-analytics and forgery protection.

Predicting intensity ranks of peptide fragment ions.

PubMed

Frank, Ari M

2009-05-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

Predicting Intensity Ranks of Peptide Fragment Ions

PubMed Central

Frank, Ari M.

2009-01-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm in to models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal MRM transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html. PMID:19256476

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data.

PubMed

Baur, Brittany; Bozdag, Serdar

2016-01-01

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.

Spatial feature analysis of a cosmic-ray sensor for measuring the soil water content: Comparison of four weighting methods

NASA Astrophysics Data System (ADS)

Cai, Jingya; Pang, Zhiguo; Fu, Jun'e.

2018-04-01

To quantitatively analyze the spatial features of a cosmic-ray sensor (CRS) (i.e., the measurement support volume of the CRS and the weight of the in situ point-scale soil water content (SWC) in terms of the regionally averaged SWC derived from the CRS) in measuring the SWC, cooperative observations based on CRS, oven drying and frequency domain reflectometry (FDR) methods are performed at the point and regional scales in a desert steppe area of the Inner Mongolia Autonomous Region. This region is flat with sparse vegetation cover consisting of only grass, thereby minimizing the effects of terrain and vegetation. Considering the two possibilities of the measurement support volume of the CRS, the results of four weighting methods are compared with the SWC monitored by FDR within an appropriate measurement support volume. The weighted average calculated using the neutron intensity-based weighting method (Ni weighting method) best fits the regionally averaged SWC measured by the CRS. Therefore, we conclude that the gyroscopic support volume and the weights determined by the Ni weighting method are the closest to the actual spatial features of the CRS when measuring the SWC. Based on these findings, a scale transformation model of the SWC from the point scale to the scale of the CRS measurement support volume is established. In addition, the spatial features simulated using the Ni weighting method are visualized by developing a software system.

Pedestrian detection from thermal images: A sparse representation based approach

NASA Astrophysics Data System (ADS)

Qi, Bin; John, Vijay; Liu, Zheng; Mita, Seiichi

2016-05-01

Pedestrian detection, a key technology in computer vision, plays a paramount role in the applications of advanced driver assistant systems (ADASs) and autonomous vehicles. The objective of pedestrian detection is to identify and locate people in a dynamic environment so that accidents can be avoided. With significant variations introduced by illumination, occlusion, articulated pose, and complex background, pedestrian detection is a challenging task for visual perception. Different from visible images, thermal images are captured and presented with intensity maps based objects' emissivity, and thus have an enhanced spectral range to make human beings perceptible from the cool background. In this study, a sparse representation based approach is proposed for pedestrian detection from thermal images. We first adopted the histogram of sparse code to represent image features and then detect pedestrian with the extracted features in an unimodal and a multimodal framework respectively. In the unimodal framework, two types of dictionaries, i.e. joint dictionary and individual dictionary, are built by learning from prepared training samples. In the multimodal framework, a weighted fusion scheme is proposed to further highlight the contributions from features with higher separability. To validate the proposed approach, experiments were conducted to compare with three widely used features: Haar wavelets (HWs), histogram of oriented gradients (HOG), and histogram of phase congruency (HPC) as well as two classification methods, i.e. AdaBoost and support vector machine (SVM). Experimental results on a publicly available data set demonstrate the superiority of the proposed approach.

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method.

PubMed

Haddadpour, Mozhdeh; Daneshvar, Sabalan; Seyedarabi, Hadi

2017-08-01

The process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time. We used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (D k ) as an assessing spectral features and Average Gradient (AG k ) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method. In this paper we used three common evaluation metrics like Average Gradient (AG k ) and the lowest Discrepancy (D k ) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method. Since that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AG k ), Discrepancy (D k ) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images. Copyright © 2017 Chang Gung University. Published by Elsevier B.V. All rights reserved.

Scientific Opportunities and Plans for FRIB

NASA Astrophysics Data System (ADS)

Bollen, Georg

2014-09-01

FRIB, the US's ``Facility for Rare Isotope Beams'' under construction at Michigan State University will be a world-leading rare isotope beam facility. FRIB will be based on a 400 kW, 200 MeV/u heavy ion linac and provide a wide variety of high-quality beams of unstable isotopes at unprecedented intensities, opening exciting research perspectives with fast, stopped, and reaccelerated beams. This talk will summarize the scientific opportunities with FRIB in the areas of nuclear science, nuclear astrophysics, and the test of fundamental interaction and symmetries, as well using isotopes from FRIB for societal benefits. Design features of FRIB and the status of the ongoing construction will be presented. FRIB, the US's ``Facility for Rare Isotope Beams'' under construction at Michigan State University will be a world-leading rare isotope beam facility. FRIB will be based on a 400 kW, 200 MeV/u heavy ion linac and provide a wide variety of high-quality beams of unstable isotopes at unprecedented intensities, opening exciting research perspectives with fast, stopped, and reaccelerated beams. This talk will summarize the scientific opportunities with FRIB in the areas of nuclear science, nuclear astrophysics, and the test of fundamental interaction and symmetries, as well using isotopes from FRIB for societal benefits. Design features of FRIB and the status of the ongoing construction will be presented. This material is based upon work supported by the U.S. Department of Energy Office of Science under Cooperative Agreement DE-SC0000661, the State of Michigan and Michigan State University. Michigan State University designs and establishes FRIB as a DOE.

A Pilot Study of Sensation-Focused Intensive Treatment for Panic Disorder with Moderate to Severe Agoraphobia: Preliminary Outcome and Benchmarking Data

ERIC Educational Resources Information Center

Bitran, Stella; Morissette, Sandra B.; Spiegel, David A.; Barlow, David H.

2008-01-01

This report presents results of a treatment for panic disorder with moderate to severe agoraphobia (PDA-MS) called sensation-focused intensive treatment (SFIT). SFIT is an 8-day intensive treatment that combines features of cognitive-behavioral treatment for panic disorder, such as interoceptive exposure and cognitive restructuring with ungraded…

Adaptive image inversion of contrast 3D echocardiography for enabling automated analysis.

PubMed

Shaheen, Anjuman; Rajpoot, Kashif

2015-08-01

Contrast 3D echocardiography (C3DE) is commonly used to enhance the visual quality of ultrasound images in comparison with non-contrast 3D echocardiography (3DE). Although the image quality in C3DE is perceived to be improved for visual analysis, however it actually deteriorates for the purpose of automatic or semi-automatic analysis due to higher speckle noise and intensity inhomogeneity. Therefore, the LV endocardial feature extraction and segmentation from the C3DE images remains a challenging problem. To address this challenge, this work proposes an adaptive pre-processing method to invert the appearance of C3DE image. The image inversion is based on an image intensity threshold value which is automatically estimated through image histogram analysis. In the inverted appearance, the LV cavity appears dark while the myocardium appears bright thus making it similar in appearance to a 3DE image. Moreover, the resulting inverted image has high contrast and low noise appearance, yielding strong LV endocardium boundary and facilitating feature extraction for segmentation. Our results demonstrate that the inverse appearance of contrast image enables the subsequent LV segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment.

PubMed

Raboshchuk, Ganna; Nadeu, Climent; Jancovic, Peter; Lilja, Alex Peiro; Kokuer, Munevver; Munoz Mahamud, Blanca; Riverola De Veciana, Ana

2018-01-01

A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage, and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modeling, and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%.

A Structured Grid Based Solution-Adaptive Technique for Complex Separated Flows

NASA Technical Reports Server (NTRS)

Thornburg, Hugh; Soni, Bharat K.; Kishore, Boyalakuntla; Yu, Robert

1996-01-01

The objective of this work was to enhance the predictive capability of widely used computational fluid dynamic (CFD) codes through the use of solution adaptive gridding. Most problems of engineering interest involve multi-block grids and widely disparate length scales. Hence, it is desirable that the adaptive grid feature detection algorithm be developed to recognize flow structures of different type as well as differing intensity, and adequately address scaling and normalization across blocks. In order to study the accuracy and efficiency improvements due to the grid adaptation, it is necessary to quantify grid size and distribution requirements as well as computational times of non-adapted solutions. Flow fields about launch vehicles of practical interest often involve supersonic freestream conditions at angle of attack exhibiting large scale separate vortical flow, vortex-vortex and vortex-surface interactions, separated shear layers and multiple shocks of different intensity. In this work, a weight function and an associated mesh redistribution procedure is presented which detects and resolves these features without user intervention. Particular emphasis has been placed upon accurate resolution of expansion regions and boundary layers. Flow past a wedge at Mach=2.0 is used to illustrate the enhanced detection capabilities of this newly developed weight function.

Prediction of treatment outcome in soft tissue sarcoma based on radiologically defined habitats

NASA Astrophysics Data System (ADS)

Farhidzadeh, Hamidreza; Chaudhury, Baishali; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Gatenby, Robert A.; Gillies, Robert J.; Raghavan, Meera

2015-03-01

Soft tissue sarcomas are malignant tumors which develop from tissues like fat, muscle, nerves, fibrous tissue or blood vessels. They are challenging to physicians because of their relative infrequency and diverse outcomes, which have hindered development of new therapeutic agents. Additionally, assessing imaging response of these tumors to therapy is also difficult because of their heterogeneous appearance on magnetic resonance imaging (MRI). In this paper, we assessed standard of care MRI sequences performed before and after treatment using 36 patients with soft tissue sarcoma. Tumor tissue was identified by manually drawing a mask on contrast enhanced images. The Otsu segmentation method was applied to segment tumor tissue into low and high signal intensity regions on both T1 post-contrast and T2 without contrast images. This resulted in four distinctive subregions or "habitats." The features used to predict metastatic tumors and necrosis included the ratio of habitat size to whole tumor size and components of 2D intensity histograms. Individual cases were correctly classified as metastatic or non-metastatic disease with 80.55% accuracy and for necrosis ≥ 90 or necrosis <90 with 75.75% accuracy by using meta-classifiers which contained feature selectors and classifiers.

A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment

PubMed Central

Nadeu, Climent; Jančovič, Peter; Lilja, Alex Peiró; Köküer, Münevver; Muñoz Mahamud, Blanca; Riverola De Veciana, Ana

2018-01-01

A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage, and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modeling, and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%. PMID:29404227

Exploring the Solar System with Stellar Occultations

NASA Technical Reports Server (NTRS)

Elliot, J. L.; Dunham, E. W.

1984-01-01

By recording the light intensity as a function of time when a planet occults a relatively bright star, the thermal structure of the upper atmosphere of the planet can be probed. The main feature of stellar occultation observations is their high spatial resolution, typically several thousand times better than the resolution achievable with ground-based imaging. Five stellar occultations have been observed. The main results of these observations are summarized. Stellar occultations have been observed on Uranus, Mars, Pallas, Neptune and the Jovian Ring.

Stochastic resonance in feedforward acupuncture networks

NASA Astrophysics Data System (ADS)

Qin, Ying-Mei; Wang, Jiang; Men, Cong; Deng, Bin; Wei, Xi-Le; Yu, Hai-Tao; Chan, Wai-Lok

2014-10-01

Effects of noises and some other network properties on the weak signal propagation are studied systematically in feedforward acupuncture networks (FFN) based on FitzHugh-Nagumo neuron model. It is found that noises with medium intensity can enhance signal propagation and this effect can be further increased by the feedforward network structure. Resonant properties in the noisy network can also be altered by several network parameters, such as heterogeneity, synapse features, and feedback connections. These results may also provide a novel potential explanation for the propagation of acupuncture signal.

SU-E-J-261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features

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

Zhang, L; Fried, D; Fave, X

Purpose: To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature’s differentiating capability. Methods: Twenty-seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre-treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre-treatment PET images (non-necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between twomore » groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2-tailed T-test was used to evaluate the differentiating capability of CT features on pre-treatment PT necrosis. Result: Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary-adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity was significantly lower in the necrosis group (p<0.05). Conclusion: Appropriate selection of image preprocessing techniques allows radiomics features to extract more useful information and thereby improve prediction models based on these features.« less

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.

Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases.

PubMed

Kostopoulos, Spiros A; Asvestas, Pantelis A; Kalatzis, Ioannis K; Sakellaropoulos, George C; Sakkis, Theofilos H; Cavouras, Dionisis A; Glotsos, Dimitris T

2017-09-01

The aim of this study was to propose features that evaluate pictorial differences between melanocytic nevus (mole) and melanoma lesions by computer-based analysis of plain photography images and to design a cross-platform, tunable, decision support system to discriminate with high accuracy moles from melanomas in different publicly available image databases. Digital plain photography images of verified mole and melanoma lesions were downloaded from (i) Edinburgh University Hospital, UK, (Dermofit, 330moles/70 melanomas, under signed agreement), from 5 different centers (Multicenter, 63moles/25 melanomas, publicly available), and from the Groningen University, Netherlands (Groningen, 100moles/70 melanomas, publicly available). Images were processed for outlining the lesion-border and isolating the lesion from the surrounding background. Fourteen features were generated from each lesion evaluating texture (4), structure (5), shape (4) and color (1). Features were subjected to statistical analysis for determining differences in pictorial properties between moles and melanomas. The Probabilistic Neural Network (PNN) classifier, the exhaustive search features selection, the leave-one-out (LOO), and the external cross-validation (ECV) methods were used to design the PR-system for discriminating between moles and melanomas. Statistical analysis revealed that melanomas as compared to moles were of lower intensity, of less homogenous surface, had more dark pixels with intensities spanning larger spectra of gray-values, contained more objects of different sizes and gray-levels, had more asymmetrical shapes and irregular outlines, had abrupt intensity transitions from lesion to background tissue, and had more distinct colors. The PR-system designed by the Dermofit images scored on the Dermofit images, using the ECV, 94.1%, 82.9%, 96.5% for overall accuracy, sensitivity, specificity, on the Multicenter Images 92.0%, 88%, 93.7% and on the Groningen Images 76.2%, 73.9%, 77.8% respectively. The PR-system as designed by the Dermofit image database could be fine-tuned to classify with good accuracy plain photography moles/melanomas images of other databases employing different image capturing equipment and protocols. Copyright © 2017 Elsevier B.V. All rights reserved.

Emotional processing in patients with mild cognitive impairment: the influence of the valence and intensity of emotional stimuli: the valence and intensity of emotional stimuli influence emotional processing in patients with mild cognitive impairment.

PubMed

Sarabia-Cobo, Carmen M; García-Rodríguez, Beatriz; Navas, M José; Ellgring, Heiner

2015-10-15

We studied the ability of individuals with mild cognitive impairment (MCI) to process emotional facial expressions (EFEs). To date, no systematic study has addressed how variation in intensity affects recognition of the different type of EFEs in such subjects. Two groups of 50 elderly subjects, 50 healthy individuals and 50 with MCI, completed a task that involved identifying 180 EFEs prepared using virtual models. Two features of the EFEs were contemplated, their valence (operationalized in six basic emotions) and five levels of intensity. At all levels of intensity, elderly individuals with MCI were significantly worse at identifying each EFE than healthy subjects. Some emotions were easier to identify than others, with happiness proving to be the easiest to identify and disgust the hardest, and intensity influenced the identification of the EFEs (the stronger the intensity, the greater the number of correct identifications). Overall, elderly individuals with MCI had a poorer capacity to process EFEs, suggesting that cognitive ability modulates the processing of emotions, where features of such stimuli also seem to play a prominent role (e.g., valence and intensity). Thus, the neurological substrates involved in emotional processing appear to be affected by MCI. Copyright © 2015 Elsevier B.V. All rights reserved.

A method for normalizing pathology images to improve feature extraction for quantitative pathology.

PubMed

Tam, Allison; Barker, Jocelyn; Rubin, Daniel

2016-01-01

With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides. To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology images by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast-limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets. The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un-normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature. ICHE may be a useful preprocessing step a digital pathology image processing pipeline.

Multitasking: Effects of processing multiple auditory feature patterns

PubMed Central

Miller, Tova; Chen, Sufen; Lee, Wei Wei; Sussman, Elyse S.

2016-01-01

ERPs and behavioral responses were measured to assess how task-irrelevant sounds interact with task processing demands and affect the ability to monitor and track multiple sound events. Participants listened to four-tone sequential frequency patterns, and responded to frequency pattern deviants (reversals of the pattern). Irrelevant tone feature patterns (duration and intensity) and respective pattern deviants were presented together with frequency patterns and frequency pattern deviants in separate conditions. Responses to task-relevant and task-irrelevant feature pattern deviants were used to test processing demands for irrelevant sound input. Behavioral performance was significantly better when there were no distracting feature patterns. Errors primarily occurred in response to the to-be-ignored feature pattern deviants. Task-irrelevant elicitation of ERP components was consistent with the error analysis, indicating a level of processing for the irrelevant features. Task-relevant elicitation of ERP components was consistent with behavioral performance, demonstrating a “cost” of performance when there were two feature patterns presented simultaneously. These results provide evidence that the brain tracked the irrelevant duration and intensity feature patterns, affecting behavioral performance. Overall, our results demonstrate that irrelevant informational streams are processed at a cost, which may be considered a type of multitasking that is an ongoing, automatic processing of taskirrelevant sensory events. PMID:25939456

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.

Universal ultrafast signatures of photoexcitations in conjugated polymers: excitons and charge-transfer polarons

NASA Astrophysics Data System (ADS)

McBranch, Duncan W.; Kraabel, Brett; Xu, Su; Wang, Hsing-Lin; Klimov, Victor I.

1999-12-01

Using subpicosecond transient absorption spectroscopy, we have investigated the primary photoexcitations in thin films and solution of several phenylene-based conjugated polymers and an oligomer. We identify two features in the transient absorption spectra and dynamics that are common to all of the materials which we have studied from this family. The first spectral feature is a photoinduced absorption (PA) band peaking near 1 eV which has intensity-dependent dynamics which match the stimulated emission dynamics exactly over two orders of magnitude in excitation density. This band is associated with singlet intrachain excitons. The second spectral feature (observed only in thin films and aggregated solutions) is a PA band peaking near 1.8 eV, which is longer-lived than the 1 eV exciton PA band, and which has dynamics that are independent (or weakly-dependent) on excitation density. This feature is attributed to charge separated (interchain) excitations. These excitations are generated through a bimolecular process. By comparing to samples in which charged excitations are created deliberately by doping with C6O, we assign these secondary species as bound polarons.

A high definition Mueller polarimetric endoscope for tissue characterisation

NASA Astrophysics Data System (ADS)

Qi, Ji; Elson, Daniel S.

2016-05-01

The contrast mechanism of medical endoscopy is mainly based on metrics of optical intensity and wavelength. As another fundamental property of light, polarization can not only reveal tissue scattering and absorption information from a different perspective, but can also provide insight into directional tissue birefringence properties to monitor pathological changes in collagen and elastin. Here we demonstrate a low cost wide field high definition Mueller polarimetric endoscope with minimal alterations to a rigid endoscope. We show that this novel endoscopic imaging modality is able to provide a number of image contrast mechanisms besides traditional unpolarized radiation intensity, including linear depolarization, circular depolarization, cross-polarization, directional birefringence and dichroism. This enhances tissue features of interest, and additionally reveals tissue micro-structure and composition, which is of central importance for tissue diagnosis and image guidance for surgery. The potential applications of the Mueller polarimetric endoscope include wide field early epithelial cancer diagnosis, surgical margin detection and energy-based tissue fusion monitoring, and could further benefit a wide range of endoscopic investigations through intra-operative guidance.

Strong-field control and enhancement of chiral response in bi-elliptical high-order harmonic generation: an analytical model

NASA Astrophysics Data System (ADS)

Ayuso, David; Decleva, Piero; Patchkovskii, Serguei; Smirnova, Olga

2018-06-01

The generation of high-order harmonics in a medium of chiral molecules driven by intense bi-elliptical laser fields can lead to strong chiroptical response in a broad range of harmonic numbers and ellipticities (Ayuso et al 2018 J. Phys. B: At. Mol. Opt. Phys. 51 06LT01). Here we present a comprehensive analytical model that can describe the most relevant features arising in the high-order harmonic spectra of chiral molecules driven by strong bi-elliptical fields. Our model recovers the physical picture underlying chiral high-order harmonic generation (HHG) based on ultrafast chiral hole motion and identifies the rotationally invariant molecular pseudoscalars responsible for chiral dynamics. Using the chiral molecule propylene oxide as an example, we show that one can control and enhance the chiral response in bi-elliptical HHG by tailoring the driving field, in particular by tuning its frequency, intensity and ellipticity, exploiting a suppression mechanism of achiral background based on the linear Stark effect.

Interferometric Near-Infrared Spectroscopy (iNIRS) for determination of optical and dynamical properties of turbid media

PubMed Central

Borycki, Dawid; Kholiqov, Oybek; Chong, Shau Poh; Srinivasan, Vivek J.

2016-01-01

We introduce and implement interferometric near-infrared spectroscopy (iNIRS), which simultaneously extracts optical and dynamical properties of turbid media through analysis of a spectral interference fringe pattern. The spectral interference fringe pattern is measured using a Mach-Zehnder interferometer with a frequency-swept narrow linewidth laser. Fourier analysis of the detected signal is used to determine time-of-flight (TOF)-resolved intensity, which is then analyzed over time to yield TOF-resolved intensity autocorrelations. This approach enables quantification of optical properties, which is not possible in conventional, continuous-wave near-infrared spectroscopy (NIRS). Furthermore, iNIRS quantifies scatterer motion based on TOF-resolved autocorrelations, which is a feature inaccessible by well-established diffuse correlation spectroscopy (DCS) techniques. We prove this by determining TOF-resolved intensity and temporal autocorrelations for light transmitted through diffusive fluid phantoms with optical thicknesses of up to 55 reduced mean free paths (approximately 120 scattering events). The TOF-resolved intensity is used to determine optical properties with time-resolved diffusion theory, while the TOF-resolved intensity autocorrelations are used to determine dynamics with diffusing wave spectroscopy. iNIRS advances the capabilities of diffuse optical methods and is suitable for in vivo tissue characterization. Moreover, iNIRS combines NIRS and DCS capabilities into a single modality. PMID:26832264

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Local Homing Navigation Based on the Moment Model for Landmark Distribution and Features

PubMed Central

Lee, Changmin; Kim, DaeEun

2017-01-01

For local homing navigation, an agent is supposed to return home based on the surrounding environmental information. According to the snapshot model, the home snapshot and the current view are compared to determine the homing direction. In this paper, we propose a novel homing navigation method using the moment model. The suggested moment model also follows the snapshot theory to compare the home snapshot and the current view, but the moment model defines a moment of landmark inertia as the sum of the product of the feature of the landmark particle with the square of its distance. The method thus uses range values of landmarks in the surrounding view and the visual features. The center of the moment can be estimated as the reference point, which is the unique convergence point in the moment potential from any view. The homing vector can easily be extracted from the centers of the moment measured at the current position and the home location. The method effectively guides homing direction in real environments, as well as in the simulation environment. In this paper, we take a holistic approach to use all pixels in the panoramic image as landmarks and use the RGB color intensity for the visual features in the moment model in which a set of three moment functions is encoded to determine the homing vector. We also tested visual homing or the moment model with only visual features, but the suggested moment model with both the visual feature and the landmark distance shows superior performance. We demonstrate homing performance with various methods classified by the status of the feature, the distance and the coordinate alignment. PMID:29149043

Efficient and sparse feature selection for biomedical text classification via the elastic net: Application to ICU risk stratification from nursing notes.

PubMed

Marafino, Ben J; Boscardin, W John; Dudley, R Adams

2015-04-01

Sparsity is often a desirable property of statistical models, and various feature selection methods exist so as to yield sparser and interpretable models. However, their application to biomedical text classification, particularly to mortality risk stratification among intensive care unit (ICU) patients, has not been thoroughly studied. To develop and characterize sparse classifiers based on the free text of nursing notes in order to predict ICU mortality risk and to discover text features most strongly associated with mortality. We selected nursing notes from the first 24h of ICU admission for 25,826 adult ICU patients from the MIMIC-II database. We then developed a pair of stochastic gradient descent-based classifiers with elastic-net regularization. We also studied the performance-sparsity tradeoffs of both classifiers as their regularization parameters were varied. The best-performing classifier achieved a 10-fold cross-validated AUC of 0.897 under the log loss function and full L2 regularization, while full L1 regularization used just 0.00025% of candidate input features and resulted in an AUC of 0.889. Using the log loss (range of AUCs 0.889-0.897) yielded better performance compared to the hinge loss (0.850-0.876), but the latter yielded even sparser models. Most features selected by both classifiers appear clinically relevant and correspond to predictors already present in existing ICU mortality models. The sparser classifiers were also able to discover a number of informative - albeit nonclinical - features. The elastic-net-regularized classifiers perform reasonably well and are capable of reducing the number of features required by over a thousandfold, with only a modest impact on performance. Copyright © 2015 Elsevier Inc. All rights reserved.

Feature-Based Retinal Image Registration Using D-Saddle Feature

PubMed Central

Hasikin, Khairunnisa; A. Karim, Noor Khairiah; Ahmedy, Fatimah

2017-01-01

Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle. PMID:29204257

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

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

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J., E-mail: bje@mayo.edu

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O{sup 6}-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiersmore » were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.« less

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

PubMed Central

Nyflot, Matthew J.; Yang, Fei; Byrd, Darrin; Bowen, Stephen R.; Sandison, George A.; Kinahan, Paul E.

2015-01-01

Abstract. Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes. PMID:26251842

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards.

PubMed

Nyflot, Matthew J; Yang, Fei; Byrd, Darrin; Bowen, Stephen R; Sandison, George A; Kinahan, Paul E

2015-10-01

Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.

Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization.

PubMed

Al-Kadi, Omar S; Chung, Daniel Y F; Carlisle, Robert C; Coussios, Constantin C; Noble, J Alison

2015-04-01

Intensity variations in image texture can provide powerful quantitative information about physical properties of biological tissue. However, tissue patterns can vary according to the utilized imaging system and are intrinsically correlated to the scale of analysis. In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing image texture, but the subtle intra-heterogeneities within a given mass are difficult to capture via this model as it works at a single spatial scale. This paper proposes a locally adaptive 3D multi-resolution Nakagami-based fractal feature descriptor that extends Nakagami-based texture analysis to accommodate subtle speckle spatial frequency tissue intensity variability in volumetric scans. Local textural fractal descriptors - which are invariant to affine intensity changes - are extracted from volumetric patches at different spatial resolutions from voxel lattice-based generated shape and scale Nakagami parameters. Using ultrasound radio-frequency datasets we found that after applying an adaptive fractal decomposition label transfer approach on top of the generated Nakagami voxels, tissue characterization results were superior to the state of art. Experimental results on real 3D ultrasonic pre-clinical and clinical datasets suggest that describing tumor intra-heterogeneity via this descriptor may facilitate improved prediction of therapy response and disease characterization. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Morphologic Features of Magnetic Resonance Imaging as a Surrogate of Capsular Contracture in Breast Cancer Patients With Implant-based Reconstructions.

PubMed

Tyagi, Neelam; Sutton, Elizabeth; Hunt, Margie; Zhang, Jing; Oh, Jung Hun; Apte, Aditya; Mechalakos, James; Wilgucki, Molly; Gelb, Emily; Mehrara, Babak; Matros, Evan; Ho, Alice

2017-02-01

Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. Currently, no objective methods are available for assessing CC. The goal of the present study was to identify image-based surrogates of CC using magnetic resonance imaging (MRI). We analyzed a retrospective data set of 50 patients who had undergone both a diagnostic MRI scan and a plastic surgeon's evaluation of the CC score (Baker's score) within a 6-month period after mastectomy and reconstructive surgery. The MRI scans were assessed for morphologic shape features of the implant and histogram features of the pectoralis muscle. The shape features, such as roundness, eccentricity, solidity, extent, and ratio length for the implant, were compared with the Baker score. For the pectoralis muscle, the muscle width and median, skewness, and kurtosis of the intensity were compared with the Baker score. Univariate analysis (UVA) using a Wilcoxon rank-sum test and multivariate analysis with the least absolute shrinkage and selection operator logistic regression was performed to determine significant differences in these features between the patient groups categorized according to their Baker's scores. UVA showed statistically significant differences between grade 1 and grade ≥2 for morphologic shape features and histogram features, except for volume and skewness. Only eccentricity, ratio length, and volume were borderline significant in differentiating grade ≤2 and grade ≥3. Features with P<.1 on UVA were used in the multivariate least absolute shrinkage and selection operator logistic regression analysis. Multivariate analysis showed a good level of predictive power for grade 1 versus grade ≥2 CC (area under the receiver operating characteristic curve 0.78, sensitivity 0.78, and specificity 0.82) and for grade ≤2 versus grade ≥3 CC (area under the receiver operating characteristic curve 0.75, sensitivity 0.75, and specificity 0.79). The morphologic shape features described on MR images were associated with the severity of CC. MRI has the potential to further improve the diagnostic ability of the Baker score in breast cancer patients who undergo implant reconstruction. Copyright © 2016 Elsevier Inc. All rights reserved.

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

PubMed Central

He, Jun; Yang, Shixi; Gan, Chunbiao

2017-01-01

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

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

PubMed

He, Jun; Yang, Shixi; Gan, Chunbiao

2017-07-04

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

Rotation covariant image processing for biomedical applications.

PubMed

Skibbe, Henrik; Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

Lumen-based detection of prostate cancer via convolutional neural networks

NASA Astrophysics Data System (ADS)

Kwak, Jin Tae; Hewitt, Stephen M.

2017-03-01

We present a deep learning approach for detecting prostate cancers. The approach consists of two steps. In the first step, we perform tissue segmentation that identifies lumens within digitized prostate tissue specimen images. Intensity- and texture-based image features are computed at five different scales, and a multiview boosting method is adopted to cooperatively combine the image features from differing scales and to identify lumens. In the second step, we utilize convolutional neural networks (CNN) to automatically extract high-level image features of lumens and to predict cancers. The segmented lumens are rescaled to reduce computational complexity and data augmentation by scaling, rotating, and flipping the rescaled image is applied to avoid overfitting. We evaluate the proposed method using two tissue microarrays (TMA) - TMA1 includes 162 tissue specimens (73 Benign and 89 Cancer) and TMA2 comprises 185 tissue specimens (70 Benign and 115 Cancer). In cross-validation on TMA1, the proposed method achieved an AUC of 0.95 (CI: 0.93-0.98). Trained on TMA1 and tested on TMA2, CNN obtained an AUC of 0.95 (CI: 0.92-0.98). This demonstrates that the proposed method can potentially improve prostate cancer pathology.

Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data

USGS Publications Warehouse

Rowan, L.C.; Schmidt, R.G.; Mars, J.C.

2006-01-01

The Reko Diq, Pakistan mineralized study area, approximately 10??km in diameter, is underlain by a central zone of hydrothermally altered rocks associated with Cu-Au mineralization. The surrounding country rocks are a variable mixture of unaltered volcanic rocks, fluvial deposits, and eolian quartz sand. Analysis of 15-band Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data of the study area, aided by laboratory spectral reflectance and spectral emittance measurements of field samples, shows that phyllically altered rocks are laterally extensive, and contain localized areas of argillically altered rocks. In the visible through shortwave-infrared (VNIR + SWIR) phyllically altered rocks are characterized by Al-OH absorption in ASTER band 6 because of molecular vibrations in muscovite, whereas argillically altered rocks have an absorption feature in band 5 resulting from alunite. Propylitically altered rocks form a peripheral zone and are present in scattered exposures within the main altered area. Chlorite and muscovite cause distinctive absorption features at 2.33 and 2.20????m, respectively, although less intense 2.33????m absorption is also present in image spectra of country rocks. Important complementary lithologic information was derived by analysis of the spectral emittance data in the 5 thermal-infrared (TIR) bands. Silicified rocks were not distinguished in the 9 VNIR + SWIR bands because of the lack of diagnostic spectral absorption features in quartz in this wavelength region. Quartz-bearing surficial deposits, as well as hydrothermally silicified rocks, were mapped in the TIR bands by using a band 13/band 12 ratio image, which is sensitive to the intensity of the quartz reststrahlen feature. Improved distinction between the quartzose surficial deposits and silicified bedrock was achieved by using matched-filter processing with TIR image spectra for reference. ?? 2006 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

Intracranial solitary fibrous tumor: imaging findings.

PubMed

Clarençon, Frédéric; Bonneville, Fabrice; Rousseau, Audrey; Galanaud, Damien; Kujas, Michèle; Naggara, Olivier; Cornu, Philippe; Chiras, Jacques

2011-11-01

To study the neuroimaging features of intracranial solitary fibrous tumors (ISFTs). Retrospective study of neuroimaging features of 9 consecutive histopathologically proven ISFT cases. Location, size, shape, density, signal intensity and gadolinium uptake were studied at CT and MRI. Data collected from diffusion-weighted imaging (DWI) (3 patients), perfusion imaging and MR spectroscopy (2 patients), and DSA (4 patients) were also analyzed. The tumors most frequently arose from the intracranial meninges (7/9), while the other lesions were intraventricular. Tumor size ranged from 2.5 to 10 cm (mean=6.6 cm). They presented multilobular shape in 6/9 patients. Most ISFTs were heterogeneous (7/9) with areas of low T2 signal intensity that strongly enhanced after gadolinium administration (6/8). Erosion of the skull was present in about half of the cases (4/9). Components with decreased apparent diffusion coefficient were seen in 2/3 ISFTs on DWI. Spectroscopy revealed elevated peaks of choline and myo-inositol. MR perfusion showed features of hyperperfusion. ISFT should be considered in cases of extra-axial, supratentorial, heterogeneous, hypervascular tumor. Areas of low T2 signal intensity that strongly enhance after gadolinium injection are suggestive of this diagnosis. Restricted diffusion and elevated peak of myo-inositol may be additional valuable features. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.

PubMed

Wu, Mike; Ghassemi, Marzyeh; Feng, Mengling; Celi, Leo A; Szolovits, Peter; Doshi-Velez, Finale

2017-05-01

The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations. We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes. We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning. The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean. Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Reflectance spectroscopy of pigmented cutaneous benign and malignant lesions

NASA Astrophysics Data System (ADS)

Borisova, E.; Jeliazkova, Al.; Pavlova, E.; Troyanova, P.; Kundurdjiev, T.; Pavlova, P.; Avramov, L.

2014-10-01

For the DRS measurements of skin benign, dysplastic and malignant lesions in vivo we applied halogen lamp (LS-1, OceanOptics Inc, Dunedin, Fl, USA) as a continuous light source in the region of 400-900 nm, optical probe (6+1 fibers) for the delivery of illumination and diffuse reflected light from the skin investigated and microspectrometer USB4000 (OceanOptics Inc., Dunedin, Fl, USA) for a storage and display of the spectra detected. As a diffuse reflectance standard Spectralon® plate was used to calibrate the spectrometer. The reflectance spectra obtained from normal skin in identical anatomic sites of different patients have similar spectral shape features, slightly differ by the reflectance intensity at different wavelengths, depending on the particular patient' skin phototype. One could find diagnostically important spectral features, related to specific intensity changes for a given wavelength due to specific pigments appearance, slope changes by value and sign for the reflectance spectra curves in a specific spectral range, disappearance or manifestation of minima, related to hemoglobin absorption at 410-420 nm, 543, 575 nm. Based on the observed peculiarities multispectral analysis of the reflectance spectra of the different lesions was used and diagnostically specific features are found. Discrimination using the DRS data obtained between benign compound and dermal nevi (45 cases), dysplastic nevi (17 cases) and pigmented malignant melanoma (41 cases) lesions is achieved with a diagnostic accuracy of 96 % for the benign nevi vs. MM, and 90 % for the dysplastic nevi vs. MM.

New decision support tool for acute lymphoblastic leukemia classification

NASA Astrophysics Data System (ADS)

Madhukar, Monica; Agaian, Sos; Chronopoulos, Anthony T.

2012-03-01

In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. The developed system includes different methods to accurately measure furthermore cell properties in microscope blood film images. The blood images are exposed to series of pre-processing steps which include color correlation, and contrast enhancement. By performing K-means clustering on the resultant images, the nuclei of the cells under consideration are obtained. Shape features and texture features are then extracted for classification. The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images.

Reflection spectra and magnetochemistry of iron oxides and natural surfaces

NASA Technical Reports Server (NTRS)

Wasilewski, P.

1978-01-01

The magnetic properties and spectral characteristics of iron oxides are distinctive. Diagnostic features in reflectance spectra (0.5 to 2.4 micron) for alpha Fe2O3, gamma Fe2O3, and FeOOH include location of Fe3(+) absorption features, intensity ratios at various wavelengths, and the curve shape between 1.2 micron and 2.4 micron. The reflection spectrum of natural rock surfaces are seldom those of the bulk rock because of weathering effects. Coatings are found to be dominated by iron oxides and clay. A simple macroscopic model of rock spectra (based on concepts of stains and coatings) is considered adequate for interpretation of LANDSAT data. The magnetic properties of materials associated with specific spectral types and systematic changes in both spectra and magnetic properties are considered.

Machine learning bandgaps of double perovskites

PubMed Central

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; Ramprasad, R.; Gubernatis, J. E.; Lookman, T.

2016-01-01

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance. PMID:26783247

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

PubMed Central

2009-01-01

Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality. PMID:19758426

Enabling phenotypic big data with PheNorm.

PubMed

Yu, Sheng; Ma, Yumeng; Gronsbell, Jessica; Cai, Tianrun; Ananthakrishnan, Ashwin N; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Liao, Katherine P; Cai, Tianxi

2018-01-01

Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference. The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Textural characterization of histopathological images for oral sub-mucous fibrosis detection.

PubMed

Krishnan, M Muthu Rama; Shah, Pratik; Choudhary, Anirudh; Chakraborty, Chandan; Paul, Ranjan Rashmi; Ray, Ajoy K

2011-10-01

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The aim of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, a systematic approach is introduced in order to grade the histopathological tissue sections into normal, OSF without dysplasia and OSF with dysplasia, which would help the oral onco-pathologists to screen the subjects rapidly. In totality, 71 textural features are extracted from epithelial region of the tissue sections using various wavelet families, Gabor-wavelet, local binary pattern, fractal dimension and Brownian motion curve, followed by preprocessing and segmentation. Wavelet families contribute a common set of 9 features, out of which 8 are significant and other 61 out of 62 obtained from the rest of the extractors are also statistically significant (p<0.05) in discriminating the three stages. Based on mean distance criteria, the best wavelet family (i.e., biorthogonal3.1 (bior3.1)) is selected for classifier design. support vector machine (SVM) is trained by 146 samples based on 69 textural features and its classification accuracy is computed for each of the combinations of wavelet family and rest of the extractors. Finally, it has been investigated that bior3.1 wavelet coefficients leads to higher accuracy (88.38%) in combination with LBP and Gabor wavelet features through three-fold cross validation. Results are shown and discussed in detail. It is shown that combining more than one texture measure instead of using just one might improve the overall accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

SU-E-J-249: Characterization of Gynecological Tumor Heterogeneity Using Texture Analysis in the Context of An 18F-FDG PET Adaptive Protocol

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

Nawrocki, J; Chino, J; Craciunescu, O

Purpose: We propose a method to examine gynecological tumor heterogeneity using texture analysis in the context of an adaptive PET protocol in order to establish if texture metrics from baseline PET-CT predict tumor response better than SUV metrics alone as well as determine texture features correlating with tumor response during radiation therapy. Methods: This IRB approved protocol included 29 women with node positive gynecological cancers visible on FDG-PET treated with EBRT to the PET positive nodes. A baseline and intra-treatment PET-CT was obtained. Tumor outcome was determined based on RECIST on posttreatment PET-CT. Primary GTVs were segmented using 40% thresholdmore » and a semi-automatic gradient-based contouring tool, PET Edge (MIM Software Inc., Cleveland, OH). SUV histogram features, Metabolic Volume (MV), and Total Lesion Glycolysis (TLG) were calculated. Four 3D texture matrices describing local and regional relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 texture features were calculated. Prognostic power of baseline features derived from gradientbased and threshold GTVs were determined using the Wilcoxon rank-sum test. Receiver Operating Characteristics and logistic regression was performed using JMP (SAS Institute Inc., Cary, NC) to find probabilities of predicting response. Changes in features during treatment were determined using the Wilcoxon signed-rank test. Results: Of the 29 patients, there were 16 complete responders, 7 partial responders, and 6 non-responders. Comparing CR/PR vs. NR for gradient-based GTVs, 7 texture values, TLG, and SUV kurtosis had a p < 0.05. Threshold GTVs yielded 4 texture features and TLG with p < 0.05. From baseline to intra-treatment, 14 texture features, SUVmean, SUVmax, MV, and TLG changed with p < 0.05. Conclusion: Texture analysis of PET imaged gynecological tumors is an effective method for early prognosis and should be used complimentary to SUV metrics, especially when using gradient based segmentation.« less

Video Extrapolation Method Based on Time-Varying Energy Optimization and CIP.

PubMed

Sakaino, Hidetomo

2016-09-01

Video extrapolation/prediction methods are often used to synthesize new videos from images. For fluid-like images and dynamic textures as well as moving rigid objects, most state-of-the-art video extrapolation methods use non-physics-based models that learn orthogonal bases from a number of images but at high computation cost. Unfortunately, data truncation can cause image degradation, i.e., blur, artifact, and insufficient motion changes. To extrapolate videos that more strictly follow physical rules, this paper proposes a physics-based method that needs only a few images and is truncation-free. We utilize physics-based equations with image intensity and velocity: optical flow, Navier-Stokes, continuity, and advection equations. These allow us to use partial difference equations to deal with the local image feature changes. Image degradation during extrapolation is minimized by updating model parameters, where a novel time-varying energy balancer model that uses energy based image features, i.e., texture, velocity, and edge. Moreover, the advection equation is discretized by high-order constrained interpolation profile for lower quantization error than can be achieved by the previous finite difference method in long-term videos. Experiments show that the proposed energy based video extrapolation method outperforms the state-of-the-art video extrapolation methods in terms of image quality and computation cost.

Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

NASA Astrophysics Data System (ADS)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are no shadows on intensity images produced from the data. These are significant advantages in developing automated classification and change detection procedures.

Improving the geomagnetic field modeling with a selection of high-quality archaeointensity data

NASA Astrophysics Data System (ADS)

Pavon-Carrasco, Francisco Javier; Gomez-Paccard, Miriam; Herve, Gwenael; Osete, Maria Luisa; Chauvin, Annick

2014-05-01

Geomagnetic field reconstructions for the last millennia are based on archeomagnetic data. However, the scatter of the archaeointensity data is very puzzling and clearly suggests that some of the intensity data might not be reliable. In this work we apply different selection criteria to the European and Western Asian archaeointensity data covering the last three millennia in order to investigate if the data selection affects geomagnetic field models results. Thanks to the recently developed archeomagnetic databases, new valuable information related to the methodology used to determine the archeointensity data is now available. We therefore used this information to rank the archaeointensity data in four quality categories depending on the methodology used during the laboratory treatment of the samples and on the number of specimens retained to calculate the mean intensities. Results show how the intensity geomagnetic field component given by the regional models hardly depends on the selected quality data used. When all the available data are used a different behavior of the geomagnetic field is observed in Western and Eastern Europe. However, when the regional model is obtained from a selection of high-quality intensity data the same features are observed at the European scale.

Creep crack growth by grain boundary cavitation under monotonic and cyclic loading

NASA Astrophysics Data System (ADS)

Wen, Jian-Feng; Srivastava, Ankit; Benzerga, Amine; Tu, Shan-Tung; Needleman, Alan

2017-11-01

Plane strain finite deformation finite element calculations of mode I crack growth under small scale creep conditions are carried out. Attention is confined to isothermal conditions and two time histories of the applied stress intensity factor: (i) a monononic increase to a plateau value subsequently held fixed; and (ii) a cyclic time variation. The crack growth calculations are based on a micromechanics constitutive relation that couples creep deformation and damage due to grain boundary cavitation. Grain boundary cavitation, with cavity growth due to both creep and diffusion, is taken as the sole failure mechanism contributing to crack growth. The influence on the crack growth rate of loading history parameters, such as the magnitude of the applied stress intensity factor, the ratio of the applied minimum to maximum stress intensity factors, the loading rate, the hold time and the cyclic loading frequency, are explored. The crack growth rate under cyclic loading conditions is found to be greater than under monotonic creep loading with the plateau applied stress intensity factor equal to its maximum value under cyclic loading conditions. Several features of the crack growth behavior observed in creep-fatigue tests naturally emerge, for example, a Paris law type relation is obtained for cyclic loading.

The Identification of Hail Storms in the Early Stage Using Time Series Analysis

NASA Astrophysics Data System (ADS)

Wang, Ping; Shi, Jinyu; Hou, Jinyi; Hu, Yan

2018-01-01

This study investigates the characteristics of hail storms and cumulonimbus storms in China from 2005 to 2016. Ten features are proposed to identify storm cells that can produce hail, especially in the early stage of hail formation. These features describe hail storms based on three factors: the height and thickness of the cell core, the radar echo intensity, and the overhang structure and the horizontal reflectivity gradient. The 10 features are transformed into two-dimensional comprehensive features by principal component analysis (PCA). The two comprehensive features are named the volume measurement comprehensive feature (VMCF) and the height-gradient comprehensive feature (HGCF). Through an analysis of 49 hail cases and 35 heavy rainfall cases with S-band radar data, the time series exhibit a distinct increase in VMCF or HGCF values in the early stage of a hail storm. However, the VMCF and HGCF values of heavy rainfall events remain relatively stable throughout the storm life cycle. An experiment involving real-storm events, including 31 hail cases and 33 heavy rainfall cases, indicated that the probability of detection of hail storms was 93.33% and the false alarm ratio was 15.66%. In the cases that could be successfully identified as hail storms, 80.00% were detected within 18 min of reaching a hail storm reflectivity of 40 dBZ.

Identification of eggs from different production systems based on hyperspectra and CS-SVM.

PubMed

Sun, J; Cong, S L; Mao, H P; Zhou, X; Wu, X H; Zhang, X D

2017-06-01

1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky-Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output. 3. The SWR-CS-SVM model performed better than the other models, including SWR-GS-SVM, SWR-GA-SVM, SWR-PSO-SVM and others based on full spectral data. The training and test classification accuracy of the SWR-CS-SVM model were respectively 99.3% and 96%. 4. SWR-CS-SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

On non-invasive 2D and 3D Chromatic White Light image sensors for age determination of latent fingerprints.

PubMed

Merkel, Ronny; Gruhn, Stefan; Dittmann, Jana; Vielhauer, Claus; Bräutigam, Anja

2012-10-10

The feasibility of 2D-intensity and 3D-topography images from a non-invasive Chromatic White Light (CWL) sensor for the age determination of latent fingerprints is investigated. The proposed method might provide the means to solve the so far unresolved issue of determining a fingerprints age in forensics. Conducting numerous experiments for an indoor crime scene using selected surfaces, different influences on the aging of fingerprints are investigated and the resulting aging variability is determined in terms of inter-person, intra-person, inter-finger and intra-finger variation. Main influence factors are shown to be the sweat composition, temperature, humidity, wind, UV-radiation, surface type, contamination of the finger with water-containing substances, resolution and measured area size, whereas contact time, contact pressure and smearing of the print seem to be of minor importance. Such influences lead to a certain experimental variability in inter-person and intra-person variation, which is higher than the inter-finger and intra-finger variation. Comparing the aging behavior of 17 different features using 1490 time series with a total of 41,520 fingerprint images, the great potential of the CWL technique in combination with the binary pixel feature from prior work is shown. Performing three different experiments for the classification of fingerprints into the two time classes [0, 5 h] and [5, 24 h], a maximum classification performance of 79.29% (kappa=0.46) is achieved for a general case, which is further improved for special cases. The statistical significance of the two best-performing features (both binary pixel versions based on 2D-intensity images) is manually shown and a feature fusion is performed, highlighting the strong dependency of the features on each other. It is concluded that such method might be combined with additional capturing devices, such as microscopes or spectroscopes, to a very promising age estimation scheme. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

D Land Cover Classification Based on Multispectral LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

2016-06-01

Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

A new medical image segmentation model based on fractional order differentiation and level set

NASA Astrophysics Data System (ADS)

Chen, Bo; Huang, Shan; Xie, Feifei; Li, Lihong; Chen, Wensheng; Liang, Zhengrong

2018-03-01

Segmenting medical images is still a challenging task for both traditional local and global methods because the image intensity inhomogeneous. In this paper, two contributions are made: (i) on the one hand, a new hybrid model is proposed for medical image segmentation, which is built based on fractional order differentiation, level set description and curve evolution; and (ii) on the other hand, three popular definitions of Fourier-domain, Grünwald-Letnikov (G-L) and Riemann-Liouville (R-L) fractional order differentiation are investigated and compared through experimental results. Because of the merits of enhancing high frequency features of images and preserving low frequency features of images in a nonlinear manner by the fractional order differentiation definitions, one fractional order differentiation definition is used in our hybrid model to perform segmentation of inhomogeneous images. The proposed hybrid model also integrates fractional order differentiation, fractional order gradient magnitude and difference image information. The widely-used dice similarity coefficient metric is employed to evaluate quantitatively the segmentation results. Firstly, experimental results demonstrated that a slight difference exists among the three expressions of Fourier-domain, G-L, RL fractional order differentiation. This outcome supports our selection of one of the three definitions in our hybrid model. Secondly, further experiments were performed for comparison between our hybrid segmentation model and other existing segmentation models. A noticeable gain was seen by our hybrid model in segmenting intensity inhomogeneous images.

Spatiotemporal change of intraseasonal oscillation intensity over the tropical Indo-Pacific Ocean associated with El Niño and La Niña events

NASA Astrophysics Data System (ADS)

Wu, Renguang; Song, Lei

2018-02-01

The present study analyzes the intraseasonal oscillation (ISO) intensity change over the tropical Indo-Pacific associated with the El Niño-Southern Oscillation (ENSO) and compares the intensity change between El Niño and La Niña years and between the 10-20-day and 30-60-day ISOs. The ISO intensity change tends to be opposite between El Niño and La Niña years in the developing and mature phases. The intensity change features a contrast between the tropical southeastern Indian Ocean and the tropical western North Pacific (WNP) in the developing phases and between the Maritime Continent and the tropical central Pacific in the mature phase. In the decaying phases, the intensity change shows notable differences between El Niño and La Niña events and between fast and slow decaying El Niño events. Large intensity change is observed over the tropical WNP in the developing summer, over the tropical southeastern Indian Ocean in the developing fall, and over the tropical WNP in the fast decaying El Niño summer due to a combined effect of vertical shear, vertical motion, and lower-level moisture. In the ENSO developing summer and in the El Niño decaying summer, the 10-20-day ISO intensity change displays a northwest-southeast tilted distribution over the tropical WNP, whereas the large 30-60-day ISO intensity change is confined to the off-equatorial WNP. In the La Niña decaying summer, the 30-60-day ISO intensity change features a large zonal contrast across the Philippines, whereas the 10-20-day ISO intensity anomaly is characterized by a north-south contrast over the tropical WNP.

Center for Neural Engineering: applications of pulse-coupled neural networks

NASA Astrophysics Data System (ADS)

Malkani, Mohan; Bodruzzaman, Mohammad; Johnson, John L.; Davis, Joel

1999-03-01

Pulsed-Coupled Neural Network (PCNN) is an oscillatory model neural network where grouping of cells and grouping among the groups that form the output time series (number of cells that fires in each input presentation also called `icon'). This is based on the synchronicity of oscillations. Recent work by Johnson and others demonstrated the functional capabilities of networks containing such elements for invariant feature extraction using intensity maps. PCNN thus presents itself as a more biologically plausible model with solid functional potential. This paper will present the summary of several projects and their results where we successfully applied PCNN. In project one, the PCNN was applied for object recognition and classification through a robotic vision system. The features (icons) generated by the PCNN were then fed into a feedforward neural network for classification. In project two, we developed techniques for sensory data fusion. The PCNN algorithm was implemented and tested on a B14 mobile robot. The PCNN-based features were extracted from the images taken from the robot vision system and used in conjunction with the map generated by data fusion of the sonar and wheel encoder data for the navigation of the mobile robot. In our third project, we applied the PCNN for speaker recognition. The spectrogram image of speech signals are fed into the PCNN to produce invariant feature icons which are then fed into a feedforward neural network for speaker identification.

Rapid extraction of image texture by co-occurrence using a hybrid data structure

NASA Astrophysics Data System (ADS)

Clausi, David A.; Zhao, Yongping

2002-07-01

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL. The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4% ( σ=3.08%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method.

Ionospheric effects of the simultaneous occurrence of a solar proton event and relativistic electron precipitation as recorded by ground-based instruments at different latitudes

NASA Astrophysics Data System (ADS)

Shirochkov, A. V.; Makarova, L. N.; Sokolov, S. N.; Sheldon, W. R.

2004-08-01

The intense event of highly relativistic electron (HRE) precipitation of May 1992 has been analyzed using data from ground-based observations (riometers and VLF phase measurements). Special attention was given to some features of this event observed at high and very high geomagnetic latitudes, since this aspect of the event was not well documented in previous studies. A remarkable feature of the HRE event of May 1992 was the simultaneous occurrence of a strong solar proton event (SPE), although reliable evidence shows that the simultaneous appearance of SPE and HRE events is not unique. It was demonstrated that a meridian chain of riometers with high latitudinal resolution is an effective and low-cost (as compared with satellite observations) tool to separate the effects of solar proton and relativistic electrons in the lower ionosphere. A significant conclusion is that the polar cap area is free from relativistic electron precipitation. Other interesting aspects of this complex geophysical phenomenon are also discussed.

Historical feature pattern extraction based network attack situation sensing algorithm.

PubMed

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

PubMed Central

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously. PMID:24892054

Patch-based automatic retinal vessel segmentation in global and local structural context.

PubMed

Cao, Shuoying; Bharath, Anil A; Parker, Kim H; Ng, Jeffrey

2012-01-01

In this paper, we extend our published work [1] and propose an automated system to segment retinal vessel bed in digital fundus images with enough adaptability to analyze images from fluorescein angiography. This approach takes into account both the global and local context and enables both vessel segmentation and microvascular centreline extraction. These tools should allow researchers and clinicians to estimate and assess vessel diameter, capillary blood volume and microvascular topology for early stage disease detection, monitoring and treatment. Global vessel bed segmentation is achieved by combining phase-invariant orientation fields with neighbourhood pixel intensities in a patch-based feature vector for supervised learning. This approach is evaluated against benchmarks on the DRIVE database [2]. Local microvascular centrelines within Regions-of-Interest (ROIs) are segmented by linking the phase-invariant orientation measures with phase-selective local structure features. Our global and local structural segmentation can be used to assess both pathological structural alterations and microemboli occurrence in non-invasive clinical settings in a longitudinal study.

Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

2018-01-01

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

Quantitative CT based radiomics as predictor of resectability of pancreatic adenocarcinoma

NASA Astrophysics Data System (ADS)

van der Putten, Joost; Zinger, Svitlana; van der Sommen, Fons; de With, Peter H. N.; Prokop, Mathias; Hermans, John

2018-02-01

In current clinical practice, the resectability of pancreatic ductal adenocarcinoma (PDA) is determined subjec- tively by a physician, which is an error-prone procedure. In this paper, we present a method for automated determination of resectability of PDA from a routine abdominal CT, to reduce such decision errors. The tumor features are extracted from a group of patients with both hypo- and iso-attenuating tumors, of which 29 were resectable and 21 were not. The tumor contours are supplied by a medical expert. We present an approach that uses intensity, shape, and texture features to determine tumor resectability. The best classification results are obtained with fine Gaussian SVM and the L0 Feature Selection algorithms. Compared to expert predictions made on the same dataset, our method achieves better classification results. We obtain significantly better results on correctly predicting non-resectability (+17%) compared to a expert, which is essential for patient treatment (negative prediction value). Moreover, our predictions of resectability exceed expert predictions by approximately 3% (positive prediction value).

Cryogenic terahertz spectrum of (+)-methamphetamine hydrochloride and assignment using solid-state density functional theory.

PubMed

Hakey, Patrick M; Allis, Damian G; Ouellette, Wayne; Korter, Timothy M

2009-04-30

The cryogenic terahertz spectrum of (+)-methamphetamine hydrochloride from 10.0 to 100.0 cm(-1) is presented, as is the complete structural analysis and vibrational assignment of the compound using solid-state density functional theory. This cryogenic investigation reveals multiple spectral features that were not previously reported in room-temperature terahertz studies of the title compound. Modeling of the compound employed eight density functionals utilizing both solid-state and isolated-molecule methods. The results clearly indicate the necessity of solid-state simulations for the accurate assignment of solid-state THz spectra. Assignment of the observed spectral features to specific atomic motions is based on the BP density functional, which provided the best-fit solid-state simulation of the experimental spectrum. The seven experimental spectral features are the result of thirteen infrared-active vibrational modes predicted at a BP/DNP level of theory with more than 90% of the total spectral intensity associated with external crystal vibrations.

A supervised learning approach for Crohn's disease detection using higher-order image statistics and a novel shape asymmetry measure.

PubMed

Mahapatra, Dwarikanath; Schueffler, Peter; Tielbeek, Jeroen A W; Buhmann, Joachim M; Vos, Franciscus M

2013-10-01

Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess rate of contrast enhancement and bowel wall thickness, and rely on extensive manual segmentation for accurate analysis. We propose a supervised learning method for the identification and localization of regions in abdominal magnetic resonance images that have been affected by CD. Low-level features like intensity and texture are used with shape asymmetry information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy-based shape asymmetry method and higher-order statistics like skewness and kurtosis. Multi-scale feature extraction renders the method robust. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.

Revealing the Coulomb interaction strength in a cuprate superconductor

DOE PAGES

Yang, S. -L.; Sobota, J. A.; He, Y.; ...

2017-12-08

Here, we study optimally doped Bi 2 Sr 2 Ca 0.92 Y 0.08 Cu 2 O 8 + δ (Bi2212) using angle-resolved two-photon photoemission spectroscopy. Three spectral features are resolved near 1.5, 2.7, and 3.6 eV above the Fermi level. By tuning the photon energy, we determine that the 2.7-eV feature arises predominantly from unoccupied states. The 1.5- and 3.6-eV features reflect unoccupied states whose spectral intensities are strongly modulated by the corresponding occupied states. These unoccupied states are thus consistent with the prediction from a cluster perturbation theory based on the single-band Hubbard model. Through this comparison, amore » Coulomb interaction strength U of 2.7 eV is extracted. Our study complements equilibrium photoemission spectroscopy and provides a direct spectroscopic measurement of the unoccupied states in cuprates. The determined Coulomb U indicates that the charge-transfer gap of optimally doped Bi2212 is 1.1 eV.« less

Multitask saliency detection model for synthetic aperture radar (SAR) image and its application in SAR and optical image fusion

NASA Astrophysics Data System (ADS)

Liu, Chunhui; Zhang, Duona; Zhao, Xintao

2018-03-01

Saliency detection in synthetic aperture radar (SAR) images is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR images. We extract four features of the SAR image, which include the intensity, orientation, uniqueness, and global contrast, as the input of the MSD model. The saliency map is generated by the multitask sparsity pursuit, which integrates the multiple features collaboratively. Detection of different scale features is also taken into consideration. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps obtained by the MSD model, we apply the saliency map of the SAR image to the SAR and color optical image fusion. The experimental results of real data show that the saliency map obtained by the MSD model helps to improve the fusion effect, and the salient areas in the SAR image can be highlighted in the fusion results.

Looking for the Weak Members of the 60+C Family in the Interstellar Medium

NASA Astrophysics Data System (ADS)

Galazutdinov, G. A.; Krełowski, J.

2017-06-01

We demonstrate, using the high resolution spectra from the ESPADONS spectrograph, fed with the 3.6 m CFH telescope, that the strength ratios of the strong-to-weak spectral features, attributed to C60+, are variable. We found that in the range of expected 9366 Å C60+ feature there are two diffuse bands centered at 9362.0±0.1 Å and 9365.3±0.1 Å with variable intensity ratio. We confidently confirm the lack of 9428 Å feature which, in the laboratory spectra of C60+, is stronger than 9366 Å. The weakest laboratory feature, near 9348.4 Å, remains below the level of detection in all spectra. The intensity ratio 9577/9365 is variable. These facts contradict the possibility of their common origin and so - the identification of some interstellar spectral features as being carried by the cation of the "soccer ball". We also refined the rest wavelength position of the strongest diffuse band in this range: it is 9576.8±0.1 Å.

SU-F-R-52: A Comparison of the Performance of Radiomic Features From Free Breathing and 4DCT Scans in Predicting Disease Recurrence in Lung Cancer SBRT Patients

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

Huynh, E; Coroller, T; Narayan, V

Purpose: There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non-invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lungmore » cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence. Methods: FB and AIP images of 113 Stage I-II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p-values were corrected for multiple testing using the false discovery rate. Results: All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65–0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60–0.61) were significant but all AIP conventional features were (range CI=0.64–0.66). Conclusion: Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments. R.M. had consulting interest with Amgen (ended in 2015).« less

An Archetype Semi-Ring Fabry-Perot (SRFP) Resonator

NASA Technical Reports Server (NTRS)

Taghavi-Larigani, Shervin; VanZyl, Jakob

2009-01-01

We introduce and demonstrate the generation of a novel resonator, termed Semi-Ring Fabry-Perot (SRFP), that exhibits unique features, such as, its use of one plane mirror, allowing the SRFP to be easily fabricated as a symmetrical device. In addition to its unique features, it exhibits advantages of ring and Fabry-Perot resonators: 1) compared to a ring resonator that only allows a transmitted intensity, the Semi-Ring Fabry-Perot (SRFP) supports standing waves, allowing both a reflected and transmitted intensity; 2) the reflected light spectrum of the SRFP resonator is much narrower than similar Fabry-Perot, implying higher finesse.

High temperature, high intensity solar array. [for Venus Radar Mapper mission

NASA Technical Reports Server (NTRS)

Smith, B. S.; Brooks, G. R.; Pinkerton, R.

1985-01-01

The solar array for the Venus Radar Mapper mission will operate in the high temperature, high intensity conditions of a low Venus orbit environment. To fulfill the performance requirements in this environment at minimum cost and mass while maximizing power density and packing factor on the panel surface, several features were introduced into the design. These features included the use of optical surface reflectors (OSR's) to reduce the operating temperature; new adhesives for conductive bonding of OSR's to avoid electrostatic discharges; custom-designed large area cells and novel shunt diode circuit and panel power harness configurations.

Assessing landscape scale wildfire exposure for highly valued resources in a Mediterranean area.

PubMed

Alcasena, Fermín J; Salis, Michele; Ager, Alan A; Arca, Bachisio; Molina, Domingo; Spano, Donatella

2015-05-01

We used a fire simulation modeling approach to assess landscape scale wildfire exposure for highly valued resources and assets (HVR) on a fire-prone area of 680 km(2) located in central Sardinia, Italy. The study area was affected by several wildfires in the last half century: some large and intense fire events threatened wildland urban interfaces as well as other socioeconomic and cultural values. Historical wildfire and weather data were used to inform wildfire simulations, which were based on the minimum travel time algorithm as implemented in FlamMap. We simulated 90,000 fires that replicated recent large fire events in the area spreading under severe weather conditions to generate detailed maps of wildfire likelihood and intensity. Then, we linked fire modeling outputs to a geospatial risk assessment framework focusing on buffer areas around HVR. The results highlighted a large variation in burn probability and fire intensity in the vicinity of HVRs, and allowed us to identify the areas most exposed to wildfires and thus to a higher potential damage. Fire intensity in the HVR buffers was mainly related to fuel types, while wind direction, topographic features, and historically based ignition pattern were the key factors affecting fire likelihood. The methodology presented in this work can have numerous applications, in the study area and elsewhere, particularly to address and inform fire risk management, landscape planning and people safety on the vicinity of HVRs.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments.

PubMed

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug; Baek, Jung Hwan; Choi, Young Jun; Ha, Eun Ju; Lee, Kang Dae; Lee, Hyoung Shin; Shin, DaeSeock; Kim, Nakyoung

2016-01-01

To develop a semiautomated computer-aided diagnosis (cad) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid cad software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrence matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of cad with visual inspection by expert radiologists based on established gold standards. Most univariate features for this proposed cad system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed cad system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, "axial ratio" and "max probability" in axial images were most frequently included in the optimal feature sets for the authors' proposed cad system, while "shape" and "calcification" in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed cad system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. The use of thyroid cad to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid cad might be considered a viable way to generate a second opinion for radiologists in clinical practice.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments

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

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug, E-mail: namkugkim@gmail.com

Purpose: To develop a semiautomated computer-aided diagnosis (CAD) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. Methods: A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid CAD software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrencemore » matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of CAD with visual inspection by expert radiologists based on established gold standards. Results: Most univariate features for this proposed CAD system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed CAD system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, “axial ratio” and “max probability” in axial images were most frequently included in the optimal feature sets for the authors’ proposed CAD system, while “shape” and “calcification” in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed CAD system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. Conclusions: The use of thyroid CAD to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid CAD might be considered a viable way to generate a second opinion for radiologists in clinical practice.« less

Learning representations for the early detection of sepsis with deep neural networks.

PubMed

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

PubMed Central

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the “curse of dimensionality”. Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods. PMID:24349277

Can two dots form a Gestalt? Measuring emergent features with the capacity coefficient.

PubMed

Hawkins, Robert X D; Houpt, Joseph W; Eidels, Ami; Townsend, James T

2016-09-01

While there is widespread agreement among vision researchers on the importance of some local aspects of visual stimuli, such as hue and intensity, there is no general consensus on a full set of basic sources of information used in perceptual tasks or how they are processed. Gestalt theories place particular value on emergent features, which are based on the higher-order relationships among elements of a stimulus rather than local properties. Thus, arbitrating between different accounts of features is an important step in arbitrating between local and Gestalt theories of perception in general. In this paper, we present the capacity coefficient from Systems Factorial Technology (SFT) as a quantitative approach for formalizing and rigorously testing predictions made by local and Gestalt theories of features. As a simple, easily controlled domain for testing this approach, we focus on the local feature of location and the emergent features of Orientation and Proximity in a pair of dots. We introduce a redundant-target change detection task to compare our capacity measure on (1) trials where the configuration of the dots changed along with their location against (2) trials where the amount of local location change was exactly the same, but there was no change in the configuration. Our results, in conjunction with our modeling tools, favor the Gestalt account of emergent features. We conclude by suggesting several candidate information-processing models that incorporate emergent features, which follow from our approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

Analysis of differences between Western and East-Asian faces based on facial region segmentation and PCA for facial expression recognition

NASA Astrophysics Data System (ADS)

Benitez-Garcia, Gibran; Nakamura, Tomoaki; Kaneko, Masahide

2017-01-01

Darwin was the first one to assert that facial expressions are innate and universal, which are recognized across all cultures. However, recent some cross-cultural studies have questioned this assumed universality. Therefore, this paper presents an analysis of the differences between Western and East-Asian faces of the six basic expressions (anger, disgust, fear, happiness, sadness and surprise) focused on three individual facial regions of eyes-eyebrows, nose and mouth. The analysis is conducted by applying PCA for two feature extraction methods: appearance-based by using the pixel intensities of facial parts, and geometric-based by handling 125 feature points from the face. Both methods are evaluated using 4 standard databases for both racial groups and the results are compared with a cross-cultural human study applied to 20 participants. Our analysis reveals that differences between Westerns and East-Asians exist mainly on the regions of eyes-eyebrows and mouth for expressions of fear and disgust respectively. This work presents important findings for a better design of automatic facial expression recognition systems based on the difference between two racial groups.

Improved disparity map analysis through the fusion of monocular image segmentations

NASA Technical Reports Server (NTRS)

Perlant, Frederic P.; Mckeown, David M.

1991-01-01

The focus is to examine how estimates of three dimensional scene structure, as encoded in a scene disparity map, can be improved by the analysis of the original monocular imagery. The utilization of surface illumination information is provided by the segmentation of the monocular image into fine surface patches of nearly homogeneous intensity to remove mismatches generated during stereo matching. These patches are used to guide a statistical analysis of the disparity map based on the assumption that such patches correspond closely with physical surfaces in the scene. Such a technique is quite independent of whether the initial disparity map was generated by automated area-based or feature-based stereo matching. Stereo analysis results are presented on a complex urban scene containing various man-made and natural features. This scene contains a variety of problems including low building height with respect to the stereo baseline, buildings and roads in complex terrain, and highly textured buildings and terrain. The improvements are demonstrated due to monocular fusion with a set of different region-based image segmentations. The generality of this approach to stereo analysis and its utility in the development of general three dimensional scene interpretation systems are also discussed.

Patients and their families weigh in on evidence-based hospital design.

PubMed

Trochelman, Kathleen; Albert, Nancy; Spence, Jacqueline; Murray, Terri; Slifcak, Ellen

2012-02-01

In 2 landmark publications, the Institute of Medicine reported on significant deficiencies in our current health care system. In response, an area of research examining the role of the physical environment in influencing outcomes for patients and staff gained momentum. The concept of evidence-based design has evolved, and the development of structural guidelines for new hospital construction was instituted by the American Institute of Architects in 2006. To determine perceptions of patients and their families of evidence-based design features in a new heart center. Hospitalized patients and their families, most of whom were in intensive care and step-down units, were surveyed and data from the Hospital Consumer Assessment of Healthcare Providers and Systems were reviewed to determine perceptions of evidence-based design features incorporated into a new heart center and to assess patients' satisfaction with the environment. Results Responses were reviewed and categorized descriptively. Five general environment topics of focus emerged: privacy, space, noise, light, and overall atmosphere. Characteristics perceived as being dissatisfying and satisfying are discussed. Critical care nurses must be aware of the current need to recognize how much the physical environment influences care delivery and take steps to maximize patients' safety, satisfaction, and quality of care.