A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Tiwari, Pallavi; Rosen, Mark; Madabhushi, Anant

2008-03-01

Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE). Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities following image registration. However multimodal data integration is non-trivial when the individual modalities include spectral and image intensity data. We propose a data combination solution wherein we project the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional embedding space via NLDR. NLDR methods preserve the relationships between the objects in the original high dimensional space when projecting them into the reduced low dimensional space. Since the original spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional space, data at the same spatial location can be integrated by concatenating the respective embedding vectors. Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared to corresponding CAD results with the individual modalities.

AccessMRS: integrating OpenMRS with smart forms on Android.

PubMed

Fazen, Louis E; Chemwolo, Benjamin T; Songok, Julia J; Ruhl, Laura J; Kipkoech, Carolyne; Green, James M; Ikemeri, Justus E; Christoffersen-Deb, Astrid

2013-01-01

We present a new open-source Android application, AccessMRS, for interfacing with an electronic medical record system (OpenMRS) and loading 'Smart Forms' on a mobile device. AccessMRS functions as a patient-centered interface for viewing OpenMRS data; managing patient information in reminders, task lists, and previous encounters; and launching patient-specific 'Smart Forms' for electronic data collection and dissemination of health information. We present AccessMRS in the context of related software applications we developed to serve Community Health Workers, including AccessInfo, AccessAdmin, AccessMaps, and AccessForms. The specific features and design of AccessMRS are detailed in relationship to the requirements that drove development: the workflows of the Kenyan Ministry of Health Community Health Volunteers (CHVs) supported by the AMPATH Primary Health Care Program. Specifically, AccessMRS was designed to improve the quality of community-based Maternal and Child Health services delivered by CHVs in Kosirai Division. AccessMRS is currently in use by more than 80 CHVs in Kenya and undergoing formal assessment of acceptability, effectiveness, and cost.

Automated time activity classification based on global positioning system (GPS) tracking data

PubMed Central

2011-01-01

Background Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. Methods We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Results Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Conclusions Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns. PMID:22082316

Automated time activity classification based on global positioning system (GPS) tracking data.

PubMed

Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph

2011-11-14

Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.

A novel approach for baseline correction in 1H-MRS signals based on ensemble empirical mode decomposition.

PubMed

Parto Dezfouli, Mohammad Ali; Dezfouli, Mohsen Parto; Rad, Hamidreza Saligheh

2014-01-01

Proton magnetic resonance spectroscopy ((1)H-MRS) is a non-invasive diagnostic tool for measuring biochemical changes in the human body. Acquired (1)H-MRS signals may be corrupted due to a wideband baseline signal generated by macromolecules. Recently, several methods have been developed for the correction of such baseline signals, however most of them are not able to estimate baseline in complex overlapped signal. In this study, a novel automatic baseline correction method is proposed for (1)H-MRS spectra based on ensemble empirical mode decomposition (EEMD). This investigation was applied on both the simulated data and the in-vivo (1)H-MRS of human brain signals. Results justify the efficiency of the proposed method to remove the baseline from (1)H-MRS signals.

The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

PubMed Central

2010-01-01

Background Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored. Results This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with any new case from any MR scanner manufacturer and 2nd, improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI). Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA) were trained and tested. Conclusions The INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses. PMID:21114820

Facial recognition techniques applied to the automated registration of patients in the emergency treatment of head injuries.

PubMed

Gooroochurn, M; Kerr, D; Bouazza-Marouf, K; Ovinis, M

2011-02-01

This paper describes the development of a registration framework for image-guided solutions to the automation of certain routine neurosurgical procedures. The registration process aligns the pose of the patient in the preoperative space to that of the intraoperative space. Computerized tomography images are used in the preoperative (planning) stage, whilst white light (TV camera) images are used to capture the intraoperative pose. Craniofacial landmarks, rather than artificial markers, are used as the registration basis for the alignment. To create further synergy between the user and the image-guided system, automated methods for extraction of these landmarks have been developed. The results obtained from the application of a polynomial neural network classifier based on Gabor features for the detection and localization of the selected craniofacial landmarks, namely the ear tragus and eye corners in the white light modality are presented. The robustness of the classifier to variations in intensity and noise is analysed. The results show that such a classifier gives good performance for the extraction of craniofacial landmarks.

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

NASA Astrophysics Data System (ADS)

Mafanya, Madodomzi; Tsele, Philemon; Botai, Joel; Manyama, Phetole; Swart, Barend; Monate, Thabang

2017-07-01

Invasive alien plants (IAPs) not only pose a serious threat to biodiversity and water resources but also have impacts on human and animal wellbeing. To support decision making in IAPs monitoring, semi-automated image classifiers which are capable of extracting valuable information in remotely sensed data are vital. This study evaluated the mapping accuracies of supervised and unsupervised image classifiers for mapping Harrisia pomanensis (a cactus plant commonly known as the Midnight Lady) using two interlinked evaluation strategies i.e. point and area based accuracy assessment. Results of the point-based accuracy assessment show that with reference to 219 ground control points, the supervised image classifiers (i.e. Maxver and Bhattacharya) mapped H. pomanensis better than the unsupervised image classifiers (i.e. K-mediuns, Euclidian Length and Isoseg). In this regard, user and producer accuracies were 82.4% and 84% respectively for the Maxver classifier. The user and producer accuracies for the Bhattacharya classifier were 90% and 95.7%, respectively. Though the Maxver produced a higher overall accuracy and Kappa estimate than the Bhattacharya classifier, the Maxver Kappa estimate of 0.8305 is not significantly (statistically) greater than the Bhattacharya Kappa estimate of 0.8088 at a 95% confidence interval. The area based accuracy assessment results show that the Bhattacharya classifier estimated the spatial extent of H. pomanensis with an average mapping accuracy of 86.1% whereas the Maxver classifier only gave an average mapping accuracy of 65.2%. Based on these results, the Bhattacharya classifier is therefore recommended for mapping H. pomanensis. These findings will aid in the algorithm choice making for the development of a semi-automated image classification system for mapping IAPs.

High-field MRS in clinical drug development.

PubMed

Ross, Brian D

2013-07-01

Magnetic resonance spectroscopy (MRS) will continue to play an ever increasing role in drug discovery because MRS does readily define biomarkers for several hundreds of clinically distinct diseases. Published evidence based medicine (EBM) surveys, which generally conclude the opposite, are seriously flawed and do a disservice to the field of drug discovery. This article presents MRS and how it has guided several hundreds of practical human 'drug discovery' endeavors since its development. Specifically, the author looks at the process of 'reverse-translation' and its influence in the expansion of the number of preclinical drug discoveries from in vivo MRS. The author also provides a structured approach of eight criteria, including EBM acceptance, which could potentially re-open the field of MRS for productive exploration of existing and repurposed drugs and cost-effective drug-discovery. MRS-guided drug discovery is poised for future expansion. The cost of clinical trials has escalated and the use of biomarkers has become increasingly useful in improving patient selection for drug trials. Clinical MRS has uncovered a treasure-trove of novel biomarkers and clinical MRS itself has become better standardized and more widely available on 'routine' clinical MRI scanners. When combined with available new MRI sequences, MRS can provide a 'one stop shop' with multiple potential outcome measures for the disease and the drug in question.

Classification of the Gabon SAR Mosaic Using a Wavelet Based Rule Classifier

NASA Technical Reports Server (NTRS)

Simard, Marc; Saatchi, Sasan; DeGrandi, Gianfranco

2000-01-01

A method is developed for semi-automated classification of SAR images of the tropical forest. Information is extracted using the wavelet transform (WT). The transform allows for extraction of structural information in the image as a function of scale. In order to classify the SAR image, a Desicion Tree Classifier is used. The method of pruning is used to optimize classification rate versus tree size. The results give explicit insight on the type of information useful for a given class.

Development and Deployment of the OpenMRS-Ebola Electronic Health Record System for an Ebola Treatment Center in Sierra Leone

PubMed Central

Jazayeri, Darius; Teich, Jonathan M; Ball, Ellen; Nankubuge, Patricia Alexandra; Rwebembera, Job; Wing, Kevin; Sesay, Alieu Amara; Kanter, Andrew S; Ramos, Glauber D; Walton, David; Cummings, Rachael; Checchi, Francesco; Fraser, Hamish S

2017-01-01

Background Stringent infection control requirements at Ebola treatment centers (ETCs), which are specialized facilities for isolating and treating Ebola patients, create substantial challenges for recording and reviewing patient information. During the 2014-2016 West African Ebola epidemic, paper-based data collection systems at ETCs compromised the quality, quantity, and confidentiality of patient data. Electronic health record (EHR) systems have the potential to address such problems, with benefits for patient care, surveillance, and research. However, no suitable software was available for deployment when large-scale ETCs opened as the epidemic escalated in 2014. Objective We present our work on rapidly developing and deploying OpenMRS-Ebola, an EHR system for the Kerry Town ETC in Sierra Leone. We describe our experience, lessons learned, and recommendations for future health emergencies. Methods We used the OpenMRS platform and Agile software development approaches to build OpenMRS-Ebola. Key features of our work included daily communications between the development team and ground-based operations team, iterative processes, and phased development and implementation. We made design decisions based on the restrictions of the ETC environment and regular user feedback. To evaluate the system, we conducted predeployment user questionnaires and compared the EHR records with duplicate paper records. Results We successfully built OpenMRS-Ebola, a modular stand-alone EHR system with a tablet-based application for infectious patient wards and a desktop-based application for noninfectious areas. OpenMRS-Ebola supports patient tracking (registration, bed allocation, and discharge); recording of vital signs and symptoms; medication and intravenous fluid ordering and monitoring; laboratory results; clinician notes; and data export. It displays relevant patient information to clinicians in infectious and noninfectious zones. We implemented phase 1 (patient tracking; drug ordering and monitoring) after 2.5 months of full-time development. OpenMRS-Ebola was used for 112 patient registrations, 569 prescription orders, and 971 medication administration recordings. We were unable to fully implement phases 2 and 3 as the ETC closed because of a decrease in new Ebola cases. The phase 1 evaluation suggested that OpenMRS-Ebola worked well in the context of the rollout, and the user feedback was positive. Conclusions To our knowledge, OpenMRS-Ebola is the most comprehensive adaptable clinical EHR built for a low-resource setting health emergency. It is designed to address the main challenges of data collection in highly infectious environments that require robust infection prevention and control measures and it is interoperable with other electronic health systems. Although we built and deployed OpenMRS-Ebola more rapidly than typical software, our work highlights the challenges of having to develop an appropriate system during an emergency rather than being able to rapidly adapt an existing one. Lessons learned from this and previous emergencies should be used to ensure that a set of well-designed, easy-to-use, pretested health software is ready for quick deployment in future. PMID:28827211

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

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

NASA Astrophysics Data System (ADS)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Development of full-field optical spatial coherence tomography system for automated identification of malaria using the multilevel ensemble classifier.

PubMed

Singla, Neeru; Srivastava, Vishal; Mehta, Dalip Singh

2018-05-01

Malaria is a life-threatening infectious blood disease affecting humans and other animals caused by parasitic protozoans belonging to the Plasmodium type especially in developing countries. The gold standard method for the detection of malaria is through the microscopic method of chemically treated blood smears. We developed an automated optical spatial coherence tomographic system using a machine learning approach for a fast identification of malaria cells. In this study, 28 samples (15 healthy, 13 malaria infected stages of red blood cells) were imaged by the developed system and 13 features were extracted. We designed a multilevel ensemble-based classifier for the quantitative prediction of different stages of the malaria cells. The proposed classifier was used by repeating k-fold cross validation dataset and achieve a high-average accuracy of 97.9% for identifying malaria infected late trophozoite stage of cells. Overall, our proposed system and multilevel ensemble model has a substantial quantifiable potential to detect the different stages of malaria infection without staining or expert. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automated anatomical labeling of bronchial branches using multiple classifiers and its application to bronchoscopy guidance based on fusion of virtual and real bronchoscopy

NASA Astrophysics Data System (ADS)

Ota, Shunsuke; Deguchi, Daisuke; Kitasaka, Takayuki; Mori, Kensaku; Suenaga, Yasuhito; Hasegawa, Yoshinori; Imaizumi, Kazuyoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi

2008-03-01

This paper presents a method for automated anatomical labeling of bronchial branches (ALBB) extracted from 3D CT datasets. The proposed method constructs classifiers that output anatomical names of bronchial branches by employing the machine-learning approach. We also present its application to a bronchoscopy guidance system. Since the bronchus has a complex tree structure, bronchoscopists easily tend to get disoriented and lose the way to a target location. A bronchoscopy guidance system is strongly expected to be developed to assist bronchoscopists. In such guidance system, automated presentation of anatomical names is quite useful information for bronchoscopy. Although several methods for automated ALBB were reported, most of them constructed models taking only variations of branching patterns into account and did not consider those of running directions. Since the running directions of bronchial branches differ greatly in individuals, they could not perform ALBB accurately when running directions of bronchial branches were different from those of models. Our method tries to solve such problems by utilizing the machine-learning approach. Actual procedure consists of three steps: (a) extraction of bronchial tree structures from 3D CT datasets, (b) construction of classifiers using the multi-class AdaBoost technique, and (c) automated classification of bronchial branches by using the constructed classifiers. We applied the proposed method to 51 cases of 3D CT datasets. The constructed classifiers were evaluated by leave-one-out scheme. The experimental results showed that the proposed method could assign correct anatomical names to bronchial branches of 89.1% up to segmental lobe branches. Also, we confirmed that it was quite useful to assist the bronchoscopy by presenting anatomical names of bronchial branches on real bronchoscopic views.

Automatic analysis of nuclear-magnetic-resonance-spectroscopy clinical research data

NASA Astrophysics Data System (ADS)

Scott, Katherine N.; Wilson, David C.; Bruner, Angela P.; Lyles, Teresa A.; Underhill, Brandon; Geiser, Edward A.; Ballinger, J. Ray; Scott, James D.; Stopka, Christine B.

1998-03-01

A major problem of P-31 nuclear magnetic spectroscopy (MRS) in vivo applications is that when large data sets are acquired, the time invested in data reduction and analysis with currently available technologies may totally overshadow the time required for data acquisition. An example is out MRS monitoring of exercise therapy for patients with peripheral vascular disease. In these, the spectral acquisition requires 90 minutes per patient study, whereas data analysis and reduction requires 6-8 hours. Our laboratory currently uses the proprietary software SA/GE developed by General Electric. However, other software packages have similar limitations. When data analysis takes this long, the researcher does not have the rapid feedback required to ascertain the quality of data acquired nor the result of the study. This highly undesirable even in a research environment, but becomes intolerable in the clinical setting. The purpose of this report is to outline progress towards the development of an automated method for eliminating the spectral analysis burden on the researcher working in the clinical setting.

A semi-automated method for bone age assessment using cervical vertebral maturation.

PubMed

Baptista, Roberto S; Quaglio, Camila L; Mourad, Laila M E H; Hummel, Anderson D; Caetano, Cesar Augusto C; Ortolani, Cristina Lúcia F; Pisa, Ivan T

2012-07-01

To propose a semi-automated method for pattern classification to predict individuals' stage of growth based on morphologic characteristics that are described in the modified cervical vertebral maturation (CVM) method of Baccetti et al. A total of 188 lateral cephalograms were collected, digitized, evaluated manually, and grouped into cervical stages by two expert examiners. Landmarks were located on each image and measured. Three pattern classifiers based on the Naïve Bayes algorithm were built and assessed using a software program. The classifier with the greatest accuracy according to the weighted kappa test was considered best. The classifier showed a weighted kappa coefficient of 0.861 ± 0.020. If an adjacent estimated pre-stage or poststage value was taken to be acceptable, the classifier would show a weighted kappa coefficient of 0.992 ± 0.019. Results from this study show that the proposed semi-automated pattern classification method can help orthodontists identify the stage of CVM. However, additional studies are needed before this semi-automated classification method for CVM assessment can be implemented in clinical practice.

Automatic blood vessel based-liver segmentation using the portal phase abdominal CT

NASA Astrophysics Data System (ADS)

Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Shimada, Mitsuo; Iinuma, Gen

2018-02-01

Liver segmentation is the basis for computer-based planning of hepatic surgical interventions. In diagnosis and analysis of hepatic diseases and surgery planning, automatic segmentation of liver has high importance. Blood vessel (BV) has showed high performance at liver segmentation. In our previous work, we developed a semi-automatic method that segments the liver through the portal phase abdominal CT images in two stages. First stage was interactive segmentation of abdominal blood vessels (ABVs) and subsequent classification into hepatic (HBVs) and non-hepatic (non-HBVs). This stage had 5 interactions that include selective threshold for bone segmentation, selecting two seed points for kidneys segmentation, selection of inferior vena cava (IVC) entrance for starting ABVs segmentation, identification of the portal vein (PV) entrance to the liver and the IVC-exit for classifying HBVs from other ABVs (non-HBVs). Second stage is automatic segmentation of the liver based on segmented ABVs as described in [4]. For full automation of our method we developed a method [5] that segments ABVs automatically tackling the first three interactions. In this paper, we propose full automation of classifying ABVs into HBVs and non- HBVs and consequently full automation of liver segmentation that we proposed in [4]. Results illustrate that the method is effective at segmentation of the liver through the portal abdominal CT images.

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

NASA Astrophysics Data System (ADS)

Janaki Sathya, D.; Geetha, K.

2017-12-01

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2.

PubMed

Thiele, Ines; Hyduke, Daniel R; Steeb, Benjamin; Fankam, Guy; Allen, Douglas K; Bazzani, Susanna; Charusanti, Pep; Chen, Feng-Chi; Fleming, Ronan M T; Hsiung, Chao A; De Keersmaecker, Sigrid C J; Liao, Yu-Chieh; Marchal, Kathleen; Mo, Monica L; Özdemir, Emre; Raghunathan, Anu; Reed, Jennifer L; Shin, Sook-il; Sigurbjörnsdóttir, Sara; Steinmann, Jonas; Sudarsan, Suresh; Swainston, Neil; Thijs, Inge M; Zengler, Karsten; Palsson, Bernhard O; Adkins, Joshua N; Bumann, Dirk

2011-01-18

Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2

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

Thiele, Ines; Hyduke, Daniel R.; Steeb, Benjamin

2011-01-01

Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of thismore » reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Finally, taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.« less

Magnetic resonance imaging and magnetic resonance spectroscopy for detection of early Alzheimer's disease.

PubMed

Westman, Eric; Wahlund, Lars-Olof; Foy, Catherine; Poppe, Michaela; Cooper, Allison; Murphy, Declan; Spenger, Christian; Lovestone, Simon; Simmons, Andrew

2011-01-01

Alzheimer's disease is the most common form of neurodegenerative disorder and early detection is of great importance if new therapies are to be effectively administered. We have investigated whether the discrimination between early Alzheimer's disease (AD) and elderly healthy control subjects can be improved by adding magnetic resonance spectroscopy (MRS) measures to magnetic resonance imaging (MRI) measures. In this study 30 AD patients and 36 control subjects were included. High resolution T1-weighted axial magnetic resonance images were obtained from each subject. Automated regional volume segmentation and cortical thickness measures were determined for the images. 1H MRS was acquired from the hippocampus and LCModel was used for metabolic quantification. Altogether, this yielded 58 different volumetric, cortical thickness and metabolite ratio variables which were used for multivariate analysis to distinguish between subjects with AD and Healthy controls. Combining MRI and MRS measures resulted in a sensitivity of 97% and a specificity of 94% compared to using MRI or MRS measures alone (sensitivity: 87%, 76%, specificity: 86%, 83% respectively). Adding the MRS measures to the MRI measures more than doubled the positive likelihood ratio from 6 to 17. Adding MRS measures to a multivariate analysis of MRI measures resulted in significantly better classification than using MRI measures alone. The method shows strong potential for discriminating between Alzheimer's disease and controls.

Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.

PubMed

Fleck, David E; Ernest, Nicholas; Adler, Caleb M; Cohen, Kelly; Eliassen, James C; Norris, Matthew; Komoroski, Richard A; Chu, Wen-Jang; Welge, Jeffrey A; Blom, Thomas J; DelBello, Melissa P; Strakowski, Stephen M

2017-06-01

Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree (GFT) design called the LITHium Intelligent Agent (LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy ( 1 H-MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and 1 H-MRS scans at baseline pre-treatment. We trained LITHIA using 18 1 H-MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Automated in vivo identification of fungal infection on human scalp using optical coherence tomography and machine learning

NASA Astrophysics Data System (ADS)

Dubey, Kavita; Srivastava, Vishal; Singh Mehta, Dalip

2018-04-01

Early identification of fungal infection on the human scalp is crucial for avoiding hair loss. The diagnosis of fungal infection on the human scalp is based on a visual assessment by trained experts or doctors. Optical coherence tomography (OCT) has the ability to capture fungal infection information from the human scalp with a high resolution. In this study, we present a fully automated, non-contact, non-invasive optical method for rapid detection of fungal infections based on the extracted features from A-line and B-scan images of OCT. A multilevel ensemble machine model is designed to perform automated classification, which shows the superiority of our classifier to the best classifier based on the features extracted from OCT images. In this study, 60 samples (30 fungal, 30 normal) were imaged by OCT and eight features were extracted. The classification algorithm had an average sensitivity, specificity and accuracy of 92.30, 90.90 and 91.66%, respectively, for identifying fungal and normal human scalps. This remarkable classifying ability makes the proposed model readily applicable to classifying the human scalp.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

PubMed Central

Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-01-01

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization. PMID:28599282

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

PubMed

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data.

PubMed

Raith, Stefan; Vogel, Eric Per; Anees, Naeema; Keul, Christine; Güth, Jan-Frederik; Edelhoff, Daniel; Fischer, Horst

2017-01-01

Chairside manufacturing based on digital image acquisition is gainingincreasing importance in dentistry. For the standardized application of these methods, it is paramount to have highly automated digital workflows that can process acquired 3D image data of dental surfaces. Artificial Neural Networks (ANNs) arenumerical methods primarily used to mimic the complex networks of neural connections in the natural brain. Our hypothesis is that an ANNcan be developed that is capable of classifying dental cusps with sufficient accuracy. This bears enormous potential for an application in chairside manufacturing workflows in the dental field, as it closes the gap between digital acquisition of dental geometries and modern computer-aided manufacturing techniques.Three-dimensional surface scans of dental casts representing natural full dental arches were transformed to range image data. These data were processed using an automated algorithm to detect candidates for tooth cusps according to salient geometrical features. These candidates were classified following common dental terminology and used as training data for a tailored ANN.For the actual cusp feature description, two different approaches were developed and applied to the available data: The first uses the relative location of the detected cusps as input data and the second method directly takes the image information given in the range images. In addition, a combination of both was implemented and investigated.Both approaches showed high performance with correct classifications of 93.3% and 93.5%, respectively, with improvements by the combination shown to be minor.This article presents for the first time a fully automated method for the classification of teeththat could be confirmed to work with sufficient precision to exhibit the potential for its use in clinical practice,which is a prerequisite for automated computer-aided planning of prosthetic treatments with subsequent automated chairside manufacturing. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development and Deployment of the OpenMRS-Ebola Electronic Health Record System for an Ebola Treatment Center in Sierra Leone.

PubMed

Oza, Shefali; Jazayeri, Darius; Teich, Jonathan M; Ball, Ellen; Nankubuge, Patricia Alexandra; Rwebembera, Job; Wing, Kevin; Sesay, Alieu Amara; Kanter, Andrew S; Ramos, Glauber D; Walton, David; Cummings, Rachael; Checchi, Francesco; Fraser, Hamish S

2017-08-21

Stringent infection control requirements at Ebola treatment centers (ETCs), which are specialized facilities for isolating and treating Ebola patients, create substantial challenges for recording and reviewing patient information. During the 2014-2016 West African Ebola epidemic, paper-based data collection systems at ETCs compromised the quality, quantity, and confidentiality of patient data. Electronic health record (EHR) systems have the potential to address such problems, with benefits for patient care, surveillance, and research. However, no suitable software was available for deployment when large-scale ETCs opened as the epidemic escalated in 2014. We present our work on rapidly developing and deploying OpenMRS-Ebola, an EHR system for the Kerry Town ETC in Sierra Leone. We describe our experience, lessons learned, and recommendations for future health emergencies. We used the OpenMRS platform and Agile software development approaches to build OpenMRS-Ebola. Key features of our work included daily communications between the development team and ground-based operations team, iterative processes, and phased development and implementation. We made design decisions based on the restrictions of the ETC environment and regular user feedback. To evaluate the system, we conducted predeployment user questionnaires and compared the EHR records with duplicate paper records. We successfully built OpenMRS-Ebola, a modular stand-alone EHR system with a tablet-based application for infectious patient wards and a desktop-based application for noninfectious areas. OpenMRS-Ebola supports patient tracking (registration, bed allocation, and discharge); recording of vital signs and symptoms; medication and intravenous fluid ordering and monitoring; laboratory results; clinician notes; and data export. It displays relevant patient information to clinicians in infectious and noninfectious zones. We implemented phase 1 (patient tracking; drug ordering and monitoring) after 2.5 months of full-time development. OpenMRS-Ebola was used for 112 patient registrations, 569 prescription orders, and 971 medication administration recordings. We were unable to fully implement phases 2 and 3 as the ETC closed because of a decrease in new Ebola cases. The phase 1 evaluation suggested that OpenMRS-Ebola worked well in the context of the rollout, and the user feedback was positive. To our knowledge, OpenMRS-Ebola is the most comprehensive adaptable clinical EHR built for a low-resource setting health emergency. It is designed to address the main challenges of data collection in highly infectious environments that require robust infection prevention and control measures and it is interoperable with other electronic health systems. Although we built and deployed OpenMRS-Ebola more rapidly than typical software, our work highlights the challenges of having to develop an appropriate system during an emergency rather than being able to rapidly adapt an existing one. Lessons learned from this and previous emergencies should be used to ensure that a set of well-designed, easy-to-use, pretested health software is ready for quick deployment in future. ©Shefali Oza, Darius Jazayeri, Jonathan M Teich, Ellen Ball, Patricia Alexandra Nankubuge, Job Rwebembera, Kevin Wing, Alieu Amara Sesay, Andrew S Kanter, Glauber D Ramos, David Walton, Rachael Cummings, Francesco Checchi, Hamish S Fraser. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.08.2017.

Multiple Rapid Swallow Maneuver Enhances the Clinical Utility of High-Resolution Manometry in Patients Showing Ineffective Esophageal Motility

PubMed Central

Min, Yang Won; Shin, Inseub; Son, Hee Jung; Rhee, Poong-Lyul

2015-01-01

Abstract The clinical significance of ineffective esophageal motility (IEM) together with multiple rapid swallow (MRS) has not been yet evaluated in the Chicago Classification v3.0. This study evaluated the adjunctive role of MRS in IEM and determined the criteria of abnormal MRS to maximize the utility of IEM. We analyzed 186 patients showing IEM or normal esophageal motility (NEM), who underwent esophageal high-resolution impedance–manometry for esophageal symptoms. Two different criteria for abnormal MRS were applied to IEM subjects, resulting in 2 corresponding subgroups: IEM-A when distal contractile integral (DCI) ratio between an average wet swallows and MRS contraction was <1 and IEM-B when MRS contraction DCI was <450 mm Hg-s-cm. One IEM subject inadequately performed MRS. Among the remaining 52 IEM subjects, 18 (34.6%) were classified into IEM-A and 23 (44.2%) into IEM-B. IEM subjects showed less complete bolus transit (median 0.0%, interquartile range 0.0–20.0% vs 60.0%, 30.0–80.0; P < 0.001) resulting in higher impaired bolus transit than NEM subjects (98.1% vs 66.9%, P = 0.001). IEM-B subjects showed additionally higher pathologic bolus exposure than NEM subjects (55.6% vs 29.3%, P = 0.001), whereas IEM-A subjects could not. Although IEM-B subjects had the highest prevalence of gastroesophageal reflux disease among the subjects groups, it did not reach statistical significance. In conclusion, IEM patients with abnormal MRS contraction have an increased risk of prolonged bolus clearance, poor bolus transit, and pathologic bolus exposure. IEM patients need to be assessed concerning whether MRS contraction DCI is <450 mm Hg-s-cm to segregate clinically relevant patients. PMID:26448010

Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis.

PubMed

Myburgh, Hermanus C; van Zijl, Willemien H; Swanepoel, DeWet; Hellström, Sten; Laurent, Claude

2016-03-01

Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations.

Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis

PubMed Central

Myburgh, Hermanus C.; van Zijl, Willemien H.; Swanepoel, DeWet; Hellström, Sten; Laurent, Claude

2016-01-01

Background Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. Methods A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. Findings An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. Interpretation The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~ 64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations. PMID:27077122

Automated Classification of Consumer Health Information Needs in Patient Portal Messages.

PubMed

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Jackson, Gretchen Purcell

2015-01-01

Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804-0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs.

Automated Classification of Consumer Health Information Needs in Patient Portal Messages

PubMed Central

Cronin, Robert M.; Fabbri, Daniel; Denny, Joshua C.; Jackson, Gretchen Purcell

2015-01-01

Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804–0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs. PMID:26958285

Towards Standardized Patient Data Exchange: Integrating a FHIR Based API for the Open Medical Record System.

PubMed

Kasthurirathne, Suranga N; Mamlin, Burke; Grieve, Grahame; Biondich, Paul

2015-01-01

Interoperability is essential to address limitations caused by the ad hoc implementation of clinical information systems and the distributed nature of modern medical care. The HL7 V2 and V3 standards have played a significant role in ensuring interoperability for healthcare. FHIR is a next generation standard created to address fundamental limitations in HL7 V2 and V3. FHIR is particularly relevant to OpenMRS, an Open Source Medical Record System widely used across emerging economies. FHIR has the potential to allow OpenMRS to move away from a bespoke, application specific API to a standards based API. We describe efforts to design and implement a FHIR based API for the OpenMRS platform. Lessons learned from this effort were used to define long term plans to transition from the legacy OpenMRS API to a FHIR based API that greatly reduces the learning curve for developers and helps enhance adhernce to standards.

Fall Detection Using Smartphone Audio Features.

PubMed

Cheffena, Michael

2016-07-01

An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

Self-Reported Minimalist Running Injury Incidence and Severity: A Pilot Study.

PubMed

Ostermann, Katrina; Ridpath, Lance; Hanna, Jandy B

2016-08-01

Minimalist running entails using shoes with a flexible thin sole and is popular in the United States. Existing literature disagrees over whether minimalist running shoes (MRS) improve perceived severity of injuries associated with running in traditional running shoes (TRS). Additionally, the perceived injury patterns associated with MRS are relatively unknown. To examine whether injury incidence and severity (ie, degree of pain) by body region change after switching to MRS, and to determine if transition times affect injury incidences or severity with MRS. Runners who were either current or previous users of MRS were recruited to complete an Internet-based survey regarding self-reported injury before switching to MRS and whether self-reported pain from that injury decreased after switching. Questions regarding whether new injuries developed in respondents after switching to MRS were also included. Analyses were calculated using t tests, Wilcoxon signed rank tests, and Fischer exact tests. Forty-seven runners completed the survey, and 16 respondents reported injuries before switching to MRS. Among these respondents, pain resulting from injuries of the feet (P=.03) and knees (P=.01) decreased. Eighteen respondents (38.3%) indicated they sustained new injuries after switching to MRS, but the severity of these did not differ significantly from no injury. Neither time allowed for transition to MRS nor use or disuse of a stretching routine during this period was correlated with an increase in the incidence or severity of injuries. After switching to MRS, respondents perceived an improvement in foot and knee injuries. Additionally, respondents using MRS reported an injury rate of 38.3%, compared with the approximately 64% that the literature reports among TRS users. Future studies should be expanded to determine the full extent of the differences in injury patterns between MRS and TRS.

A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

NASA Astrophysics Data System (ADS)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

Bone marrow fat quantification in the presence of trabecular bone: initial comparison between water-fat imaging and single-voxel MRS

PubMed Central

Karampinos, Dimitrios C.; Melkus, Gerd; Baum, Thomas; Bauer, Jan S.; Rummeny, Ernst J.; Krug, Roland

2013-01-01

Purpose The purpose of the present study was to test the relative performance of chemical shift-based water-fat imaging in measuring bone marrow fat fraction in the presence of trabecular bone, having as reference standard the single-voxel magnetic resonance spectroscopy (MRS). Methods Six-echo gradient echo imaging and single-voxel MRS measurements were performed on the proximal femur of seven healthy volunteers. The bone marrow fat spectrum was characterized based on the magnitude of measurable fat peaks and an a priori knowledge of the chemical structure of triglycerides, in order to accurately extract the water peak from the overlapping broad fat peaks in MRS. The imaging-based fat fraction results were then compared to the MRS-based results both without and with taking into consideration the presence of short T2* water components in MRS. Results There was a significant underestimation of the fat fraction using the MRS model not accounting for short T2* species with respect to the imaging-based water fraction. A good equivalency was observed between the fat fraction using the MRS model accounting for short T2* species and the imaging-based fat fraction (R2=0.87). Conclusion The consideration of the short T2* water species effect on bone marrow fat quantification is essential when comparing MRS-based and imaging-based fat fraction results. PMID:23657998

The development of a strategy for the implementation of automation in a bioanalytical laboratory.

PubMed

Mole, D; Mason, R J; McDowall, R D

1993-03-01

Laboratory automation is equipment, instrumentation, software and techniques that are classified into four groups: instrument automation; communications; data to information conversion; and information management. This new definition is necessary to understand the role that automation can play in achieving the aims and objectives of a laboratory within its organization. To undertake automation projects effectively, a laboratory automation strategy is outlined which requires an intimate knowledge of an organization and the target environment to implement individual automation projects.

Development and Validation of Noninvasive Magnetic Resonance Relaxometry for the In Vivo Assessment of Tissue-Engineered Graft Oxygenation

PubMed Central

Einstein, Samuel A.; Weegman, Bradley P.; Firpo, Meri T.; Papas, Klearchos K.

2016-01-01

Techniques to monitor the oxygen partial pressure (pO2) within implanted tissue-engineered grafts (TEGs) are critically necessary for TEG development, but current methods are invasive and inaccurate. In this study, we developed an accurate and noninvasive technique to monitor TEG pO2 utilizing proton (1H) or fluorine (19F) magnetic resonance spectroscopy (MRS) relaxometry. The value of the spin-lattice relaxation rate constant (R1) of some biocompatible compounds is sensitive to dissolved oxygen (and temperature), while insensitive to other external factors. Through this physical mechanism, MRS can measure the pO2 of implanted TEGs. We evaluated six potential MRS pO2 probes and measured their oxygen and temperature sensitivities and their intrinsic R1 values at 16.4 T. Acellular TEGs were constructed by emulsifying porcine plasma with perfluoro-15-crown-5-ether, injecting the emulsion into a macroencapsulation device, and cross-linking the plasma with a thrombin solution. A multiparametric calibration equation containing R1, pO2, and temperature was empirically generated from MRS data and validated with fiber optic (FO) probes in vitro. TEGs were then implanted in a dorsal subcutaneous pocket in a murine model and evaluated with MRS up to 29 days postimplantation. R1 measurements from the TEGs were converted to pO2 values using the established calibration equation and these in vivo pO2 measurements were simultaneously validated with FO probes. Additionally, MRS was used to detect increased pO2 within implanted TEGs that received supplemental oxygen delivery. Finally, based on a comparison of our MRS data with previously reported data, ultra-high-field (16.4 T) is shown to have an advantage for measuring hypoxia with 19F MRS. Results from this study show MRS relaxometry to be a precise, accurate, and noninvasive technique to monitor TEG pO2 in vitro and in vivo. PMID:27758135

Development and Validation of Noninvasive Magnetic Resonance Relaxometry for the In Vivo Assessment of Tissue-Engineered Graft Oxygenation.

PubMed

Einstein, Samuel A; Weegman, Bradley P; Firpo, Meri T; Papas, Klearchos K; Garwood, Michael

2016-11-01

Techniques to monitor the oxygen partial pressure (pO 2 ) within implanted tissue-engineered grafts (TEGs) are critically necessary for TEG development, but current methods are invasive and inaccurate. In this study, we developed an accurate and noninvasive technique to monitor TEG pO 2 utilizing proton ( 1 H) or fluorine ( 19 F) magnetic resonance spectroscopy (MRS) relaxometry. The value of the spin-lattice relaxation rate constant (R 1 ) of some biocompatible compounds is sensitive to dissolved oxygen (and temperature), while insensitive to other external factors. Through this physical mechanism, MRS can measure the pO 2 of implanted TEGs. We evaluated six potential MRS pO 2 probes and measured their oxygen and temperature sensitivities and their intrinsic R 1 values at 16.4 T. Acellular TEGs were constructed by emulsifying porcine plasma with perfluoro-15-crown-5-ether, injecting the emulsion into a macroencapsulation device, and cross-linking the plasma with a thrombin solution. A multiparametric calibration equation containing R 1 , pO 2 , and temperature was empirically generated from MRS data and validated with fiber optic (FO) probes in vitro. TEGs were then implanted in a dorsal subcutaneous pocket in a murine model and evaluated with MRS up to 29 days postimplantation. R 1 measurements from the TEGs were converted to pO 2 values using the established calibration equation and these in vivo pO 2 measurements were simultaneously validated with FO probes. Additionally, MRS was used to detect increased pO 2 within implanted TEGs that received supplemental oxygen delivery. Finally, based on a comparison of our MRS data with previously reported data, ultra-high-field (16.4 T) is shown to have an advantage for measuring hypoxia with 19 F MRS. Results from this study show MRS relaxometry to be a precise, accurate, and noninvasive technique to monitor TEG pO 2 in vitro and in vivo.

MARC: Mrs. Avram's Remarkable Contribution.

ERIC Educational Resources Information Center

Rather, Lucia J.; Wiggins, Beacher

1989-01-01

This discussion of the contributions of Henriette D. Avram to the field of librarianship covers her role in establishing MARC formats as the international standard for library automation and her work in the retrospective conversion of catalogs, establishment of library standards, and creation of information networks. An interview with Dr. Avram is…

Automated software development workstation

NASA Technical Reports Server (NTRS)

Prouty, Dale A.; Klahr, Philip

1988-01-01

A workstation is being developed that provides a computational environment for all NASA engineers across application boundaries, which automates reuse of existing NASA software and designs, and efficiently and effectively allows new programs and/or designs to be developed, catalogued, and reused. The generic workstation is made domain specific by specialization of the user interface, capturing engineering design expertise for the domain, and by constructing/using a library of pertinent information. The incorporation of software reusability principles and expert system technology into this workstation provide the obvious benefits of increased productivity, improved software use and design reliability, and enhanced engineering quality by bringing engineering to higher levels of abstraction based on a well tested and classified library.

Validation of a new classifier for the automated analysis of the human epidermal growth factor receptor 2 (HER2) gene amplification in breast cancer specimens

PubMed Central

2013-01-01

Amplification of the human epidermal growth factor receptor 2 (HER2) is a prognostic marker for poor clinical outcome and a predictive marker for therapeutic response to targeted therapies in breast cancer patients. With the introduction of anti-HER2 therapies, accurate assessment of HER2 status has become essential. Fluorescence in situ hybridization (FISH) is a widely used technique for the determination of HER2 status in breast cancer. However, the manual signal enumeration is time-consuming. Therefore, several companies like MetaSystem have developed automated image analysis software. Some of these signal enumeration software employ the so called “tile-sampling classifier”, a programming algorithm through which the software quantifies fluorescent signals in images on the basis of square tiles of fixed dimensions. Considering that the size of tile does not always correspond to the size of a single tumor cell nucleus, some users argue that this analysis method might not completely reflect the biology of cells. For that reason, MetaSystems has developed a new classifier which is able to recognize nuclei within tissue sections in order to determine the HER2 amplification status on nuclei basis. We call this new programming algorithm “nuclei-sampling classifier”. In this study, we evaluated the accuracy of the “nuclei-sampling classifier” in determining HER2 gene amplification by FISH in nuclei of breast cancer cells. To this aim, we randomly selected from our cohort 64 breast cancer specimens (32 nonamplified and 32 amplified) and we compared results obtained through manual scoring and through this new classifier. The new classifier automatically recognized individual nuclei. The automated analysis was followed by an optional human correction, during which the user interacted with the software in order to improve the selection of cell nuclei automatically selected. Overall concordance between manual scoring and automated nuclei-sampling analysis was 98.4% (100% for nonamplified cases and 96.9% for amplified cases). However, after human correction, concordance between the two methods was 100%. We conclude that the nuclei-based classifier is a new available tool for automated quantitative HER2 FISH signals analysis in nuclei in breast cancer specimen and it can be used for clinical purposes. PMID:23379971

Automated detection of geological landforms on Mars using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Palafox, Leon F.; Hamilton, Christopher W.; Scheidt, Stephen P.; Alvarez, Alexander M.

2017-04-01

The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

Automated detection of geological landforms on Mars using Convolutional Neural Networks.

PubMed

Palafox, Leon F; Hamilton, Christopher W; Scheidt, Stephen P; Alvarez, Alexander M

2017-04-01

The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.

PubMed

Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Ed; Neuman, Michael

2007-01-01

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

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

NASA Astrophysics Data System (ADS)

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

2010-07-01

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

Performance comparison of classifiers for differentiation among obstructive lung diseases based on features of texture analysis at HRCT

NASA Astrophysics Data System (ADS)

Lee, Youngjoo; Seo, Joon Beom; Kang, Bokyoung; Kim, Dongil; Lee, June Goo; Kim, Song Soo; Kim, Namkug; Kang, Suk Ho

2007-03-01

The performance of classification algorithms for differentiating among obstructive lung diseases based on features from texture analysis using HRCT (High Resolution Computerized Tomography) images was compared. HRCT can provide accurate information for the detection of various obstructive lung diseases, including centrilobular emphysema, panlobular emphysema and bronchiolitis obliterans. Features on HRCT images can be subtle, however, particularly in the early stages of disease, and image-based diagnosis is subject to inter-observer variation. To automate the diagnosis and improve the accuracy, we compared three types of automated classification systems, naÃve Bayesian classifier, ANN (Artificial Neural Net) and SVM (Support Vector Machine), based on their ability to differentiate among normal lung and three types of obstructive lung diseases. To assess the performance and cross-validation of these three classifiers, 5 folding methods with 5 randomly chosen groups were used. For a more robust result, each validation was repeated 100 times. SVM showed the best performance, with 86.5% overall sensitivity, significantly different from the other classifiers (one way ANOVA, p<0.01). We address the characteristics of each classifier affecting performance and the issue of which classifier is the most suitable for clinical applications, and propose an appropriate method to choose the best classifier and determine its optimal parameters for optimal disease discrimination. These results can be applied to classifiers for differentiation of other diseases.

Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches.

PubMed

Abu, Arpah; Leow, Lee Kien; Ramli, Rosli; Omar, Hasmahzaiti

2016-12-22

Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs. At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community. This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

Adaptation of a web-based, open source electronic medical record system platform to support a large study of tuberculosis epidemiology

PubMed Central

2012-01-01

Background In 2006, we were funded by the US National Institutes of Health to implement a study of tuberculosis epidemiology in Peru. The study required a secure information system to manage data from a target goal of 16,000 subjects who needed to be followed for at least one year. With previous experience in the development and deployment of web-based medical record systems for TB treatment in Peru, we chose to use the OpenMRS open source electronic medical record system platform to develop the study information system. Supported by a core technical and management team and a large and growing worldwide community, OpenMRS is now being used in more than 40 developing countries. We adapted the OpenMRS platform to better support foreign languages. We added a new module to support double data entry, linkage to an existing laboratory information system, automatic upload of GPS data from handheld devices, and better security and auditing of data changes. We added new reports for study managers, and developed data extraction tools for research staff and statisticians. Further adaptation to handle direct entry of laboratory data occurred after the study was launched. Results Data collection in the OpenMRS system began in September 2009. By August 2011 a total of 9,256 participants had been enrolled, 102,274 forms and 13,829 laboratory results had been entered, and there were 208 users. The system is now entirely supported by the Peruvian study staff and programmers. Conclusions The information system served the study objectives well despite requiring some significant adaptations mid-stream. OpenMRS has more tools and capabilities than it did in 2008, and requires less adaptations for future projects. OpenMRS can be an effective research data system in resource poor environments, especially for organizations using or considering it for clinical care as well as research. PMID:23131180

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

PubMed Central

Kavitha, Muthu Subash; Ganesh Kumar, Pugalendhi; Park, Soon-Yong; Huh, Kyung-Hoe; Heo, Min-Suk; Kurita, Takio; Asano, Akira; An, Seo-Yong

2016-01-01

Objectives: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. Methods: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. Results: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942–0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. Conclusions: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage. PMID:27186991

ADC Quantification of the Vertebral Bone Marrow Water Component: Removing the Confounding Effect of Residual Fat.

PubMed

Dieckmeyer, Michael; Ruschke, Stefan; Eggers, Holger; Kooijman, Hendrik; Rummeny, Ernst J; Kirschke, Jan S; Baum, Thomas; Karampinos, Dimitrios C

2017-10-01

To remove the confounding effect of unsuppressed fat on the imaging-based apparent diffusion coefficient (ADC) of the vertebral bone marrow water component when using spectrally selective fat suppression and to compare and validate the proposed quantification strategy against diffusion-weighted magnetic resonance spectroscopy (DW-MRS). Twelve subjects underwent diffusion-weighted imaging (DWI) and DW-MRS of the vertebral bone marrow. A theoretical model was developed to take into account and correct the effects of residual fat on ADC, incorporating additional measurements for proton density fat fraction (PDFF) and water T 2 (T 2w ). Uncorrected and corrected DWI-based ADC was compared with DW-MRS-based ADC using the Bland-Altman method. There was a systematic bias equal to 0.118 ± 0.116 × 10 -3 mm 2 /s between DWI and DW-MRS when no correction was performed. Taking into account measured PDFF and constant T 2w reduced the bias to 0.006 ± 0.128 × 10 -3 mm 2 /s. Using the proposed approach with both individually measured PDFF and T 2w reduced both the bias and the limits of agreement between DWI and DW-MRS (0.018 ± 0.065 × 10 -3 mm 2 /s). By taking into account the presence of residual fat in a modified signal model that incorporates additional individual measurements of PDFF and T 2w , good agreement of imaging-based ADC with MRS-based ADC can be achieved in vertebral bone marrow. Magn Reson Med 78:1432-1441, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

A new algorithm for reducing the workload of experts in performing systematic reviews.

PubMed

Matwin, Stan; Kouznetsov, Alexandre; Inkpen, Diana; Frunza, Oana; O'Blenis, Peter

2010-01-01

To determine whether a factorized version of the complement naïve Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment. The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Weight engineering (WE) techniques were added to reduce underestimation for Medical Subject Headings (MeSH)-based and Publication Type (PubType)-based features. Cross-validation experiments were performed to evaluate the classifier's parameters and performance. Work saved over sampling (WSS) at no less than a 95% recall was used as the main measure of performance. The minimum workload reduction for a systematic review for one topic, achieved with a FCNB/WE classifier, was 8.5%; the maximum was 62.2% and the average over the 15 topics was 33.5%. This is 15.0% higher than the average workload reduction obtained using a voting perceptron-based automated citation classification system. The FCNB/WE classifier is simple, easy to implement, and produces significantly better results in reducing the workload than previously achieved. The results support it being a useful algorithm for machine-learning-based automation of systematic reviews of drug class efficacy for disease treatment.

Automated road network extraction from high spatial resolution multi-spectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a road network. The extracted road network is evaluated against a reference dataset using a line segment matching algorithm. The entire process is unsupervised and fully automated. Based on extensive experimentation on a variety of remotely-sensed multi-spectral images, the proposed methodology achieves a moderate success in automating road network extraction from high spatial resolution multi-spectral imagery.

LANDSAT demonstration/application and GIS integration in south central Alaska

NASA Technical Reports Server (NTRS)

Burns, A. W.; Derrenbacher, W.

1981-01-01

Automated geographic information systems were developed for two sites in Southcentral Alaska to serve as tests for both the process of integrating classified LANDSAT data into a comprehensive environmental data base and the process of using automated information in land capability/suitability analysis and environmental planning. The Big Lake test site, located approximately 20 miles north of the City of Anchorage, comprises an area of approximately 150 square miles. The Anchorage Hillside test site, lying approximately 5 miles southeast of the central part of the city, extends over an area of some 25 square miles. Map construction and content is described.

Automated classification of self-grooming in mice using open-source software.

PubMed

van den Boom, Bastijn J G; Pavlidi, Pavlina; Wolf, Casper J H; Mooij, Adriana H; Willuhn, Ingo

2017-09-01

Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. We trained a freely available, automated classifier, Janelia Automatic Animal Behavior Annotator (JAABA), to quantify self-grooming duration and number of bouts based on video recordings of SAPAP3 knockout mice (a mouse line that self-grooms excessively) and wild-type animals. We compared the JAABA classifier with human expert observers to test its ability to measure self-grooming in three scenarios: mice in an open field, mice on an elevated plus-maze, and tethered mice in an open field. In each scenario, the classifier identified both grooming and non-grooming with great accuracy and correlated highly with results obtained by human observers. Consistently, the JAABA classifier confirmed previous reports of excessive grooming in SAPAP3 knockout mice. Thus far, manual analysis was regarded as the only valid quantification method for self-grooming. We demonstrate that the JAABA classifier is a valid and reliable scoring tool, more cost-efficient than manual scoring, easy to use, requires minimal effort, provides high throughput, and prevents inter-rater variability. We introduce the JAABA classifier as an efficient analysis tool for the assessment of rodent self-grooming with expert quality. In our "how-to" instructions, we provide all information necessary to implement behavioral classification with JAABA. Copyright © 2017 Elsevier B.V. All rights reserved.

An Automated Method for Navigation Assessment for Earth Survey Sensors Using Island Targets

NASA Technical Reports Server (NTRS)

Patt, F. S.; Woodward, R. H.; Gregg, W. W.

1997-01-01

An automated method has been developed for performing navigation assessment on satellite-based Earth sensor data. The method utilizes islands as targets which can be readily located in the sensor data and identified with reference locations. The essential elements are an algorithm for classifying the sensor data according to source, a reference catalogue of island locations, and a robust pattern-matching algorithm for island identification. The algorithms were developed and tested for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), an ocean colour sensor. This method will allow navigation error statistics to be automatically generated for large numbers of points, supporting analysis over large spatial and temporal ranges.

Automated navigation assessment for earth survey sensors using island targets

NASA Technical Reports Server (NTRS)

Patt, Frederick S.; Woodward, Robert H.; Gregg, Watson W.

1997-01-01

An automated method has been developed for performing navigation assessment on satellite-based Earth sensor data. The method utilizes islands as targets which can be readily located in the sensor data and identified with reference locations. The essential elements are an algorithm for classifying the sensor data according to source, a reference catalog of island locations, and a robust pattern-matching algorithm for island identification. The algorithms were developed and tested for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), an ocean color sensor. This method will allow navigation error statistics to be automatically generated for large numbers of points, supporting analysis over large spatial and temporal ranges.

Automated classification of optical coherence tomography images of human atrial tissue

NASA Astrophysics Data System (ADS)

Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

2016-10-01

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

32 CFR 806b.35 - Balancing protection.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., Computer Security, 5 for procedures on safeguarding personal information in automated records. 5 http://www... automated system with a log-on protocol. Others may require more sophisticated security protection based on the sensitivity of the information. Classified computer systems or those with established audit and...

32 CFR 806b.35 - Balancing protection.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., Computer Security, 5 for procedures on safeguarding personal information in automated records. 5 http://www... automated system with a log-on protocol. Others may require more sophisticated security protection based on the sensitivity of the information. Classified computer systems or those with established audit and...

32 CFR 806b.35 - Balancing protection.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., Computer Security, 5 for procedures on safeguarding personal information in automated records. 5 http://www... automated system with a log-on protocol. Others may require more sophisticated security protection based on the sensitivity of the information. Classified computer systems or those with established audit and...

32 CFR 806b.35 - Balancing protection.

Code of Federal Regulations, 2011 CFR

2011-07-01

..., Computer Security, 5 for procedures on safeguarding personal information in automated records. 5 http://www... automated system with a log-on protocol. Others may require more sophisticated security protection based on the sensitivity of the information. Classified computer systems or those with established audit and...

32 CFR 806b.35 - Balancing protection.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., Computer Security, 5 for procedures on safeguarding personal information in automated records. 5 http://www... automated system with a log-on protocol. Others may require more sophisticated security protection based on the sensitivity of the information. Classified computer systems or those with established audit and...

A Comparison of Rule-Based, K-Nearest Neighbor, and Neural Net Classifiers for Automated

Treesearch

Tai-Hoon Cho; Richard W. Conners; Philip A. Araman

1991-01-01

Over the last few years the authors have been involved in research aimed at developing a machine vision system for locating and identifying surface defects on materials. The particular problem being studied involves locating surface defects on hardwood lumber in a species independent manner. Obviously, the accurate location and identification of defects is of paramount...

Mobile Phone-Based Questionnaire for Assessing 3 Months Modified Rankin Score After Acute Stroke: A Pilot Study.

PubMed

Cooray, Charith; Matusevicius, Marius; Wahlgren, Nils; Ahmed, Niaz

2015-10-01

In many countries, a majority of stroke patients are not assessed for long-term functional outcome owing to limited resources and time. We investigated whether automatic assessment of the modified Rankin Scale (mRS) based on a mobile phone questionnaire may serve as an alternative to mRS assessments at clinical visits after stroke. We enrolled 62 acute stroke patients admitted to our stroke unit during March to May 2014. Forty-eight patients completed the study. During the stay, patients and/or caregivers were equipped with a mobile phone application in their personal mobile phones. The mobile phone application contained a set of 20 questions, based on the Rankin Focused Assessment, which we previously tested in a pilot study. Three months after inclusion, the mobile phone application automatically prompted the study participants to answer the mRS questionnaire in the mobile phones. Each question or a group of questions in the questionnaire corresponded to a certain mRS score. Using a predefined protocol, the highest mRS score question where the study participant had answered yes was deemed the final mobile mRS score. A few days later, a study personnel performed a clinical visit mRS assessment. The 2 assessments were compared using quadratic weighing κ-statistics. Mean age was 67 years (38% females), and median baseline National Institutes of Health Stroke Scale (NIHSS) score was 5 (interquartile range 2-10.5, range 0-23). Median and mean clinical visit mRS at 3 months was 2 and 2.3, respectively. We found a 62.5% agreement between clinical visit and mobile mRS assessment, weighted kappa 0.89 (95% confidence interval 0.82-0.96), and unweighted kappa 0.53 (95% confidence interval 0.36-0.70). Dichotomized mRS outcome separating functionally independent (mRS score 0-2) from dependent (mRS score 3-5) showed 83% agreement and unweighted kappa of 0.66 (95% confidence interval 0.45-0.87). Mobile phone-based automatic assessments of mRS performed well in comparison with clinical visit mRS and could be used as an alternative in stroke follow-up. © 2015 American Heart Association, Inc.

A tool for developing an automatic insect identification system based on wing outlines

PubMed Central

Yang, He-Ping; Ma, Chun-Sen; Wen, Hui; Zhan, Qing-Bin; Wang, Xin-Li

2015-01-01

For some insect groups, wing outline is an important character for species identification. We have constructed a program as the integral part of an automated system to identify insects based on wing outlines (DAIIS). This program includes two main functions: (1) outline digitization and Elliptic Fourier transformation and (2) classifier model training by pattern recognition of support vector machines and model validation. To demonstrate the utility of this program, a sample of 120 owlflies (Neuroptera: Ascalaphidae) was split into training and validation sets. After training, the sample was sorted into seven species using this tool. In five repeated experiments, the mean accuracy for identification of each species ranged from 90% to 98%. The accuracy increased to 99% when the samples were first divided into two groups based on features of their compound eyes. DAIIS can therefore be a useful tool for developing a system of automated insect identification. PMID:26251292

Human factors for capacity building: lessons learned from the OpenMRS implementers network.

PubMed

Seebregts, C J; Mamlin, B W; Biondich, P G; Fraser, H S F; Wolfe, B A; Jazayeri, D; Miranda, J; Blaya, J; Sinha, C; Bailey, C T; Kanter, A S

2010-01-01

The overall objective of this project was to investigate ways to strengthen the OpenMRS community by (i) developing capacity and implementing a network focusing specifically on the needs of OpenMRS implementers, (ii) strengthening community-driven aspects of OpenMRS and providing a dedicated forum for implementation-specific issues, and; (iii) providing regional support for OpenMRS implementations as well as mentorship and training. The methods used included (i) face-to-face networking using meetings and workshops; (ii) online collaboration tools, peer support and mentorship programmes; (iii) capacity and community development programmes, and; (iv) community outreach programmes. The community-driven approach, combined with a few simple interventions, has been a key factor in the growth and success of the OpenMRS Implementers Network. It has contributed to implementations in at least twenty-three different countries using basic online tools; and provided mentorship and peer support through an annual meeting, workshops and an internship program. The OpenMRS Implementers Network has formed collaborations with several other open source networks and is evolving regional OpenMRS Centres of Excellence to provide localized support for OpenMRS development and implementation. These initiatives are increasing the range of functionality and sustainability of open source software in the health domain, resulting in improved adoption and enterprise-readiness. Social organization and capacity development activities are important in growing a successful community-driven open source software model.

APEX_SCOPE: A graphical user interface for visualization of multi-modal data in inter-disciplinary studies.

PubMed

Kanbar, Lara J; Shalish, Wissam; Precup, Doina; Brown, Karen; Sant'Anna, Guilherme M; Kearney, Robert E

2017-07-01

In multi-disciplinary studies, different forms of data are often collected for analysis. For example, APEX, a study on the automated prediction of extubation readiness in extremely preterm infants, collects clinical parameters and cardiorespiratory signals. A variety of cardiorespiratory metrics are computed from these signals and used to assign a cardiorespiratory pattern at each time. In such a situation, exploratory analysis requires a visualization tool capable of displaying these different types of acquired and computed signals in an integrated environment. Thus, we developed APEX_SCOPE, a graphical tool for the visualization of multi-modal data comprising cardiorespiratory signals, automated cardiorespiratory metrics, automated respiratory patterns, manually classified respiratory patterns, and manual annotations by clinicians during data acquisition. This MATLAB-based application provides a means for collaborators to view combinations of signals to promote discussion, generate hypotheses and develop features.

Objective automated quantification of fluorescence signal in histological sections of rat lens.

PubMed

Talebizadeh, Nooshin; Hagström, Nanna Zhou; Yu, Zhaohua; Kronschläger, Martin; Söderberg, Per; Wählby, Carolina

2017-08-01

Visual quantification and classification of fluorescent signals is the gold standard in microscopy. The purpose of this study was to develop an automated method to delineate cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section. A region of interest representing the lens epithelium was manually demarcated in each input image. Thereafter, individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding with an algorithm developed in Matlab™. Fluorescence signal was quantified within nuclei, cytoplasms and juxtaposed backgrounds. The classification of cells as labelled or not labelled was based on comparison of the fluorescence signal within cells with local background. The classification rule was thereafter optimized as compared with visual classification of a limited dataset. The performance of the automated classification was evaluated by asking 11 independent blinded observers to classify all cells (n = 395) in one lens image. Time consumed by the automatic algorithm and visual classification of cells was recorded. On an average, 77% of the cells were correctly classified as compared with the majority vote of the visual observers. The average agreement among visual observers was 83%. However, variation among visual observers was high, and agreement between two visual observers was as low as 71% in the worst case. Automated classification was on average 10 times faster than visual scoring. The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal with an accuracy comparable with the variability among visual observers. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.

PubMed

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-10-01

To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%. A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

PubMed Central

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-01-01

Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors’ classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors’ automatic classification and manual segmentation were 91.6% ± 2.0%. Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. PMID:23039675

A comparison of rule-based and machine learning approaches for classifying patient portal messages.

PubMed

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

2017-09-01

Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g., Temporal_Concept, which maps to 'morning', 'evening' and Idea_or_Concept which maps to 'appointment' and 'refill'). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g., Telephone mapping to 'phone', 'insurance'). For social and informational communications, the most predictive variables were words (e.g., social: 'thanks', 'much', informational: 'question', 'mean'). This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types, although NLP variables were most predictive for medical communication types. As adoption of patient portals increases, automated techniques could assist in understanding and managing growing volumes of messages. Further work is needed to improve classification performance to potentially support message triage and answering. Copyright © 2017 Elsevier B.V. All rights reserved.

What do they do? Interactions between village doctors and medical representatives in Chakaria, Bangladesh.

PubMed

Rahman, M Hafizur; Agarwal, Smisha; Tuddenham, Susan; Peto, Heather; Iqbal, Mohammad; Bhuiya, Abbas; Peters, David H

2015-07-01

Informally trained village doctors supply the majority of healthcare services to the rural poor in many developing countries. This study describes the demographic and socio-economic differences between medical representatives (MRs) and village doctors in rural Bangladesh, and explores the nature of their interactions. This study was conducted in Chakaria, a rural sub-district of Bangladesh. Focus group discussions and in-depth interviews were conducted, along with a quantitative survey to understand practice perceptions. Data analysis was performed using grounded theory and bivariate statistical tests. We surveyed 43 MRs and 83 village doctors through 22 focus group discussions and 33 in-depth interviews. MRs have a higher average per capita monthly expenditure compared to village doctors. MRs are better educated with 98% having bachelor's degrees whereas 84% of village doctors have twelfth grade education or less (p<0.001). MRs are the principal information source about new medications for the village doctors. Furthermore, incentives offered by MRs and credit availability influence the prescription practices of village doctors. MRs being the key player in providing information about drugs to village doctors might influence their prescription practices. Improvements in the quality of healthcare delivered to the rural poor in informal provider-based health markets require stricter regulations and educational initiatives for providers and MRs. © The Author 2014. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Development of the network architecture of the Canadian MSAT system

NASA Technical Reports Server (NTRS)

Davies, N. George; Shoamanesh, Alireza; Leung, Victor C. M.

1988-01-01

A description is given of the present concept for the Canadian Mobile Satellite (MSAT) System and the development of the network architecture which will accommodate the planned family of three categories of service: a mobile radio service (MRS), a mobile telephone service (MTS), and a mobile data service (MDS). The MSAT satellite will have cross-strapped L-band and Ku-band transponders to provide communications services between L-band mobile terminals and fixed base stations supporting dispatcher-type MRS, gateway stations supporting MTS interconnections to the public telephone network, data hub stations supporting the MDS, and the network control center. The currently perceived centralized architecture with demand assignment multiple access for the circuit switched MRS, MTS and permanently assigned channels for the packet switched MDS is discussed.

Development of the network architecture of the Canadian MSAT system

NASA Astrophysics Data System (ADS)

Davies, N. George; Shoamanesh, Alireza; Leung, Victor C. M.

1988-05-01

A description is given of the present concept for the Canadian Mobile Satellite (MSAT) System and the development of the network architecture which will accommodate the planned family of three categories of service: a mobile radio service (MRS), a mobile telephone service (MTS), and a mobile data service (MDS). The MSAT satellite will have cross-strapped L-band and Ku-band transponders to provide communications services between L-band mobile terminals and fixed base stations supporting dispatcher-type MRS, gateway stations supporting MTS interconnections to the public telephone network, data hub stations supporting the MDS, and the network control center. The currently perceived centralized architecture with demand assignment multiple access for the circuit switched MRS, MTS and permanently assigned channels for the packet switched MDS is discussed.

Data mining framework for identification of myocardial infarction stages in ultrasound: A hybrid feature extraction paradigm (PART 2).

PubMed

Sudarshan, Vidya K; Acharya, U Rajendra; Ng, E Y K; Tan, Ru San; Chou, Siaw Meng; Ghista, Dhanjoo N

2016-04-01

Early expansion of infarcted zone after Acute Myocardial Infarction (AMI) has serious short and long-term consequences and contributes to increased mortality. Thus, identification of moderate and severe phases of AMI before leading to other catastrophic post-MI medical condition is most important for aggressive treatment and management. Advanced image processing techniques together with robust classifier using two-dimensional (2D) echocardiograms may aid for automated classification of the extent of infarcted myocardium. Therefore, this paper proposes novel algorithms namely Curvelet Transform (CT) and Local Configuration Pattern (LCP) for an automated detection of normal, moderately infarcted and severely infarcted myocardium using 2D echocardiograms. The methodology extracts the LCP features from CT coefficients of echocardiograms. The obtained features are subjected to Marginal Fisher Analysis (MFA) dimensionality reduction technique followed by fuzzy entropy based ranking method. Different classifiers are used to differentiate ranked features into three classes normal, moderate and severely infarcted based on the extent of damage to myocardium. The developed algorithm has achieved an accuracy of 98.99%, sensitivity of 98.48% and specificity of 100% for Support Vector Machine (SVM) classifier using only six features. Furthermore, we have developed an integrated index called Myocardial Infarction Risk Index (MIRI) to detect the normal, moderately and severely infarcted myocardium using a single number. The proposed system may aid the clinicians in faster identification and quantification of the extent of infarcted myocardium using 2D echocardiogram. This system may also aid in identifying the person at risk of developing heart failure based on the extent of infarcted myocardium. Copyright © 2016 Elsevier Ltd. All rights reserved.

Liquid-Based Medium Used to Prepare Cytological Breast Nipple Fluid Improves the Quality of Cellular Samples Automatic Collection

PubMed Central

Zonta, Marco Antonio; Velame, Fernanda; Gema, Samara; Filassi, Jose Roberto; Longatto-Filho, Adhemar

2014-01-01

Background Breast cancer is the second cause of death in women worldwide. The spontaneous breast nipple discharge may contain cells that can be analyzed for malignancy. Halo® Mamo Cyto Test (HMCT) was recently developed as an automated system indicated to aspirate cells from the breast ducts. The objective of this study was to standardize the methodology of sampling and sample preparation of nipple discharge obtained by the automated method Halo breast test and perform cytological evaluation in samples preserved in liquid medium (SurePath™). Methods We analyzed 564 nipple fluid samples, from women between 20 and 85 years old, without history of breast disease and neoplasia, no pregnancy, and without gynecologic medical history, collected by HMCT method and preserved in two different vials with solutions for transport. Results From 306 nipple fluid samples from method 1, 199 (65%) were classified as unsatisfactory (class 0), 104 (34%) samples were classified as benign findings (class II), and three (1%) were classified as undetermined to neoplastic cells (class III). From 258 samples analyzed in method 2, 127 (49%) were classified as class 0, 124 (48%) were classified as class II, and seven (2%) were classified as class III. Conclusion Our study suggests an improvement in the quality and quantity of cellular samples when the association of the two methodologies is performed, Halo breast test and the method in liquid medium. PMID:29147397

Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment.

PubMed

Nanni, Loris; Lumini, Alessandra; Zaffonato, Nicolò

2018-05-15

Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very active in the challenge of designing automated approaches to achieve an early and certain diagnosis. Recently an international competition among AD predictors has been organized: "A Machine learning neuroimaging challenge for automated diagnosis of Mild Cognitive Impairment" (MLNeCh). This competition is based on pre-processed sets of T1-weighted Magnetic Resonance Images (MRI) to be classified in four categories: stable AD, individuals with MCI who converted to AD, individuals with MCI who did not convert to AD and healthy controls. In this work, we propose a method to perform early diagnosis of AD, which is evaluated on MLNeCh dataset. Since the automatic classification of AD is based on the use of feature vectors of high dimensionality, different techniques of feature selection/reduction are compared in order to avoid the curse-of-dimensionality problem, then the classification method is obtained as the combination of Support Vector Machines trained using different clusters of data extracted from the whole training set. The multi-classifier approach proposed in this work outperforms all the stand-alone method tested in our experiments. The final ensemble is based on a set of classifiers, each trained on a different cluster of the training data. The proposed ensemble has the great advantage of performing well using a very reduced version of the data (the reduction factor is more than 90%). The MATLAB code for the ensemble of classifiers will be publicly available 1 to other researchers for future comparisons. Copyright © 2017 Elsevier B.V. All rights reserved.

Value of Quantitative Collateral Scoring on CT Angiography in Patients with Acute Ischemic Stroke.

PubMed

Boers, A M M; Sales Barros, R; Jansen, I G H; Berkhemer, O A; Beenen, L F M; Menon, B K; Dippel, D W J; van der Lugt, A; van Zwam, W H; Roos, Y B W E M; van Oostenbrugge, R J; Slump, C H; Majoie, C B L M; Marquering, H A

2018-06-01

Many studies have emphasized the relevance of collateral flow in patients presenting with acute ischemic stroke. Our aim was to evaluate the relationship of the quantitative collateral score on baseline CTA with the outcome of patients with acute ischemic stroke and test whether the timing of the CTA acquisition influences this relationship. From the Multicenter Randomized Clinical Trial of Endovascular Treatment of Acute Ischemic Stroke in the Netherlands (MR CLEAN) data base, all baseline thin-slice CTA images of patients with acute ischemic stroke with intracranial large-vessel occlusion were retrospectively collected. The quantitative collateral score was calculated as the ratio of the vascular appearance of both hemispheres and was compared with the visual collateral score. Primary outcomes were 90-day mRS score and follow-up infarct volume. The relation with outcome and the association with treatment effect were estimated. The influence of the CTA acquisition phase on the relation of collateral scores with outcome was determined. A total of 442 patients were included. The quantitative collateral score strongly correlated with the visual collateral score (ρ = 0.75) and was an independent predictor of mRS (adjusted odds ratio = 0.81; 95% CI, .77-.86) and follow-up infarct volume (exponent β = 0.88; P < .001) per 10% increase. The quantitative collateral score showed areas under the curve of 0.71 and 0.69 for predicting functional independence (mRS 0-2) and follow-up infarct volume of >90 mL, respectively. We found significant interaction of the quantitative collateral score with the endovascular therapy effect in unadjusted analysis on the full ordinal mRS scale ( P = .048) and on functional independence ( P = .049). Modification of the quantitative collateral score by acquisition phase on outcome was significant (mRS: P = .004; follow-up infarct volume: P < .001) in adjusted analysis. Automated quantitative collateral scoring in patients with acute ischemic stroke is a reliable and user-independent measure of the collateral capacity on baseline CTA and has the potential to augment the triage of patients with acute stroke for endovascular therapy. © 2018 by American Journal of Neuroradiology.

Automated Classification of Pathology Reports.

PubMed

Oleynik, Michel; Finger, Marcelo; Patrão, Diogo F C

2015-01-01

This work develops an automated classifier of pathology reports which infers the topography and the morphology classes of a tumor using codes from the International Classification of Diseases for Oncology (ICD-O). Data from 94,980 patients of the A.C. Camargo Cancer Center was used for training and validation of Naive Bayes classifiers, evaluated by the F1-score. Measures greater than 74% in the topographic group and 61% in the morphologic group are reported. Our work provides a successful baseline for future research for the classification of medical documents written in Portuguese and in other domains.

Automated analysis of clonal cancer cells by intravital imaging

PubMed Central

Coffey, Sarah Earley; Giedt, Randy J; Weissleder, Ralph

2013-01-01

Longitudinal analyses of single cell lineages over prolonged periods have been challenging particularly in processes characterized by high cell turn-over such as inflammation, proliferation, or cancer. RGB marking has emerged as an elegant approach for enabling such investigations. However, methods for automated image analysis continue to be lacking. Here, to address this, we created a number of different multicolored poly- and monoclonal cancer cell lines for in vitro and in vivo use. To classify these cells in large scale data sets, we subsequently developed and tested an automated algorithm based on hue selection. Our results showed that this method allows accurate analyses at a fraction of the computational time required by more complex color classification methods. Moreover, the methodology should be broadly applicable to both in vitro and in vivo analyses. PMID:24349895

An Assessment of Smallsat Technology to Future Exploration Missions

NASA Technical Reports Server (NTRS)

Chan, Steve

1997-01-01

This reports the results of a general study for NASA Lewis in relation to the use of small satellites for a Mars Relay Satellite (MRS) that supports communications between Mars and Earth: commands to, and telemetry from, Mars Landers and Rover. The scope of the study encompasses a survey of small satellites, those that are lower than 800 kg in mass, by NASA, DoD, and commercial companies. Additionally, surveys in advanced technologies in the area of composite materials, propulsion subsystems, battery subsystems, communications components and subsystems, and ground operations are also provided, A summary of NASA Mars Programs and their status as relevant to MRS is also included. Attempts to draw detailed cost conclusion is generally not possible due to its proprietary nature. In any event, cost is driven by market demands rather than new technologies. A preliminary comparison with the cost estimate of the S-Tel/OSC report did suggest the possibility of cost savings for the MRS by the use of production busses. On the other hand, cost savings in normalized terms from the use of automated ground systems were obtained with some degree of details.

Artificial intelligence techniques applied to the development of a decision–support system for diagnosing celiac disease

PubMed Central

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2013-01-01

Background Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. Objective To develop a clinical decision–support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. Methods A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. Results The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision–support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k = 0.68 (p < 0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician’s diagnostic impression and the gold standard k = 0. 64 (p < 0.0001). There was moderate agreement between the physician’s diagnostic impression and CDSS k = 0.46 (p = 0.0008). Conclusions The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. PMID:21917512

Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease.

PubMed

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2011-11-01

Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. To develop a clinical decision-support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision-support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k=0.68 (p<0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician's diagnostic impression and the gold standard k=0. 64 (p<0.0001). There was moderate agreement between the physician's diagnostic impression and CDSS k=0.46 (p=0.0008). The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

PubMed Central

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Halim, Zaini Abdul; Ibrahim, Haidi; Ali, Syed Salim Syed

2012-01-01

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category. PMID:23202043

Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch.

PubMed

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Abdul Halim, Zaini; Ibrahim, Haidi; Syed Ali, Syed Salim

2012-10-22

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

PubMed

Ramasubbu, Rajamannar; Brown, Matthew R G; Cortese, Filmeno; Gaxiola, Ismael; Goodyear, Bradley; Greenshaw, Andrew J; Dursun, Serdar M; Greiner, Russell

2016-01-01

Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers. Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14-19), severe depression (HRSD 20-23), and very severe depression (HRSD ≥ 24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls. The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups. Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

A minimum spanning forest based classification method for dedicated breast CT images

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

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei, E-mail: bfei@emory.edu

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting modelmore » used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.« less

Automatic quality control in clinical (1)H MRSI of brain cancer.

PubMed

Pedrosa de Barros, Nuno; McKinley, Richard; Knecht, Urspeter; Wiest, Roland; Slotboom, Johannes

2016-05-01

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1)H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.

Telephony-based voice pathology assessment using automated speech analysis.

PubMed

Moran, Rosalyn J; Reilly, Richard B; de Chazal, Philip; Lacy, Peter D

2006-03-01

A system for remotely detecting vocal fold pathologies using telephone-quality speech is presented. The system uses a linear classifier, processing measurements of pitch perturbation, amplitude perturbation and harmonic-to-noise ratio derived from digitized speech recordings. Voice recordings from the Disordered Voice Database Model 4337 system were used to develop and validate the system. Results show that while a sustained phonation, recorded in a controlled environment, can be classified as normal or pathologic with accuracy of 89.1%, telephone-quality speech can be classified as normal or pathologic with an accuracy of 74.2%, using the same scheme. Amplitude perturbation features prove most robust for telephone-quality speech. The pathologic recordings were then subcategorized into four groups, comprising normal, neuromuscular pathologic, physical pathologic and mixed (neuromuscular with physical) pathologic. A separate classifier was developed for classifying the normal group from each pathologic subcategory. Results show that neuromuscular disorders could be detected remotely with an accuracy of 87%, physical abnormalities with an accuracy of 78% and mixed pathology voice with an accuracy of 61%. This study highlights the real possibility for remote detection and diagnosis of voice pathology.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

NASA Astrophysics Data System (ADS)

Yin, Yin; Fotin, Sergei V.; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter

2012-02-01

Manual delineation of the prostate is a challenging task for a clinician due to its complex and irregular shape. Furthermore, the need for precisely targeting the prostate boundary continues to grow. Planning for radiation therapy, MR-ultrasound fusion for image-guided biopsy, multi-parametric MRI tissue characterization, and context-based organ retrieval are examples where accurate prostate delineation can play a critical role in a successful patient outcome. Therefore, a robust automated full prostate segmentation system is desired. In this paper, we present an automated prostate segmentation system for 3D MR images. In this system, the prostate is segmented in two steps: the prostate displacement and size are first detected, and then the boundary is refined by a shape model. The detection approach is based on normalized gradient fields cross-correlation. This approach is fast, robust to intensity variation and provides good accuracy to initialize a prostate mean shape model. The refinement model is based on a graph-search based framework, which contains both shape and topology information during deformation. We generated the graph cost using trained classifiers and used coarse-to-fine search and region-specific classifier training. The proposed algorithm was developed using 261 training images and tested on another 290 cases. The segmentation performance using mean DSC ranging from 0.89 to 0.91 depending on the evaluation subset demonstrates state of the art performance. Running time for the system is about 20 to 40 seconds depending on image size and resolution.

Object-based classification of semi-arid wetlands

NASA Astrophysics Data System (ADS)

Halabisky, Meghan; Moskal, L. Monika; Hall, Sonia A.

2011-01-01

Wetlands are valuable ecosystems that benefit society. However, throughout history wetlands have been converted to other land uses. For this reason, timely wetland maps are necessary for developing strategies to protect wetland habitat. The goal of this research was to develop a time-efficient, automated, low-cost method to map wetlands in a semi-arid landscape that could be scaled up for use at a county or state level, and could lay the groundwork for expanding to forested areas. Therefore, it was critical that the research project contain two components: accurate automated feature extraction and the use of low-cost imagery. For that reason, we tested the effectiveness of geographic object-based image analysis (GEOBIA) to delineate and classify wetlands using freely available true color aerial photographs provided through the National Agriculture Inventory Program. The GEOBIA method produced an overall accuracy of 89% (khat = 0.81), despite the absence of infrared spectral data. GEOBIA provides the automation that can save significant resources when scaled up while still providing sufficient spatial resolution and accuracy to be useful to state and local resource managers and policymakers.

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features.

PubMed

Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

2017-08-01

Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

NASA Astrophysics Data System (ADS)

Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

2017-08-01

Objective. Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

The Proposal of the Model for Developing Dispatch System for Nationwide One-Day Integrative Planning

NASA Astrophysics Data System (ADS)

Kim, Hyun Soo; Choi, Hyung Rim; Park, Byung Kwon; Jung, Jae Un; Lee, Jin Wook

The problems of dispatch planning for container truck are classified as the pickup and delivery problems, which are highly complex issues that consider various constraints in the real world. However, in case of the current situation, it is developed by the control system so that it requires the automated planning system under the view of nationwide integrative planning. Therefore, the purpose of this study is to suggest model to develop the automated dispatch system through the constraint satisfaction problem and meta-heuristic technique-based algorithm. In the further study, the practical system is developed and evaluation is performed in aspect of various results. This study suggests model to undergo the study which promoted the complexity of the problems by considering the various constraints which were not considered in the early study. However, it is suggested that it is necessary to add the study which includes the real-time monitoring function for vehicles and cargos based on the information technology.

Knee X-ray image analysis method for automated detection of Osteoarthritis

PubMed Central

Shamir, Lior; Ling, Shari M.; Scott, William W.; Bos, Angelo; Orlov, Nikita; Macura, Tomasz; Eckley, D. Mark; Ferrucci, Luigi; Goldberg, Ilya G.

2008-01-01

We describe a method for automated detection of radiographic Osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades (normal, doubtful, minimal and moderate). Image analysis is performed by first identifying a set of image content descriptors and image transforms that are informative for the detection of OA in the X-rays, and assigning weights to these image features using Fisher scores. Then, a simple weighted nearest neighbor rule is used in order to predict the KL grade to which a given test X-ray sample belongs. The dataset used in the experiment contained 350 X-ray images classified manually by their KL grades. Experimental results show that moderate OA (KL grade 3) and minimal OA (KL grade 2) can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively. Doubtful OA (KL grade 1) was detected automatically with a much lower accuracy of 57%. The source code developed and used in this study is available for free download at www.openmicroscopy.org. PMID:19342330

Automated segmentation of dental CBCT image with prior-guided sequential random forests

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

Wang, Li; Gao, Yaozong; Shi, Feng

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimatemore » the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method for CBCT segmentation.« less

Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning

NASA Astrophysics Data System (ADS)

Brown, James M.; Campbell, J. Peter; Beers, Andrew; Chang, Ken; Donohue, Kyra; Ostmo, Susan; Chan, R. V. Paul; Dy, Jennifer; Erdogmus, Deniz; Ioannidis, Stratis; Chiang, Michael F.; Kalpathy-Cramer, Jayashree

2018-03-01

Retinopathy of prematurity (ROP) is a disease that affects premature infants, where abnormal growth of the retinal blood vessels can lead to blindness unless treated accordingly. Infants considered at risk of severe ROP are monitored for symptoms of plus disease, characterized by arterial tortuosity and venous dilation at the posterior pole, with a standard photographic definition. Disagreement among ROP experts in diagnosing plus disease has driven the development of computer-based methods that classify images based on hand-crafted features extracted from the vasculature. However, most of these approaches are semi-automated, which are time-consuming and subject to variability. In contrast, deep learning is a fully automated approach that has shown great promise in a wide variety of domains, including medical genetics, informatics and imaging. Convolutional neural networks (CNNs) are deep networks which learn rich representations of disease features that are highly robust to variations in acquisition and image quality. In this study, we utilized a U-Net architecture to perform vessel segmentation and then a GoogLeNet to perform disease classification. The classifier was trained on 3,000 retinal images and validated on an independent test set of patients with different observed progressions and treatments. We show that our fully automated algorithm can be used to monitor the progression of plus disease over multiple patient visits with results that are consistent with the experts' consensus diagnosis. Future work will aim to further validate the method on larger cohorts of patients to assess its applicability within the clinic as a treatment monitoring tool.

Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species

PubMed Central

Soleymani, Ali; Pennekamp, Frank; Petchey, Owen L.; Weibel, Robert

2015-01-01

Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates). Previously, morphological attributes and simple movement metrics, such as speed, were used for classifying ciliate species. Here, we demonstrate that adding advanced movement features, in particular such based on discrete wavelet transform, to morphological features can improve classification. These results may have practical applications in automated monitoring of waste water facilities as well as environmental monitoring of aquatic systems. PMID:26680591

Between-Region Genetic Divergence Reflects the Mode and Tempo of Tumor Evolution

PubMed Central

Sun, Ruping; Hu, Zheng; Sottoriva, Andrea; Graham, Trevor A.; Harpak, Arbel; Ma, Zhicheng; Fischer, Jared M.; Shibata, Darryl; Curtis, Christina

2017-01-01

Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multi-region sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, revealing different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, accumulate intra-tumor heterogeneity, and ultimately how they may be more effectively treated. PMID:28581503

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

PubMed

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P; Zelikowsky, Moriel; Navonne, Santiago G; Perona, Pietro; Anderson, David J

2015-09-22

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

PubMed Central

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P.; Zelikowsky, Moriel; Navonne, Santiago G.; Perona, Pietro; Anderson, David J.

2015-01-01

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics. PMID:26354123

[Research Progress of Raman Spectroscopy on Dyestuff Identification of Ancient Relics and Artifacts].

PubMed

He, Qiu-ju; Wang, Li-qin

2016-02-01

As the birthplace of Silk Road, China has a long dyeing history. The valuable information about the production time, the source of dyeing material, dyeing process and preservation status were existed in organic dyestuff deriving from cultural relics and artifacts. However, because of the low contents, complex compositions and easily degraded of dyestuff, it is always a challenging task to identify the dyestuff in relics analyzing field. As a finger-print spectrum, Raman spectroscopy owns unique superiorities in dyestuff identification. Thus, the principle, characteristic, limitation, progress and development direction of micro-Raman spectroscopy (MRS/µ-Raman), near infrared reflection and Fourier transform Raman spectroscopy (NIR-FT-Raman), surface-enhanced Raman spectroscopy (SERS) and resonance raman spectroscopy (RRS) have been introduced in this paper. Furthermore, the features of Raman spectra of gardenia, curcumin and other natural dyestuffs were classified by MRS technology, and then the fluorescence phenomena of purpurin excitated with different wavelength laser was compared and analyzed. At last, gray green silver colloidal particles were made as the base, then the colorant of madder was identified combining with thin layer chromatography (TLC) separation technology and SERS, the result showed that the surface enhancement effect of silver colloidal particles could significantly reduce fluorescence background of the Raman spectra. It is pointed out that Raman spectroscopy is a rapid and convenient molecular structure qualitative methodology, which has broad application prospect in dyestuff analysis of cultural relics and artifacts. We propose that the combination of multi-Raman spectroscopy, separation technology and long distance transmission technology are the development trends of Raman spectroscopy.

Optimization of a Multi-Stage ATR System for Small Target Identification

NASA Technical Reports Server (NTRS)

Lin, Tsung-Han; Lu, Thomas; Braun, Henry; Edens, Western; Zhang, Yuhan; Chao, Tien- Hsin; Assad, Christopher; Huntsberger, Terrance

2010-01-01

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

Detection of cerebral NAD+ in humans at 7T.

PubMed

de Graaf, Robin A; De Feyter, Henk M; Brown, Peter B; Nixon, Terence W; Rothman, Douglas L; Behar, Kevin L

2017-09-01

To develop 1 H-based MR detection of nicotinamide adenine dinucleotide (NAD + ) in the human brain at 7T and validate the 1 H results with NAD + detection based on 31 P-MRS. 1 H-MR detection of NAD + was achieved with a one-dimensional double-spin-echo method on a slice parallel to the surface coil transceiver. Perturbation of the water resonance was avoided through the use of frequency-selective excitation. 31 P-MR detection of NAD + was performed with an unlocalized pulse-acquire sequence. Both 1 H- and 31 P-MRS allowed the detection of NAD + signals on every subject in 16 min. Spectral fitting provided an NAD + concentration of 107 ± 28 μM for 1 H-MRS and 367 ± 78 μM and 312 ± 65 μM for 31 P-MRS when uridine diphosphate glucose (UDPG) was excluded and included, respectively, as an overlapping signal. NAD + detection by 1 H-MRS is a simple method that comes at the price of reduced NMR visibility. NAD + detection by 31 P-MRS has near-complete NMR visibility, but it is complicated by spectral overlap with NADH and UDPG. Overall, the 1 H- and 31 P-MR methods both provide exciting opportunities to study NAD + metabolism on human brain in vivo. © 2016 International Society for Magnetic Resonance in Medicine. Magn Reson Med 78:828-835, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

False alarm reduction by the And-ing of multiple multivariate Gaussian classifiers

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2003-09-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. This paper describes a method for training several multivariate Gaussian classifiers such that their And-ing dramatically reduces false alarms while maintaining a high probability of classification. This training approach is referred to as the Focused- Training method. This work extends our 2001-2002 work where the Focused-Training method was used with three other types of classifiers: the Attractor-based K-Nearest Neighbor Neural Network (a type of radial-basis, probabilistic neural network), the Optimal Discrimination Filter Classifier (based linear discrimination theory), and the Quadratic Penalty Function Support Vector Machine (QPFSVM). Although our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to a wide range of pattern recognition and automatic target recognition (ATR) problems.

Inhibition of protein synthesis and malaria parasite development by drug targeting of methionyl-tRNA synthetases.

PubMed

Hussain, Tahir; Yogavel, Manickam; Sharma, Amit

2015-04-01

Aminoacyl-tRNA synthetases (aaRSs) are housekeeping enzymes that couple cognate tRNAs with amino acids to transmit genomic information for protein translation. The Plasmodium falciparum nuclear genome encodes two P. falciparum methionyl-tRNA synthetases (PfMRS), termed PfMRS(cyt) and PfMRS(api). Phylogenetic analyses revealed that the two proteins are of primitive origin and are related to heterokonts (PfMRS(cyt)) or proteobacteria/primitive bacteria (PfMRS(api)). We show that PfMRS(cyt) localizes in parasite cytoplasm, while PfMRS(api) localizes to apicoplasts in asexual stages of malaria parasites. Two known bacterial MRS inhibitors, REP3123 and REP8839, hampered Plasmodium growth very effectively in the early and late stages of parasite development. Small-molecule drug-like libraries were screened against modeled PfMRS structures, and several "hit" compounds showed significant effects on parasite growth. We then tested the effects of the hit compounds on protein translation by labeling nascent proteins with (35)S-labeled cysteine and methionine. Three of the tested compounds reduced protein synthesis and also blocked parasite growth progression from the ring stage to the trophozoite stage. Drug docking studies suggested distinct modes of binding for the three compounds, compared with the enzyme product methionyl adenylate. Therefore, this study provides new targets (PfMRSs) and hit compounds that can be explored for development as antimalarial drugs. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Neither short-term nor long-term administration of oral choline alters metabolite concentrations in human brain.

PubMed

Dechent, P; Pouwels, P J; Frahm, J

1999-08-01

This study reexamined conflicting proton magnetic resonance spectroscopy (MRS) reports of increased or unaffected choline-containing compounds (Cho) in human brain in response to a single dose of 50 mg/kg choline bitartrate. The present work was based on a well-established strategy for quantitative proton MRS (2.0 T, STEAM localization sequence, TR/TE/TM = 6000/20/10 ms, LCModel automated spectral evaluation) that allows the determination of cerebral metabolite concentrations rather than T1-weighted resonance intensity ratios. Moreover, the investigations were extended to a possible long-term effect of oral choline by monitoring the continuous ingestion of 2 x 16 g of lecithin per day for 4 weeks. Six young healthy volunteers participated in each study and metabolite concentrations were determined in standardized locations in gray matter, white matter, cerebellum, and thalamus. Neither for short-term nor for long-term administration of choline do the data reveal statistically significant deviations from the basal concentrations of Cho, total N-acetyl-containing compounds (neuronal markers), total creatine, and myo-inositol (glial marker) in any of the investigated brain regions. Previous reports of increased Cho are not confirmed.

Development and Validation of the Nursing Home Minimum Data Set 3.0 Mortality Risk Score (MRS3).

PubMed

Thomas, Kali S; Ogarek, Jessica A; Teno, Joan M; Gozalo, Pedro L; Mor, Vincent

2018-03-05

To develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments. We developed a training cohort of Medicare beneficiaries newly admitted to U.S. NHs during 2012 (N=1,426,815) and a testing cohort from 2013 (N=1,160,964). Data came from the MDS 3.0 assessments linked to the Medicare Beneficiary Summary File. Using the training dataset, we developed a composite MDS 3.0 Mortality Risk Score (MRS3) consisting of 17 clinical items and patients' age groups based on their relation to 30-day mortality. We assessed the calibration and discrimination of the MRS3 in predicting 30-day and 60-day mortality and compared its performance to the Charlson Comorbidity Index and the clinician's assessment of 6-month prognosis measured at admission. The 30-day and 60-day mortality rate for the testing population was 2.8% and 5.6%, respectively. Results from logistic regression models suggest that the MRS3 performed well in predicting death within 30 and 60 days (C-Statistics of 0.744 (95%CL = 0.741, 0.747) and 0.709 (95%CL=0.706, 0.711), respectively). The MRS3 was a superior predictor of mortality compared to the Charlson Comorbidity Index (C-statistics of 0.611 (95%CL=0.607, 0.615) and 0.608 (95%CL=0.605, 0.610)) and the clinicians' assessments of patients' 6-month prognoses (C-statistics of 0.543 (95%CL=0.542, 0.545) and 0.528 (95%CL=0.527, 0.529). The MRS3 is a good predictor of mortality and can be useful in guiding decision-making, informing plans of care, and adjusting for patients' risk of mortality.

Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study

NASA Astrophysics Data System (ADS)

Polan, Daniel F.; Brady, Samuel L.; Kaufman, Robert A.

2016-09-01

There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment. Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the TWS plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 n , (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21 patient image sections, were analyzed. The automated algorithm produced segmentation of seven material classes with a median DSC of 0.86  ±  0.03 for pediatric patient protocols, and 0.85  ±  0.04 for adult patient protocols. Additionally, 100 randomly selected patient examinations were segmented and analyzed, and a mean sensitivity of 0.91 (range: 0.82-0.98), specificity of 0.89 (range: 0.70-0.98), and accuracy of 0.90 (range: 0.76-0.98) were demonstrated. In this study, we demonstrate that this fully automated segmentation tool was able to produce fast and accurate segmentation of the neck and trunk of the body over a wide range of patient habitus and scan parameters.

Intra- and inter-examination repeatability of magnetic resonance spectroscopy, magnitude-based MRI, and complex-based MRI for estimation of hepatic proton density fat fraction in overweight and obese children and adults.

PubMed

Tyagi, Avishkar; Yeganeh, Omid; Levin, Yakir; Hooker, Jonathan C; Hamilton, Gavin C; Wolfson, Tanya; Gamst, Anthony; Zand, Amir K; Heba, Elhamy; Loomba, Rohit; Schwimmer, Jeffrey; Middleton, Michael S; Sirlin, Claude B

2015-10-01

Determine intra- and inter-examination repeatability of magnitude-based magnetic resonance imaging (MRI-M), complex-based magnetic resonance imaging (MRI-C), and magnetic resonance spectroscopy (MRS) at 3T for estimating hepatic proton density fat fraction (PDFF), and using MRS as a reference, confirm MRI-M and MRI-C accuracy. Twenty-nine overweight and obese pediatric (n = 20) and adult (n = 9) subjects (23 male, 6 female) underwent three same-day 3T MR examinations. In each examination MRI-M, MRI-C, and single-voxel MRS were acquired three times. For each MRI acquisition, hepatic PDFF was estimated at the MRS voxel location. Intra- and inter-examination repeatability were assessed by computing standard deviations (SDs) and intra-class correlation coefficients (ICCs). Aggregate SD was computed for each method as the square root of the average of first repeat variances. MRI-M and MRI-C PDFF estimation accuracy was assessed using linear regression with MRS as a reference. For MRI-M, MRI-C, and MRS acquisitions, respectively, mean intra-examination SDs were 0.25%, 0.42%, and 0.49%; mean intra-examination ICCs were 0.999, 0.997, and 0.995; mean inter-examination SDs were 0.42%, 0.45%, and 0.46%; and inter-examination ICCs were 0.995, 0.992, and 0.990. Aggregate SD for each method was <0.9%. Using MRS as a reference, regression slope, intercept, average bias, and R (2), respectively, for MRI-M were 0.99%, 1.73%, 1.61%, and 0.986, and for MRI-C were 0.96%, 0.43%, 0.40%, and 0.991. MRI-M, MRI-C, and MRS showed high intra- and inter-examination hepatic PDFF estimation repeatability in overweight and obese subjects. Longitudinal hepatic PDFF change >1.8% (twice the maximum aggregate SD) may represent real change rather than measurement imprecision. Further research is needed to assess whether examinations performed on different days or with different MR technologists affect repeatability of MRS voxel placement and MRS-based PDFF measurements.

Intra- and inter-examination repeatability of magnetic resonance spectroscopy, magnitude-based MRI, and complex-based MRI for estimation of hepatic proton density fat fraction in overweight and obese children and adults

PubMed Central

Tyagi, Avishkar; Yeganeh, Omid; Levin, Yakir; Hooker, Jonathan C.; Hamilton, Gavin C.; Wolfson, Tanya; Gamst, Anthony; Zand, Amir K.; Heba, Elhamy; Loomba, Rohit; Schwimmer, Jeffrey; Middleton, Michael S.; Sirlin, Claude B.

2016-01-01

Purpose Determine intra- and inter-examination repeatability of magnitude-based magnetic resonance imaging (MRI-M), complex-based magnetic resonance imaging (MRI-C), and magnetic resonance spectroscopy (MRS) at 3T for estimating hepatic proton density fat fraction (PDFF), and using MRS as a reference, confirm MRI-M and MRI-C accuracy. Methods Twenty-nine overweight and obese pediatric (n = 20) and adult (n = 9) subjects (23 male, 6 female) underwent three same-day 3T MR examinations. In each examination MRI-M, MRI-C, and single-voxel MRS were acquired three times. For each MRI acquisition, hepatic PDFF was estimated at the MRS voxel location. Intra- and inter-examination repeatability were assessed by computing standard deviations (SDs) and intra-class correlation coefficients (ICCs). Aggregate SD was computed for each method as the square root of the average of first repeat variances. MRI-M and MRI-C PDFF estimation accuracy was assessed using linear regression with MRS as a reference. Results For MRI-M, MRI-C, and MRS acquisitions, respectively, mean intra-examination SDs were 0.25%, 0.42%, and 0.49%; mean intra-examination ICCs were 0.999, 0.997, and 0.995; mean inter-examination SDs were 0.42%, 0.45%, and 0.46%; and inter-examination ICCs were 0.995, 0.992, and 0.990. Aggregate SD for each method was <0.9%. Using MRS as a reference, regression slope, intercept, average bias, and R2, respectively, for MRI-M were 0.99%, 1.73%, 1.61%, and 0.986, and for MRI-C were 0.96%, 0.43%, 0.40%, and 0.991. Conclusion MRI-M, MRI-C, and MRS showed high intra- and inter-examination hepatic PDFF estimation repeatability in overweight and obese subjects. Longitudinal hepatic PDFF change >1.8% (twice the maximum aggregate SD) may represent real change rather than measurement imprecision. Further research is needed to assess whether examinations performed on different days or with different MR technologists affect repeatability of MRS voxel placement and MRS-based PDFF measurements. PMID:26350282

Automated multi-lesion detection for referable diabetic retinopathy in indigenous health care.

PubMed

Pires, Ramon; Carvalho, Tiago; Spurling, Geoffrey; Goldenstein, Siome; Wainer, Jacques; Luckie, Alan; Jelinek, Herbert F; Rocha, Anderson

2015-01-01

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.

Automated Multi-Lesion Detection for Referable Diabetic Retinopathy in Indigenous Health Care

PubMed Central

Pires, Ramon; Carvalho, Tiago; Spurling, Geoffrey; Goldenstein, Siome; Wainer, Jacques; Luckie, Alan; Jelinek, Herbert F.; Rocha, Anderson

2015-01-01

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening. PMID:26035836

Toward automated assessment of health Web page quality using the DISCERN instrument.

PubMed

Allam, Ahmed; Schulz, Peter J; Krauthammer, Michael

2017-05-01

As the Internet becomes the number one destination for obtaining health-related information, there is an increasing need to identify health Web pages that convey an accurate and current view of medical knowledge. In response, the research community has created multicriteria instruments for reliably assessing online medical information quality. One such instrument is DISCERN, which measures health Web page quality by assessing an array of features. In order to scale up use of the instrument, there is interest in automating the quality evaluation process by building machine learning (ML)-based DISCERN Web page classifiers. The paper addresses 2 key issues that are essential before constructing automated DISCERN classifiers: (1) generation of a robust DISCERN training corpus useful for training classification algorithms, and (2) assessment of the usefulness of the current DISCERN scoring schema as a metric for evaluating the performance of these algorithms. Using DISCERN, 272 Web pages discussing treatment options in breast cancer, arthritis, and depression were evaluated and rated by trained coders. First, different consensus models were compared to obtain a robust aggregated rating among the coders, suitable for a DISCERN ML training corpus. Second, a new DISCERN scoring criterion was proposed (features-based score) as an ML performance metric that is more reflective of the score distribution across different DISCERN quality criteria. First, we found that a probabilistic consensus model applied to the DISCERN instrument was robust against noise (random ratings) and superior to other approaches for building a training corpus. Second, we found that the established DISCERN scoring schema (overall score) is ill-suited to measure ML performance for automated classifiers. Use of a probabilistic consensus model is advantageous for building a training corpus for the DISCERN instrument, and use of a features-based score is an appropriate ML metric for automated DISCERN classifiers. The code for the probabilistic consensus model is available at https://bitbucket.org/A_2/em_dawid/ . © 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

Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions.

PubMed

Shen, Simon; Syal, Karan; Tao, Nongjian; Wang, Shaopeng

2015-12-01

We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion.

Automated anatomical labeling of bronchial branches extracted from CT datasets based on machine learning and combination optimization and its application to bronchoscope guidance.

PubMed

Mori, Kensaku; Ota, Shunsuke; Deguchi, Daisuke; Kitasaka, Takayuki; Suenaga, Yasuhito; Iwano, Shingo; Hasegawa, Yosihnori; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi

2009-01-01

This paper presents a method for the automated anatomical labeling of bronchial branches extracted from 3D CT images based on machine learning and combination optimization. We also show applications of anatomical labeling on a bronchoscopy guidance system. This paper performs automated labeling by using machine learning and combination optimization. The actual procedure consists of four steps: (a) extraction of tree structures of the bronchus regions extracted from CT images, (b) construction of AdaBoost classifiers, (c) computation of candidate names for all branches by using the classifiers, (d) selection of best combination of anatomical names. We applied the proposed method to 90 cases of 3D CT datasets. The experimental results showed that the proposed method can assign correct anatomical names to 86.9% of the bronchial branches up to the sub-segmental lobe branches. Also, we overlaid the anatomical names of bronchial branches on real bronchoscopic views to guide real bronchoscopy.

(Un)Official Knowledge and Identity: An Emerging Bilingual's Journey into Hybridity

ERIC Educational Resources Information Center

López-Robertson, Julia; Schramm-Pate, Susan

2013-01-01

Gabriela Montserrat (pseudonym) is a Mexican-American child classified by her school district as an "emerging bilingual" and is the focus of this qualitative case study that took place at a public elementary school located in a suburban community in the southwestern US in Mrs Pérez's (pseudonym) second-grade classroom. The student's use…

Automated aural classification used for inter-species discrimination of cetaceans.

PubMed

Binder, Carolyn M; Hines, Paul C

2014-04-01

Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

Classification of yeast cells from image features to evaluate pathogen conditions

NASA Astrophysics Data System (ADS)

van der Putten, Peter; Bertens, Laura; Liu, Jinshuo; Hagen, Ferry; Boekhout, Teun; Verbeek, Fons J.

2007-01-01

Morphometrics from images, image analysis, may reveal differences between classes of objects present in the images. We have performed an image-features-based classification for the pathogenic yeast Cryptococcus neoformans. Building and analyzing image collections from the yeast under different environmental or genetic conditions may help to diagnose a new "unseen" situation. Diagnosis here means that retrieval of the relevant information from the image collection is at hand each time a new "sample" is presented. The basidiomycetous yeast Cryptococcus neoformans can cause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related to virulence. This paper reports on the approach towards developing classifiers for detecting potentially more or less virulent cells in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. The classifier can henceforth be used for automating screening and annotating existing image collections. In addition we will present our methods for creating samples, collecting images, image preprocessing, identifying "yeast cells" and creating feature extraction from the images. We compare various expertise based and fully automated methods of feature selection and benchmark a range of classification algorithms and illustrate successful application to this particular domain.

Automated Classification of ROSAT Sources Using Heterogeneous Multiwavelength Source Catalogs

NASA Technical Reports Server (NTRS)

McGlynn, Thomas; Suchkov, A. A.; Winter, E. L.; Hanisch, R. J.; White, R. L.; Ochsenbein, F.; Derriere, S.; Voges, W.; Corcoran, M. F.

2004-01-01

We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

A system for classifying disease comorbidity status from medical discharge summaries using automated hotspot and negated concept detection.

PubMed

Ambert, Kyle H; Cohen, Aaron M

2009-01-01

OBJECTIVE Free-text clinical reports serve as an important part of patient care management and clinical documentation of patient disease and treatment status. Free-text notes are commonplace in medical practice, but remain an under-used source of information for clinical and epidemiological research, as well as personalized medicine. The authors explore the challenges associated with automatically extracting information from clinical reports using their submission to the Integrating Informatics with Biology and the Bedside (i2b2) 2008 Natural Language Processing Obesity Challenge Task. DESIGN A text mining system for classifying patient comorbidity status, based on the information contained in clinical reports. The approach of the authors incorporates a variety of automated techniques, including hot-spot filtering, negated concept identification, zero-vector filtering, weighting by inverse class-frequency, and error-correcting of output codes with linear support vector machines. MEASUREMENTS Performance was evaluated in terms of the macroaveraged F1 measure. RESULTS The automated system performed well against manual expert rule-based systems, finishing fifth in the Challenge's intuitive task, and 13(th) in the textual task. CONCLUSIONS The system demonstrates that effective comorbidity status classification by an automated system is possible.

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

Stinnett, Jacob; Sullivan, Clair J.; Xiong, Hao

Low-resolution isotope identifiers are widely deployed for nuclear security purposes, but these detectors currently demonstrate problems in making correct identifications in many typical usage scenarios. While there are many hardware alternatives and improvements that can be made, performance on existing low resolution isotope identifiers should be able to be improved by developing new identification algorithms. We have developed a wavelet-based peak extraction algorithm and an implementation of a Bayesian classifier for automated peak-based identification. The peak extraction algorithm has been extended to compute uncertainties in the peak area calculations. To build empirical joint probability distributions of the peak areas andmore » uncertainties, a large set of spectra were simulated in MCNP6 and processed with the wavelet-based feature extraction algorithm. Kernel density estimation was then used to create a new component of the likelihood function in the Bayesian classifier. Furthermore, identification performance is demonstrated on a variety of real low-resolution spectra, including Category I quantities of special nuclear material.« less

A Visual Galaxy Classification Interface and its Classroom Application

NASA Astrophysics Data System (ADS)

Kautsch, Stefan J.; Phung, Chau; VanHilst, Michael; Castro, Victor H

2014-06-01

Galaxy morphology is an important topic in modern astronomy to understand questions concerning the evolution and formation of galaxies and their dark matter content. In order to engage students in exploring galaxy morphology, we developed a web-based, graphical interface that allows students to visually classify galaxy images according to various morphological types. The website is designed with HTML5, JavaScript, PHP, and a MySQL database. The classification interface provides hands-on research experience and training for students and interested clients, and allows them to contribute to studies of galaxy morphology. We present the first results of a pilot study and compare the visually classified types using our interface with that from automated classification routines.

In vivo classification of human skin burns using machine learning and quantitative features captured by optical coherence tomography

NASA Astrophysics Data System (ADS)

Singla, Neeru; Srivastava, Vishal; Singh Mehta, Dalip

2018-02-01

We report the first fully automated detection of human skin burn injuries in vivo, with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Our proposed automated procedure entails building a machine-learning-based classifier by extracting quantitative features from normal and burn tissue images recorded by OCT. In this study, 56 samples (28 normal, 28 burned) were imaged by OCT and eight features were extracted. A linear model classifier was trained using 34 samples and 22 samples were used to test the model. Sensitivity of 91.6% and specificity of 90% were obtained. Our results demonstrate the capability of a computer-aided technique for accurately and automatically identifying burn tissue resection margins during surgical treatment.

New MYC IHC Classifier Integrating Quantitative Architecture Parameters to Predict MYC Gene Translocation in Diffuse Large B-Cell Lymphoma

PubMed Central

Dong, Wei-Feng; Canil, Sarah; Lai, Raymond; Morel, Didier; Swanson, Paul E.; Izevbaye, Iyare

2018-01-01

A new automated MYC IHC classifier based on bivariate logistic regression is presented. The predictor relies on image analysis developed with the open-source ImageJ platform. From a histologic section immunostained for MYC protein, 2 dimensionless quantitative variables are extracted: (a) relative distance between nuclei positive for MYC IHC based on euclidean minimum spanning tree graph and (b) coefficient of variation of the MYC IHC stain intensity among MYC IHC-positive nuclei. Distance between positive nuclei is suggested to inversely correlate MYC gene rearrangement status, whereas coefficient of variation is suggested to inversely correlate physiological regulation of MYC protein expression. The bivariate classifier was compared with 2 other MYC IHC classifiers (based on percentage of MYC IHC positive nuclei), all tested on 113 lymphomas including mostly diffuse large B-cell lymphomas with known MYC fluorescent in situ hybridization (FISH) status. The bivariate classifier strongly outperformed the “percentage of MYC IHC-positive nuclei” methods to predict MYC+ FISH status with 100% sensitivity (95% confidence interval, 94-100) associated with 80% specificity. The test is rapidly performed and might at a minimum provide primary IHC screening for MYC gene rearrangement status in diffuse large B-cell lymphomas. Furthermore, as this bivariate classifier actually predicts “permanent overexpressed MYC protein status,” it might identify nontranslocation-related chromosomal anomalies missed by FISH. PMID:27093450

A volumetric pulmonary CT segmentation method with applications in emphysema assessment

NASA Astrophysics Data System (ADS)

Silva, José Silvestre; Silva, Augusto; Santos, Beatriz S.

2006-03-01

A segmentation method is a mandatory pre-processing step in many automated or semi-automated analysis tasks such as region identification and densitometric analysis, or even for 3D visualization purposes. In this work we present a fully automated volumetric pulmonary segmentation algorithm based on intensity discrimination and morphologic procedures. Our method first identifies the trachea as well as primary bronchi and then the pulmonary region is identified by applying a threshold and morphologic operations. When both lungs are in contact, additional procedures are performed to obtain two separated lung volumes. To evaluate the performance of the method, we compared contours extracted from 3D lung surfaces with reference contours, using several figures of merit. Results show that the worst case generally occurs at the middle sections of high resolution CT exams, due the presence of aerial and vascular structures. Nevertheless, the average error is inferior to the average error associated with radiologist inter-observer variability, which suggests that our method produces lung contours similar to those drawn by radiologists. The information created by our segmentation algorithm is used by an identification and representation method in pulmonary emphysema that also classifies emphysema according to its severity degree. Two clinically proved thresholds are applied which identify regions with severe emphysema, and with highly severe emphysema. Based on this thresholding strategy, an application for volumetric emphysema assessment was developed offering new display paradigms concerning the visualization of classification results. This framework is easily extendable to accommodate other classifiers namely those related with texture based segmentation as it is often the case with interstitial diseases.

Investigation of the neuroprotective effects of bee-venom acupuncture in a mouse model of Parkinson's disease by using immunohistochemistry and In-vivo 1H magnetic resonance spectroscopy at 9.4 T

NASA Astrophysics Data System (ADS)

Yoon, Moon-Hyun; Lee, Do-Wan; Kim, Hyun-Jin; Chung, Jin-Yeung; Doo, Ah-Reum; Park, Hi-Joon; Kim, Seung-Nam; Choe, Bo-Young

2013-01-01

Neuroprotective therapeutics slows down the degeneration process in animal models of Parkinson's disease (PD). The neuronal survival in PD animal models is often measured by using immunohistochemistry. However, dynamic changes in the pathology of the brain cannot be explored with this technique. Application of in-vivo 1H magnetic resonance spectroscopy (1H MRS) can cover this shortcoming, as these techniques are non-invasive and can be repeated over time in the same animal. Thus, the sensitivity of both techniques to measure changes in the PD pathology was explored in an experiment studying the neuroprotective effects of the vigilance enhancer bee-venom (BV) in a mouse model of PD. The mice were pre-treated with 0.02-ml BV administered to the acupuncture point GB34 (Yangneungcheon) once every 3 days for 2 weeks. Three groups were classified as control, MPTP-intoxicated PD model and BV-treated mice. Outer volume suppression combined with the ultra-short echo-time STEAM (TE = 2.2 ms, TM = 20 ms, TR = 5000 ms) was used for localized in-vivo 1H MRS. Based on the 1H MRS spectral analysis, substantial changes of the neurochemical profiles were evaluated in the three investigated groups. In particular, the glutamate complex (Glx)/creatine (Cr) ratio (7.72 ± 1.25) in the PD group was significantly increased compared to that in the control group (3.93 ± 2.21, P = 0.001). Compared to the baseline values, the Glx/Cr ratio of the BV-treated group was significantly decreased 2 weeks after MPTP intoxication (one-way ANOVA, p < 0.05). In conclusion, the present study demonstrated that neurochemical alterations occurred in the three groups and that the neuroprotective effects of the BV acupuncture in a mouse model of PD could be quantified by using immunohistochemistry and 1H MRS.

Automation of static and dynamic non-dispersive liquid phase microextraction. Part 1: Approaches based on extractant drop-, plug-, film- and microflow-formation.

PubMed

Alexovič, Michal; Horstkotte, Burkhard; Solich, Petr; Sabo, Ján

2016-02-04

Simplicity, effectiveness, swiftness, and environmental friendliness - these are the typical requirements for the state of the art development of green analytical techniques. Liquid phase microextraction (LPME) stands for a family of elegant sample pretreatment and analyte preconcentration techniques preserving these principles in numerous applications. By using only fractions of solvent and sample compared to classical liquid-liquid extraction, the extraction kinetics, the preconcentration factor, and the cost efficiency can be increased. Moreover, significant improvements can be made by automation, which is still a hot topic in analytical chemistry. This review surveys comprehensively and in two parts the developments of automation of non-dispersive LPME methodologies performed in static and dynamic modes. Their advantages and limitations and the reported analytical performances are discussed and put into perspective with the corresponding manual procedures. The automation strategies, techniques, and their operation advantages as well as their potentials are further described and discussed. In this first part, an introduction to LPME and their static and dynamic operation modes as well as their automation methodologies is given. The LPME techniques are classified according to the different approaches of protection of the extraction solvent using either a tip-like (needle/tube/rod) support (drop-based approaches), a wall support (film-based approaches), or microfluidic devices. In the second part, the LPME techniques based on porous supports for the extraction solvent such as membranes and porous media are overviewed. An outlook on future demands and perspectives in this promising area of analytical chemistry is finally given. Copyright © 2015 Elsevier B.V. All rights reserved.

Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining.

PubMed

Zhang, Ling; Kong, Hui; Ting Chin, Chien; Liu, Shaoxiong; Fan, Xinmin; Wang, Tianfu; Chen, Siping

2014-03-01

Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 μm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals. © 2013 International Society for Advancement of Cytometry.

Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.

PubMed

Kim, Jihoon; Grillo, Janice M; Boxwala, Aziz A; Jiang, Xiaoqian; Mandelbaum, Rose B; Patel, Bhakti A; Mikels, Debra; Vinterbo, Staal A; Ohno-Machado, Lucila

2011-01-01

Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10(-6). Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful.

Anomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs

PubMed Central

Kim, Jihoon; Grillo, Janice M; Boxwala, Aziz A; Jiang, Xiaoqian; Mandelbaum, Rose B; Patel, Bhakti A; Mikels, Debra; Vinterbo, Staal A; Ohno-Machado, Lucila

2011-01-01

Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10−6. Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful. PMID:22195129

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy.

PubMed

Yang, Guang; Nawaz, Tahir; Barrick, Thomas R; Howe, Franklyn A; Slabaugh, Greg

2015-12-01

Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

Creating an automated chiller fault detection and diagnostics tool using a data fault library.

PubMed

Bailey, Margaret B; Kreider, Jan F

2003-07-01

Reliable, automated detection and diagnosis of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section.

Proton magnetic resonance spectroscopy for assessment of human body composition.

PubMed

Kamba, M; Kimura, K; Koda, M; Ogawa, T

2001-02-01

The usefulness of magnetic resonance spectroscopy (MRS)-based techniques for assessment of human body composition has not been established. We compared a proton MRS-based technique with the total body water (TBW) method to determine the usefulness of the former technique for assessment of human body composition. Proton magnetic resonance spectra of the chest to abdomen, abdomen to pelvis, and pelvis to thigh regions were obtained from 16 volunteers by using single, free induction decay measurement with a clinical magnetic resonance system operating at 1.5 T. The MRS-derived metabolite ratio was determined as the ratio of fat methyl and methylene proton resonance to water proton resonance. The peak areas for the chest to abdomen and the pelvis to thigh regions were normalized to an external reference (approximately 2200 g benzene) and a weighted average of the MRS-derived metabolite ratios for the 2 positions was calculated. TBW for each subject was determined by the deuterium oxide dilution technique. The MRS-derived metabolite ratios were significantly correlated with the ratio of body fat to lean body mass estimated by TBW. The MRS-derived metabolite ratio for the abdomen to pelvis region correlated best with the ratio of body fat to lean body mass on simple regression analyses (r = 0.918). The MRS-derived metabolite ratio for the abdomen to pelvis region and that for the pelvis to thigh region were selected for a multivariate regression model (R = 0.947, adjusted R(2) = 0.881). This MRS-based technique is sufficiently accurate for assessment of human body composition.

Automated detection of tuberculosis on sputum smeared slides using stepwise classification

NASA Astrophysics Data System (ADS)

Divekar, Ajay; Pangilinan, Corina; Coetzee, Gerrit; Sondh, Tarlochan; Lure, Fleming Y. M.; Kennedy, Sean

2012-03-01

Routine visual slide screening for identification of tuberculosis (TB) bacilli in stained sputum slides under microscope system is a tedious labor-intensive task and can miss up to 50% of TB. Based on the Shannon cofactor expansion on Boolean function for classification, a stepwise classification (SWC) algorithm is developed to remove different types of false positives, one type at a time, and to increase the detection of TB bacilli at different concentrations. Both bacilli and non-bacilli objects are first analyzed and classified into several different categories including scanty positive, high concentration positive, and several non-bacilli categories: small bright objects, beaded, dim elongated objects, etc. The morphological and contrast features are extracted based on aprior clinical knowledge. The SWC is composed of several individual classifiers. Individual classifier to increase the bacilli counts utilizes an adaptive algorithm based on a microbiologist's statistical heuristic decision process. Individual classifier to reduce false positive is developed through minimization from a binary decision tree to classify different types of true and false positive based on feature vectors. Finally, the detection algorithm is was tested on 102 independent confirmed negative and 74 positive cases. A multi-class task analysis shows high accordance rate for negative, scanty, and high-concentration as 88.24%, 56.00%, and 97.96%, respectively. A binary-class task analysis using a receiver operating characteristics method with the area under the curve (Az) is also utilized to analyze the performance of this detection algorithm, showing the superior detection performance on the high-concentration cases (Az=0.913) and cases mixed with high-concentration and scanty cases (Az=0.878).

Development of Automated Whistle and Click Classifiers for Odontocete Species in the Western Atlantic Ocean and the Waters Surrounding the Hawaiian Islands

DTIC Science & Technology

2015-09-30

for Odontocete Species in the Western Atlantic Ocean and the Waters Surrounding the Hawaiian Islands Julie N. Oswald & Tina M. Yack Bio-Waves... Atlantic Ocean, the temperate Pacific Ocean and the waters surrounding the Hawaiian Islands. These classifiers will also incorporate ancillary...and echolocation click classifiers for odontocete species in the northwest Atlantic Ocean, the waters surrounding the Hawaiian Islands and the

An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

PubMed

Lee, Unseok; Chang, Sungyul; Putra, Gian Anantrio; Kim, Hyoungseok; Kim, Dong Hwan

2018-01-01

A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

Automated computer-based detection of encounter behaviours in groups of honeybees.

PubMed

Blut, Christina; Crespi, Alessandro; Mersch, Danielle; Keller, Laurent; Zhao, Linlin; Kollmann, Markus; Schellscheidt, Benjamin; Fülber, Carsten; Beye, Martin

2017-12-15

Honeybees form societies in which thousands of members integrate their behaviours to act as a single functional unit. We have little knowledge on how the collaborative features are regulated by workers' activities because we lack methods that enable collection of simultaneous and continuous behavioural information for each worker bee. In this study, we introduce the Bee Behavioral Annotation System (BBAS), which enables the automated detection of bees' behaviours in small observation hives. Continuous information on position and orientation were obtained by marking worker bees with 2D barcodes in a small observation hive. We computed behavioural and social features from the tracking information to train a behaviour classifier for encounter behaviours (interaction of workers via antennation) using a machine learning-based system. The classifier correctly detected 93% of the encounter behaviours in a group of bees, whereas 13% of the falsely classified behaviours were unrelated to encounter behaviours. The possibility of building accurate classifiers for automatically annotating behaviours may allow for the examination of individual behaviours of worker bees in the social environments of small observation hives. We envisage that BBAS will be a powerful tool for detecting the effects of experimental manipulation of social attributes and sub-lethal effects of pesticides on behaviour.

Robust automated classification of first-motion polarities for focal mechanism determination with machine learning

NASA Astrophysics Data System (ADS)

Ross, Z. E.; Meier, M. A.; Hauksson, E.

2017-12-01

Accurate first-motion polarities are essential for determining earthquake focal mechanisms, but are difficult to measure automatically because of picking errors and signal to noise issues. Here we develop an algorithm for reliable automated classification of first-motion polarities using machine learning algorithms. A classifier is designed to identify whether the first-motion polarity is up, down, or undefined by examining the waveform data directly. We first improve the accuracy of automatic P-wave onset picks by maximizing a weighted signal/noise ratio for a suite of candidate picks around the automatic pick. We then use the waveform amplitudes before and after the optimized pick as features for the classification. We demonstrate the method's potential by training and testing the classifier on tens of thousands of hand-made first-motion picks by the Southern California Seismic Network. The classifier assigned the same polarity as chosen by an analyst in more than 94% of the records. We show that the method is generalizable to a variety of learning algorithms, including neural networks and random forest classifiers. The method is suitable for automated processing of large seismic waveform datasets, and can potentially be used in real-time applications, e.g. for improving the source characterizations of earthquake early warning algorithms.

Context-based automated defect classification system using multiple morphological masks

DOEpatents

Gleason, Shaun S.; Hunt, Martin A.; Sari-Sarraf, Hamed

2002-01-01

Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, but the classification of those defects is still performed manually by technicians. This invention includes novel digital image analysis techniques that generate unique feature vector descriptions of semiconductor defects as well as classifiers that use these descriptions to automatically categorize the defects into one of a set of pre-defined classes. Feature extraction techniques based on multiple-focus images, multiple-defect mask images, and segmented semiconductor wafer images are used to create unique feature-based descriptions of the semiconductor defects. These feature-based defect descriptions are subsequently classified by a defect classifier into categories that depend on defect characteristics and defect contextual information, that is, the semiconductor process layer(s) with which the defect comes in contact. At the heart of the system is a knowledge database that stores and distributes historical semiconductor wafer and defect data to guide the feature extraction and classification processes. In summary, this invention takes as its input a set of images containing semiconductor defect information, and generates as its output a classification for the defect that describes not only the defect itself, but also the location of that defect with respect to the semiconductor process layers.

Pairwise mixture model for unmixing partial volume effect in multi-voxel MR spectroscopy of brain tumour patients

NASA Astrophysics Data System (ADS)

Olliverre, Nathan; Asad, Muhammad; Yang, Guang; Howe, Franklyn; Slabaugh, Gregory

2017-03-01

Multi-Voxel Magnetic Resonance Spectroscopy (MV-MRS) provides an important and insightful technique for the examination of the chemical composition of brain tissue, making it an attractive medical imaging modality for the examination of brain tumours. MRS, however, is affected by the issue of the Partial Volume Effect (PVE), where the signals of multiple tissue types can be found within a single voxel and provides an obstacle to the interpretation of the data. The PVE results from the low resolution achieved in MV-MRS images relating to the signal to noise ratio (SNR). To counteract PVE, this paper proposes a novel Pairwise Mixture Model (PMM), that extends a recently reported Signal Mixture Model (SMM) for representing the MV-MRS signal as normal, low or high grade tissue types. Inspired by Conditional Random Field (CRF) and its continuous variant the PMM incorporates the surrounding voxel neighbourhood into an optimisation problem, the solution of which provides an estimation to a set of coefficients. The values of the estimated coefficients represents the amount of each tissue type (normal, low or high) found within a voxel. These coefficients can then be visualised as a nosological rendering using a coloured grid representing the MV-MRS image overlaid on top of a structural image, such as a Magnetic Resonance Image (MRI). Experimental results show an accuracy of 92.69% in classifying patient tumours as either low or high grade compared against the histopathology for each patient. Compared to 91.96% achieved by the SMM, the proposed PMM method demonstrates the importance of incorporating spatial coherence into the estimation as well as its potential clinical usage.

Automated noninvasive classification of renal cancer on multiphase CT

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

Linguraru, Marius George; Wang, Shijun; Shah, Furhawn

2011-10-15

Purpose: To explore the added value of the shape of renal lesions for classifying renal neoplasms. To investigate the potential of computer-aided analysis of contrast-enhanced computed-tomography (CT) to quantify and classify renal lesions. Methods: A computer-aided clinical tool based on adaptive level sets was employed to analyze 125 renal lesions from contrast-enhanced abdominal CT studies of 43 patients. There were 47 cysts and 78 neoplasms: 22 Von Hippel-Lindau (VHL), 16 Birt-Hogg-Dube (BHD), 19 hereditary papillary renal carcinomas (HPRC), and 21 hereditary leiomyomatosis and renal cell cancers (HLRCC). The technique quantified the three-dimensional size and enhancement of lesions. Intrapatient and interphasemore » registration facilitated the study of lesion serial enhancement. The histograms of curvature-related features were used to classify the lesion types. The areas under the curve (AUC) were calculated for receiver operating characteristic curves. Results: Tumors were robustly segmented with 0.80 overlap (0.98 correlation) between manual and semi-automated quantifications. The method further identified morphological discrepancies between the types of lesions. The classification based on lesion appearance, enhancement and morphology between cysts and cancers showed AUC = 0.98; for BHD + VHL (solid cancers) vs. HPRC + HLRCC AUC = 0.99; for VHL vs. BHD AUC = 0.82; and for HPRC vs. HLRCC AUC = 0.84. All semi-automated classifications were statistically significant (p < 0.05) and superior to the analyses based solely on serial enhancement. Conclusions: The computer-aided clinical tool allowed the accurate quantification of cystic, solid, and mixed renal tumors. Cancer types were classified into four categories using their shape and enhancement. Comprehensive imaging biomarkers of renal neoplasms on abdominal CT may facilitate their noninvasive classification, guide clinical management, and monitor responses to drugs or interventions.« less

Correction of oral contrast artifacts in CT-based attenuation correction of PET images using an automated segmentation algorithm.

PubMed

Ahmadian, Alireza; Ay, Mohammad R; Bidgoli, Javad H; Sarkar, Saeed; Zaidi, Habib

2008-10-01

Oral contrast is usually administered in most X-ray computed tomography (CT) examinations of the abdomen and the pelvis as it allows more accurate identification of the bowel and facilitates the interpretation of abdominal and pelvic CT studies. However, the misclassification of contrast medium with high-density bone in CT-based attenuation correction (CTAC) is known to generate artifacts in the attenuation map (mumap), thus resulting in overcorrection for attenuation of positron emission tomography (PET) images. In this study, we developed an automated algorithm for segmentation and classification of regions containing oral contrast medium to correct for artifacts in CT-attenuation-corrected PET images using the segmented contrast correction (SCC) algorithm. The proposed algorithm consists of two steps: first, high CT number object segmentation using combined region- and boundary-based segmentation and second, object classification to bone and contrast agent using a knowledge-based nonlinear fuzzy classifier. Thereafter, the CT numbers of pixels belonging to the region classified as contrast medium are substituted with their equivalent effective bone CT numbers using the SCC algorithm. The generated CT images are then down-sampled followed by Gaussian smoothing to match the resolution of PET images. A piecewise calibration curve was then used to convert CT pixel values to linear attenuation coefficients at 511 keV. The visual assessment of segmented regions performed by an experienced radiologist confirmed the accuracy of the segmentation and classification algorithms for delineation of contrast-enhanced regions in clinical CT images. The quantitative analysis of generated mumaps of 21 clinical CT colonoscopy datasets showed an overestimation ranging between 24.4% and 37.3% in the 3D-classified regions depending on their volume and the concentration of contrast medium. Two PET/CT studies known to be problematic demonstrated the applicability of the technique in clinical setting. More importantly, correction of oral contrast artifacts improved the readability and interpretation of the PET scan and showed substantial decrease of the SUV (104.3%) after correction. An automated segmentation algorithm for classification of irregular shapes of regions containing contrast medium was developed for wider applicability of the SCC algorithm for correction of oral contrast artifacts during the CTAC procedure. The algorithm is being refined and further validated in clinical setting.

Combined diffusion imaging and MR spectroscopy in the diagnosis of human prion diseases.

PubMed

Galanaud, Damien; Haik, S; Linguraru, M G; Ranjeva, J-P; Faucheux, B; Kaphan, E; Ayache, N; Chiras, J; Cozzone, P; Dormont, D; Brandel, J-P

2010-08-01

The physiopathologic bases underlying the signal intensity changes and reduced diffusibility observed in prion diseases (TSEs) are still poorly understood. We evaluated the interest of MRS combined with DWI both as a diagnostic tool and a way to understand the mechanism underlying signal intensity and ADC changes in this setting. We designed a prospective study of multimodal MR imaging in patients with suspected TSEs. Forty-five patients with a suspicion of TSE and 11 age-matched healthy volunteers were included. The MR imaging protocol included T1, FLAIR, and DWI sequences. MRS was performed on the cerebellum, pulvinar, right lenticular nucleus, and frontal cortex. MR images were assessed visually, and ADC values were calculated. Among the 45 suspected cases, 31 fulfilled the criteria for probable or definite TSEs (19 sCJDs, 3 iCJDs, 2 vCJDs, and 7 genetic TSEs); and 14 were classified as AltDs. High signals in the cortex and/or basal ganglia were observed in 26/31 patients with TSEs on FLAIR and 29/31 patients on DWI. In the basal ganglia, high DWI signals corresponded to a decreased ADC. Metabolic alterations, increased mIns, and decreased NAA were observed in all patients with TSEs. ADC values and metabolic changes were not correlated; this finding suggests that neuronal stress (vacuolization), neuronal loss, and astrogliosis do not alone explain the decrease of ADC. MRS combined with other MR imaging is of interest in the diagnosis of TSE and provides useful information for understanding physiopathologic processes underlying prion diseases.

A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images

PubMed Central

Hoffman, R.A.; Kothari, S.; Phan, J.H.; Wang, M.D.

2016-01-01

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x106 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered. PMID:27532012

A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images.

PubMed

Hoffman, R A; Kothari, S; Phan, J H; Wang, M D

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x10 6 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered.

Automated detection of radioisotopes from an aircraft platform by pattern recognition analysis of gamma-ray spectra.

PubMed

Dess, Brian W; Cardarelli, John; Thomas, Mark J; Stapleton, Jeff; Kroutil, Robert T; Miller, David; Curry, Timothy; Small, Gary W

2018-03-08

A generalized methodology was developed for automating the detection of radioisotopes from gamma-ray spectra collected from an aircraft platform using sodium-iodide detectors. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, multivariate classification models based on nonparametric linear discriminant analysis were developed for application to spectra that were preprocessed through a combination of altitude-based scaling and digital filtering. Training sets of spectra for use in building classification models were assembled from a combination of background spectra collected in the field and synthesized spectra obtained by superimposing laboratory-collected spectra of target radioisotopes onto field backgrounds. This approach eliminated the need for field experimentation with radioactive sources for use in building classification models. Through a bi-Gaussian modeling procedure, the discriminant scores that served as the outputs from the classification models were related to associated confidence levels. This provided an easily interpreted result regarding the presence or absence of the signature of a specific radioisotope in each collected spectrum. Through the use of this approach, classifiers were built for cesium-137 ( 137 Cs) and cobalt-60 ( 60 Co), two radioisotopes that are of interest in airborne radiological monitoring applications. The optimized classifiers were tested with field data collected from a set of six geographically diverse sites, three of which contained either 137 Cs, 60 Co, or both. When the optimized classification models were applied, the overall percentages of correct classifications for spectra collected at these sites were 99.9 and 97.9% for the 60 Co and 137 Cs classifiers, respectively. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

PubMed

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

2007-08-01

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

Development and evaluation of a hand tracker using depth images captured from an overhead perspective.

PubMed

Czarnuch, Stephen; Mihailidis, Alex

2015-03-27

We present the development and evaluation of a robust hand tracker based on single overhead depth images for use in the COACH, an assistive technology for people with dementia. The new hand tracker was designed to overcome limitations experienced by the COACH in previous clinical trials. We train a random decision forest classifier using ∼5000 manually labeled, unbalanced, training images. Hand positions from the classifier are translated into task actions based on proximity to environmental objects. Tracker performance is evaluated using a large set of ∼24 000 manually labeled images captured from 41 participants in a fully-functional washroom, and compared to the system's previous colour-based hand tracker. Precision and recall were 0.994 and 0.938 for the depth tracker compared to 0.981 and 0.822 for the colour tracker with the current data, and 0.989 and 0.466 in the previous study. The improved tracking performance supports integration of the depth-based tracker into the COACH toward unsupervised, real-world trials. Implications for Rehabilitation The COACH is an intelligent assistive technology that can enable people with cognitive disabilities to stay at home longer, supporting the concept of aging-in-place. Automated prompting systems, a type of intelligent assistive technology, can help to support the independent completion of activities of daily living, increasing the independence of people with cognitive disabilities while reducing the burden of care experienced by caregivers. Robust motion tracking using depth imaging supports the development of intelligent assistive technologies like the COACH. Robust motion tracking also has application to other forms of assistive technologies including gaming, human-computer interaction and automated assessments.

Prognostic factors for acute encephalopathy with bright tree appearance.

PubMed

Azuma, Junji; Nabatame, Shin; Nakano, Sayaka; Iwatani, Yoshiko; Kitai, Yukihiro; Tominaga, Koji; Kagitani-Shimono, Kuriko; Okinaga, Takeshi; Yamamoto, Takehisa; Nagai, Toshisaburo; Ozono, Keiichi

2015-02-01

To determine the prognostic factors for encephalopathy with bright tree appearance (BTA) in the acute phase through retrospective case evaluation. We recruited 10 children with encephalopathy who presented with BTA and classified them into 2 groups. Six patients with evident regression and severe psychomotor developmental delay after encephalopathy were included in the severe group, while the remaining 4 patients with mild mental retardation were included in the mild group. We retrospectively analyzed their clinical symptoms, laboratory data, and magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) findings. Patients in the severe group developed subsequent complications such as epilepsy and severe motor impairment. Univariate analysis revealed that higher maximum lactate dehydrogenase (LDH) levels (p=0.055) were a weak predictor of poor outcome. Maximum creatinine levels were significantly higher (p<0.05) and minimal platelet counts were significantly lower (p<0.05) in the severe group than in the mild group. Acute renal failure was not observed in any patient throughout the study. MRS of the BTA lesion during the BTA period showed elevated lactate levels in 5 children in the severe group and 1 child in the mild group. MRI performed during the chronic phase revealed severe brain atrophy in all patients in the severe group. Higher creatinine and LDH levels and lower platelet counts in the acute phase correlated with poor prognosis. Increased lactate levels in the BTA lesion during the BTA period on MRS may predict severe physical and mental disability. Copyright © 2014 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Global brain atrophy and metabolic dysfunction in LGI1 encephalitis: A prospective multimodal MRI study.

PubMed

Szots, Monika; Blaabjerg, Morten; Orsi, Gergely; Iversen, Pernille; Kondziella, Daniel; Madsen, Camilla G; Garde, Ellen; Magnusson, Peter O; Barsi, Peter; Nagy, Ferenc; Siebner, Hartwig R; Illes, Zsolt

2017-05-15

Chronic cognitive deficits are frequent in leucin-rich glioma-inactivated 1 protein (LGI1) encephalitis. We examined structural and metabolic brain abnormalities following LGI1 encephalitis and correlated findings with acute and follow-up clinical outcomes. Nine patients underwent prospective multimodal 3 Tesla MRI 33.1±18months after disease onset, including automated volumetry, diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS). Data were compared to 9 age- and sex-matched healthy controls. Although extratemporal lesions were not present on MRI in the acute stage, tract-based spatial statistics analyses of DTI during follow-up showed widespread changes in the cerebral and cerebellar white matter (WM), most prominent in the anterior parts of the corona radiata, capsula interna and corpus callosum. MRS revealed lower glutamine/glutamate WM levels compared to controls. Higher cerebellar gray matter volume was associated with better function at disease onset (measured by the modified Rankin Scale), and higher putaminal volume was associated with better cognition by Addenbrooke's Cognitive Examination test at 23.4±7.6months. Poor clinical outcome following LGI1 encephalitis is associated with global brain atrophy and disintegration of white matter tracts. The pathological changes affect not only temporomesial structures but also frontal lobes and the cerebellum. Copyright © 2017 Elsevier B.V. All rights reserved.

A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA

NASA Astrophysics Data System (ADS)

Wagstaff, Kiri L.; Tang, Benyang; Thompson, David R.; Khudikyan, Shakeh; Wyngaard, Jane; Deller, Adam T.; Palaniswamy, Divya; Tingay, Steven J.; Wayth, Randall B.

2016-08-01

Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80%-90% of the candidates, with an accuracy greater than 98%, leaving only the 10%-20% most promising candidates to be reviewed by humans.

Uncertainty analysis of wavelet-based feature extraction for isotope identification on NaI gamma-ray spectra

DOE PAGES

Stinnett, Jacob; Sullivan, Clair J.; Xiong, Hao

2017-03-02

Low-resolution isotope identifiers are widely deployed for nuclear security purposes, but these detectors currently demonstrate problems in making correct identifications in many typical usage scenarios. While there are many hardware alternatives and improvements that can be made, performance on existing low resolution isotope identifiers should be able to be improved by developing new identification algorithms. We have developed a wavelet-based peak extraction algorithm and an implementation of a Bayesian classifier for automated peak-based identification. The peak extraction algorithm has been extended to compute uncertainties in the peak area calculations. To build empirical joint probability distributions of the peak areas andmore » uncertainties, a large set of spectra were simulated in MCNP6 and processed with the wavelet-based feature extraction algorithm. Kernel density estimation was then used to create a new component of the likelihood function in the Bayesian classifier. Furthermore, identification performance is demonstrated on a variety of real low-resolution spectra, including Category I quantities of special nuclear material.« less

Automated Classification of Phonological Errors in Aphasic Language

PubMed Central

Ahuja, Sanjeev B.; Reggia, James A.; Berndt, Rita S.

1984-01-01

Using heuristically-guided state space search, a prototype program has been developed to simulate and classify phonemic errors occurring in the speech of neurologically-impaired patients. Simulations are based on an interchangeable rule/operator set of elementary errors which represent a theory of phonemic processing faults. This work introduces and evaluates a novel approach to error simulation and classification, it provides a prototype simulation tool for neurolinguistic research, and it forms the initial phase of a larger research effort involving computer modelling of neurolinguistic processes.

Proton magnetic resonance spectroscopy of tubercular breast abscess: report of a case.

PubMed

Das, Chandan Jyoti; Medhi, Kunjahari

2008-01-01

In vivo proton magnetic resonance spectroscopy (H-MRS) is a functional imaging modality. When magnetic resonance imaging is coupled with H-MRS, it results in accurate metabolic characterization of various lesions. Proton magnetic resonance spectroscopy has an established role in evaluating malignant breast lesions, and the increasing number of published literature supports the role of H-MRS in patients with breast cancer. However, H-MRS can be of help in evaluating benign breast disease. We present a case of tubercular breast abscess, initial diagnosis of which was suggested based on characteristic lipid pick on H-MRS and was subsequently confirmed by fine needle aspiration biopsy of the breast lesion.

Classifying a Person's Degree of Accessibility From Natural Body Language During Social Human-Robot Interactions.

PubMed

McColl, Derek; Jiang, Chuan; Nejat, Goldie

2017-02-01

For social robots to be successfully integrated and accepted within society, they need to be able to interpret human social cues that are displayed through natural modes of communication. In particular, a key challenge in the design of social robots is developing the robot's ability to recognize a person's affective states (emotions, moods, and attitudes) in order to respond appropriately during social human-robot interactions (HRIs). In this paper, we present and discuss social HRI experiments we have conducted to investigate the development of an accessibility-aware social robot able to autonomously determine a person's degree of accessibility (rapport, openness) toward the robot based on the person's natural static body language. In particular, we present two one-on-one HRI experiments to: 1) determine the performance of our automated system in being able to recognize and classify a person's accessibility levels and 2) investigate how people interact with an accessibility-aware robot which determines its own behaviors based on a person's speech and accessibility levels.

Multimodal Neurodiagnostic Tool for Exploration Missions

NASA Technical Reports Server (NTRS)

Lee, Yong Jin

2015-01-01

Linea Research Corporation has developed a neurodiagnostic tool that detects behavioral stress markers for astronauts on long-duration space missions. Lightweight and compact, the device is unobtrusive and requires minimal time and effort for the crew to use. The system provides a real-time functional imaging of cortical activity during normal activities. In Phase I of the project, Linea Research successfully monitored cortical activity using multiparameter sensor modules. Using electroencephalography (EEG) and functional near-infrared spectroscopy signals, the company obtained photoplethysmography and electrooculography signals to compute the heart rate and frequency of eye movement. The company also demonstrated the functionality of an algorithm that automatically classifies the varying degrees of cognitive loading based on physiological parameters. In Phase II, Linea Research developed the flight-capable neurodiagnostic device. Worn unobtrusively on the head, the device detects and classifies neurophysiological markers associated with decrements in behavior state and cognition. An automated algorithm identifies key decrements and provides meaningful and actionable feedback to the crew and ground-based medical staff.

Ectodermal Influx and Cell Hypertrophy Provide Early Growth for All Murine Mammary Rudiments, and Are Differentially Regulated among Them by Gli3

PubMed Central

Lee, May Yin; Racine, Victor; Jagadpramana, Peter; Sun, Li; Yu, Weimiao; Du, Tiehua; Spencer-Dene, Bradley; Rubin, Nicole; Le, Lendy; Ndiaye, Delphine; Bellusci, Saverio; Kratochwil, Klaus; Veltmaat, Jacqueline M.

2011-01-01

Mammary gland development starts in utero with one or several pairs of mammary rudiments (MRs) budding from the surface ectodermal component of the mammalian embryonic skin. Mice develop five pairs, numbered MR1 to MR5 from pectoral to inguinal position. We have previously shown that Gli3Xt-J/Xt-J mutant embryos, which lack the transcription factor Gli3, do not form MR3 and MR5. We show here that two days after the MRs emerge, Gli3Xt-J/Xt-J MR1 is 20% smaller, and Gli3Xt-J/Xt-J MR2 and MR4 are 50% smaller than their wild type (wt) counterparts. Moreover, while wt MRs sink into the underlying dermis, Gli3Xt-J/Xt-J MR4 and MR2 protrude outwardly, to different extents. To understand why each of these five pairs of functionally identical organs has its own, distinct response to the absence of Gli3, we determined which cellular mechanisms regulate growth of the individual MRs, and whether and how Gli3 regulates these mechanisms. We found a 5.5 to 10.7-fold lower cell proliferation rate in wt MRs compared to their adjacent surface ectoderm, indicating that MRs do not emerge or grow via locally enhanced cell proliferation. Cell-tracing experiments showed that surface ectodermal cells are recruited toward the positions where MRs emerge, and contribute to MR growth during at least two days. During the second day of MR development, peripheral cells within the MRs undergo hypertrophy, which also contributes to MR growth. Limited apoptotic cell death counterbalances MR growth. The relative contribution of each of these processes varies among the five MRs. Furthermore, each of these processes is impaired in the absence of Gli3, but to different extents in each MR. This differential involvement of Gli3 explains the variation in phenotype among Gli3Xt-J/Xt-J MRs, and may help to understand the variation in numbers and positions of mammary glands among mammals. PMID:22046263

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

PubMed

Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

2016-01-01

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning.

PubMed

Mirsky, Simcha K; Barnea, Itay; Levi, Mattan; Greenspan, Hayit; Shaked, Natan T

2017-09-01

Currently, the delicate process of selecting sperm cells to be used for in vitro fertilization (IVF) is still based on the subjective, qualitative analysis of experienced clinicians using non-quantitative optical microscopy techniques. In this work, a method was developed for the automated analysis of sperm cells based on the quantitative phase maps acquired through use of interferometric phase microscopy (IPM). Over 1,400 human sperm cells from 8 donors were imaged using IPM, and an algorithm was designed to digitally isolate sperm cell heads from the quantitative phase maps while taking into consideration both the cell 3D morphology and contents, as well as acquire features describing sperm head morphology. A subset of these features was used to train a support vector machine (SVM) classifier to automatically classify sperm of good and bad morphology. The SVM achieves an area under the receiver operating characteristic curve of 88.59% and an area under the precision-recall curve of 88.67%, as well as precisions of 90% or higher. We believe that our automatic analysis can become the basis for objective and automatic sperm cell selection in IVF. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Magnetic resonance spectroscopy.

PubMed

Hope, P L; Moorcraft, J

1991-09-01

MRS is a noninvasive technique that does not use ionizing radiation and can be used to measure relative metabolite concentrations in human tissues and organs in vivo. Phosphorus MRS can be used to study energy metabolites and intracellular pH. The first neonatal applications were described in 1983 in a study of cerebral metabolism. Since then, the value of cerebral MRS as research tool and an investigative technique has been confirmed, and its prognostic power in asphyxiated infants has been established. Techniques of spatial localization and quantitation have been developed, but studies of other organs and the use of other nuclei remain at a very preliminary stage. Considering the huge potential of MRS and the proliferation of high field magnets primarily designed for imaging, there has been a disappointing lack of progress in the development of clinical and research applications of spectroscopy. The logistic differences of studying sick infants in strong magnetic fields make MRS a time-consuming and labor-intensive investigation, which will inevitably limit its widespread routine use. Research studies are hampered by the diversity of spectroscopic and signal processing techniques, which make comparisons of data from different groups impossible. Some techniques for the assessment of cerebral hemodynamics such as doppler ultrasound and near infrared spectroscopy have the advantage of being available at the cotside, but MRS is unique in providing quantitative information about a wide range of intracellular metabolites. The altricial development of MRS as a clinical investigative tool in neonatology can be ascribed partly to practical difficulties, but these should not detract from the exciting possibilities opened up by a technique that gives a noninvasive insight into intracellular chemistry. The metabolic information from MRS is an invaluable addition to the information provided by other techniques and will certainly play an important role in unraveling the sequence of events between an hypoxic-ischemic insult and cell death. A better understanding of these mechanisms is a prerequisite to the development of rational therapeutic maneuvers following asphyxial insults.

Assessing the skeletal age from a hand radiograph: automating the Tanner-Whitehouse method

NASA Astrophysics Data System (ADS)

Niemeijer, Meindert; van Ginneken, Bram; Maas, Casper A.; Beek, Frederik J. A.; Viergever, Max A.

2003-05-01

The skeletal maturity of children is usually assessed from a standard radiograph of the left hand and wrist. An established clinical method to determine the skeletal maturity is the Tanner-Whitehouse (TW2) method. This method divides the skeletal development into several stages (labelled A, B, ...,I). We are developing an automated system based on this method. In this work we focus on assigning a stage to one region of interest (ROI), the middle phalanx of the third finger. We classify each ROI as follows. A number of ROIs which have been assigned a certain stage by a radiologist are used to construct a mean image for that stage. For a new input ROI, landmarks are detected by using an Active Shape Model. These are used to align the mean images with the input image. Subsequently the correlation between each transformed mean stage image and the input is calculated. The input ROI can be assigned to the stage with the highest correlation directly, or the values can be used as features in a classifier. The method was tested on 71 cases ranging from stage E to I. The ROI was staged correctly in 73.2% of all cases and in 97.2% of all incorrectly staged cases the error was not more than one stage.

An integrated one-step system to extract, analyze and annotate all relevant information from image-based cell screening of chemical libraries.

PubMed

Rabal, Obdulia; Link, Wolfgang; Serelde, Beatriz G; Bischoff, James R; Oyarzabal, Julen

2010-04-01

Here we report the development and validation of a complete solution to manage and analyze the data produced by image-based phenotypic screening campaigns of small-molecule libraries. In one step initial crude images are analyzed for multiple cytological features, statistical analysis is performed and molecules that produce the desired phenotypic profile are identified. A naïve Bayes classifier, integrating chemical and phenotypic spaces, is built and utilized during the process to assess those images initially classified as "fuzzy"-an automated iterative feedback tuning. Simultaneously, all this information is directly annotated in a relational database containing the chemical data. This novel fully automated method was validated by conducting a re-analysis of results from a high-content screening campaign involving 33 992 molecules used to identify inhibitors of the PI3K/Akt signaling pathway. Ninety-two percent of confirmed hits identified by the conventional multistep analysis method were identified using this integrated one-step system as well as 40 new hits, 14.9% of the total, originally false negatives. Ninety-six percent of true negatives were properly recognized too. A web-based access to the database, with customizable data retrieval and visualization tools, facilitates the posterior analysis of annotated cytological features which allows identification of additional phenotypic profiles; thus, further analysis of original crude images is not required.

Orthogonal array deciphering MRS medium requirements for isolated Lactobacillus rhamnosus ZY with cell properties characterization.

PubMed

Zhang, Yu; Ng, I-Son; Yao, Chuanyi; Lu, Yinghua

2014-09-01

Lactobacillus rhamnosus is a well-known lactic acid bacterium (LAB), but a new ZY strain was isolated for the first time from commercial probiotic powder recently. Although many studies have focused on developing cost-effective media for the production of LAB, the de Man, Rogosa and Sharpe (MRS) medium is still the most common medium for bioprocesses. The aim of the current study is to decipher the composition of MRS based on a statistical approach, which will allow a higher biomass of Lactobacillus to be obtained. In Taguchi's approach, an L27 orthogonal array was adopted to evaluate the significance of 10 ingredients in MRS, in which the effects of the components were ranked according to their effect on biomass at OD600 as dextrose > MnSO4·H2O > beef extract > CH3COONa > MgSO4 > yeast extract > proteose peptone > K2HPO4 > ammonium citrate > Tween 80. Although the individual trace elements of ammonium citrate, K2HPO4, CH3COONa and MgSO4 in MRS had an insignificant influence on the biomass after statistical analysis, the total elimination of trace elements would predominantly affect the cell growth of Lactobacillus. Further characterization of the cell properties through attenuated total reflectance of Fourier transform infrared (ATR-FTIR) spectroscopy and protein identification via SDS-PAGE coupled with tandem mass spectrometry implied that dextrose as major carbon source in MRS played the most crucial role for L. rhamnosus production. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Brain Natriuretic Peptide Is a Powerful Predictor of Outcome in Stroke Patients with Atrial Fibrillation

PubMed Central

Maruyama, Kenji; Uchiyama, Shinichiro; Shiga, Tsuyoshi; Iijima, Mutsumi; Ishizuka, Kentaro; Hoshino, Takao; Kitagawa, Kazuo

2017-01-01

Background Since stroke patients with nonvalvular atrial fibrillation (NVAF) have poor outcomes in general, the prediction of outcomes following discharge is of utmost concern for these patients. We previously reported that brain natriuretic peptide (BNP) levels were significantly higher in NVAF patients with larger infarcts, higher modified Rankin Scale (mRS) score, and higher CHADS2 score. In the present study, we evaluated an array of variables, including BNP, in order to determine significant predictors for functional outcome in patients with NVAF after acute ischemic stroke (AIS). Methods A total of 615 consecutive patients with AIS within 48 h of symptom onset, admitted to our hospital between April 2010 and October 2015, were retrospectively searched. Among these patients, we enrolled consecutive patients with NVAF. We evaluated the mRS score 3 months after onset of stroke and investigated associations between mRS score and the following clinical and echocardiographic variables. Categorical variables included male sex, current smoking, alcohol intake, hypertension, diabetes mellitus, dyslipidemia, coronary artery disease, peripheral artery disease, use of antiplatelet drugs, anticoagulants, or tissue plasminogen activator (tPA), and infarct size. Continuous variables included age, systolic blood pressure (SBP), diastolic blood pressure, hemoglobin, creatinine, D-dimer, brain natriuretic peptide (BNP), left atrial diameter, left ventricular ejection fraction (EF), and early mitral inflow velocity/diastolic mitral annular velocity (E/e’). We also analyzed the association of prestroke CHADS2, CHA2DS2-VASc, and R2CHADS2 scores, and National Institutes of Health Stroke Scale (NIHSS) score on admission with mRS score 3 months after the onset of stroke. Patients were classified into 2 groups according to mRS score: an mRS score ≤2 was defined as good outcome, an mRS score ≥3 was defined as poor outcome. To clarify the correlations between categorical or continuous variables and mRS score, uni- and multivariate logistic regression models using the stepwise variable selection method were applied. Results Among 157 patients with NVAF after AIS, 63.7% were male and the mean age was 75.9 years. In univariate regression analysis, poor outcome (mRS score ≥3) was associated with use of tPA, infarct size, age, SBP, BNP, EF, and NIHSS score. In multivariate regression analysis, BNP levels (odds ratio [OR] 6.40; 95% confidence interval [CI] 1.26–32.43; p = 0.0235) and NIHSS score (OR 2.87; 95% CI 1.84–4.47; p < 0.001) were significantly associated with poor outcome (mRS score ≥3) after adjusting for use of tPA, infarct size, age, BNP, EF, and NIHSS score. Conclusions Apart from NIHSS score, BNP was a very useful predictor for long-term outcomes of patients with NVAF after AIS. PMID:28253498

Automated assessment of cognitive health using smart home technologies.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2013-01-01

The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments. This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

Automated Assessment of Cognitive Health Using Smart Home Technologies

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2014-01-01

BACKGROUND The goal of this work is to develop intelligent systems to monitor the well being of individuals in their home environments. OBJECTIVE This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve = 0.80, g-mean = 0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained. PMID:23949177

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

PubMed Central

Srinivasan, Pratul P.; Kim, Leo A.; Mettu, Priyatham S.; Cousins, Scott W.; Comer, Grant M.; Izatt, Joseph A.; Farsiu, Sina

2014-01-01

We present a novel fully automated algorithm for the detection of retinal diseases via optical coherence tomography (OCT) imaging. Our algorithm utilizes multiscale histograms of oriented gradient descriptors as feature vectors of a support vector machine based classifier. The spectral domain OCT data sets used for cross-validation consisted of volumetric scans acquired from 45 subjects: 15 normal subjects, 15 patients with dry age-related macular degeneration (AMD), and 15 patients with diabetic macular edema (DME). Our classifier correctly identified 100% of cases with AMD, 100% cases with DME, and 86.67% cases of normal subjects. This algorithm is a potentially impactful tool for the remote diagnosis of ophthalmic diseases. PMID:25360373

The Association Between Duration of Resuscitation and Favorable Outcome After Out-of-Hospital Cardiac Arrest: Implications for Prolonging or Terminating Resuscitation

PubMed Central

Reynolds, Joshua C.; Grunau, Brian E.; Rittenberger, Jon C.; Sawyer, Kelly N.; Kurz, Michael C.; Callaway, Clifton W.

2016-01-01

Background Little evidence guides the appropriate duration of resuscitation in out-of-hospital cardiac arrest (OHCA), and case features justifying longer or shorter durations are ill-defined. We estimated the impact of resuscitation duration on the probability of favorable functional outcome in OHCA using a large, multi-center cohort. Methods Secondary analysis of a North American, single blind, multi-center, cluster-randomized clinical trial (ROC-PRIMED) of consecutive adults with non-traumatic, EMS-treated, OHCA. Primary exposure was duration of resuscitation in minutes (onset of professional resuscitation to return of spontaneous circulation [ROSC] or termination of resuscitation). Primary outcome was survival to hospital discharge with favorable outcome (modified Rankin scale [mRS] 0-3). Subjects were additionally classified as survival with unfavorable outcome (mRS 4-5), ROSC without survival (mRS 6), or without ROSC. Subject accrual was plotted as a function of resuscitation duration, and the dynamic probability of favorable outcome at discharge was estimated for the whole cohort and subgroups. Adjusted logistic regression models tested the association between resuscitation duration and survival with favorable outcome. Results The primary cohort included 11,368 subjects (median age 69 years [IQR: 56-81 years]; 7,121 men [62.6%]). Of these, 4,023 (35.4%) achieved ROSC, 1,232 (10.8%) survived to hospital discharge, and 905 (8.0%) had mRS 0-3 at discharge. Distribution of CPR duration differed by outcome (p<0.00001). For CPR duration up to 37.0 minutes (95%CI 34.9-40.9 minutes), 99% with eventual mRS 0-3 at discharge achieved ROSC. Dynamic probability of mRS 0-3 at discharge declined over elapsed resuscitation duration, but subjects with initial shockable cardiac rhythm, witnessed cardiac arrest, and bystander CPR were more likely to survive with favorable outcome after prolonged efforts (30-40 minutes). Adjusting for prehospital (OR 0.93; 95%CI 0.92-0.95) and inpatient (OR 0.97; 95%CI 0.95-0.99) covariates, resuscitation duration was associated with survival to discharge with mRS 0-3. Conclusions Shorter resuscitation duration was associated with likelihood of favorable outcome at hospital discharge. Subjects with favorable case features were more likely to survive prolonged resuscitation up to 47 minutes. PMID:27760796

Diffusion tensor imaging and MR spectroscopy of microstructural alterations and metabolite concentration changes in the auditory neural pathway of pediatric congenital sensorineural hearing loss patients.

PubMed

Wu, Chunxiao; Huang, Lexing; Tan, Hui; Wang, Yanting; Zheng, Hongyi; Kong, Lingmei; Zheng, Wenbin

2016-05-15

Our objective was to evaluate age-dependent changes in microstructure and metabolism in the auditory neural pathway, of children with profound sensorineural hearing loss (SNHL), and to differentiate between good and poor surgical outcome cochlear implantation (CI) patients by using diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS). Ninety-two SNHL children (49 males, 43 females; mean age, 4.9 years) were studied by conventional MR imaging, DTI and MRS. Patients were divided into three groups: Group A consisted of children≤1 years old (n=20), Group B consisted of children 1-3 years old (n=31), and group C consisted of children 3-14 years old (n=41). Among the 31 patients (19 males and 12 females, 12m- 14y ) with CI, 18 patients (mean age 4.8±0.7 years) with a categories of auditory performance (CAP) score over five were classified into the good outcome group and 13 patients (mean age, 4.4±0.7 years) with a CAP score below five were classified into the poor outcome group. Two DTI parameters, fractional anisotropy (FA) and apparent diffusion coefficient (ADC), were measured in the superior temporal gyrus (STG) and auditory radiation. Regions of interest for metabolic change measurements were located inside the STG. DTI values were measured based on region-of-interest analysis and MRS values for correlation analysis with CAP scores. Compared with healthy individuals, 92 SNHL patients displayed decreased FA values in the auditory radiation and STG (p<0.05). Only decreased FA values in the auditory radiation was observed in Group A. Decreased FA values in the auditory radiation and STG were both observed in B and C groups. However, in Group C, the N-acetyl aspartate/creatinine ratio in the STG was also significantly decreased (p<0.05). Correlation analyses at 12 months post-operation revealed strong correlations between the FA, in the auditory radiation, and CAP scores (r=0.793, p<0.01). DTI and MRS can be used to evaluate microstructural alterations and metabolite concentration changes in the auditory neural pathway that are not detectable by conventional MR imaging. The observed changes in FA suggest that children with SNHL have a developmental delay in myelination in the auditory neural pathway, and it also display greater metabolite concentration changes in the auditory cortex in older children, suggest that early cochlear implantation might be more effective in restoring hearing in children with SNHL. This article is part of a Special Issue entitled SI: Brain and Memory. Copyright © 2014 Elsevier B.V. All rights reserved.

Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS

NASA Astrophysics Data System (ADS)

Vinding, Mads S.; Laustsen, Christoffer; Maximov, Ivan I.; Søgaard, Lise Vejby; Ardenkjær-Larsen, Jan H.; Nielsen, Niels Chr.

2013-02-01

Aimed at 13C metabolic magnetic resonance imaging (MRI) and spectroscopy (MRS) applications, we demonstrate that dynamic nuclear polarization (DNP) may be combined with optimal control 2D spatial selection to simultaneously obtain high sensitivity and well-defined spatial restriction. This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-13C]pyruvate on a 4.7 T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region-of-interest (ROI) single metabolite signals available for higher image resolution or single-peak spectra. The 2D spatial-selective rf pulses were designed using a novel Krotov-based optimal control approach capable of iteratively fast providing successful pulse sequences in the absence of qualified initial guesses. The technique may be important for early detection of abnormal metabolism, monitoring disease progression, and drug research.

Automated classification of mouse pup isolation syllables: from cluster analysis to an Excel-based "mouse pup syllable classification calculator".

PubMed

Grimsley, Jasmine M S; Gadziola, Marie A; Wenstrup, Jeffrey J

2012-01-01

Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified 10 syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.

Automating document classification for the Immune Epitope Database

PubMed Central

Wang, Peng; Morgan, Alexander A; Zhang, Qing; Sette, Alessandro; Peters, Bjoern

2007-01-01

Background The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose. Results We here report our experience in automating this process using Naïve Bayes classifiers trained on 20,910 abstracts classified by domain experts. Improvements on the basic classifier performance were made by a) utilizing information stored in PubMed beyond the abstract itself b) applying standard feature selection criteria and c) extracting domain specific feature patterns that e.g. identify peptides sequences. We have implemented the classifier into the curation process determining if abstracts are clearly relevant, clearly irrelevant, or if no certain classification can be made, in which case the abstracts are manually classified. Testing this classification scheme on an independent dataset, we achieve 95% sensitivity and specificity in the 51.1% of abstracts that were automatically classified. Conclusion By implementing text classification, we have sped up the reference selection process without sacrificing sensitivity or specificity of the human expert classification. This study provides both practical recommendations for users of text classification tools, as well as a large dataset which can serve as a benchmark for tool developers. PMID:17655769

Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

NASA Astrophysics Data System (ADS)

Heleno, S.; Matias, M.; Pina, P.; Sousa, A. J.

2015-09-01

A method for semi-automatic landslide detection, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a Support Vector Machine classifier on a GeoEye-1 multispectral image, sensed 3 days after the major damaging landslide event that occurred in Madeira island (20 February 2010), with a pre-event LIDAR Digital Elevation Model. The testing is developed in a 15 km2-wide study area, where 95 % of the landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier east facing-slopes.

Unresolved Galaxy Classifier for ESA/Gaia mission: Support Vector Machines approach

NASA Astrophysics Data System (ADS)

Bellas-Velidis, Ioannis; Kontizas, Mary; Dapergolas, Anastasios; Livanou, Evdokia; Kontizas, Evangelos; Karampelas, Antonios

A software package Unresolved Galaxy Classifier (UGC) is being developed for the ground-based pipeline of ESA's Gaia mission. It aims to provide an automated taxonomic classification and specific parameters estimation analyzing Gaia BP/RP instrument low-dispersion spectra of unresolved galaxies. The UGC algorithm is based on a supervised learning technique, the Support Vector Machines (SVM). The software is implemented in Java as two separate modules. An offline learning module provides functions for SVM-models training. Once trained, the set of models can be repeatedly applied to unknown galaxy spectra by the pipeline's application module. A library of galaxy models synthetic spectra, simulated for the BP/RP instrument, is used to train and test the modules. Science tests show a very good classification performance of UGC and relatively good regression performance, except for some of the parameters. Possible approaches to improve the performance are discussed.

An ordinal classification approach for CTG categorization.

PubMed

Georgoulas, George; Karvelis, Petros; Gavrilis, Dimitris; Stylios, Chrysostomos D; Nikolakopoulos, George

2017-07-01

Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

Automatic stage identification of Drosophila egg chamber based on DAPI images

PubMed Central

Jia, Dongyu; Xu, Qiuping; Xie, Qian; Mio, Washington; Deng, Wu-Min

2016-01-01

The Drosophila egg chamber, whose development is divided into 14 stages, is a well-established model for developmental biology. However, visual stage determination can be a tedious, subjective and time-consuming task prone to errors. Our study presents an objective, reliable and repeatable automated method for quantifying cell features and classifying egg chamber stages based on DAPI images. The proposed approach is composed of two steps: 1) a feature extraction step and 2) a statistical modeling step. The egg chamber features used are egg chamber size, oocyte size, egg chamber ratio and distribution of follicle cells. Methods for determining the on-site of the polytene stage and centripetal migration are also discussed. The statistical model uses linear and ordinal regression to explore the stage-feature relationships and classify egg chamber stages. Combined with machine learning, our method has great potential to enable discovery of hidden developmental mechanisms. PMID:26732176

SeqReporter: automating next-generation sequencing result interpretation and reporting workflow in a clinical laboratory.

PubMed

Roy, Somak; Durso, Mary Beth; Wald, Abigail; Nikiforov, Yuri E; Nikiforova, Marina N

2014-01-01

A wide repertoire of bioinformatics applications exist for next-generation sequencing data analysis; however, certain requirements of the clinical molecular laboratory limit their use: i) comprehensive report generation, ii) compatibility with existing laboratory information systems and computer operating system, iii) knowledgebase development, iv) quality management, and v) data security. SeqReporter is a web-based application developed using ASP.NET framework version 4.0. The client-side was designed using HTML5, CSS3, and Javascript. The server-side processing (VB.NET) relied on interaction with a customized SQL server 2008 R2 database. Overall, 104 cases (1062 variant calls) were analyzed by SeqReporter. Each variant call was classified into one of five report levels: i) known clinical significance, ii) uncertain clinical significance, iii) pending pathologists' review, iv) synonymous and deep intronic, and v) platform and panel-specific sequence errors. SeqReporter correctly annotated and classified 99.9% (859 of 860) of sequence variants, including 68.7% synonymous single-nucleotide variants, 28.3% nonsynonymous single-nucleotide variants, 1.7% insertions, and 1.3% deletions. One variant of potential clinical significance was re-classified after pathologist review. Laboratory information system-compatible clinical reports were generated automatically. SeqReporter also facilitated quality management activities. SeqReporter is an example of a customized and well-designed informatics solution to optimize and automate the downstream analysis of clinical next-generation sequencing data. We propose it as a model that may envisage the development of a comprehensive clinical informatics solution. Copyright © 2014 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Inspection of wear particles in oils by using a fuzzy classifier

NASA Astrophysics Data System (ADS)

Hamalainen, Jari J.; Enwald, Petri

1994-11-01

The reliability of stand-alone machines and larger production units can be improved by automated condition monitoring. Analysis of wear particles in lubricating or hydraulic oils helps diagnosing the wear states of machine parts. This paper presents a computer vision system for automated classification of wear particles. Digitized images from experiments with a bearing test bench, a hydraulic system with an industrial company, and oil samples from different industrial sources were used for algorithm development and testing. The wear particles were divided into four classes indicating different wear mechanisms: cutting wear, fatigue wear, adhesive wear, and abrasive wear. The results showed that the fuzzy K-nearest neighbor classifier utilized gave the same distribution of wear particles as the classification by a human expert.

Evaluation of hyperpolarized [1-¹³C]-pyruvate by magnetic resonance to detect ionizing radiation effects in real time.

PubMed

Sandulache, Vlad C; Chen, Yunyun; Lee, Jaehyuk; Rubinstein, Ashley; Ramirez, Marc S; Skinner, Heath D; Walker, Christopher M; Williams, Michelle D; Tailor, Ramesh; Court, Laurence E; Bankson, James A; Lai, Stephen Y

2014-01-01

Ionizing radiation (IR) cytotoxicity is primarily mediated through reactive oxygen species (ROS). Since tumor cells neutralize ROS by utilizing reducing equivalents, we hypothesized that measurements of reducing potential using real-time hyperpolarized (HP) magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) can serve as a surrogate marker of IR induced ROS. This hypothesis was tested in a pre-clinical model of anaplastic thyroid carcinoma (ATC), an aggressive head and neck malignancy. Human ATC cell lines were utilized to test IR effects on ROS and reducing potential in vitro and [1-¹³C] pyruvate HP-MRS/MRSI imaging of ATC orthotopic xenografts was used to study in vivo effects of IR. IR increased ATC intra-cellular ROS levels resulting in a corresponding decrease in reducing equivalent levels. Exogenous manipulation of cellular ROS and reducing equivalent levels altered ATC radiosensitivity in a predictable manner. Irradiation of ATC xenografts resulted in an acute drop in reducing potential measured using HP-MRS, reflecting the shunting of reducing equivalents towards ROS neutralization. Residual tumor tissue post irradiation demonstrated heterogeneous viability. We have adapted HP-MRS/MRSI to non-invasively measure IR mediated changes in tumor reducing potential in real time. Continued development of this technology could facilitate the development of an adaptive clinical algorithm based on real-time adjustments in IR dose and dose mapping.

SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

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

Wahi-Anwar, M; Lo, P; Kim, H

Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifiesmore » the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel component to automatically verify image acquisition parameters and automated adherence to specifications. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant support from: U01 CA181156.« less

Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

PubMed Central

Karaçalı, Bilge; Vamvakidou, Alexandra P; Tözeren, Aydın

2007-01-01

Background Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images. Methods Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system. Results Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images. Conclusion Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development. PMID:17822559

N-acetylaspartate, choline, myoinositol, glutamine and glutamate (glx) concentration changes in proton MR spectroscopy (1H MRS) in patients with mild cognitive impairment (MCI).

PubMed

Walecki, Jerzy; Barcikowska, Maria; Ćwikła, Jarosław B; Gabryelewicz, Tomasz

2011-12-01

Purpose of study was evaluation of regional metabolic disorders using 1H MRS in patients with MCI, as a predictor of clinical conversion to dementia based on clinical follow-up. The study group consisted of 31 subjects with diagnosis of MCI based on criteria the Mayo Clinic Group. ¹H MRS was performed with a single-voxel method using PRESS sequence. The volume of interest (VOI) was located in the hippocampal formation and posterior part of the cingulated gyrus. Patients had annual clinical control at least twice. At the beginning, 9 had amnestic MCI and the others had multidomain MCI. During follow-up (median 3 yrs) 8 subjects had stable disease (SD), 13 had disease progression (DP) and 10 develop Alzheimer disease (AD). Baseline metabolic ratios (1H MRS) between 3 groups indicated significant difference (P < 0.05) in left frontal lobe in mI/H20 ratio, between patients with SD (0.27) and DP. In comparing the groups with DP and AD, a significant difference in NAA/Cr (1.77 vs. 1.43) was found. A significant difference within left temporal external lobes was found between SD and DP in NAA/H2O ratio (0.55 vs. 0.51). An additional significant difference within medial temporal lobe was found between DP and AD in Glx/H2O ratio (0.44 vs. 0.34) on the right side. 1H MRS seems to be sensitive method allows prediction of which patients are liable to progress from MCI to AD. Combined with other biomarkers of disease staging, it is an important approach in the preclinical AD diagnosis, as well as the assessment of dementia progression.

Design of a fused phantom for quantitative evaluation of brain metabolites and enhanced quality assurance testing for magnetic resonance imaging and spectroscopy.

PubMed

Song, Kyu-Ho; Kim, Sang-Young; Lee, Do-Wan; Jung, Jin-Young; Lee, Jung-Hoon; Baek, Hyeon-Man; Choe, Bo-Young

2015-11-30

Magnetic resonance imaging and spectroscopy (MRI-MRS) is a useful tool for the identification and evaluation of chemical changes in anatomical regions. Quality assurance (QA) is performed in either images or spectra using QA phantom. Therefore, consistent and uniform technical MRI-MRS QA is crucial. Here we developed an MRI-MRS fused phantom along with the inserts for metabolite quantification to simultaneously optimize QA parameters for both MRI and MRS. T1- and T2-weighted images were obtained and MRS was performed with point-resolved spectroscopy. Using the fused phantom, the results of measuring MRI factors were: geometric distortion, <2% and ± 2 mm; image intensity uniformity, 83.09 ± 1.33%; percent-signal ghosting, 0.025 ± 0.004; low-contrast object detectability, 27.85 ± 0.80. In addition, the signal-to-noise ratio of N-acetyl-aspartate was consistently high (42.00 ± 5.66). In previous studies, MR phantoms could not obtain information from both images and spectra in the MR scanner simultaneously. Here we designed and developed a phantom for accurate and consistent QA within the acceptance range. It is important to take into account variations in the QA value using the MRI-MRS phantom, when comparing to other clinical or research MR scanners. The MRI-MRS QA factors obtained simultaneously using the phantom can facilitate evaluation of both images and spectra, and provide guidelines for obtaining MRI and MRS QA factors simultaneously. Copyright © 2015 Elsevier B.V. All rights reserved.

Learning to Do Concept-Based Pragmatics Instruction: Teacher Development and L2 Pedagogical Content Knowledge

ERIC Educational Resources Information Center

van Compernolle, Rémi A.; Henery, Ashlie

2015-01-01

This article explores the development of pedagogical content knowledge in relation to one teacher's experience in learning to engage in a Vygotskian approach to teaching second language (L2) pragmatics known as "concept-based pragmatics instruction" (CBPI). The teacher, Mrs. Hanks, was a PhD candidate in second language acquisition at…

Automated structural classification of lipids by machine learning.

PubMed

Taylor, Ryan; Miller, Ryan H; Miller, Ryan D; Porter, Michael; Dalgleish, James; Prince, John T

2015-03-01

Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms. We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification. Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at https://www.github.com/princelab/lipid_classifier. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes.

PubMed

Das, Barnan; Cook, Diane J; Krishnan, Narayanan C; Schmitter-Edgecombe, Maureen

2016-08-01

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step towards automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

Automated identification of diagnosis and co-morbidity in clinical records.

PubMed

Cano, C; Blanco, A; Peshkin, L

2009-01-01

Automated understanding of clinical records is a challenging task involving various legal and technical difficulties. Clinical free text is inherently redundant, unstructured, and full of acronyms, abbreviations and domain-specific language which make it challenging to mine automatically. There is much effort in the field focused on creating specialized ontology, lexicons and heuristics based on expert knowledge of the domain. However, ad-hoc solutions poorly generalize across diseases or diagnoses. This paper presents a successful approach for a rapid prototyping of a diagnosis classifier based on a popular computational linguistics platform. The corpus consists of several hundred of full length discharge summaries provided by Partners Healthcare. The goal is to identify a diagnosis and assign co-morbidi-ty. Our approach is based on the rapid implementation of a logistic regression classifier using an existing toolkit: LingPipe (http://alias-i.com/lingpipe). We implement and compare three different classifiers. The baseline approach uses character 5-grams as features. The second approach uses a bag-of-words representation enriched with a small additional set of features. The third approach reduces a feature set to the most informative features according to the information content. The proposed systems achieve high performance (average F-micro 0.92) for the task. We discuss the relative merit of the three classifiers. Supplementary material with detailed results is available at: http:// decsai.ugr.es/~ccano/LR/supplementary_ material/ We show that our methodology for rapid prototyping of a domain-unaware system is effective for building an accurate classifier for clinical records.

Fully-automated identification of fish species based on otolith contour: using short-time Fourier transform and discriminant analysis (STFT-DA).

PubMed

Salimi, Nima; Loh, Kar Hoe; Kaur Dhillon, Sarinder; Chong, Ving Ching

2016-01-01

Background. Fish species may be identified based on their unique otolith shape or contour. Several pattern recognition methods have been proposed to classify fish species through morphological features of the otolith contours. However, there has been no fully-automated species identification model with the accuracy higher than 80%. The purpose of the current study is to develop a fully-automated model, based on the otolith contours, to identify the fish species with the high classification accuracy. Methods. Images of the right sagittal otoliths of 14 fish species from three families namely Sciaenidae, Ariidae, and Engraulidae were used to develop the proposed identification model. Short-time Fourier transform (STFT) was used, for the first time in the area of otolith shape analysis, to extract important features of the otolith contours. Discriminant Analysis (DA), as a classification technique, was used to train and test the model based on the extracted features. Results. Performance of the model was demonstrated using species from three families separately, as well as all species combined. Overall classification accuracy of the model was greater than 90% for all cases. In addition, effects of STFT variables on the performance of the identification model were explored in this study. Conclusions. Short-time Fourier transform could determine important features of the otolith outlines. The fully-automated model proposed in this study (STFT-DA) could predict species of an unknown specimen with acceptable identification accuracy. The model codes can be accessed at http://mybiodiversityontologies.um.edu.my/Otolith/ and https://peerj.com/preprints/1517/. The current model has flexibility to be used for more species and families in future studies.

Applying machine learning classification techniques to automate sky object cataloguing

NASA Astrophysics Data System (ADS)

Fayyad, Usama M.; Doyle, Richard J.; Weir, W. Nick; Djorgovski, Stanislav

1993-08-01

We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3 (Generalized ID3) and O-B Tree, two inductive learning techniques, learns classification decision trees from examples. These classifiers will then be applied to new data. These developmnent process is highly interactive, with astronomer input playing a vital role. Astronomers refine the feature set used to construct sky object descriptions, and evaluate the performance of the automated classification technique on new data. This paper gives an overview of the machine learning techniques with an emphasis on their general applicability, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our machine learning approach is well-suited to the problem. The primary benefit of the approach is increased data reduction throughput. Another benefit is consistency of classification. The classification rules which are the product of the inductive learning techniques will form an objective, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically catalogued data.

["And suddenly I have a tumor" - the situation of family S./N].

PubMed

Ries-Gisler, Tobias; Spirig, Rebecca

2014-04-01

Many families affected by a terminal illness need professional help and support. In order to be able to cope with emotional stress, loss of light-heartedness and changes in family structure thorough information is important for patients and their families. The Calgary Family-Assessment and Calgary Family Intervention-Model are suitable to determine the needs of concerned families and hence to offer appropriate interventions. In an instrumental case study the situation of Mrs. S.2 and her family was analyzed. Mrs. S. is suffering from an inoperable adenocarcinoma. An assessment classified the different information given during the first meeting and determined the focus of the interventions. Interventions concentrated on cognitive and emotional support. The case study showed how family models for nurses could systematically guide professionals in supporting families. Listening and spending time with the concerned person and their families showed to offer important factors, which were perceived as very helpful by the families.

Examination of magnetic resonance soundings in the Central Platter River Basin for ground water model enhancements

NASA Astrophysics Data System (ADS)

Abraham, J. D.; Kress, W. H.; Cannia, J. C.; Steele, G. V.; Smith, B. D.; Woodward, D.

2008-12-01

In 2007, the USGS in cooperation with the Central Platte Natural Resources District, central Nebraska, initiated a four year study to test the usefulness of magnetic resonance rounding (MRS) to gather information on aquifer characteristics. Magnetic resonance sounding is a ground surface applied tool which has the potential to measure hydraulic conductivity at depth using noninvasive means. This in turn will provide a low cost alternative to traditional aquifer tests. MRS also will allow for collection of large data sets of aquifer properties during short periods of time. The work is under way in Dawson County near Lexington, Nebraska to characterize the hydrogeology of the Quaternary-age alluvial and underlying Tertiary-age Ogallala Group aquifers that occur within the Platte River Valley. This county was selected because it lies in an area of Nebraska that has major groundwater- surface water management issues which have stimulated the development of regional and local groundwater models. Data used to evaluate the MRS during this study were derived from traditional constant discharge aquifer tests, borehole flow meter tests, lithologic descriptions, borehole geophysics, and time-domain electromagnetic soundings. This study presents methods and interpretation of MRS. The MRS-derived hydraulic conductivity data will be compared to hydraulic conductivity data from two separate constant discharge pumping tests of the alluvium and Ogallala Group aquifers at Site 72 The MRS-derived hydraulic conductivity data will also be compared to conductivity estimates based on data from a borehole flow meter test. This information can potentially be incorporated into groundwater models of the area to provide improved data sets of aquifer characteristics. The research will document an integrated MRS, surface geophysical, borehole geophysical, borehole flow meter and aquifer test approach in which the hydrostratigraphy of the Platte River alluvial aquifer and Ogallala aquifer can be described.

Challenges in the Assessment and Treatment of Health Anxiety: The Case of Mrs. A.

ERIC Educational Resources Information Center

McCabe, Randi E.; Antony, Martin M.

2004-01-01

Health anxiety can present a challenge for clinicians, both from the perspective of assigning a "DSM-IV" diagnosis and in developing an appropriate treatment plan. The case of Mrs. A. illustrates some of the complexities that arise in the diagnosis and treatment of health anxiety. Mrs. A. is a 60-year-old retired teacher who presented to a…

Automated Essay Grading using Machine Learning Algorithm

NASA Astrophysics Data System (ADS)

Ramalingam, V. V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu

2018-04-01

Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator’s time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.

Rapid and easy detection of low-level resistance to vancomycin in methicillin-resistant Staphylococcus aureus by matrix-assisted laser desorption ionization time-of-flight mass spectrometry.

PubMed

Asakura, Kota; Azechi, Takuya; Sasano, Hiroshi; Matsui, Hidehito; Hanaki, Hideaki; Miyazaki, Motoyasu; Takata, Tohru; Sekine, Miwa; Takaku, Tomoiku; Ochiai, Tomonori; Komatsu, Norio; Shibayama, Keigo; Katayama, Yuki; Yahara, Koji

2018-01-01

Vancomycin-intermediately resistant Staphylococcus aureus (VISA) and heterogeneous VISA (hVISA) are associated with treatment failure. hVISA contains only a subpopulation of cells with increased minimal inhibitory concentrations, and its detection is problematic because it is classified as vancomycin-susceptible by standard susceptibility testing and the gold-standard method for its detection is impractical in clinical microbiology laboratories. Recently, a research group developed a machine-learning classifier to distinguish VISA and hVISA from vancomycin-susceptible S. aureus (VSSA) according to matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) data. Nonetheless, the sensitivity of hVISA classification was found to be 76%, and the program was not completely automated with a graphical user interface. Here, we developed a more accurate machine-learning classifier for discrimination of hVISA from VSSA and VISA among MRSA isolates in Japanese hospitals by means of MALDI-TOF MS data. The classifier showed 99% sensitivity of hVISA classification. Furthermore, we clarified the procedures for preparing samples and obtaining MALDI-TOF MS data and developed all-in-one software, hVISA Classifier, with a graphical user interface that automates the classification and is easy for medical workers to use; it is publicly available at https://github.com/bioprojects/hVISAclassifier. This system is useful and practical for screening MRSA isolates for the hVISA phenotype in clinical microbiology laboratories and thus should improve treatment of MRSA infections.

Rapid and easy detection of low-level resistance to vancomycin in methicillin-resistant Staphylococcus aureus by matrix-assisted laser desorption ionization time-of-flight mass spectrometry

PubMed Central

Asakura, Kota; Azechi, Takuya; Sasano, Hiroshi; Matsui, Hidehito; Hanaki, Hideaki; Miyazaki, Motoyasu; Takata, Tohru; Sekine, Miwa; Takaku, Tomoiku; Ochiai, Tomonori; Komatsu, Norio; Shibayama, Keigo

2018-01-01

Vancomycin-intermediately resistant Staphylococcus aureus (VISA) and heterogeneous VISA (hVISA) are associated with treatment failure. hVISA contains only a subpopulation of cells with increased minimal inhibitory concentrations, and its detection is problematic because it is classified as vancomycin-susceptible by standard susceptibility testing and the gold-standard method for its detection is impractical in clinical microbiology laboratories. Recently, a research group developed a machine-learning classifier to distinguish VISA and hVISA from vancomycin-susceptible S. aureus (VSSA) according to matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) data. Nonetheless, the sensitivity of hVISA classification was found to be 76%, and the program was not completely automated with a graphical user interface. Here, we developed a more accurate machine-learning classifier for discrimination of hVISA from VSSA and VISA among MRSA isolates in Japanese hospitals by means of MALDI-TOF MS data. The classifier showed 99% sensitivity of hVISA classification. Furthermore, we clarified the procedures for preparing samples and obtaining MALDI-TOF MS data and developed all-in-one software, hVISA Classifier, with a graphical user interface that automates the classification and is easy for medical workers to use; it is publicly available at https://github.com/bioprojects/hVISAclassifier. This system is useful and practical for screening MRSA isolates for the hVISA phenotype in clinical microbiology laboratories and thus should improve treatment of MRSA infections. PMID:29522576

Effects of a Self-Exercise Program on Activities of Daily Living in Patients After Acute Stroke: A Propensity Score Analysis Based on the Japan Association of Rehabilitation Database.

PubMed

Shiraishi, Nariaki; Suzuki, Yusuke; Matsumoto, Daisuke; Jeong, Seungwon; Sugiyama, Motoya; Kondo, Katsunori

2017-03-01

To investigate whether self-exercise programs for patients after stroke contribute to improved activities of daily living (ADL) at hospital discharge. Retrospective, observational, propensity score (PS)-matched case-control study. General hospitals. Participants included patients after stroke (N=1560) hospitalized between January 3, 2006, and December 26, 2012, satisfying the following criteria: (1) data on age, sex, duration from stroke to hospital admission, length of stay, FIM score, modified Rankin Scale (mRS) score, Glasgow Coma Scale score, Japan Stroke Scale score, and self-exercise program participation were available; and (2) admitted within 7 days after stroke onset, length of stay was between 7 and 60 days, prestroke mRS score was ≤2, and not discharged because of FIM or mRS exacerbation. A total of 780 PS-matched pairs were selected for each of the self-exercise program and no-self-exercise program groups. Self-exercise program participation. At discharge, FIM motor score, FIM cognitive score, FIM motor score gain (discharge value - admission value), FIM motor score gain rate (gain/length of stay), a binary variable divided by the median FIM motor score gain rate (high efficiency or no-high efficiency), and mRS score. Patients were classified into a self-exercise program (n=780) or a no-self-exercise program (n=780) group. After matching, there were no significant between-group differences, except motor system variables. The receiver operating characteristic curve for PS had an area under the curve value of .71 with a 95% confidence interval of .68 to .73, and the model was believed to have a relatively favorable fit. A logistic regression analysis of PS-matched pairs suggested that the self-exercise program was effective, with an overall odds ratio for ADL (high efficiency or no-high efficiency) of 2.2 (95% confidence ratio, 1.75-2.70). SEPs may contribute to improving ADL. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Histogram deconvolution - An aid to automated classifiers

NASA Technical Reports Server (NTRS)

Lorre, J. J.

1983-01-01

It is shown that N-dimensional histograms are convolved by the addition of noise in the picture domain. Three methods are described which provide the ability to deconvolve such noise-affected histograms. The purpose of the deconvolution is to provide automated classifiers with a higher quality N-dimensional histogram from which to obtain classification statistics.

Automated detection of neovascularization for proliferative diabetic retinopathy screening.

PubMed

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

2016-08-01

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

An Automated Classification Technique for Detecting Defects in Battery Cells

NASA Technical Reports Server (NTRS)

McDowell, Mark; Gray, Elizabeth

2006-01-01

Battery cell defect classification is primarily done manually by a human conducting a visual inspection to determine if the battery cell is acceptable for a particular use or device. Human visual inspection is a time consuming task when compared to an inspection process conducted by a machine vision system. Human inspection is also subject to human error and fatigue over time. We present a machine vision technique that can be used to automatically identify defective sections of battery cells via a morphological feature-based classifier using an adaptive two-dimensional fast Fourier transformation technique. The initial area of interest is automatically classified as either an anode or cathode cell view as well as classified as an acceptable or a defective battery cell. Each battery cell is labeled and cataloged for comparison and analysis. The result is the implementation of an automated machine vision technique that provides a highly repeatable and reproducible method of identifying and quantifying defects in battery cells.

A Minimum Spanning Forest Based Method for Noninvasive Cancer Detection with Hyperspectral Imaging

PubMed Central

Pike, Robert; Lu, Guolan; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-01-01

Goal The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine (SVM) classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection. PMID:26285052

Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population.

PubMed

Areeckal, A S; Jayasheelan, N; Kamath, J; Zawadynski, S; Kocher, M; David S, S

2018-03-01

We propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects. We propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers. Cortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws' masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass. In our pilot study, the proposed segmentation method shows 89.9 and 93.5% accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3% and test accuracy of 88.5%. An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.

Automated tissue classification of intracardiac optical coherence tomography images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

2016-03-01

Remodeling of the myocardium is associated with increased risk of arrhythmia and heart failure. Our objective is to automatically identify regions of fibrotic myocardium, dense collagen, and adipose tissue, which can serve as a way to guide radiofrequency ablation therapy or endomyocardial biopsies. Using computer vision and machine learning, we present an automated algorithm to classify tissue compositions from cardiac optical coherence tomography (OCT) images. Three dimensional OCT volumes were obtained from 15 human hearts ex vivo within 48 hours of donor death (source, NDRI). We first segmented B-scans using a graph searching method. We estimated the boundary of each region by minimizing a cost function, which consisted of intensity, gradient, and contour smoothness. Then, features, including texture analysis, optical properties, and statistics of high moments, were extracted. We used a statistical model, relevance vector machine, and trained this model with abovementioned features to classify tissue compositions. To validate our method, we applied our algorithm to 77 volumes. The datasets for validation were manually segmented and classified by two investigators who were blind to our algorithm results and identified the tissues based on trichrome histology and pathology. The difference between automated and manual segmentation was 51.78 +/- 50.96 μm. Experiments showed that the attenuation coefficients of dense collagen were significantly different from other tissue types (P < 0.05, ANOVA). Importantly, myocardial fibrosis tissues were different from normal myocardium in entropy and kurtosis. The tissue types were classified with an accuracy of 84%. The results show good agreements with histology.

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.

PubMed

Jin, Bo; Krishnan, Balu; Adler, Sophie; Wagstyl, Konrad; Hu, Wenhan; Jones, Stephen; Najm, Imad; Alexopoulos, Andreas; Zhang, Kai; Zhang, Jianguo; Ding, Meiping; Wang, Shuang; Wang, Zhong Irene

2018-05-01

Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In this study, we utilized surface-based MRI morphometry and machine learning for automated lesion detection in a mixed cohort of patients with FCD type II from 3 different epilepsy centers. Sixty-one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different MRI scanners. T1-volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using k-fold cross-validation. The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the ROC analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal MRI than patients with unequivocally positive MRI. Subgroup analysis showed the size of the training group and normal control database impacted classifier performance. Automated surface-based MRI morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve FCD detection in presurgical evaluation for patients with pharmacoresistant epilepsy. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Association of Computed Tomography Ischemic Lesion Location With Functional Outcome in Acute Large Vessel Occlusion Ischemic Stroke.

PubMed

Ernst, Marielle; Boers, Anna M M; Aigner, Annette; Berkhemer, Olvert A; Yoo, Albert J; Roos, Yvo B; Dippel, Diederik W J; van der Lugt, Aad; van Oostenbrugge, Robert J; van Zwam, Wim H; Fiehler, Jens; Marquering, Henk A; Majoie, Charles B L M

2017-09-01

Ischemic lesion volume (ILV) assessed by follow-up noncontrast computed tomography correlates only moderately with clinical end points, such as the modified Rankin Scale (mRS). We hypothesized that the association between follow-up noncontrast computed tomography ILV and outcome as assessed with mRS 3 months after stroke is strengthened when taking the mRS relevance of the infarct location into account. An anatomic atlas with 66 areas was registered to the follow-up noncontrast computed tomographic images of 254 patients from the MR CLEAN trial (Multicenter Randomized Clinical Trial of Endovascular Treatment of Acute Ischemic Stroke in the Netherlands). The anatomic brain areas were divided into brain areas of high, moderate, and low mRS relevance as reported in the literature. Based on this distinction, the ILV in brain areas of high, moderate, and low mRS relevance was assessed for each patient. Binary and ordinal logistic regression analyses with and without adjustment for known confounders were performed to assess the association between the ILVs of different mRS relevance and outcome. The odds for a worse outcome (higher mRS) were markedly higher given an increase of ILV in brain areas of high mRS relevance (odds ratio, 1.42; 95% confidence interval, 1.31-1.55 per 10 mL) compared with an increase in total ILV (odds ratios, 1.16; 95% confidence interval, 1.12-1.19 per 10 mL). Regression models using ILV in brain areas of high mRS relevance instead of total ILV showed a higher quality. The association between follow-up noncontrast computed tomography ILV and outcome as assessed with mRS 3 months after stroke is strengthened by accounting for the mRS relevance of the affected brain areas. Future prediction models should account for the ILV in brain areas of high mRS relevance. © 2017 American Heart Association, Inc.

Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease

PubMed Central

Pour, Elias Khalili; Pourreza, Hamidreza; Zamani, Kambiz Ameli; Mahmoudi, Alireza; Sadeghi, Arash Mir Mohammad; Shadravan, Mahla; Karkhaneh, Reza; Pour, Ramak Rouhi

2017-01-01

Purpose To design software with a novel algorithm, which analyzes the tortuosity and vascular dilatation in fundal images of retinopathy of prematurity (ROP) patients with an acceptable accuracy for detecting plus disease. Methods Eighty-seven well-focused fundal images taken with RetCam were classified to three groups of plus, non-plus, and pre-plus by agreement between three ROP experts. Automated algorithms in this study were designed based on two methods: the curvature measure and distance transform for assessment of tortuosity and vascular dilatation, respectively as two major parameters of plus disease detection. Results Thirty-eight plus, 12 pre-plus, and 37 non-plus images, which were classified by three experts, were tested by an automated algorithm and software evaluated the correct grouping of images in comparison to expert voting with three different classifiers, k-nearest neighbor, support vector machine and multilayer perceptron network. The plus, pre-plus, and non-plus images were analyzed with 72.3%, 83.7%, and 84.4% accuracy, respectively. Conclusions The new automated algorithm used in this pilot scheme for diagnosis and screening of patients with plus ROP has acceptable accuracy. With more improvements, it may become particularly useful, especially in centers without a skilled person in the ROP field. PMID:29022295

Developing an Automatic Crawling System for Populating a Digital Repository of Professional Development Resources: A Pilot Study

ERIC Educational Resources Information Center

Park, Jung-ran; Yang, Chris; Tosaka, Yuji; Ping, Qing; Mimouni, Houda El

2016-01-01

This study is a part of the larger project that develops a sustainable digital repository of professional development resources on emerging data standards and technologies for data organization and management in libraries. Toward that end, the project team developed an automated workflow to crawl for, monitor, and classify relevant web objects…

Classifying magnetic resonance image modalities with convolutional neural networks

NASA Astrophysics Data System (ADS)

Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

2018-02-01

Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

The partial volume effect in the quantification of 1H magnetic resonance spectroscopy in Alzheimer's disease and aging.

PubMed

Mato Abad, Virginia; Quirós, Alicia; García-Álvarez, Roberto; Loureiro, Javier Pereira; Alvarez-Linera, Juan; Frank, Ana; Hernández-Tamames, Juan Antonio

2014-01-01

1H-MRS variability increases due to normal aging and also as a result of atrophy in grey and white matter caused by neurodegeneration. In this work, an automatic process was developed to integrate data from spectra and high-resolution anatomical images to quantify metabolites, taking into account tissue partial volumes within the voxel of interest avoiding additional spectra acquisitions required for partial volume correction. To evaluate this method, we use a cohort of 135 subjects (47 male and 88 female, aged between 57 and 99 years) classified into 4 groups: 38 healthy participants, 20 amnesic mild cognitive impairment patients, 22 multi-domain mild cognitive impairment patients, and 55 Alzheimer's disease patients. Our findings suggest that knowing the voxel composition of white and grey matter and cerebrospinal fluid is necessary to avoid partial volume variations in a single-voxel study and to decrease part of the variability found in metabolites quantification, particularly in those studies involving elder patients and neurodegenerative diseases. The proposed method facilitates the use of 1H-MRS techniques in statistical studies in Alzheimer's disease, because it provides more accurate quantitative measurements, reduces the inter-subject variability, and improves statistical results when performing group comparisons.

Discriminating bot accounts based solely on temporal features of microblog behavior

NASA Astrophysics Data System (ADS)

Pan, Junshan; Liu, Ying; Liu, Xiang; Hu, Hanping

2016-05-01

As the largest microblog service in China, Sina Weibo has attracted numerous automated applications (known as bots) due to its popularity and open architecture. We classify the active users from Sina Weibo into human, bot-based and hybrid groups based solely on the study of temporal features of their posting behavior. The anomalous burstiness parameter and time-interval entropy value are exploited to characterize automation. We also reveal different behavior patterns among the three types of users regarding their reposting ratio, daily rhythm and active days. Our findings may help Sina Weibo manage a better community and should be considered for dynamic models of microblog behaviors.

Automated high resolution full-field spatial coherence tomography for quantitative phase imaging of human red blood cells

NASA Astrophysics Data System (ADS)

Singla, Neeru; Dubey, Kavita; Srivastava, Vishal; Ahmad, Azeem; Mehta, D. S.

2018-02-01

We developed an automated high-resolution full-field spatial coherence tomography (FF-SCT) microscope for quantitative phase imaging that is based on the spatial, rather than the temporal, coherence gating. The Red and Green color laser light was used for finding the quantitative phase images of unstained human red blood cells (RBCs). This study uses morphological parameters of unstained RBCs phase images to distinguish between normal and infected cells. We recorded the single interferogram by a FF-SCT microscope for red and green color wavelength and average the two phase images to further reduced the noise artifacts. In order to characterize anemia infected from normal cells different morphological features were extracted and these features were used to train machine learning ensemble model to classify RBCs with high accuracy.

Oscillometric Blood Pressure Estimation: Past, Present, and Future.

PubMed

Forouzanfar, Mohamad; Dajani, Hilmi R; Groza, Voicu Z; Bolic, Miodrag; Rajan, Sreeraman; Batkin, Izmail

2015-01-01

The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors.

Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

PubMed

Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

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.

Building an automated SOAP classifier for emergency department reports.

PubMed

Mowery, Danielle; Wiebe, Janyce; Visweswaran, Shyam; Harkema, Henk; Chapman, Wendy W

2012-02-01

Information extraction applications that extract structured event and entity information from unstructured text can leverage knowledge of clinical report structure to improve performance. The Subjective, Objective, Assessment, Plan (SOAP) framework, used to structure progress notes to facilitate problem-specific, clinical decision making by physicians, is one example of a well-known, canonical structure in the medical domain. Although its applicability to structuring data is understood, its contribution to information extraction tasks has not yet been determined. The first step to evaluating the SOAP framework's usefulness for clinical information extraction is to apply the model to clinical narratives and develop an automated SOAP classifier that classifies sentences from clinical reports. In this quantitative study, we applied the SOAP framework to sentences from emergency department reports, and trained and evaluated SOAP classifiers built with various linguistic features. We found the SOAP framework can be applied manually to emergency department reports with high agreement (Cohen's kappa coefficients over 0.70). Using a variety of features, we found classifiers for each SOAP class can be created with moderate to outstanding performance with F(1) scores of 93.9 (subjective), 94.5 (objective), 75.7 (assessment), and 77.0 (plan). We look forward to expanding the framework and applying the SOAP classification to clinical information extraction tasks. Copyright © 2011. Published by Elsevier Inc.

Automated diagnosis of dry eye using infrared thermography images

NASA Astrophysics Data System (ADS)

Acharya, U. Rajendra; Tan, Jen Hong; Koh, Joel E. W.; Sudarshan, Vidya K.; Yeo, Sharon; Too, Cheah Loon; Chua, Chua Kuang; Ng, E. Y. K.; Tong, Louis

2015-07-01

Dry Eye (DE) is a condition of either decreased tear production or increased tear film evaporation. Prolonged DE damages the cornea causing the corneal scarring, thinning and perforation. There is no single uniform diagnosis test available to date; combinations of diagnostic tests are to be performed to diagnose DE. The current diagnostic methods available are subjective, uncomfortable and invasive. Hence in this paper, we have developed an efficient, fast and non-invasive technique for the automated identification of normal and DE classes using infrared thermography images. The features are extracted from nonlinear method called Higher Order Spectra (HOS). Features are ranked using t-test ranking strategy. These ranked features are fed to various classifiers namely, K-Nearest Neighbor (KNN), Nave Bayesian Classifier (NBC), Decision Tree (DT), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM) to select the best classifier using minimum number of features. Our proposed system is able to identify the DE and normal classes automatically with classification accuracy of 99.8%, sensitivity of 99.8%, and specificity if 99.8% for left eye using PNN and KNN classifiers. And we have reported classification accuracy of 99.8%, sensitivity of 99.9%, and specificity if 99.4% for right eye using SVM classifier with polynomial order 2 kernel.

Chemical shift-based MRI to measure fat fractions in dystrophic skeletal muscle

PubMed Central

Triplett, William T.; Baligand, Celine; Forbes, Sean C.; Willcocks, Rebecca J.; Lott, Donovan J.; DeVos, Soren; Pollaro, Jim; Rooney, William D.; Sweeney, H. Lee; Bönnemann, Carsten; Wang, Dah-Jyuu; Vandenborne, Krista; Walter, Glenn A.

2014-01-01

Purpose The relationship between FF determined based on multiple TE, unipolar GE images and 1H-MRS was evaluated using different models for fat-water decomposition, signal-to-noise ratios (SNR), and excitation flip angles. Methods A combination of single voxel proton spectroscopy (1H-MRS) and gradient echo (GE) imaging was used to determine muscle fat fractions (FF) in both normal and dystrophic muscles. In order to cover a large range of FF, the soleus and vastus lateralis muscles of 22 unaffected control (CON), 16 subjects with Collagen VI (COL6), and 71 subjects with Duchenne muscular dystrophy (DMD) were studied. 1H-MRS based FF were corrected for the increased muscle 1H2O T1 and T2 values observed in dystrophic muscles. Results Excellent agreement was found between co-registered FF derived from GE images fit to a multipeak model with noise bias correction and the relaxation corrected 1H-MRS FF (y= 0.93×+0.003; R2=0.96) across the full range of FF. Relaxation corrected 1H-MRS FF and imaging based FF were significantly elevated (p<0.01) in both COL6 and DMD muscles. Conclusion FF, T2, and T1 were all sensitive to muscle involvement in dystrophic muscle. MRI offered an additional advantage over single voxel spectroscopy in that the tissue heterogeneity in FF could be readily determined. PMID:24006208

New data clustering for RBF classifier of agriculture products from x-ray images

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

1999-08-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a subsystem for automated non-invasive detection of defective product items on a conveyor belt. We discuss the use of clustering and how it is vital to achieve useful classification. New clustering methods using class identify and new cluster classes are advanced and shown to be of use for this application. Radial basis function neural net classifiers are emphasized. We expect our results to be of use for other classifiers and applications.

Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents.

PubMed

Romm, H; Ainsbury, E; Barnard, S; Barrios, L; Barquinero, J F; Beinke, C; Deperas, M; Gregoire, E; Koivistoinen, A; Lindholm, C; Moquet, J; Oestreicher, U; Puig, R; Rothkamm, K; Sommer, S; Thierens, H; Vandersickel, V; Vral, A; Wojcik, A

2013-08-30

Mass casualty scenarios of radiation exposure require high throughput biological dosimetry techniques for population triage in order to rapidly identify individuals who require clinical treatment. The manual dicentric assay is a highly suitable technique, but it is also very time consuming and requires well trained scorers. In the framework of the MULTIBIODOSE EU FP7 project, semi-automated dicentric scoring has been established in six European biodosimetry laboratories. Whole blood was irradiated with a Co-60 gamma source resulting in 8 different doses between 0 and 4.5Gy and then shipped to the six participating laboratories. To investigate two different scoring strategies, cell cultures were set up with short term (2-3h) or long term (24h) colcemid treatment. Three classifiers for automatic dicentric detection were applied, two of which were developed specifically for these two different culture techniques. The automation procedure included metaphase finding, capture of cells at high resolution and detection of dicentric candidates. The automatically detected dicentric candidates were then evaluated by a trained human scorer, which led to the term 'semi-automated' being applied to the analysis. The six participating laboratories established at least one semi-automated calibration curve each, using the appropriate classifier for their colcemid treatment time. There was no significant difference between the calibration curves established, regardless of the classifier used. The ratio of false positive to true positive dicentric candidates was dose dependent. The total staff effort required for analysing 150 metaphases using the semi-automated approach was 2 min as opposed to 60 min for manual scoring of 50 metaphases. Semi-automated dicentric scoring is a useful tool in a large scale radiation accident as it enables high throughput screening of samples for fast triage of potentially exposed individuals. Furthermore, the results from the participating laboratories were comparable which supports networking between laboratories for this assay. Copyright © 2013 Elsevier B.V. All rights reserved.

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2016-06-01

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

21 CFR 864.5220 - Automated differential cell counter.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated differential cell counter. 864.5220... § 864.5220 Automated differential cell counter. (a) Identification. An automated differential cell... have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood...

21 CFR 864.5220 - Automated differential cell counter.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Automated differential cell counter. 864.5220... § 864.5220 Automated differential cell counter. (a) Identification. An automated differential cell... have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood...

21 CFR 864.5220 - Automated differential cell counter.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Automated differential cell counter. 864.5220... § 864.5220 Automated differential cell counter. (a) Identification. An automated differential cell... have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood...

21 CFR 864.5220 - Automated differential cell counter.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Automated differential cell counter. 864.5220... § 864.5220 Automated differential cell counter. (a) Identification. An automated differential cell... have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood...

21 CFR 864.5220 - Automated differential cell counter.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Automated differential cell counter. 864.5220... § 864.5220 Automated differential cell counter. (a) Identification. An automated differential cell... have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood...

DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.

PubMed

Park, Bo-Yong; Lee, Mi Ji; Lee, Seung-Hak; Cha, Jihoon; Chung, Chin-Sang; Kim, Sung Tae; Park, Hyunjin

2018-01-01

Migraineurs show an increased load of white matter hyperintensities (WMHs) and more rapid deep WMH progression. Previous methods for WMH segmentation have limited efficacy to detect small deep WMHs. We developed a new fully automated detection pipeline, DEWS (DEep White matter hyperintensity Segmentation framework), for small and superficially-located deep WMHs. A total of 148 non-elderly subjects with migraine were included in this study. The pipeline consists of three components: 1) white matter (WM) extraction, 2) WMH detection, and 3) false positive reduction. In WM extraction, we adjusted the WM mask to re-assign misclassified WMHs back to WM using many sequential low-level image processing steps. In WMH detection, the potential WMH clusters were detected using an intensity based threshold and region growing approach. For false positive reduction, the detected WMH clusters were classified into final WMHs and non-WMHs using the random forest (RF) classifier. Size, texture, and multi-scale deep features were used to train the RF classifier. DEWS successfully detected small deep WMHs with a high positive predictive value (PPV) of 0.98 and true positive rate (TPR) of 0.70 in the training and test sets. Similar performance of PPV (0.96) and TPR (0.68) was attained in the validation set. DEWS showed a superior performance in comparison with other methods. Our proposed pipeline is freely available online to help the research community in quantifying deep WMHs in non-elderly adults.

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

NASA Astrophysics Data System (ADS)

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

2012-11-01

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

Automated mango fruit assessment using fuzzy logic approach

NASA Astrophysics Data System (ADS)

Hasan, Suzanawati Abu; Kin, Teoh Yeong; Sauddin@Sa'duddin, Suraiya; Aziz, Azlan Abdul; Othman, Mahmod; Mansor, Ab Razak; Parnabas, Vincent

2014-06-01

In term of value and volume of production, mango is the third most important fruit product next to pineapple and banana. Accurate size assessment of mango fruits during harvesting is vital to ensure that they are classified to the grade accordingly. However, the current practice in mango industry is grading the mango fruit manually using human graders. This method is inconsistent, inefficient and labor intensive. In this project, a new method of automated mango size and grade assessment is developed using RGB fiber optic sensor and fuzzy logic approach. The calculation of maximum, minimum and mean values based on RGB fiber optic sensor and the decision making development using minimum entropy formulation to analyse the data and make the classification for the mango fruit. This proposed method is capable to differentiate three different grades of mango fruit automatically with 77.78% of overall accuracy compared to human graders sorting. This method was found to be helpful for the application in the current agricultural industry.

Automated detection of fundus photographic red lesions in diabetic retinopathy.

PubMed

Larsen, Michael; Godt, Jannik; Larsen, Nicolai; Lund-Andersen, Henrik; Sjølie, Anne Katrin; Agardh, Elisabet; Kalm, Helle; Grunkin, Michael; Owens, David R

2003-02-01

To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes. Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed. Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648). Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.

Tenacity of Collateral Perfusion in Proximal Cerebral Arterial Occlusions 6-12 h after Onset.

PubMed

Kim, Beom Joon; Kim, Hyeran; Jeong, Han-Gil; Yang, Mi Hwa; Jung, Cheol Kyu; Han, Moon-Ku; Kim, Jae Hyoung; Demchuk, Andrew M; Bae, Hee-Joon

2018-06-07

Clinical trials have shown that benefits of endovascular recanalization (EVT) for acute ischemic stroke patients with sizable penumbral tissues seems plausible even beyond 6 h after their last seen normal (LSN). Persistency of ischemic penumbra remains unclear in delayed periods. From a prospective stroke registry database, we identified 111 acute ischemic stroke patients who had internal carotid artery or middle cerebral artery occlusion with baseline National Institutes of Health Stroke Scale scores ≥6 points and arrived 6-12 h after LSN. Baseline information and functional outcomes were prospectively collected as a clinical registry. Attending physicians made treatment decisions for EVT based on the current guidelines and institutional protocols. MR image parameters, including the volume of diffusion-restricted lesions and mapping of the -hypoperfused area, were quantified using automated commercial software. Binary logistic regression analysis models, with modified Rankin Scale (mRS) scores of 0-1 at 3 months after stroke included as a dependent variable, were constructed. Between 6 and 12 h after onset, 58% had a mismatch ratio of ≥1.8 at baseline and 42% had favorable imaging profiles as determined by DEFUSE 2 study. After 9 h, there was a mismatch ratio of ≥1.8 in 47 and 38% favorable profiles. EVT was performed in 54% of cases. A 3-month mRS score of 0-1 was found in 19% (25% in EVT and 12% in medical treatment groups) of cases. EVT was associated with an increased OR of having a mRS score of 0-1 at 3 months after stroke (adjusted OR 7.59 [95% CI 1.28-61.60]). Penumbral tissues were persistent in a substantial proportion of anterior circulation occlusion cases 6-12 h after LSN. EVT at 6-12 h in a predominantly Asian cohort resulted in better outcomes. © 2018 S. Karger AG, Basel.

CIRSS vertical data integration, San Bernardino County study phases 1-A, 1-B

NASA Technical Reports Server (NTRS)

Christenson, J.; Michel, R. (Principal Investigator)

1981-01-01

User needs, data types, data automation, and preliminary applications are described for an effort to assemble a single data base for San Bernardino County from data bases which exist at several administrative levels. Each of the data bases used was registered and converted to a grid-based data file at a resolution of 4 acres and used to create a multivariable data base for the entire study area. To this data base were added classified LANDSAT data from 1976 and 1979. The resulting data base thus integrated in a uniform format all of the separately automated data within the study area. Several possible interactions between existing geocoded data bases and LANDSAT data were tested. The use of LANDSAT to update existing data base is to be tested.

Influenza vaccination coverage among medical residents: an Italian multicenter survey.

PubMed

Costantino, Claudio; Mazzucco, Walter; Azzolini, Elena; Baldini, Cesare; Bergomi, Margherita; Biafiore, Alessio Daniele; Bianco, Manuela; Borsari, Lucia; Cacciari, Paolo; Cadeddu, Chiara; Camia, Paola; Carluccio, Eugenia; Conti, Andrea; De Waure, Chiara; Di Gregori, Valentina; Fabiani, Leila; Fallico, Roberto; Filisetti, Barbara; Flacco, Maria E; Franco, Elisabetta; Furnari, Roberto; Galis, Veronica; Gallea, Maria R; Gallone, Maria F; Gallone, Serena; Gelatti, Umberto; Gilardi, Francesco; Giuliani, Anna R; Grillo, Orazio C; Lanati, Niccolò; Mascaretti, Silvia; Mattei, Antonella; Micò, Rocco; Morciano, Laura; Nante, Nicola; Napoli, Giuseppe; Nobile, Carmelo Giuseppe; Palladino, Raffaele; Parisi, Salvatore; Passaro, Maria; Pelissero, Gabriele; Quarto, Michele; Ricciardi, Walter; Romano, Gabriele; Rustico, Ennio; Saponari, Anita; Schioppa, Francesco S; Signorelli, Carlo; Siliquini, Roberta; Trabacchi, Valeria; Triassi, Maria; Varetta, Alessia; Ziglio, Andrea; Zoccali, Angela; Vitale, Francesco; Amodio, Emanuele

2014-01-01

Although influenza vaccination is recognized to be safe and effective, recent studies have confirmed that immunization coverage among health care workers remain generally low, especially among medical residents (MRs). Aim of the present multicenter study was to investigate attitudes and determinants associated with acceptance of influenza vaccination among Italian MRs. A survey was performed in 2012 on MRs attending post-graduate schools of 18 Italian Universities. Each participant was interviewed via an anonymous, self-administered, web-based questionnaire including questions on attitudes regarding influenza vaccination. A total of 2506 MRs were recruited in the survey and 299 (11.9%) of these stated they had accepted influenza vaccination in 2011-2012 season. Vaccinated MRs were older (P = 0.006), working in clinical settings (P = 0.048), and vaccinated in the 2 previous seasons (P<0.001 in both seasons). Moreover, MRs who had recommended influenza vaccination to their patients were significantly more compliant with influenza vaccination uptake in 2011-2012 season (P<0.001). "To avoid spreading influenza among patients" was recognized as the main reason for accepting vaccination by less than 15% of vaccinated MRs. Italian MRs seem to have a very low compliance with influenza vaccination and they seem to accept influenza vaccination as a habit that is unrelated to professional and ethical responsibility. Otherwise, residents who refuse vaccination in the previous seasons usually maintain their behaviors. Promoting correct attitudes and good practice in order to improve the influenza immunization rates of MRs could represent a decisive goal for increasing immunization coverage among health care workers of the future.

HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening.

PubMed

Charoenkwan, Phasit; Hwang, Eric; Cutler, Robert W; Lee, Hua-Chin; Ko, Li-Wei; Huang, Hui-Ling; Ho, Shinn-Ying

2013-01-01

High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable MatLab project at http://iclab.life.nctu.edu.tw/HCS-Neurons. Few automatic methods focus on analyzing multi-neuron images collected from HCS used in drug discovery. We provided an automatic HCS-based method for generating accurate classifiers to classify neurons based on their phenotypic changes upon drug treatments. The proposed HCS-neurons method is helpful in identifying and classifying chemical or biological molecules that alter the morphology of a group of neurons in HCS.

Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches.

PubMed

Yousef Kalafi, Elham; Tan, Wooi Boon; Town, Christopher; Dhillon, Sarinder Kaur

2016-12-22

Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods. Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%. The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Removal of correlated noise online for in situ measurements by using multichannel magnetic resonance sounding system

NASA Astrophysics Data System (ADS)

Lin, Tingting; Zhang, Siyuan; Zhang, Yang; Wan, Ling; Lin, Jun

2017-01-01

Compared with the other geophysical approaches, magnetic resonance sounding (MRS) technique is direct and nondestructive in subsurface water exploration. It provides water content distribution and estimates hydrogeological properties. The biggest challenge is that MRS measurement always suffers bad signal-to-noise ratio, and it can be carried out only far from sources of noise. To solve this problem, a series of de-noising methods are developed. However, most of them are post-processing, leading the data quality uncontrolled for in situ measurements. In the present study, a new approach that removal of correlated noise online is found to overcome the restriction. Based on LabVIEW, a method is provided to enable online data quality control by the way of realizing signal acquisition and noise filtering simultaneously. Using one or more reference coils, adaptive noise cancellation based on LabVIEW to eliminate the correlated noise is available for in situ measurements. The approach was examined through numerical simulation and field measurements. The correlated noise is mitigated effectively and the application of MRS measurements is feasible in high-level noise environment. The method shortens the measurement time and improves the measurement efficiency.

Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

PubMed

Maity, Maitreya; Dhane, Dhiraj; Mungle, Tushar; Maiti, A K; Chakraborty, Chandan

2017-10-26

Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance. The proposed methodology for automated evaluation of parasites includes pre-processing of blood smear microscopic images followed by erythrocytes segmentation. To differentiate between different parasites; a total of 138 quantitative features characterising colour, morphology, and texture are extracted from segmented erythrocytes. An integrated pattern classification framework is designed where four feature selection methods viz. Correlation-based Feature Selection (CFS), Chi-square, Information Gain, and RELIEF are employed with three different classifiers i.e. Naive Bayes', C4.5, and Instance-Based Learning (IB1) individually. Optimal features subset with the best classifier is selected for achieving maximum diagnostic precision. It is seen that the proposed method achieved with 99.2% sensitivity and 99.6% specificity by combining CFS and C4.5 in comparison with other methods. Moreover, the web-based tool is entirely designed using open standards like Java for a web application, ImageJ for image processing, and WEKA for data mining considering its feasibility in rural places with minimal health care facilities.

Decreased frontal N-acetylaspartate levels in adolescents concurrently using both methamphetamine and marijuana.

PubMed

Sung, Young-Hoon; Carey, Paul D; Stein, Dan J; Ferrett, Helen L; Spottiswoode, Bruce S; Renshaw, Perry F; Yurgelun-Todd, Deborah A

2013-06-01

The potential neurochemical toxicity associated with methamphetamine (MA) or marijuana (MJ) use on the developing adolescent brain is unclear, particularly with regard to individuals with concomitant use of MA and MJ (MA+MJ). In this study, proton magnetic resonance spectroscopy (MRS) was utilized to measure in vivo brain N-acetylaspartate plus N-acetylaspartyl glutamate (tNAA, an indicator of intact neuronal integrity) levels. Three adolescent groups from Cape Town, South Africa completed MRS scans as well as clinical measures including a drug use history. Subjects included (1) nine MA (age=15.7±1.37), (2) eight MA+MJ (age=16.2±1.16) using adolescents and (3) ten healthy controls (age=16.8±0.62). Single voxel spectra were acquired from midfrontal gray matter using a point-resolved spectroscopy sequence (PRESS). The MRS data were post-processed in the fully automated approach for quantitation of metabolite ratios to phosphocreatine plus creatine (PCr+Cr). A significant reduction in frontal tNAA/PCr+Cr ratios was seen in the MA+MJ group compared to the healthy controls (p=0.01, by 7.2%) and to the MA group (p=0.04, by 6.9%). Significant relationships were also observed between decreased tNAA/PCr+Cr ratios and drug use history of MA or MJ (total cumulative lifetime dose, age of onset, and duration of MA and MJ exposure) only in the MA+MJ group (all p<0.05). These findings suggest that in adolescents, concomitant heavy MA+MJ use may contribute to altered brain metabolites in frontal gray matter. The significant associations between the abnormal tNAA/PCr+Cr ratios and the drug use history suggest that MA+MJ abuse may induce neurotoxicity in a dose-responsive manner in adolescent brain. Copyright © 2013 Elsevier B.V. All rights reserved.

Automated high-grade prostate cancer detection and ranking on whole slide images

NASA Astrophysics Data System (ADS)

Huang, Chao-Hui; Racoceanu, Daniel

2017-03-01

Recently, digital pathology (DP) has been largely improved due to the development of computer vision and machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV) from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa; second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform high-accuracy detection ( 95:57% +/- 2:1%) and excellent performance of ranking. Hence, the proposed system has a great potential to support the daily tasks in the medical routine of clinical pathology.

Automatic discrimination of fine roots in minirhizotron images.

PubMed

Zeng, Guang; Birchfield, Stanley T; Wells, Christina E

2008-01-01

Minirhizotrons provide detailed information on the production, life history and mortality of fine roots. However, manual processing of minirhizotron images is time-consuming, limiting the number and size of experiments that can reasonably be analysed. Previously, an algorithm was developed to automatically detect and measure individual roots in minirhizotron images. Here, species-specific root classifiers were developed to discriminate detected roots from bright background artifacts. Classifiers were developed from training images of peach (Prunus persica), freeman maple (Acer x freemanii) and sweetbay magnolia (Magnolia virginiana) using the Adaboost algorithm. True- and false-positive rates for classifiers were estimated using receiver operating characteristic curves. Classifiers gave true positive rates of 89-94% and false positive rates of 3-7% when applied to nontraining images of the species for which they were developed. The application of a classifier trained on one species to images from another species resulted in little or no reduction in accuracy. These results suggest that a single root classifier can be used to distinguish roots from background objects across multiple minirhizotron experiments. By incorporating root detection and discrimination algorithms into an open-source minirhizotron image analysis application, many analysis tasks that are currently performed by hand can be automated.

Clastic rocks associated with the Midcontinent rift system in Iowa

USGS Publications Warehouse

Anderson, Raymond R.; McKay, Robert M.

1997-01-01

The Middle Proterozoic Midcontinent Rift System (MRS) of North America is a failed rift that formed in response to region-wide stresses about 1,100 Ma. In Iowa, the MRS is buried beneath 2,200?3,500 ft of Paleozoic and Mesozoic sedimentary rocks and Quaternary glaciogenic deposits. An extremely large volume of sediments was deposited within basins associated with the rift at several stages during its development. Although the uplift of a rift-axial horst resulted in the erosional removal of most of these clastic rocks from the central region of the MRS in Iowa, thick sequences are preserved in a series of horst-bounding basins. Recent studies incorporating petrographic analysis, geophysical modeling, and other analytical procedures have led to the establishment of a preliminary stratigraphy for these clastic rocks and interpretations of basin geometries. This information has allowed the refinement of existing theories and history of MRS formation in Iowa. Additionally, drill samples previously interpreted as indicating the existence of early Paleozoic basins overlying the Proterozoic MRS basins were re-examined. Samples previously interpreted as deep-lying Paleozoic rocks are now known to have caved from upper levels of the drillhole and were out of stratigraphic position. No deep Paleozoic basins exist in this area. These investigations led to the development of petrographic parameters useful in differentiating the Proterozoic MRS Red clastics from Paleozoic clastic rocks having similar lithologies.

Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis.

PubMed

Banks, Jamie L; Marotta, Charles A

2007-03-01

The modified Rankin scale (mRS), a clinician-reported measure of global disability, is widely applied for evaluating stroke patient outcomes and as an end point in randomized clinical trials. Extensive evidence on the validity of the mRS exists across a large but fragmented literature. As new treatments for acute ischemic stroke are submitted for agency approval, an appreciation of the mRS's attributes, specifically its relationship to other stroke evaluation scales, would be valuable for decision-makers to properly assess the impact of a new drug on treatment paradigms. The purpose of this report is to assemble and systematically assess the properties of the mRS to provide decision-makers with pertinent evaluative information. A Medline search was conducted to identify reports in the peer-reviewed medical literature (1957-2006) that provide information on the structure, validation, scoring, and psychometric properties of the mRS and its use in clinical trials. The selection of articles was based on defined criteria that included relevance, study design and use of appropriate statistical methods. Of 224 articles identified by the literature search, 50 were selected for detailed assessment. Inter-rater reliability with the mRS is moderate and improves with structured interviews (kappa 0.56 versus 0.78); strong test-re-test reliability (kappa=0.81 to 0.95) has been reported. Numerous studies demonstrate the construct validity of the mRS by its relationships to physiological indicators such as stroke type, lesion size, perfusion and neurological impairment. Convergent validity between the mRS and other disability scales is well documented. Patient comorbidities and socioeconomic factors should be considered in properly applying and interpreting the mRS. Recent analyses suggest that randomized clinical trials of acute stroke treatments may require a smaller sample size if the mRS is used as a primary end point rather than the Barthel Index. Multiple types of evidence attest to the validity and reliability of the mRS. The reported data support the view that the mRS is a valuable instrument for assessing the impact of new stroke treatments.

Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals.

PubMed

Connolly, Brian; Matykiewicz, Pawel; Bretonnel Cohen, K; Standridge, Shannon M; Glauser, Tracy A; Dlugos, Dennis J; Koh, Susan; Tham, Eric; Pestian, John

2014-01-01

The constant progress in computational linguistic methods provides amazing opportunities for discovering information in clinical text and enables the clinical scientist to explore novel approaches to care. However, these new approaches need evaluation. We describe an automated system to compare descriptions of epilepsy patients at three different organizations: Cincinnati Children's Hospital, the Children's Hospital Colorado, and the Children's Hospital of Philadelphia. To our knowledge, there have been no similar previous studies. In this work, a support vector machine (SVM)-based natural language processing (NLP) algorithm is trained to classify epilepsy progress notes as belonging to a patient with a specific type of epilepsy from a particular hospital. The same SVM is then used to classify notes from another hospital. Our null hypothesis is that an NLP algorithm cannot be trained using epilepsy-specific notes from one hospital and subsequently used to classify notes from another hospital better than a random baseline classifier. The hypothesis is tested using epilepsy progress notes from the three hospitals. We are able to reject the null hypothesis at the 95% level. It is also found that classification was improved by including notes from a second hospital in the SVM training sample. With a reasonably uniform epilepsy vocabulary and an NLP-based algorithm able to use this uniformity to classify epilepsy progress notes across different hospitals, we can pursue automated comparisons of patient conditions, treatments, and diagnoses across different healthcare settings. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Targeting MRS-Defined Dominant Intraprostatic Lesions with Inverse-Planned High Dose Rate Brachytherapy

DTIC Science & Technology

2010-06-01

known frequency positions of each of these peaks [30]. Spectroscopic voxels were classified using the standardized scoring system proposed by Jung et...indicative of malignancy (red voxels). The center image shows the voxel classifications described by Jung et al, and the right image shows the suspicious...J. Star-Lack, D. B. Vigneron, J. Pauly , J. Kurhanewicz, S. J. Nelson, Journal of Magnetic Resonance Imaging 7(4), 745 (1997). [26] J. Star-Lack, S

The good, the bad and the ugly of marine reserves for fishery yields

PubMed Central

De Leo, Giulio A.; Micheli, Fiorenza

2015-01-01

Marine reserves (MRs) are used worldwide as a means of conserving biodiversity and protecting depleted populations. Despite major investments in MRs, their environmental and social benefits have proven difficult to demonstrate and are still debated. Clear expectations of the possible outcomes of MR establishment are needed to guide and strengthen empirical assessments. Previous models show that reserve establishment in overcapitalized, quota-based fisheries can reduce both catch and population abundance, thereby negating fisheries and even conservation benefits. By using a stage-structured, spatially explicit stochastic model, we show that catches under quota-based fisheries that include a network of MRs can exceed maximum sustainable yield (MSY) under conventional quota management if reserves provide protection to old, large spawners that disproportionally contribute to recruitment outside the reserves. Modelling results predict that the net fishery benefit of MRs is lost when gains in fecundity of old, large individuals are small, is highest in the case of sedentary adults with high larval dispersal, and decreases with adult mobility. We also show that environmental variability may mask fishery benefits of reserve implementation and that MRs may buffer against collapse when sustainable catch quotas are exceeded owing to stock overestimation or systematic overfishing. PMID:26460129

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

PubMed

Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

2014-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications

PubMed Central

Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

2014-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu PMID:24131510

Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

PubMed Central

Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

Accurate determination of imaging modality using an ensemble of text- and image-based classifiers.

PubMed

Kahn, Charles E; Kalpathy-Cramer, Jayashree; Lam, Cesar A; Eldredge, Christina E

2012-02-01

Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals. Each classifier assigned one of eight imaging modalities--computed tomography, graphic, magnetic resonance imaging, nuclear medicine, positron emission tomography, photograph, ultrasound, or radiograph-to each image based on visual and/or textual information. Three physicians determined the modality of 5,000 randomly selected images as a reference standard. A "Simple Vote" ensemble classifier assigned each image to the modality that received the greatest number of individual classifiers' votes. A "Weighted Vote" classifier weighted each individual classifier's vote based on performance over a training set. For each image, this classifier's output was the imaging modality that received the greatest weighted vote score. We measured precision, recall, and F score (the harmonic mean of precision and recall) for each classifier. Individual classifiers' F scores ranged from 0.184 to 0.892. The simple vote and weighted vote classifiers correctly assigned 4,565 images (F score, 0.913; 95% confidence interval, 0.905-0.921) and 4,672 images (F score, 0.934; 95% confidence interval, 0.927-0.941), respectively. The weighted vote classifier performed significantly better than all individual classifiers. An ensemble classifier correctly determined the imaging modality of 93% of figures in our sample. The imaging modality of figures published in radiology journals can be determined with high accuracy, which will improve systems for image retrieval.

Brain metabolite differences in one-year-old infants born small at term and association with neurodevelopmental outcome.

PubMed

Simões, Rui V; Cruz-Lemini, Mónica; Bargalló, Núria; Gratacós, Eduard; Sanz-Cortés, Magdalena

2015-08-01

We assessed brain metabolite levels by magnetic resonance spectroscopy (MRS) in 1-year-old infants born small at term, as compared with infants born appropriate for gestational age (AGA), and their association with neurodevelopment at 2 years of age. A total of 40 infants born small (birthweight <10th centile for gestational age) and 30 AGA infants underwent brain MRS at age 1 year on a 3-T scanner. Small-born infants were subclassified as late intrauterine growth restriction or as small for gestational age, based on the presence or absence of prenatal Doppler and birthweight predictors of an adverse perinatal outcome, respectively. Single-voxel proton magnetic resonance spectroscopy ((1)H-MRS) data were acquired from the frontal lobe at short echo time. Neurodevelopment was evaluated at 2 years of age using the Bayley Scales of Infant and Toddler Development, Third Edition, assessing cognitive, language, motor, social-emotional, and adaptive behavior scales. As compared with AGA controls, infants born small showed significantly higher levels of glutamate and total N-acetylaspartate (NAAt) to creatine (Cr) ratio at age 1 year, and lower Bayley Scales of Infant and Toddler Development, Third Edition scores at 2 years. The subgroup with late intrauterine growth restriction further showed lower estimated glutathione levels at age 1 year. Significant correlations were observed for estimated glutathione levels with adaptive scores, and for myo-inositol with language scores. Significant associations were also noticed for NAA/Cr with cognitive scores, and for glutamate/Cr with motor scores. Infants born small show brain metabolite differences at 1 year of age, which are correlated with later neurodevelopment. These results support further research on MRS to develop imaging biomarkers of abnormal neurodevelopment. Copyright © 2015 Elsevier Inc. All rights reserved.

AUTOMATED PRODUCTION OF SEAGRASS MAPS FROM SIDESCAN SONAR IMAGERY: ACCURACY, VARIABILITY AND PATCH RESOLUTION

EPA Science Inventory

Maps of seagrass beds are useful for monitoring estuarine condition, managing habitats, and modeling estuarine processes. We recently developed inexpensive methods for collecting and classifying sidescan sonar (SSS) imagery for seagrass presence in turbid waters as shallow as 1-...

Automated Vocal Analysis of Children with Hearing Loss and Their Typical and Atypical Peers

PubMed Central

VanDam, Mark; Oller, D. Kimbrough; Ambrose, Sophie E.; Gray, Sharmistha; Richards, Jeffrey A.; Xu, Dongxin; Gilkerson, Jill; Silbert, Noah H.; Moeller, Mary Pat

2014-01-01

Objectives This study investigated automatic assessment of vocal development in children with hearing loss as compared with children who are typically developing, have language delays, and autism spectrum disorder. Statistical models are examined for performance in a classification model and to predict age within the four groups of children. Design The vocal analysis system analyzed over 1900 whole-day, naturalistic acoustic recordings from 273 toddlers and preschoolers comprising children who were typically developing, hard of hearing, language delayed, or autistic. Results Samples from children who were hard-of-hearing patterned more similarly to those of typically-developing children than to the language-delayed or autistic samples. The statistical models were able to classify children from the four groups examined and estimate developmental age based on automated vocal analysis. Conclusions This work shows a broad similarity between children with hearing loss and typically developing children, although children with hearing loss show some delay in their production of speech. Automatic acoustic analysis can now be used to quantitatively compare vocal development in children with and without speech-related disorders. The work may serve to better distinguish among various developmental disorders and ultimately contribute to improved intervention. PMID:25587667

An ensemble-of-classifiers based approach for early diagnosis of Alzheimer's disease: classification using structural features of brain images.

PubMed

Farhan, Saima; Fahiem, Muhammad Abuzar; Tauseef, Huma

2014-01-01

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.

Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit.

PubMed

Simpson, Robin; Devenyi, Gabriel A; Jezzard, Peter; Hennessy, T Jay; Near, Jamie

2017-01-01

To introduce a new toolkit for simulation and processing of magnetic resonance spectroscopy (MRS) data, and to demonstrate some of its novel features. The FID appliance (FID-A) is an open-source, MATLAB-based software toolkit for simulation and processing of MRS data. The software is designed specifically for processing data with multiple dimensions (eg, multiple radiofrequency channels, averages, spectral editing dimensions). It is equipped with functions for importing data in the formats of most major MRI vendors (eg, Siemens, Philips, GE, Agilent) and for exporting data into the formats of several common processing software packages (eg, LCModel, jMRUI, Tarquin). This paper introduces the FID-A software toolkit and uses examples to demonstrate its novel features, namely 1) the use of a spectral registration algorithm to carry out useful processing routines automatically, 2) automatic detection and removal of motion-corrupted scans, and 3) the ability to perform several major aspects of the MRS computational workflow from a single piece of software. This latter feature is illustrated through both high-level processing of in vivo GABA-edited MEGA-PRESS MRS data, as well as detailed quantum mechanical simulations to generate an accurate LCModel basis set for analysis of the same data. All of the described processing steps resulted in a marked improvement in spectral quality compared with unprocessed data. Fitting of MEGA-PRESS data using a customized basis set resulted in improved fitting accuracy compared with a generic MEGA-PRESS basis set. The FID-A software toolkit enables high-level processing of MRS data and accurate simulation of in vivo MRS experiments. Magn Reson Med 77:23-33, 2017. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Automation of assembly of electrical products

NASA Astrophysics Data System (ADS)

Lebedenko, V. A.

1984-10-01

Approaches to the operation of a production line with a free rather than rigid tempo and cycles are discussed, along with the installation of a interoperational transport with trays rather than a continuously moving convey belt and the use of standard technological equipment in lieu of small scale mechanization. The design of a production line which follow these principles is examined. The advantages as well as the disadvantages of such a system are considered in the development of an automated flexible production line with robotized technological complex. A single flexible assembly line for the hookup operations with the most important link, the automatic manipulator for joining the product components together, consists of modules classifiable into six groups: (1) movers of the base parts; (2) feeders and orientators of parts; (3) joiners of parts; (4) carriers and holders of parts; (5) inspection and control systems; and (6) fasteners of parts.

Automated segmentation algorithm for detection of changes in vaginal epithelial morphology using optical coherence tomography

NASA Astrophysics Data System (ADS)

Chitchian, Shahab; Vincent, Kathleen L.; Vargas, Gracie; Motamedi, Massoud

2012-11-01

We have explored the use of optical coherence tomography (OCT) as a noninvasive tool for assessing the toxicity of topical microbicides, products used to prevent HIV, by monitoring the integrity of the vaginal epithelium. A novel feature-based segmentation algorithm using a nearest-neighbor classifier was developed to monitor changes in the morphology of vaginal epithelium. The two-step automated algorithm yielded OCT images with a clearly defined epithelial layer, enabling differentiation of normal and damaged tissue. The algorithm was robust in that it was able to discriminate the epithelial layer from underlying stroma as well as residual microbicide product on the surface. This segmentation technique for OCT images has the potential to be readily adaptable to the clinical setting for noninvasively defining the boundaries of the epithelium, enabling quantifiable assessment of microbicide-induced damage in vaginal tissue.

Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

PubMed Central

2013-01-01

Background Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning. Results In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, Nterminal-Center-Cterminal composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction accuracy for distinguishing plastid vs. non-plastids and only 20% in classifying various plastid-types, indicating the need and importance of machine learning algorithms. Conclusion The current work is a first attempt to develop a methodology for classifying various plastid-type proteins. The prediction modules have also been made available as a web tool, PLpred available at http://bioinfo.okstate.edu/PLpred/ for real time identification/characterization. We believe this tool will be very useful in the functional annotation of various genomes. PMID:24266945

Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media.

PubMed

Comfort, Shaun; Perera, Sujan; Hudson, Zoe; Dorrell, Darren; Meireis, Shawman; Nagarajan, Meenakshi; Ramakrishnan, Cartic; Fine, Jennifer

2018-06-01

There is increasing interest in social digital media (SDM) as a data source for pharmacovigilance activities; however, SDM is considered a low information content data source for safety data. Given that pharmacovigilance itself operates in a high-noise, lower-validity environment without objective 'gold standards' beyond process definitions, the introduction of large volumes of SDM into the pharmacovigilance workflow has the potential to exacerbate issues with limited manual resources to perform adverse event identification and processing. Recent advances in medical informatics have resulted in methods for developing programs which can assist human experts in the detection of valid individual case safety reports (ICSRs) within SDM. In this study, we developed rule-based and machine learning (ML) models for classifying ICSRs from SDM and compared their performance with that of human pharmacovigilance experts. We used a random sampling from a collection of 311,189 SDM posts that mentioned Roche products and brands in combination with common medical and scientific terms sourced from Twitter, Tumblr, Facebook, and a spectrum of news media blogs to develop and evaluate three iterations of an automated ICSR classifier. The ICSR classifier models consisted of sub-components to annotate the relevant ICSR elements and a component to make the final decision on the validity of the ICSR. Agreement with human pharmacovigilance experts was chosen as the preferred performance metric and was evaluated by calculating the Gwet AC1 statistic (gKappa). The best performing model was tested against the Roche global pharmacovigilance expert using a blind dataset and put through a time test of the full 311,189-post dataset. During this effort, the initial strict rule-based approach to ICSR classification resulted in a model with an accuracy of 65% and a gKappa of 46%. Adding an ML-based adverse event annotator improved the accuracy to 74% and gKappa to 60%. This was further improved by the addition of an additional ML ICSR detector. On a blind test set of 2500 posts, the final model demonstrated a gKappa of 78% and an accuracy of 83%. In the time test, it took the final model 48 h to complete a task that would have taken an estimated 44,000 h for human experts to perform. The results of this study indicate that an effective and scalable solution to the challenge of ICSR detection in SDM includes a workflow using an automated ML classifier to identify likely ICSRs for further human SME review.

A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification.

PubMed

LeVan, P; Urrestarazu, E; Gotman, J

2006-04-01

To devise an automated system to remove artifacts from ictal scalp EEG, using independent component analysis (ICA). A Bayesian classifier was used to determine the probability that 2s epochs of seizure segments decomposed by ICA represented EEG activity, as opposed to artifact. The classifier was trained using numerous statistical, spectral, and spatial features. The system's performance was then assessed using separate validation data. The classifier identified epochs representing EEG activity in the validation dataset with a sensitivity of 82.4% and a specificity of 83.3%. An ICA component was considered to represent EEG activity if the sum of the probabilities that its epochs represented EEG exceeded a threshold predetermined using the training data. Otherwise, the component represented artifact. Using this threshold on the validation set, the identification of EEG components was performed with a sensitivity of 87.6% and a specificity of 70.2%. Most misclassified components were a mixture of EEG and artifactual activity. The automated system successfully rejected a good proportion of artifactual components extracted by ICA, while preserving almost all EEG components. The misclassification rate was comparable to the variability observed in human classification. Current ICA methods of artifact removal require a tedious visual classification of the components. The proposed system automates this process and removes simultaneously multiple types of artifacts.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.

PubMed

Yang, Guang; Raschke, Felix; Barrick, Thomas R; Howe, Franklyn A

2015-09-01

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method. In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis. © 2014 Wiley Periodicals, Inc.

1H Magnetic Resonance Spectroscopy of live human sperm

PubMed Central

Calvert, S J; Paley, M N; Pacey, A A

2017-01-01

Abstract STUDY QUESTION Can 1H Magnetic Resonance Spectroscopy (MRS) be used to obtain information about the molecules and metabolites in live human spermatozoa? SUMMARY ANSWER Percoll-based density gradient centrifugation (DGC) followed by a further two washing steps, yielded enough sperm with minimal contamination (<0.01%) from seminal fluid to permit effective MRS which detected significant differences (P < 0.05) in the choline/glycerophosphocholine (GPC), lipid and lactate regions of the 1H MRS spectrum between sperm in the pellet and those from the 40%/80% interface. WHAT IS KNOWN ALREADY Current methods to examine sperm are either limited in their value (e.g. semen analysis) or are destructive (e.g. immunohistochemistry, sperm DNA testing). A few studies have previously used MRS to examine sperm, but these have either looked at seminal plasma from men with different ejaculate qualities or at the molecules present in pooled samples of lyophilized sperm. STUDY DESIGN, SAMPLES/MATERIALS, METHODS Sperm suspended in phosphate buffered saline (PBS) at 37°C were examined by 1H MRS scanning using a 1H excitation-sculpting solvent suppression sequence after recovery from fresh ejaculates by one of three different methods: (i) simple centrifugation; (ii) DGC with one wash; or (iii) DGC with two washes. In the case of DGC, sperm were collected both from the pellet (‘80%’ sperm) and the 40/80 interface (‘40%’ sperm). Spectrum processing was carried out using custom Matlab scripts to determine; the degree of seminal plasma/Percoll contamination, the minimum sperm concentration for 1H MRS detection and differences between the 1H MRS spectra of ‘40%’ and ‘80%’ sperm. MAIN RESULTS AND THE ROLE OF CHANCE DGC with two washes minimized the 1H MRS peak intensity for both seminal plasma and Percoll/PBS solution contamination while retaining sperm specific peaks. For the MRS scanner used in this study, the minimum sperm concentration required to produce a choline/GPC 1H MRS peak greater than 3:1 signal to noise ratio (SNR) was estimated at ~3 × 106/ml. The choline/GPC and lactate/lipid regions of the 1H spectrum were significantly different by two-way ANOVA analysis (P < 0.0001; n = 20). ROC curve analysis of these region showed significant ability to distinguish between the two sperm populations: choline/GPC ROC AUC = 0.65–0.67, lactate/lipid ROC AUC = 0.86–0.87. LIMITATIONS, REASONS FOR CAUTION Only 3–4 semen samples were used to assess the efficacy of each sperm washing protocol that were examined. The estimated minimum sperm concentration required for MRS is specific to the hardware used in our study and may be different in other spectrometers. Spectrum binning is a low resolution analysis method that sums MRS peaks within a chemical shift range. This can obscure the identity of which metabolite(s) are responsible for differences between sperm populations. Further work is required to determine the relative contribution of somatic cells to the MRS spectrum from the ‘40%’ and ‘80%’ sperm. WIDER IMPLICATIONS OF THE FINDINGS 1H MRS can provide information about the molecules present in live human sperm and may therefore permit the study of the underlying functional biology or metabolomics of live sperm. Given the relatively low concentration of sperm required to obtain a suitable MRS signal (~3 × 106/ml), this could be carried out on sperm from men with oligo-, astheno- or teratozoospermia. This may lead to the development of new diagnostic tests or ultimately novel treatments for male factor infertility. STUDY FUNDING AND COMPETING INTEREST(S) This work was supported by the Medical Research Council Grant MR/M010473/1. The authors declare no conflicts of interest. PMID:28431025

1H Magnetic Resonance Spectroscopy of live human sperm.

PubMed

Reynolds, S; Calvert, S J; Paley, M N; Pacey, A A

2017-07-01

Can 1H Magnetic Resonance Spectroscopy (MRS) be used to obtain information about the molecules and metabolites in live human spermatozoa? Percoll-based density gradient centrifugation (DGC) followed by a further two washing steps, yielded enough sperm with minimal contamination (<0.01%) from seminal fluid to permit effective MRS which detected significant differences (P < 0.05) in the choline/glycerophosphocholine (GPC), lipid and lactate regions of the 1H MRS spectrum between sperm in the pellet and those from the 40%/80% interface. Current methods to examine sperm are either limited in their value (e.g. semen analysis) or are destructive (e.g. immunohistochemistry, sperm DNA testing). A few studies have previously used MRS to examine sperm, but these have either looked at seminal plasma from men with different ejaculate qualities or at the molecules present in pooled samples of lyophilized sperm. Sperm suspended in phosphate buffered saline (PBS) at 37°C were examined by 1H MRS scanning using a 1H excitation-sculpting solvent suppression sequence after recovery from fresh ejaculates by one of three different methods: (i) simple centrifugation; (ii) DGC with one wash; or (iii) DGC with two washes. In the case of DGC, sperm were collected both from the pellet ('80%' sperm) and the 40/80 interface ('40%' sperm). Spectrum processing was carried out using custom Matlab scripts to determine; the degree of seminal plasma/Percoll contamination, the minimum sperm concentration for 1H MRS detection and differences between the 1H MRS spectra of '40%' and '80%' sperm. DGC with two washes minimized the 1H MRS peak intensity for both seminal plasma and Percoll/PBS solution contamination while retaining sperm specific peaks. For the MRS scanner used in this study, the minimum sperm concentration required to produce a choline/GPC 1H MRS peak greater than 3:1 signal to noise ratio (SNR) was estimated at ~3 × 106/ml. The choline/GPC and lactate/lipid regions of the 1H spectrum were significantly different by two-way ANOVA analysis (P < 0.0001; n = 20). ROC curve analysis of these region showed significant ability to distinguish between the two sperm populations: choline/GPC ROC AUC = 0.65-0.67, lactate/lipid ROC AUC = 0.86-0.87. Only 3-4 semen samples were used to assess the efficacy of each sperm washing protocol that were examined. The estimated minimum sperm concentration required for MRS is specific to the hardware used in our study and may be different in other spectrometers. Spectrum binning is a low resolution analysis method that sums MRS peaks within a chemical shift range. This can obscure the identity of which metabolite(s) are responsible for differences between sperm populations. Further work is required to determine the relative contribution of somatic cells to the MRS spectrum from the '40%' and '80%' sperm. 1H MRS can provide information about the molecules present in live human sperm and may therefore permit the study of the underlying functional biology or metabolomics of live sperm. Given the relatively low concentration of sperm required to obtain a suitable MRS signal (~3 × 106/ml), this could be carried out on sperm from men with oligo-, astheno- or teratozoospermia. This may lead to the development of new diagnostic tests or ultimately novel treatments for male factor infertility. This work was supported by the Medical Research Council Grant MR/M010473/1. The authors declare no conflicts of interest. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved.

Automated defect spatial signature analysis for semiconductor manufacturing process

DOEpatents

Tobin, Jr., Kenneth W.; Gleason, Shaun S.; Karnowski, Thomas P.; Sari-Sarraf, Hamed

1999-01-01

An apparatus and method for performing automated defect spatial signature alysis on a data set representing defect coordinates and wafer processing information includes categorizing data from the data set into a plurality of high level categories, classifying the categorized data contained in each high level category into user-labeled signature events, and correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

PubMed

Zhang, Jianhua; Yin, Zhong; Wang, Rubin

2017-01-01

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

Method for Controlled Mitochondrial Perturbation during Phosphorus MRS in Children

PubMed Central

Cree-Green, Melanie; Newcomer, Bradley R.; Brown, Mark; Hull, Amber; West, Amy D.; Singel, Debra; Reusch, Jane E.B.; McFann, Kim; Regensteiner, Judith G.; Nadeau, Kristen J.

2014-01-01

Introduction Insulin resistance (IR) is increasingly prevalent in children, and may be related to muscle mitochondrial dysfunction, necessitating development of mitochondrial assessment techniques. Recent studies used 31Phosphorus magnetic resonance spectroscopy (31P-MRS), a non-invasive technique appealing for clinical research. 31P-MRS requires exercise at a precise percentage of maximum volitional contraction (MVC). MVC measurement in children, particularly with disease, is problematic due to variability in perception of effort and motivation. We therefore developed a method to predict MVC, using maximal calf muscle cross-sectional area (MCSA) to assure controlled and reproducible muscle metabolic perturbations. Methods Data were collected from 66 sedentary 12–20 year-olds. Plantar flexion-volitional MVC was assessed using a MRI-compatible exercise treadle device. MCSA of the calf muscles were measured from MRI images. Data from the first 26 participants were utilized to model the relationship between MVC and MCSA (predicted MVC = 24.763+0.0047*MCSA). This model was then applied to the subsequent 40 participants. Results Volitional vs. model-predicted mean MVC was 43.9±0.8 kg vs. 44.2±1.81 (P=0.90). 31P-MRS results when predicted and volitional MVC were similar showed expected changes during volitional MVC-based exercise. In contrast, volitional MVC was markedly lower than predicted in 4 participants, and produced minimal metabolic perturbation. Upon repeat testing, these individuals could perform their predicted MVC with coaching, which produced expected metabolic perturbations. Conclusions Compared to using MVC testing alone, utilizing MRI to predict muscle strength allows for a more accurate and standardized 31P-MRS protocol during exercise in children. This method overcomes a major obstacle in assessing mitochondrial function in youth. These studies have importance as we seek to determine the role of mitochondrial function in youth with IR and diabetes and response to interventions. PMID:24576856

Research in interactive scene analysis

NASA Technical Reports Server (NTRS)

Tenenbaum, J. M.; Barrow, H. G.; Weyl, S. A.

1976-01-01

Cooperative (man-machine) scene analysis techniques were developed whereby humans can provide a computer with guidance when completely automated processing is infeasible. An interactive approach promises significant near-term payoffs in analyzing various types of high volume satellite imagery, as well as vehicle-based imagery used in robot planetary exploration. This report summarizes the work accomplished over the duration of the project and describes in detail three major accomplishments: (1) the interactive design of texture classifiers; (2) a new approach for integrating the segmentation and interpretation phases of scene analysis; and (3) the application of interactive scene analysis techniques to cartography.

Long-term outcome of refractory status epilepticus in adults: A retrospective population-based study.

PubMed

Kantanen, Anne-Mari; Reinikainen, Matti; Parviainen, Ilkka; Kälviäinen, Reetta

2017-07-01

Refractory status epilepticus (RSE) is a neurological emergency with significant morbidity and mortality. We aimed to analyze the long-term outcome of intensive care unit (ICU)-treated RSE and super-refractory status epilepticus (SRSE) patients in a population based cohort. A retrospective study of ICU- and anesthesia-treated RSE patients in Kuopio University Hospital's (KUH) special responsibility area hospitals in the central and eastern part of Finland from Jan. 1, 2010 to Dec. 31, 2012 was conducted. KUH's catchment area consists of five hospitals-one university hospital and four central hospitals-and covers a population of 840 000. We included all consecutive adult (16 years or older) RSE patients admitted in the participating ICUs during the 3-year period and excluded patients with postanoxic etiologies. We used a modified Rankin Scale (mRS) as a long-term (1-year) outcome measure: good (mRS 0-3, recovered to baseline function) or poor (mRS 4-6, major functional deficit or death). We identified 75 patients with ICU- and anesthesia-treated RSE, corresponding to an annual incidence of 3.0 (95% confidence interval (CI) 2.4-3.8). 21% of the patients were classified as SRSE, with the annual incidence being 0.6/100 000 (95% CI 0.4-1.0). For RSE, the ICU mortality was 0%, hospital mortality was 7% (95% CI 1.2%-12.8%) (n=5), and one-year mortality was 23% (CI 95% 13.4%-32.5%) (n=17). 48% (n=36) of RSE patients recovered to baseline, and 29% (n=22) showed neurological deficit at 1year. Poor outcome (mRS 4-6) was recorded for 52% (n=39) of the patients. Older age was associated with poorer outcome at 1year (p=0.03). For SRSE, hospital mortality was 6% (n=1) and 1-year mortality was 19% (n=3) (95%CI 0%-38.2%). During 1-year follow-up, nearly 50% of the ICU-treated RSE patients recovered to baseline function, whereas 30% showed new functional defects and 20% died. SRSE does not have a necessarily poorer outcome. The outcome is worse in older patients and in patients with progressive or fatal etiologies. SE should be treated with generalized anesthesia only in refractory cases after failure of adequately used first- and second-line antiepileptic drugs. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Altered white matter metabolism in delayed neurologic sequelae after carbon monoxide poisoning: A proton magnetic resonance spectroscopic study.

PubMed

Kuroda, Hiroshi; Fujihara, Kazuo; Mugikura, Shunji; Takahashi, Shoki; Kushimoto, Shigeki; Aoki, Masashi

2016-01-15

Proton magnetic resonance spectroscopy ((1)H-MRS) was recently used to examine altered metabolism in the white matter (WM) of patients experiencing carbon monoxide (CO) poisoning; however, only a small number of patients with delayed neurologic sequelae (DNS) were analyzed. We aimed to detect altered metabolism in the WM of patients with DNS using (1)H-MRS; to explore its clinical relevance in the management of patients experiencing CO poisoning. Patients experiencing acute CO poisoning underwent (1)H-MRS and cerebrospinal fluid (CSF) examination within 1week and at 1month after acute poisoning. Metabolites including choline-containing compounds (Cho), creatine (Cr), N-acetylaspartate (NAA), and lactate were measured from the periventricular WM. Myelin basic protein (MBP) concentrations were measured in CSF. Fifty-two patients experiencing acute CO poisoning (15 with DNS, 37 without DNS; median age, 49years; 65% males) underwent (1)H-MRS. Within 1week, NAA/Cr ratios, reflecting neuroaxonal viability, were lower in patients with DNS than in those without DNS (P<0.05). At 1month, when 9 of 15 patients (60%) developed DNS, Cho/Cr ratios were higher, and NAA/Cr and NAA/Cho ratios lower in patients with DNS (P=0.0001, <0.0001, and <0.0001, respectively), indicating increased membrane metabolism and decreased neuroaxonal viability. (1)H-MRS parameter abnormalities correlated with the elevation of MBP in CSF. The presence of a lactate peak was a predictor for a poor long-term outcome. (1)H-MRS within 1week may be useful for predicting DNS development; (1)H-MRS at 1month may be useful for discriminating patients with DNS and predicting long-term outcomes. Copyright © 2015 Elsevier B.V. All rights reserved.

Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

PubMed

Donnelly-Kehoe, Patricio Andres; Pascariello, Guido Orlando; Gómez, Juan Carlos

2018-05-15

We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients. We propose to segregate participants in three groups according to Mini Mental State Examination score (MMSEs), searching for the main nMF in each group. Then we use them to develop a Multi Classifier System (MCS). We compare the MCS against a single classifier scheme using both MMSEs+nMF and nMF only. We repeat this comparison using three state-of-the-art classification algorithms. The MCS showed the best performance on both Accuracy and Area Under the Receiver Operating Curve (AUC) in comparison with single classifiers. The multiclass AUC for the MCS classification on Test Dataset were 0.83 for HC, 0.76 for cMCI, 0.65 for MCI and 0.95 for AD. Furthermore, MCS's optimum accuracy on Neurodegenerative Disease (ND) detection (AD+cMCI vs MCI+HC) was 81.0% (AUC=0.88), while the single classifiers got 71.3% (AUC=0.86) and 63.1% (AUC=0.79) for MMSEs+nMF and only nMF respectively. The proposed MCS showed a better performance than using all nMF into a single state-of-the-art classifier. These findings suggest that using cognitive scoring, e.g. MMSEs, in the design of a Multi Classifier System improves performance by allowing a better selection of MRI-based features. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated turn pike using PLC and SCADA

NASA Astrophysics Data System (ADS)

Silpa Sreedhar, P.; Aiswarya, P.; Kathirvelan, J.

2017-11-01

We propose a smart turnpike based on Programmable Logic Controller (PLC) and Supervisory Control and Data Acquisition Systems (SCADA) in this paper. In this work, the basic idea is to measure the weight of the vehicles and classify them according to its weight to the respective lanes. It is difficult for the turnpike people to monitor the whole process all the time. So, this PLC based diversion system can be implemented in turnpikes to reduce the difficulties. This method will work based on weight sensors (piezo-resistive) whose output will be fed to a PLC, which will control the vehicle diversion. Using SCADA software, the whole process can be monitored from a remote area. The algorithm developed in this successfully installed in real time system.

Magnetic Resonance Spectroscopy for Evaluating Portal-Systemic Encephalopathy in Patients with Chronic Hepatic Schistosomiasis Japonicum.

PubMed

Li, Ying; Mei, Lihong; Qiang, Jinwei; Ju, Shuai; Zhao, Shuhui

2016-12-01

Portal-systemic encephalopathy (PSE) is classified as type B hepatic encephalopathy. Portal-systemic shunting rather than liver dysfunction is the main cause of PSE in chronic hepatic schistosomiasis japonicum (HSJ) patients. Owing to lack of detectable evidence of intrinsic liver disease, chronic HSJ patients with PSE are frequently clinically undetected or misdiagnosed, especially chronic HSJ patients with covert PSE (subclinical encephalopathy). In this study, we investigated whether magnetic resonance spectroscopy (MRS) could be a useful tool for diagnosing PSE in chronic HSJ patients. Magnetic resonance (MR) T1-weighted imaging, diffusion-weighted imaging, and MRS were performed in 41 chronic HSJ patients with suspected PSE and in 21 age-matched controls. The T1 signal intensity index (T1SI) and apparent diffusion coefficient (ADC) value were obtained in the Globus pallidus. Liver function was also investigated via serum ammonia and liver function tests. Higher T1SI and ADC values, increased lactate and glutamine levels, and decreased myo-inositol were found in the bilateral Globus pallidus in chronic HSJ patients with PSE. No significantly abnormal serum ammonia or liver function tests were observed in chronic HSJ patients with PSE. On the basis of these findings, we propose a diagnostic procedure for PSE in chronic HSJ patients. This study reveals that MRS can be useful for diagnosing PSE in chronic HSJ patients.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

PubMed

Yu, Sheng; Liao, Katherine P; Shaw, Stanley Y; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Cai, Tianxi

2015-09-01

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mobile phone technology identifies and recruits trained citizens to perform CPR on out-of-hospital cardiac arrest victims prior to ambulance arrival.

PubMed

Ringh, Mattias; Fredman, David; Nordberg, Per; Stark, Tomas; Hollenberg, Jacob

2011-12-01

In a two-parted study, evaluate a new concept were mobile phone technology is used to dispatch lay responders to nearby out-of-hospital cardiac arrests (OHCAs). Mobile phone positioning systems (MPS) can geographically locate selected mobile phone users at any given moment. A mobile phone service using MPS was developed and named Mobile Life Saver (MLS). Simulation study: 25 volunteers named mobile responders (MRs) were connected to MLS. Ambulance time intervals from 22 consecutive OHCAs in 2005 were used as controls. The MRs randomly moved in Stockholm city centre and were dispatched to simulated OHCAs (identical to controls) if they were within a 350 m distance. Real life study: during 25 weeks 1271-1801 MRs trained in CPR were connected to MLS. MLS was activated at the dispatch centre in parallel with ambulance dispatch when an OHCA was suspected. The MRs were dispatched if they were within 500 m from the suspected OHCA. Simulation study: mean response time for the MRs compared to historical ambulance time intervals was reduced by 2 min 20s (44%), p<0.001, (95% CI, 1 min 5s - 3 min 35s). The MRs reached the simulated OHCA prior to the historical control in 72% of cases. Real life study: the MLS was triggered 92 times. In 45% of all suspected and in 56% of all true OHCAs the MRs arrived prior to ambulance. CPR was performed by MRs in 17% of all true OHCAs and in 30% of all true OHCAs if MRs arrived prior to ambulance. Mobile phone technology can be used to identify and recruit nearby CPR-trained citizens to OHCAs for bystander CPR prior to ambulance arrival. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Multiple Rapid Swallow Responses During Esophageal High-Resolution Manometry Reflect Esophageal Body Peristaltic Reserve

PubMed Central

Shaker, Anisa; Stoikes, Nathaniel; Drapekin, Jesse; Kushnir, Vladimir; Brunt, L. Michael; Gyawali, C. Prakash

2014-01-01

OBJECTIVES Dysphagia may develop following antireflux surgery as a consequence of poor esophageal peristaltic reserve. We hypothesized that suboptimal contraction response following multiple rapid swallows (MRS) could be associated with chronic transit symptoms following antireflux surgery. METHODS Wet swallow and MRS responses on esophageal high-resolution manometry (HRM) were characterized collectively in the esophageal body (distal contractile integral (DCI)), and individually in each smooth muscle contraction segment (S2 and S3 amplitudes) in 63 patients undergoing antireflux surgery and in 18 healthy controls. Dysphagia was assessed using symptom questionnaires. The MRS/wet swallow ratios were calculated for S2 and S3 peak amplitudes and DCI. MRS responses were compared in patients with and without late postoperative dysphagia following antireflux surgery. RESULTS Augmentation of smooth muscle contraction (MRS/wet swallow ratios > 1.0) as measured collectively by DCI was seen in only 11.1% with late postoperative dysphagia, compared with 63.6% in those with no dysphagia and 78.1% in controls (P≤0.02 for each comparison). Similar results were seen with S3 but not S2 peak amplitude ratios. Receiver operating characteristics identified a DCI MRS/wet swallow ratio threshold of 0.85 in segregating patients with late postoperative dysphagia from those with no postoperative dysphagia with a sensitivity of 0.67 and specificity of 0.64. CONCLUSIONS Lack of augmentation of smooth muscle contraction following MRS is associated with late postoperative dysphagia following antireflux surgery, suggesting that MRS responses could assess esophageal smooth muscle peristaltic reserve. Further research is warranted to determine if antireflux surgery needs to be tailored to the MRS response. PMID:24019081

Diffusion tensor imaging and magnetic resonance spectroscopy assessment of cancellous bone quality in femoral neck of healthy, osteopenic and osteoporotic subjects at 3T: Preliminary experience.

PubMed

Manenti, Guglielmo; Capuani, Silvia; Fanucci, Ezio; Assako, Elie Parfait; Masala, Salvatore; Sorge, Roberto; Iundusi, Riccardo; Tarantino, Umberto; Simonetti, Giovanni

2013-07-01

We assessed the potential of diffusion tensor imaging (DTI) in combination with proton magnetic resonance spectroscopy (1H-MRS), in cancellous bone quality evaluation of the femoral neck in postmenopausal women. DTI allows for non-invasive microarchitectural characterization of heterogeneous tissue. In this work we hypothesized that DTI parameters mean diffusivity (MD) and fractional anisotropy (FA) of bone marrow water, can provide information about microstructural changes that occur with the development of osteoporosis disease. Because osteoporosis is associated with increased bone marrow fat content, which in principal can alter DTI parameters, the goal of this study was to examine the potential of MD and FA, in combination with bone marrow fat fraction (FF), to discriminate between healthy, osteopenic and osteoporotic subjects, classified according to DXA criteria. Forty postmenopausal women (mean age, 68.7 years; range 52-81 years), underwent a Dual-energy X-ray absorptiometry (DXA) examination in femoral neck, to be classified as healthy (n=12), osteopenic (n=14) and osteoporotic (n=14) subjects. 1H-MRS and DTI (with b value=2500 s/mm2) of femoral neck were obtained in each subject at 3T. The study protocol was approved by local Ethics Committee. MD, FA, FF and MD/FF, FA/FF were obtained and compared among the three bone-density groups. One-way ANOVA with multiple comparisons Bonferroni test and Pearson correlation analysis were applied. Receiver operating characteristic (ROC) curve analysis was also performed. Reproducibility of DTI measures was satisfactory. CV was approximately 2%-3% for MD and 4%-5% for FA measurements. Moreover, no significant difference was found in both MD and FA measurements between two separate sessions (median 34 days apart) comprised of six healthy volunteers. FF was able to discriminate between healthy and osteoporotic subjects only. Conversely MD and FA were able to discriminate healthy from osteopenic and healthy from osteoporotic subjects, but they were not able to discriminate between osteopenic and osteoporotic patients. A significant correlation between MD and FF was observed in healthy group only. A moderate correlation was found between MD and T-score when all groups together are considered. No significant correlation was found between MD and T-score within groups. A significant positive correlation between FA and FF was found in both osteopenic and osteoporotic groups. Vice-versa no correlation between FA and FF was observed in healthy group. A high significant positive correlation was found between FA and T-score in all groups together, in healthy and in osteoporotic groups. MD/FF and FA/FF are characterized by a higher sensitivity and specificity compared to MD and FA in the discrimination between healthy, and osteoporotic subjects. MD/FF vs. FA/FF graph extracted from femoral neck, identify all healthy individuals according to DXA results. DTI-(1)H-MRS protocol performed in femoral neck seems to be highly sensitive and specific in identifying healthy subjects. A MR exam is more expensive when compared to a DXA investigation. However, even though DXA BMD evaluation has been the accepted standard for osteoporosis diagnosis, DXA result has a low predictive value on patients' risk for future fractures. Thus, new approaches for examining patients at risk for developing osteoporosis would be desirable. Preliminary results showed here suggest that future studies on a larger population based on DTI assessment in the femoral neck, in combination with 1H-MRS investigations, might allow screening of high-risk populations and the establishment of cut-off values of normality, with potential application of the method to single subjects. Copyright © 2013 Elsevier Inc. All rights reserved.

Evolutionary and biological metaphors for engineering design

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

Jakiela, M.

1994-12-31

Since computing became generally available, there has been strong interest in using computers to assist and automate engineering design processes. Specifically, for design optimization and automation, nonlinear programming and artificial intelligence techniques have been extensively studied. New computational techniques, based upon the natural processes of evolution, adaptation, and learing, are showing promise because of their generality and robustness. This presentation will describe the use of two such techniques, genetic algorithms and classifier systems, for a variety of engineering design problems. Structural topology optimization, meshing, and general engineering optimization are shown as example applications.

Methodological Approaches to Online Scoring of Essays.

ERIC Educational Resources Information Center

Chung, Gregory K. W. K.; O'Neil, Harold F., Jr.

This report examines the feasibility of scoring essays using computer-based techniques. Essays have been incorporated into many of the standardized testing programs. Issues of validity and reliability must be addressed to deploy automated approaches to scoring fully. Two approaches that have been used to classify documents, surface- and word-based…

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation

NASA Astrophysics Data System (ADS)

Nieten, Joseph L.; Burke, Roger

1993-03-01

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital Holography

NASA Astrophysics Data System (ADS)

Davies, E. J.; Buscombe, D.; Graham, G.; Nimmo-Smith, A.

2013-12-01

The use of digital holography to image suspended particles in-situ using submersible systems is on the ascendancy. Such systems allow visualization of the in-focus particles without the depth-of-field issues associated with conventional imaging. The size and concentration of all particles, and each individual particle, can be rapidly and automatically assessed. The automated methods by which to extract these quantities can be readily evaluated using manual measurements. These methods are not possible using instruments based on optical and acoustic (back- or forward-) scattering, so-called 'sediment surrogate' methods, which are sensitive to the bulk quantities of all suspended particles in a sample volume, and rely on mathematically inverting a measured signal to derive the property of interest. Depending on the intended application, the number of holograms required to elucidate a process could range from tens to millions. Therefore manual particle extraction is not feasible for most data-sets. This has created a pressing need among the growing community of holography users, for accurate, automated processing which is comparable in output to more well-established in-situ sizing techniques such as laser diffraction. Here we discuss the computational considerations required to focus and segment individual particles from raw digital holograms, and then size and classify these particles by type; all using unsupervised (automated) image processing. To do so, we draw upon imagery from both controlled laboratory conditions to near-shore coastal environments, using different holographic system designs, and constituting a significant variety in particle types, sizes and shapes. We evaluate the success of these techniques, and suggest directions for future developments.

An entirely automated method to score DSS-induced colitis in mice by digital image analysis of pathology slides

PubMed Central

Kozlowski, Cleopatra; Jeet, Surinder; Beyer, Joseph; Guerrero, Steve; Lesch, Justin; Wang, Xiaoting; DeVoss, Jason; Diehl, Lauri

2013-01-01

SUMMARY The DSS (dextran sulfate sodium) model of colitis is a mouse model of inflammatory bowel disease. Microscopic symptoms include loss of crypt cells from the gut lining and infiltration of inflammatory cells into the colon. An experienced pathologist requires several hours per study to score histological changes in selected regions of the mouse gut. In order to increase the efficiency of scoring, Definiens Developer software was used to devise an entirely automated method to quantify histological changes in the whole H&E slide. When the algorithm was applied to slides from historical drug-discovery studies, automated scores classified 88% of drug candidates in the same way as pathologists’ scores. In addition, another automated image analysis method was developed to quantify colon-infiltrating macrophages, neutrophils, B cells and T cells in immunohistochemical stains of serial sections of the H&E slides. The timing of neutrophil and macrophage infiltration had the highest correlation to pathological changes, whereas T and B cell infiltration occurred later. Thus, automated image analysis enables quantitative comparisons between tissue morphology changes and cell-infiltration dynamics. PMID:23580198

Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures

PubMed Central

Hwang, Kristy S.; Avila, David; Elashoff, David; Kohannim, Omid; Teng, Edmond; Sokolow, Sophie; Jack, Clifford R.; Jagust, William J.; Shaw, Leslie; Trojanowski, John Q.; Weiner, Michael W.; Thompson, Paul M.

2015-01-01

Background: The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort. Methods: We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF β-amyloid 1–42 (Aβ42) ≤192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio ≥1.5. We trained our classifier in the subcohort with CSF Aβ42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Aβ42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia. Results: The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] = 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier self-tuning was allowed, AUC = 0.74. The 36-month disease-progression classifier achieved AUC = 0.75 and accuracy = 71%. Conclusions: Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future. Classification of evidence: This study provides Class II evidence that a classification algorithm based on cognitive, imaging, and peripheral blood protein measures identifies patients with brain amyloid on PiB-PET with moderate accuracy (sensitivity 68%, specificity 78%). PMID:25609767

Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures.

PubMed

Apostolova, Liana G; Hwang, Kristy S; Avila, David; Elashoff, David; Kohannim, Omid; Teng, Edmond; Sokolow, Sophie; Jack, Clifford R; Jagust, William J; Shaw, Leslie; Trojanowski, John Q; Weiner, Michael W; Thompson, Paul M

2015-02-17

The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort. We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF β-amyloid 1-42 (Aβ42) ≤ 192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio ≥ 1.5. We trained our classifier in the subcohort with CSF Aβ42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Aβ42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia. The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] = 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier self-tuning was allowed, AUC = 0.74. The 36-month disease-progression classifier achieved AUC = 0.75 and accuracy = 71%. Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future. This study provides Class II evidence that a classification algorithm based on cognitive, imaging, and peripheral blood protein measures identifies patients with brain amyloid on PiB-PET with moderate accuracy (sensitivity 68%, specificity 78%). © 2015 American Academy of Neurology.

A retrospective survey on injuries in Croatian football/soccer referees

PubMed Central

2013-01-01

Background Injury among soccer referees is rarely studied, especially with regard to differences in the quality level of the refereeing. Additionally, we have found no study that has reported injury occurrence during official physical fitness testing for soccer referees. The aim of this study was to investigate the frequency, type and consequences of match-related and fitness-testing related injuries among soccer referees of different competitive levels. Methods We studied 342 soccer referees (all males; mean age 32.9 ± 5.02 years). The study was retrospective, and a self-administered questionnaire was used. In the first phase of the study, the questionnaire was tested for its reliability and applicability. The questionnaire included morphological/anthropometric data, refereeing variables, and musculoskeletal disorders together with the consequences. Results The sample comprised 157 main referees (MR; mean age 31.4 ± 4.9 years) and 185 assistant referees (AR; mean age 34.1 ± 5.1 years) divided into: international level (Union of European Football Associations-UEFA) referees (N = 18; 6 MRs; 12 ARs) ; 1st (N = 78; 31 MRs; 47 ARs), 2nd (N = 91; 45 MRs; 46 ARs); or 3rd national level referees (N = 155; 75 MRs; 80 ARs). In total, 29% (95%CI: 0.23–0.37) of the MRs and 30% (95%CI: 0.22–0.36) of the ARs had experienced an injury during the previous year, while 13% (95%CI: 0.05–0.14) of the MRs, and 19% (95%CI: 0.14–0.25) of the ARs suffered from an injury that occurred during fitness testing. There was an obvious increase in injury severity as the refereeing advanced at the national level, but the UEFA referees were the least injured of all referees. The results showed a relatively high prevalence of injuries to the upper leg (i.e., quadriceps and hamstrings) during physical fitness testing for all but the UEFA referees. During game refereeing, the ankles and lower legs were the most commonly injured regions. The MRs primarily injured their ankles. The ARs experienced lower leg and lower back disorders. However, the overall injury rate was equal for both groups, with 5.29 (95%CI: 2.23–8.30) and 4.58 (95%CI: 2.63–6.54) injuries per 1000 hours of refereeing for MRs and ARs, respectively. Conclusion In addition to the reported risk of injury during soccer games, physical fitness testing should be classified as a risk for injury among soccer referees. Special attention should be given to (I) lower leg injuries during games and (II) upper leg injuries during physical fitness tests. A higher physical fitness level and a qualitative approach to training are recognized as protective factors against injury. Subsequent studies should investigate the specific predictors of injuries among referees. PMID:23497316

How automated image analysis techniques help scientists in species identification and classification?

PubMed

Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

2017-09-04

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

Genome-Wide Comparative Gene Family Classification

PubMed Central

Frech, Christian; Chen, Nansheng

2010-01-01

Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species. PMID:20976221

Targeting MRS-Defined Dominant Intraprostatic Lesions with Inverse-Planned High Dose Rate Brachytherapy

DTIC Science & Technology

2011-06-01

peaks [32]. Spectroscopic voxels were classified using the stan- dardized scoring system proposed by Jung et al. [33] where 1 = definitely normal, 2...prostate with flyback echo-pla- nar encoding. Magn Reson Imaging 2007; 25: 1288-1292. 30. Schricker AA, Pauly JM, Kurhanewicz J et al. Dualband spec...method for automatic quantifi- cation of 1-D Spectra with low signal to noise ratio. J Magn Reson 1987; 75: 229-243. 33. Jung JA, Coakley FV, Vigneron DB

Effect of bovine colostrum, cheese whey, and spray-dried porcine plasma on the in vitro growth of probiotic bacteria and Escherichia coli.

PubMed

Champagne, Claude P; Raymond, Yves; Pouliot, Yves; Gauthier, Sylvie F; Lessard, Martin

2014-05-01

The aim of this study is to evaluate the effects of defatted colostrum (Col), defatted decaseinated colostrum whey, cheese whey, and spray-dried porcine plasma (SDPP) as supplements of a growth medium (de Man - Rogosa - Sharpe (MRS) broth) on the multiplication of lactic acid bacteria, probiotic bacteria, and potentially pathogenic Escherichia coli. Using automated spectrophotometry (in vitro system), we evaluated the effect of the 4 supplements on maximum growth rate (μ(max)), lag time (LagT), and biomass (OD(max)) of 12 lactic acid bacteria and probiotic bacteria and of an E. coli culture. Enrichment of MRS broth with a Col concentration of 10 g/L increased the μ(max) of 5 of the 12 strains by up to 55%. Negative effects of Col or SDPP on growth rates were also observed with 3 probiotic strains; in one instance μ(max) was reduced by 40%. The most effective inhibitor of E. coli growth was SDPP, and this effect was not linked to its lysozyme content. The positive effect of enrichment with the dairy-based ingredient might be linked to enrichment in sugars and increased buffering power of the medium. These in vitro data suggest that both Col and SDPP could be considered as supplements to animal feeds to improve intestinal health because of their potential to promote growth of probiotic bacteria and to inhibit growth of pathogenic bacteria such as E. coli.

CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns.

PubMed

Goodson, Summer G; White, Sarah; Stevans, Alicia M; Bhat, Sanjana; Kao, Chia-Yu; Jaworski, Scott; Marlowe, Tamara R; Kohlmeier, Martin; McMillan, Leonard; Zeisel, Steven H; O'Brien, Deborah A

2017-11-01

The ability to accurately monitor alterations in sperm motility is paramount to understanding multiple genetic and biochemical perturbations impacting normal fertilization. Computer-aided sperm analysis (CASA) of human sperm typically reports motile percentage and kinematic parameters at the population level, and uses kinematic gating methods to identify subpopulations such as progressive or hyperactivated sperm. The goal of this study was to develop an automated method that classifies all patterns of human sperm motility during in vitro capacitation following the removal of seminal plasma. We visually classified CASA tracks of 2817 sperm from 18 individuals and used a support vector machine-based decision tree to compute four hyperplanes that separate five classes based on their kinematic parameters. We then developed a web-based program, CASAnova, which applies these equations sequentially to assign a single classification to each motile sperm. Vigorous sperm are classified as progressive, intermediate, or hyperactivated, and nonvigorous sperm as slow or weakly motile. This program correctly classifies sperm motility into one of five classes with an overall accuracy of 89.9%. Application of CASAnova to capacitating sperm populations showed a shift from predominantly linear patterns of motility at initial time points to more vigorous patterns, including hyperactivated motility, as capacitation proceeds. Both intermediate and hyperactivated motility patterns were largely eliminated when sperm were incubated in noncapacitating medium, demonstrating the sensitivity of this method. The five CASAnova classifications are distinctive and reflect kinetic parameters of washed human sperm, providing an accurate, quantitative, and high-throughput method for monitoring alterations in motility. © The Authors 2017. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Advancements in automated tissue segmentation pipeline for contrast-enhanced CT scans of adult and pediatric patients

NASA Astrophysics Data System (ADS)

Somasundaram, Elanchezhian; Kaufman, Robert; Brady, Samuel

2017-03-01

The development of a random forests machine learning technique is presented for fully-automated neck, chest, abdomen, and pelvis tissue segmentation of CT images using Trainable WEKA (Waikato Environment for Knowledge Analysis) Segmentation (TWS) plugin of FIJI (ImageJ, NIH). The use of a single classifier model to segment six tissue classes (lung, fat, muscle, solid organ, blood/contrast agent, bone) in the CT images is studied. An automated unbiased scheme to sample pixels from the training images and generate a balanced training dataset over the seven classes is also developed. Two independent training datasets are generated from a pool of 4 adult (>55 kg) and 3 pediatric patients (<=55 kg) with 7 manually contoured slices for each patient. Classifier training investigated 28 image filters comprising a total of 272 features. Highly correlated and insignificant features are eliminated using Correlated Feature Subset (CFS) selection with Best First Search (BFS) algorithms in WEKA. The 2 training models (from the 2 training datasets) had 74 and 71 input training features, respectively. The study also investigated the effect of varying the number of trees (25, 50, 100, and 200) in the random forest algorithm. The performance of the 2 classifier models are evaluated on inter-patient intra-slice, intrapatient inter-slice and inter-patient inter-slice test datasets. The Dice similarity coefficients (DSC) and confusion matrices are used to understand the performance of the classifiers across the tissue segments. The effect of number of features in the training input on the performance of the classifiers for tissue classes with less than optimal DSC values is also studied. The average DSC values for the two training models on the inter-patient intra-slice test data are: 0.98, 0.89, 0.87, 0.79, 0.68, and 0.84, for lung, fat, muscle, solid organ, blood/contrast agent, and bone, respectively. The study demonstrated that a robust segmentation accuracy for lung, muscle and fat tissue classes. For solid-organ, blood/contrast and bone, the performance of the segmentation pipeline improved significantly by using the advanced capabilities of WEKA. However, further improvements are needed to reduce the noise in the segmentation.

Neural network classification of sweet potato embryos

NASA Astrophysics Data System (ADS)

Molto, Enrique; Harrell, Roy C.

1993-05-01

Somatic embryogenesis is a process that allows for the in vitro propagation of thousands of plants in sub-liter size vessels and has been successfully applied to many significant species. The heterogeneity of maturity and quality of embryos produced with this technique requires sorting to obtain a uniform product. An automated harvester is being developed at the University of Florida to sort embryos in vitro at different stages of maturation in a suspension culture. The system utilizes machine vision to characterize embryo morphology and a fluidic based separation device to isolate embryos associated with a pre-defined, targeted morphology. Two different backpropagation neural networks (BNN) were used to classify embryos based on information extracted from the vision system. One network utilized geometric features such as embryo area, length, and symmetry as inputs. The alternative network utilized polar coordinates of an embryo's perimeter with respect to its centroid as inputs. The performances of both techniques were compared with each other and with an embryo classification method based on linear discriminant analysis (LDA). Similar results were obtained with all three techniques. Classification efficiency was improved by reducing the dimension of the feature vector trough a forward stepwise analysis by LDA. In order to enhance the purity of the sample selected as harvestable, a reject to classify option was introduced in the model and analyzed. The best classifier performances (76% overall correct classifications, 75% harvestable objects properly classified, homogeneity improvement ratio 1.5) were obtained using 8 features in a BNN.

Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

PubMed

Joshi, Vinayak S; Reinhardt, Joseph M; Garvin, Mona K; Abramoff, Michael D

2014-01-01

The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44% correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42%.

Automated classification of multiphoton microscopy images of ovarian tissue using deep learning.

PubMed

Huttunen, Mikko J; Hassan, Abdurahman; McCloskey, Curtis W; Fasih, Sijyl; Upham, Jeremy; Vanderhyden, Barbara C; Boyd, Robert W; Murugkar, Sangeeta

2018-06-01

Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. Here, we demonstrate a potential complementary approach for diagnosis. We show that multiphoton microscopy images from unstained, reproductive tissues can be robustly classified using deep learning techniques. We fine-train four pretrained convolutional neural networks using over 200 murine tissue images based on combined second-harmonic generation and two-photon excitation fluorescence contrast, to classify the tissues either as healthy or associated with high-grade serous carcinoma with over 95% sensitivity and 97% specificity. Our approach shows promise for applications involving automated disease diagnosis. It could also be readily applied to other tissues, diseases, and related classification problems. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Automated Scoring of L2 Spoken English with Random Forests

ERIC Educational Resources Information Center

Kobayashi, Yuichiro; Abe, Mariko

2016-01-01

The purpose of the present study is to assess second language (L2) spoken English using automated scoring techniques. Automated scoring aims to classify a large set of learners' oral performance data into a small number of discrete oral proficiency levels. In automated scoring, objectively measurable features such as the frequencies of lexical and…

Machine learning in soil classification.

PubMed

Bhattacharya, B; Solomatine, D P

2006-03-01

In a number of engineering problems, e.g. in geotechnics, petroleum engineering, etc. intervals of measured series data (signals) are to be attributed a class maintaining the constraint of contiguity and standard classification methods could be inadequate. Classification in this case needs involvement of an expert who observes the magnitude and trends of the signals in addition to any a priori information that might be available. In this paper, an approach for automating this classification procedure is presented. Firstly, a segmentation algorithm is developed and applied to segment the measured signals. Secondly, the salient features of these segments are extracted using boundary energy method. Based on the measured data and extracted features to assign classes to the segments classifiers are built; they employ Decision Trees, ANN and Support Vector Machines. The methodology was tested in classifying sub-surface soil using measured data from Cone Penetration Testing and satisfactory results were obtained.

High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles.

PubMed

Li, Fuhai; Zhou, Xiaobo; Zhu, Jinmin; Ma, Jinwen; Huang, Xudong; Wong, Stephen T C

2007-10-09

High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study. The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system. The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.

Hepatic fat and adenosine triphosphate measurement in overweight and obese adults using 1H and 31P magnetic resonance spectroscopy

PubMed Central

Solga, Steven F.; Horska, Alena; Hemker, Susanne; Crawford, Stephen; Diggs, Charalett; Diehl, Anna Mae; Brancati, Frederick L.; Clark, Jeanne M.

2009-01-01

Background/Aims Magnetic resonance spectroscopy (MRS) measures hepatic fat and adenosine triphosphate (ATP), but magnetic resonance studies are challenging in obese subjects. We aimed to evaluate the inter- and intrarater reliability and stability of hepatic fat and ATP measurements in a cohort of overweight and obese adults. Methods We measured hepatic fat and ATP using proton MRS (1H MRS) and phosphorus MRS (31P MRS) at baseline in adults enrolled in the Action for Health in Diabetes (Look AHEAD) clinical trial at one site. Using logistic regression, we determined factors associated with successful MRS data acquisition. We calculated the intra- and inter-rater reliability for hepatic fat and ATP based on 20 scans analysed twice by two readers. We also calculated the stability of these measures three times on five healthy volunteers. Results Of 244 participants recruited into our ancillary study, 185 agreed to MRS. We obtained usable hepatic fat data from 151 (82%) and ATP data from 105 (58%). Obesity was the strongest predictor of failed data acquisition; every unit increase in the body mass index reduced the likelihood of successful fat data by 11% and ATP data by 14%. The inter- and intrarater reliability were excellent for fat (intraclass correlation coefficient = 0.99), but substantially more variable for ATP. Fat measures appeared relatively stable, but this was less true for ATP. Conclusions Obesity can hinder 1H and 31P MRS data acquisition and subsequent analysis. This impact was greater for hepatic ATP than hepatic fat. PMID:18331237

A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System.

PubMed

Frohlich, Holger; Claes, Kasper; De Wolf, Catherine; Van Damme, Xavier; Michel, Anne

2018-05-01

Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored. We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net). Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation. A machine learning method for automated analysis of data from the Noldus Catwalk system was established. Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.

The good, the bad and the ugly of marine reserves for fishery yields.

PubMed

De Leo, Giulio A; Micheli, Fiorenza

2015-11-05

Marine reserves (MRs) are used worldwide as a means of conserving biodiversity and protecting depleted populations. Despite major investments in MRs, their environmental and social benefits have proven difficult to demonstrate and are still debated. Clear expectations of the possible outcomes of MR establishment are needed to guide and strengthen empirical assessments. Previous models show that reserve establishment in overcapitalized, quota-based fisheries can reduce both catch and population abundance, thereby negating fisheries and even conservation benefits. By using a stage-structured, spatially explicit stochastic model, we show that catches under quota-based fisheries that include a network of MRs can exceed maximum sustainable yield (MSY) under conventional quota management if reserves provide protection to old, large spawners that disproportionally contribute to recruitment outside the reserves. Modelling results predict that the net fishery benefit of MRs is lost when gains in fecundity of old, large individuals are small, is highest in the case of sedentary adults with high larval dispersal, and decreases with adult mobility. We also show that environmental variability may mask fishery benefits of reserve implementation and that MRs may buffer against collapse when sustainable catch quotas are exceeded owing to stock overestimation or systematic overfishing. © 2015 The Author(s).

Optimising mHealth helpdesk responsiveness in South Africa: towards automated message triage

PubMed Central

Engelhard, Matthew; Copley, Charles; Watson, Jacqui; Pillay, Yogan; Barron, Peter

2018-01-01

In South Africa, a national-level helpdesk was established in August 2014 as a social accountability mechanism for improving governance, allowing recipients of public sector services to send complaints, compliments and questions directly to a team of National Department of Health (NDoH) staff members via text message. As demand increases, mechanisms to streamline and improve the helpdesk must be explored. This work aims to evaluate the need for and feasibility of automated message triage to improve helpdesk responsiveness to high-priority messages. Drawing from 65 768 messages submitted between October 2016 and July 2017, the quality of helpdesk message handling was evaluated via detailed inspection of (1) a random sample of 481 messages and (2) messages reporting mistreatment of women, as identified using expert-curated keywords. Automated triage was explored by training a naïve Bayes classifier to replicate message labels assigned by NDoH staff. Classifier performance was evaluated on 12 526 messages withheld from the training set. 90 of 481 (18.7%) NDoH responses were scored as suboptimal or incorrect, with median response time of 4.0 hours. 32 reports of facility-based mistreatment and 39 of partner and family violence were identified; NDoH response time and appropriateness for these messages were not superior to the random sample (P>0.05). The naïve Bayes classifier had average accuracy of 85.4%, with ≥98% specificity for infrequently appearing (<50%) labels. These results show that helpdesk handling of mistreatment of women could be improved. Keyword matching and naïve Bayes effectively identified uncommon messages of interest and could support automated triage to improve handling of high-priority messages. PMID:29713508

Annalisa Gnoleba, MSA | Division of Cancer Prevention

Cancer.gov

Mrs. Annalisa Gnoleba is the Public Health Analyst for the Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute. In this position, Mrs. Gnoleba serves as the analyst for developing and formulating short and long range public health program goals, objectives and policies. |

Cooking up an open source EMR for developing countries: OpenMRS - a recipe for successful collaboration.

PubMed

Mamlin, Burke W; Biondich, Paul G; Wolfe, Ben A; Fraser, Hamish; Jazayeri, Darius; Allen, Christian; Miranda, Justin; Tierney, William M

2006-01-01

Millions of people are continue to die each year from HIV/AIDS. The majority of infected persons (>95%) live in the developing world. A worthy response to this pandemic will require coordinated, scalable, and flexible information systems. We describe the OpenMRS system, an open source, collaborative effort that can serve as a foundation for EMR development in developing countries. We report our progress to date, lessons learned, and future directions.

Materials & Engineering: Propelling Innovation MRS Bulletin Special Issue Session

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

Rao, Gopal

Materials enable engineering; and, engineering in turn depends on materials to transform design concepts and equations into physical entities. This relationship continues to grow with expanding societal demand for new products and processes. MRS Bulletin, a publication of the Materials Research Society (MRS) and Cambridge University Press, planned a special issue for December 2015 on Materials and Engineering: Propelling Innovation. This special issue of MRS Bulletin captured the unique relationship between materials and engineering, which are closely intertwined. A special half day session at the 2015 MRS Fall Meeting in Boston captured this discussion through presentations by high level expertsmore » followed by a panel discussion on what it takes to translate materials discoveries into products to benefit society. The Special Session included presentations by experts who are practitioners in materials as well as engineering applications, followed by a panel discussion. Participants discussed state-of-the-art in materials applications in engineering, as well as how engineering needs have pushed materials developments, as also reflected in the 20 or so articles published in the special issue of MRS Bulletin. As expected, the discussions spanned the broad spectrum of materials and provided very strong interdisciplinary interactions and discussions by participants and presenters.« less

Automated Clinical Assessment from Smart home-based Behavior Data

PubMed Central

Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

2016-01-01

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis. PMID:26292348

Learning to recognize rat social behavior: Novel dataset and cross-dataset application.

PubMed

Lorbach, Malte; Kyriakou, Elisavet I; Poppe, Ronald; van Dam, Elsbeth A; Noldus, Lucas P J J; Veltkamp, Remco C

2018-04-15

Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings. Copyright © 2017 Elsevier B.V. All rights reserved.

Robust through-the-wall radar image classification using a target-model alignment procedure.

PubMed

Smith, Graeme E; Mobasseri, Bijan G

2012-02-01

A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) ≤ 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates ≥ 97%. © 2011 IEEE

Automated analysis of urinary stone composition using Raman spectroscopy: pilot study for the development of a compact portable system for immediate postoperative ex vivo application.

PubMed

Miernik, Arkadiusz; Eilers, Yvan; Bolwien, Carsten; Lambrecht, Armin; Hauschke, Dieter; Rebentisch, Gunter; Lossin, Phillipp S; Hesse, Albrecht; Rassweiler, Jens J; Wetterauer, Ulrich; Schoenthaler, Martin

2013-11-01

We evaluate a compact portable system for immediate automated postoperative ex vivo analysis of urinary stone composition using Raman spectroscopy. Analysis of urinary stone composition provides essential information for the treatment and metaphylaxis of urolithiasis. Currently infrared spectroscopy and x-ray diffraction are used for urinary stone analysis. However, these methods may require complex sample preparation and costly laboratory equipment. In contrast, Raman spectrometers could be a simple and quick strategy for immediate stone analysis. Pure samples of 9 stone components and 159 human urinary calculi were analyzed by Raman spectroscopy using a microscope coupled system at 2 excitation wavelengths. Signal-to-noise ratio, peak positions and the distinctness of the acquired Raman spectra were analyzed and compared. Background fluorescence was removed mathematically. Corrected Raman spectra were used as a reference library for automated classification of native human urinary stones (50). The results were then compared to standard infrared spectroscopy. Signal-to-noise ratio was superior at an excitation wavelength of 532 nm. An automated, computer based classifier was capable of matching spectra from patient samples with those of pure stone components. Consecutive analysis of 50 human stones demonstrated 100% sensitivity and specificity compared to infrared spectroscopy (for components with more than 25% of total composition). Our pilot study indicates that Raman spectroscopy is a valid and reliable technique for determining urinary stone composition. Thus, we propose that the development of a compact and portable system based on Raman spectroscopy for immediate, postoperative stone analysis could represent an invaluable tool for the metaphylaxis of urolithiasis. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Toward Automated Cochlear Implant Fitting Procedures Based on Event-Related Potentials.

PubMed

Finke, Mareike; Billinger, Martin; Büchner, Andreas

Cochlear implants (CIs) restore hearing to the profoundly deaf by direct electrical stimulation of the auditory nerve. To provide an optimal electrical stimulation pattern the CI must be individually fitted to each CI user. To date, CI fitting is primarily based on subjective feedback from the user. However, not all CI users are able to provide such feedback, for example, small children. This study explores the possibility of using the electroencephalogram (EEG) to objectively determine if CI users are able to hear differences in tones presented to them, which has potential applications in CI fitting or closed loop systems. Deviant and standard stimuli were presented to 12 CI users in an active auditory oddball paradigm. The EEG was recorded in two sessions and classification of the EEG data was performed with shrinkage linear discriminant analysis. Also, the impact of CI artifact removal on classification performance and the possibility to reuse a trained classifier in future sessions were evaluated. Overall, classification performance was above chance level for all participants although performance varied considerably between participants. Also, artifacts were successfully removed from the EEG without impairing classification performance. Finally, reuse of the classifier causes only a small loss in classification performance. Our data provide first evidence that EEG can be automatically classified on single-trial basis in CI users. Despite the slightly poorer classification performance over sessions, classifier and CI artifact correction appear stable over successive sessions. Thus, classifier and artifact correction weights can be reused without repeating the set-up procedure in every session, which makes the technique easier applicable. With our present data, we can show successful classification of event-related cortical potential patterns in CI users. In the future, this has the potential to objectify and automate parts of CI fitting procedures.

Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier.

PubMed

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Rao, A R

2016-11-05

DNA barcoding is a molecular diagnostic method that allows automated and accurate identification of species based on a short and standardized fragment of DNA. To this end, an attempt has been made in this study to develop a computational approach for identifying the species by comparing its barcode with the barcode sequence of known species present in the reference library. Each barcode sequence was first mapped onto a numeric feature vector based on k-mer frequencies and then Random forest methodology was employed on the transformed dataset for species identification. The proposed approach outperformed similarity-based, tree-based, diagnostic-based approaches and found comparable with existing supervised learning based approaches in terms of species identification success rate, while compared using real and simulated datasets. Based on the proposed approach, an online web interface SPIDBAR has also been developed and made freely available at http://cabgrid.res.in:8080/spidbar/ for species identification by the taxonomists. Copyright © 2016 Elsevier B.V. All rights reserved.

Association Between Duration of Resuscitation and Favorable Outcome After Out-of-Hospital Cardiac Arrest: Implications for Prolonging or Terminating Resuscitation.

PubMed

Reynolds, Joshua C; Grunau, Brian E; Rittenberger, Jon C; Sawyer, Kelly N; Kurz, Michael C; Callaway, Clifton W

2016-12-20

Little evidence guides the appropriate duration of resuscitation in out-of-hospital cardiac arrest, and case features justifying longer or shorter durations are ill defined. We estimated the impact of resuscitation duration on the probability of favorable functional outcome in out-of-hospital cardiac arrest using a large, multicenter cohort. This was a secondary analysis of a North American, single-blind, multicenter, cluster-randomized, clinical trial (ROC-PRIMED [Resuscitation Outcomes Consortium Prehospital Resuscitation Using an Impedance Valve and Early Versus Delayed]) of consecutive adults with nontraumatic, emergency medical services-treated out-of-hospital cardiac arrest. Primary exposure was duration of resuscitation in minutes (onset of professional resuscitation to return of spontaneous circulation [ROSC] or termination of resuscitation). Primary outcome was survival to hospital discharge with favorable outcome (modified Rankin scale [mRS] score of 0-3). Subjects were additionally classified as survival with unfavorable outcome (mRS score of 4-5), ROSC without survival (mRS score of 6), or without ROSC. Subject accrual was plotted as a function of resuscitation duration, and the dynamic probability of favorable outcome at discharge was estimated for the whole cohort and subgroups. Adjusted logistic regression models tested the association between resuscitation duration and survival with favorable outcome. The primary cohort included 11 368 subjects (median age, 69 years [interquartile range, 56-81 years]; 7121 men [62.6%]). Of these, 4023 (35.4%) achieved ROSC, 1232 (10.8%) survived to hospital discharge, and 905 (8.0%) had an mRS score of 0 to 3 at discharge. Distribution of cardiopulmonary resuscitation duration differed by outcome (P<0.00001). For cardiopulmonary resuscitation duration up to 37.0 minutes (95% confidence interval, 34.9-40.9 minutes), 99% with an eventual mRS score of 0 to 3 at discharge achieved ROSC. The dynamic probability of an mRS score of 0 to 3 at discharge declined over elapsed resuscitation duration, but subjects with initial shockable cardiac rhythm, witnessed cardiac arrest, and bystander cardiopulmonary resuscitation were more likely to survive with favorable outcome after prolonged efforts (30-40 minutes). After adjustment for prehospital (odds ratio, 0.93; 95% confidence interval, 0.92-0.95) and inpatient (odds ratio, 0.97; 95% confidence interval, 0.95-0.99) covariates, resuscitation duration was associated with survival to discharge with an mRS score of 0 to 3. Shorter resuscitation duration was associated with likelihood of favorable outcome at hospital discharge. Subjects with favorable case features were more likely to survive prolonged resuscitation up to 47 minutes. URL: http://clinicaltrials.gov. Unique identifier: NCT00394706. © 2016 American Heart Association, Inc.

Automatic classification of hyperactive children: comparing multiple artificial intelligence approaches.

PubMed

Delavarian, Mona; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Dibajnia, Parvin

2011-07-12

Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis. In this study, we tried to differentiate and classify (diagnose) 306 children with many similar symptoms and different behavioral disorders such as ADHD, depression, anxiety, comorbid depression and anxiety and conduct disorder with high accuracy. Classification was based on the symptoms and their severity. With examining 16 different available classifiers, by using "Prtools", we have proposed nearest mean classifier as the most accurate classifier with 96.92% accuracy in this research. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Classification of resistance to passive motion using minimum probability of error criterion.

PubMed

Chan, H C; Manry, M T; Kondraske, G V

1987-01-01

Neurologists diagnose many muscular and nerve disorders by classifying the resistance to passive motion of patients' limbs. Over the past several years, a computer-based instrument has been developed for automated measurement and parameterization of this resistance. In the device, a voluntarily relaxed lower extremity is moved at constant velocity by a motorized driver. The torque exerted on the extremity by the machine is sampled, along with the angle of the extremity. In this paper a computerized technique is described for classifying a patient's condition as 'Normal' or 'Parkinson disease' (rigidity), from the torque versus angle curve for the knee joint. A Legendre polynomial, fit to the curve, is used to calculate a set of eight normally distributed features of the curve. The minimum probability of error approach is used to classify the curve as being from a normal or Parkinson disease patient. Data collected from 44 different subjects was processes and the results were compared with an independent physician's subjective assessment of rigidity. There is agreement in better than 95% of the cases, when all of the features are used.

RADARSAT-2 Polarimetric Radar Imaging for Lake Ice Mapping

NASA Astrophysics Data System (ADS)

Pan, F.; Kang, K.; Duguay, C. R.

2016-12-01

Changes in lake ice dates and duration are useful indicators for assessing long-term climate trends and variability in northern countries. Lake ice cover observations are also a valuable data source for predictions with numerical ice and weather forecasting models. In recent years, satellite remote sensing has assumed a greater role in providing observations of lake ice cover extent for both modeling and climate monitoring purposes. Polarimetric radar imaging has become a promising tool for lake ice mapping at high latitudes where meteorological conditions and polar darkness severely limit observations from optical sensors. In this study, we assessed and characterized the physical scattering mechanisms of lake ice from fully polarimetric RADARSAT-2 datasets obtained over Great Bear Lake, Canada, with the intent of classifying open water and different ice types during the freeze-up and break-up periods. Model-based and eigen-based decompositions were employed to construct the coherency matrix into deterministic scattering mechanisms. These procedures as well as basic polarimetric parameters were integrated into modified convolutional neural networks (CNN). The CNN were modified via introduction of a Markov random field into the higher iterative layers of networks for acquiring updated priors and classifying ice and open water areas over the lake. We show that the selected polarimetric parameters can help with interpretation of radar-ice/water interactions and can be used successfully for water-ice segmentation, including different ice types. As more satellite SAR sensors are being launched or planned, such as the Sentinel-1a/b series and the upcoming RADARSAT Constellation Mission, the rapid volume growth of data and their analysis require the development of robust automated algorithms. The approach developed in this study was therefore designed with the intent of moving towards fully automated mapping of lake ice for consideration by ice services.

Errors detected in pediatric oral liquid medication doses prepared in an automated workflow management system.

PubMed

Bledsoe, Sarah; Van Buskirk, Alex; Falconer, R James; Hollon, Andrew; Hoebing, Wendy; Jokic, Sladan

2018-02-01

The effectiveness of barcode-assisted medication preparation (BCMP) technology on detecting oral liquid dose preparation errors. From June 1, 2013, through May 31, 2014, a total of 178,344 oral doses were processed at Children's Mercy, a 301-bed pediatric hospital, through an automated workflow management system. Doses containing errors detected by the system's barcode scanning system or classified as rejected by the pharmacist were further reviewed. Errors intercepted by the barcode-scanning system were classified as (1) expired product, (2) incorrect drug, (3) incorrect concentration, and (4) technological error. Pharmacist-rejected doses were categorized into 6 categories based on the root cause of the preparation error: (1) expired product, (2) incorrect concentration, (3) incorrect drug, (4) incorrect volume, (5) preparation error, and (6) other. Of the 178,344 doses examined, 3,812 (2.1%) errors were detected by either the barcode-assisted scanning system (1.8%, n = 3,291) or a pharmacist (0.3%, n = 521). The 3,291 errors prevented by the barcode-assisted system were classified most commonly as technological error and incorrect drug, followed by incorrect concentration and expired product. Errors detected by pharmacists were also analyzed. These 521 errors were most often classified as incorrect volume, preparation error, expired product, other, incorrect drug, and incorrect concentration. BCMP technology detected errors in 1.8% of pediatric oral liquid medication doses prepared in an automated workflow management system, with errors being most commonly attributed to technological problems or incorrect drugs. Pharmacists rejected an additional 0.3% of studied doses. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

New trend of MRI diagnosis based on the function and metabolism in the central nervous system.

PubMed

Harada, Masafumi

2006-08-01

The movement of a subject is a major problem in MRI experiments and diagnosis. At first, this review introduces a new technology named the "Propeller Technique" which can improve the motion artifact by changing the data sampling method in the K trajectory. Our experience of a case who underwent measurement by Propeller technique is reported and the effect of this technique is explained. One of the recent hot topics is the appearance of the clinical 3T MR instrument, with its characteristic differences from that at 1.5T. The advantage of 3T is that it facilitates the evaluation of functional and metabolic information using MR spectroscopy (MRS) and functional MRI. The application of proton MRS in clinical cases is shown and the standard method to use proton MRS in a clinical setting is demonstrated. Furthermore, the new techniques, which can measure important metabolites in small amount such as neurotransmitters, was developed using a high signal to noise ratio and frequency resolution, which are advantages of 3T.

Automated mapping of soybean and corn using phenology

NASA Astrophysics Data System (ADS)

Zhong, Liheng; Hu, Lina; Yu, Le; Gong, Peng; Biging, Gregory S.

2016-09-01

For the two of the most important agricultural commodities, soybean and corn, remote sensing plays a substantial role in delivering timely information on the crop area for economic, environmental and policy studies. Traditional long-term mapping of soybean and corn is challenging as a result of the high cost of repeated training data collection, the inconsistency in image process and interpretation, and the difficulty of handling the inter-annual variability of weather and crop progress. In this study, we developed an automated approach to map soybean and corn in the state of Paraná, Brazil for crop years 2010-2015. The core of the approach is a decision tree classifier with rules manually built based on expert interaction for repeated use. The automated approach is advantageous for its capacity of multi-year mapping without the need to re-train or re-calibrate the classifier. Time series MODerate-resolution Imaging Spectroradiometer (MODIS) reflectance product (MCD43A4) were employed to derive vegetation phenology to identify soybean and corn based on crop calendar. To deal with the phenological similarity between soybean and corn, the surface reflectance of the shortwave infrared band scaled to a phenological stage was used to fully separate the two crops. Results suggested that the mapped areas of soybean and corn agreed with official statistics at the municipal level. The resultant map in the crop year 2012 was evaluated using an independent reference data set, and the overall accuracy and Kappa coefficient were 87.2% and 0.804 respectively. As a result of mixed pixel effect at the 500 m resolution, classification results were biased depending on topography. In the flat, broad and highly-cropped areas, uncultivated lands were likely to be identified as soybean or corn, causing over-estimation of cropland area. By contrast, scattered crop fields in mountainous regions with dense natural vegetation tend to be overlooked. For future mapping efforts, it has great potential to apply the automated mapping algorithm to other image series at various scales especially high-resolution images.

Electronic Derivative Classifier/Reviewing Official

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

Harris, Joshua C; McDuffie, Gregory P; Light, Ken L

2017-02-17

The electronic Derivative Classifier, Reviewing Official (eDC/RO) is a web based document management and routing system that reduces security risks and increases workflow efficiencies. The system automates the upload, notification review request, and document status tracking of documents for classification review on a secure server. It supports a variety of document formats (i.e., pdf, doc, docx, xls, xlsx, xlsm, ppt, pptx, vsd, vsdx and txt), and allows for the dynamic placement of classification markings such as the classification level, category and caveats on the document, in addition to a document footer and digital signature.

Segmentation of images of abdominal organs.

PubMed

Wu, Jie; Kamath, Markad V; Noseworthy, Michael D; Boylan, Colm; Poehlman, Skip

2008-01-01

Abdominal organ segmentation, which is, the delineation of organ areas in the abdomen, plays an important role in the process of radiological evaluation. Attempts to automate segmentation of abdominal organs will aid radiologists who are required to view thousands of images daily. This review outlines the current state-of-the-art semi-automated and automated methods used to segment abdominal organ regions from computed tomography (CT), magnetic resonance imaging (MEI), and ultrasound images. Segmentation methods generally fall into three categories: pixel based, region based and boundary tracing. While pixel-based methods classify each individual pixel, region-based methods identify regions with similar properties. Boundary tracing is accomplished by a model of the image boundary. This paper evaluates the effectiveness of the above algorithms with an emphasis on their advantages and disadvantages for abdominal organ segmentation. Several evaluation metrics that compare machine-based segmentation with that of an expert (radiologist) are identified and examined. Finally, features based on intensity as well as the texture of a small region around a pixel are explored. This review concludes with a discussion of possible future trends for abdominal organ segmentation.

Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic

PubMed Central

Shukla, Chinmay A

2017-01-01

The implementation of automation in the multistep flow synthesis is essential for transforming laboratory-scale chemistry into a reliable industrial process. In this review, we briefly introduce the role of automation based on its application in synthesis viz. auto sampling and inline monitoring, optimization and process control. Subsequently, we have critically reviewed a few multistep flow synthesis and suggested a possible control strategy to be implemented so that it helps to reliably transfer the laboratory-scale synthesis strategy to a pilot scale at its optimum conditions. Due to the vast literature in multistep synthesis, we have classified the literature and have identified the case studies based on few criteria viz. type of reaction, heating methods, processes involving in-line separation units, telescopic synthesis, processes involving in-line quenching and process with the smallest time scale of operation. This classification will cover the broader range in the multistep synthesis literature. PMID:28684977

Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging.

PubMed

Gundupalli, Sathish Paulraj; Hait, Subrata; Thakur, Atul

2017-12-01

There has been a significant rise in municipal solid waste (MSW) generation in the last few decades due to rapid urbanization and industrialization. Due to the lack of source segregation practice, a need for automated segregation of recyclables from MSW exists in the developing countries. This paper reports a thermal imaging based system for classifying useful recyclables from simulated MSW sample. Experimental results have demonstrated the possibility to use thermal imaging technique for classification and a robotic system for sorting of recyclables in a single process step. The reported classification system yields an accuracy in the range of 85-96% and is comparable with the existing single-material recyclable classification techniques. We believe that the reported thermal imaging based system can emerge as a viable and inexpensive large-scale classification-cum-sorting technology in recycling plants for processing MSW in developing countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

Object-based image analysis for cadastral mapping using satellite images

NASA Astrophysics Data System (ADS)

Kohli, D.; Crommelinck, S.; Bennett, R.; Koeva, M.; Lemmen, C.

2017-10-01

Cadasters together with land registry form a core ingredient of any land administration system. Cadastral maps comprise of the extent, ownership and value of land which are essential for recording and updating land records. Traditional methods for cadastral surveying and mapping often prove to be labor, cost and time intensive: alternative approaches are thus being researched for creating such maps. With the advent of very high resolution (VHR) imagery, satellite remote sensing offers a tremendous opportunity for (semi)-automation of cadastral boundaries detection. In this paper, we explore the potential of object-based image analysis (OBIA) approach for this purpose by applying two segmentation methods, i.e. MRS (multi-resolution segmentation) and ESP (estimation of scale parameter) to identify visible cadastral boundaries. Results show that a balance between high percentage of completeness and correctness is hard to achieve: a low error of commission often comes with a high error of omission. However, we conclude that the resulting segments/land use polygons can potentially be used as a base for further aggregation into tenure polygons using participatory mapping.

Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs

PubMed Central

Kim, Youngjun; Gobbel, Glenn Temple; Matheny, Michael E; Redd, Andrew; Bray, Bruce E; Heidenreich, Paul; Bolton, Dan; Heavirland, Julia; Kelly, Natalie; Reeves, Ruth; Kalsy, Megha; Goldstein, Mary Kane; Meystre, Stephane M

2018-01-01

Background We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. Objective To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. Methods We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications. We used documents from 1083 unique inpatients from eight VA medical centers to develop a reference standard (RS) to train (n=314) and test (n=769) the Congestive Heart Failure Information Extraction Framework (CHIEF). We also conducted semi-structured interviews (n=15) for stakeholder feedback on implementation of the CHIEF. Results The CHIEF classified each hospitalization in the test set with a sensitivity (SN) of 98.9% and positive predictive value of 98.7%, compared with an RS and SN of 98.5% for available External Peer Review Program assessments. Of the 1083 patients available for the NLP system, the CHIEF evaluated and classified 100% of cases. Stakeholders identified potential implementation facilitators and clinical uses of the CHIEF. Conclusions The CHIEF provided complete data for all patients in the cohort and could potentially improve the efficiency, timeliness, and utility of HF quality measurements. PMID:29335238

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

PubMed

Marafino, Ben J; Davies, Jason M; Bardach, Naomi S; Dean, Mitzi L; Dudley, R Adams

2014-01-01

Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. We selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures. SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

PubMed

Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

2017-12-01

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive thresholding are applied to the corresponding cancer response maps for PCa foci localization. Evaluation based on 160 patient data with 12-core systematic TRUS-guided prostate biopsy as the reference standard demonstrates that our system achieves a sensitivity of 0.46, 0.92 and 0.97 at 0.1, 1 and 10 false positives per normal/benign patient which is significantly superior to two state-of-the-art CNN-based methods (Oquab et al., 2015; Zhou et al., 2015) and 6-core systematic prostate biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue

PubMed Central

Bjornsson, Christopher S; Lin, Gang; Al-Kofahi, Yousef; Narayanaswamy, Arunachalam; Smith, Karen L; Shain, William; Roysam, Badrinath

2009-01-01

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic ‘divide and conquer’ methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick (~100 μm) slices of rat brain tissue were labeled using 3 – 5 fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81–92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system. PMID:18294697

Lung partitioning for x-ray CAD applications

NASA Astrophysics Data System (ADS)

Annangi, Pavan; Raja, Anand

2011-03-01

Partitioning the inside region of lung into homogeneous regions becomes a crucial step in any computer-aided diagnosis applications based on chest X-ray. The ribs, air pockets and clavicle occupy major space inside the lung as seen in the chest x-ray PA image. Segmenting the ribs and clavicle to partition the lung into homogeneous regions forms a crucial step in any CAD application to better classify abnormalities. In this paper we present two separate algorithms to segment ribs and the clavicle bone in a completely automated way. The posterior ribs are segmented based on Phase congruency features and the clavicle is segmented using Mean curvature features followed by Radon transform. Both the algorithms work on the premise that the presentation of each of these anatomical structures inside the left and right lung has a specific orientation range within which they are confined to. The search space for both the algorithms is limited to the region inside the lung, which is obtained by an automated lung segmentation algorithm that was previously developed in our group. Both the algorithms were tested on 100 images of normal and patients affected with Pneumoconiosis.

The mortality risk score and the ADG score: two points-based scoring systems for the Johns Hopkins aggregated diagnosis groups to predict mortality in a general adult population cohort in Ontario, Canada.

PubMed

Austin, Peter C; Walraven, Carl van

2011-10-01

Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults. To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults. The ADG Score derives weights for the individual ADG diagnosis groups. : Retrospective cohort constructed using population-based administrative data. All 10,498,413 residents of Ontario, Canada, between the age of 20 and 100 years who were alive on their birthday in 2007, participated in this study. Participants were randomly divided into derivation and validation samples. : Death within 1 year. In the derivation cohort, the MRS ranged from -21 to 139 (median value 29, IQR 17 to 44). In the validation group, a logistic regression model with the MRS as the sole predictor significantly predicted the risk of 1-year mortality with a c-statistic of 0.917. A regression model with age, sex, and the ADG Score has similar performance. Both methods accurately predicted the risk of 1-year mortality across the 20 vigintiles of risk. The MRS combined values for a person's age, sex, and the John Hopkins ADGs to accurately predict 1-year mortality in adults. The ADG Score is a weighted score representing the presence or absence of the 32 ADG diagnosis groups. These scores will facilitate health services researchers conducting risk adjustment using administrative health care databases.

Automated analysis of angle closure from anterior chamber angle images.

PubMed

Baskaran, Mani; Cheng, Jun; Perera, Shamira A; Tun, Tin A; Liu, Jiang; Aung, Tin

2014-10-21

To evaluate a novel software capable of automatically grading angle closure on EyeCam angle images in comparison with manual grading of images, with gonioscopy as the reference standard. In this hospital-based, prospective study, subjects underwent gonioscopy by a single observer, and EyeCam imaging by a different operator. The anterior chamber angle in a quadrant was classified as closed if the posterior trabecular meshwork could not be seen. An eye was classified as having angle closure if there were two or more quadrants of closure. Automated grading of the angle images was performed using customized software. Agreement between the methods was ascertained by κ statistic and comparison of area under receiver operating characteristic curves (AUC). One hundred forty subjects (140 eyes) were included, most of whom were Chinese (102/140, 72.9%) and women (72/140, 51.5%). Angle closure was detected in 61 eyes (43.6%) with gonioscopy in comparison with 59 eyes (42.1%, P = 0.73) using manual grading, and 67 eyes (47.9%, P = 0.24) with automated grading of EyeCam images. The agreement for angle closure diagnosis between gonioscopy and both manual (κ = 0.88; 95% confidence interval [CI), 0.81-0.96) and automated grading of EyeCam images was good (κ = 0.74; 95% CI, 0.63-0.85). The AUC for detecting eyes with gonioscopic angle closure was comparable for manual and automated grading (AUC 0.974 vs. 0.954, P = 0.31) of EyeCam images. Customized software for automated grading of EyeCam angle images was found to have good agreement with gonioscopy. Human observation of the EyeCam images may still be needed to avoid gross misclassification, especially in eyes with extensive angle closure. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Less versus more radical surgery in stage IB1 cervical cancer: A population-based study of long-term survival.

PubMed

Tseng, Jill H; Aloisi, Alessia; Sonoda, Yukio; Gardner, Ginger J; Zivanovic, Oliver; Abu-Rustum, Nadeem R; Leitao, Mario M

2018-05-15

Standard surgical treatment for women with stage IB1 cervical cancer consists of radical hysterectomy. This study assesses survival outcomes of those treated with less radical surgery (LRS; conization, trachelectomy, simple hysterectomy) compared to more radical surgery (MRS; modified radical, radical hysterectomy). Using the Surveillance, Epidemiology and End Results database, we identified women <45 years with FIGO stage IB1 cervical cancer diagnosed from 1/1998 to 12/2012. Only those who underwent lymph node (LN) assessment were analyzed. Disease-specific survivals (DSSs) of LRS were compared with those of MRS. Of 2571 patients, 807 underwent LRS and 1764 underwent MRS, all with LN assessment. For LRS vs. MRS, 28% vs. 23% were diagnosed with adenocarcinoma (p = 0.024), 31% vs. 39% had G3 disease (p < 0.001), 40% vs. 45% had tumor size >2 cm (p < 0.001), and 27% vs. 29% received adjuvant radiation therapy (p = 0.005). Median follow-up was 79 months (range, 0-179). Ten-year DSS for LRS vs. MRS was 93.5% vs. 92.3% (p = 0.511). There was no difference in 10-year DSS when stratified by tumor size ≤2 cm (LRS 95.1% vs. MRS 95.6%, p = 0.80) or > 2 cm (LRS 90.1% vs. MRS 88.2%, p = 0.48). Factors independently associated with increased risk of death included adenosquamous histology (HR 2.37), G3 disease (HR 2.86), tumors >2 cm (HR 1.82), and LN positivity (HR 2.42). Compared to MRS, LRS was not associated with a higher risk of death. In a select group of young women with stage IB1 cervical cancer, LRS compared to MRS does not appear to compromise DSS. Copyright © 2018 Elsevier Inc. All rights reserved.

Spectral autofluorescence imaging of the retina for drusen detection

NASA Astrophysics Data System (ADS)

Foubister, James J.; Gorman, Alistair; Harvey, Andy; Hemert, Jano van

2018-02-01

The presence and characteristics of drusen in retinal images, namely their size, location, and distribution, can be used to aid in the diagnosis and monitoring of Age Related Macular Degeneration (AMD); one of the leading causes for blindness in the elderly population. Current imaging techniques are effective at determining the presence and number of drusen, but fail when it comes to classifying their size and form. These distinctions are important for correctly characterising the disease, especially in the early stages where the development of just one larger drusen can indicate progression. Another challenge for automated detection is in distinguishing them from other retinal features, such as cotton wool spots. We describe the development of a multi-spectral scanning-laser ophthalmoscope that records images of retinal autofluorescence (AF) in four spectral bands. This will offer the potential to detect drusen with improved contrast based on spectral discrimination for automated classification. The resulting improved specificity and sensitivity for their detection offers more reliable characterisation of AMD. We present proof of principle images prior to further system optimisation and clinical trials for assessment of enhanced detection of drusen.

10. Photocopy of an engraving of a stained glass window ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

10. Photocopy of an engraving of a stained glass window design by Johann Friedrich Overbeck (1789-1869) on which two of the chancel windows in the Church of the Holy Cross are thought to have been based. This copy is of a photocopy obtained from the Treasury of Notre Dame de Paris, Paris, France, by the late Mrs. Walter C. White of Stateburg, South Carolina. Mrs. White's photocopy is in the possession of Mrs. Richard K. Anderson of the Borough House at Stateburg. - Church of the Holy Cross, State Route 261, Stateburg, Sumter County, SC

Rehabilitation after Clipping of Cerebral Aneurysms without Acute Subarachnoid Hemorrhage: Outcome Analysis of 100 Consecutive Cases.

PubMed

Jödicke, Andreas; Bauer, Karsten; Hajdukova, Andrea

2018-06-11

 Discharge to rehabilitation is reported in large studies as one important outcome parameter based on hospital codes. Because neurologic outcome scores (e.g., the modified Rankin Scale [mRS]) are missing in International Classification of Diseases (ICD) databases, rehabilitation indirectly serves as a kind of surrogate parameter for overall outcome. Reported fractions of patients with rehabilitation, however, largely differ between studies and seem high for patients with aneurysm clipping. Variances in rehabilitation fractions seem to largely differ between treatments (clipping versus coiling) for unruptured intracranial aneurysms, so we analyzed our patients for percentage of and potential factors predicting rehabilitation.  From July 2007 to September 2013, 100 consecutive patients with at least one cerebral aneurysm underwent aneurysm clipping. Aneurysms were classified as incidental, associated, pretreated (coil compaction after subarachnoid hemorrhage), and symptomatic (oculomotor nerve compression, microemboli), and they were assigned to their anatomical location. Complications (infection, hemorrhage, cerebrospinal fluid fistula, transient and permanent neurologic deficit, reoperation) and outcome (mRS at 6 months; clip occlusion rate by postoperative digital subtraction angiography) as well as frequency and type of rehabilitation were analyzed and correlated retrospectively. Multiple aneurysms clipped in one procedure were not counted separately regarding complications or outcome (i.e., one patient, one outcome).  The overall complication rate was 17% including 10% early and 3% permanent neurologic deficits and 7% reoperations. There were no deaths. Overall, 98% of patients had a good outcome (mRS 0-2). Clip occlusion rate was 97.9%. Multivariate logistic regression analysis identified aneurysm location as the only significant independent factor for risk of complication ( p  < 0.001) and complication as the only significant independent risk factor for rehabilitation ( p  = 0.003). Rehabilitation was indicated or requested by the patient as early neurologic rehabilitation (5%), inpatient follow-up (15%), and outpatient follow-up (15%). The long-term care rate was 2%.  Microsurgery of unruptured and not acutely ruptured aneurysms (including post-coil and associated aneurysms) has a low rate of rehabilitation with a low risk of a permanent neurologic deficit, long-term care, or early neurologic rehabilitation. The rate of rehabilitation is well below reported risks from studies based on ICD-based health care analysis. Rehabilitation per se is not a good indicator for outcome. Georg Thieme Verlag KG Stuttgart · New York.

An expert support system for breast cancer diagnosis using color wavelet features.

PubMed

Issac Niwas, S; Palanisamy, P; Chibbar, Rajni; Zhang, W J

2012-10-01

Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2017-02-01

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

An automated microphysiological assay for toxicity evaluation.

PubMed

Eggert, S; Alexander, F A; Wiest, J

2015-08-01

Screening a newly developed drug, food additive or cosmetic ingredient for toxicity is a critical preliminary step before it can move forward in the development pipeline. Due to the sometimes dire consequences when a harmful agent is overlooked, toxicologists work under strict guidelines to effectively catalogue and classify new chemical agents. Conventional assays involve long experimental hours and many manual steps that increase the probability of user error; errors that can potentially manifest as inaccurate toxicology results. Automated assays can overcome many potential mistakes that arise due to human error. In the presented work, we created and validated a novel, automated platform for a microphysiological assay that can examine cellular attributes with sensors measuring changes in cellular metabolic rate, oxygen consumption, and vitality mediated by exposure to a potentially toxic agent. The system was validated with low buffer culture medium with varied conductivities that caused changes in the measured impedance on integrated impedance electrodes.

Remote surface inspection system

NASA Astrophysics Data System (ADS)

Hayati, S.; Balaram, J.; Seraji, H.; Kim, W. S.; Tso, K.; Prasad, V.

1993-02-01

This paper reports on an on-going research and development effort in remote surface inspection of space platforms such as the Space Station Freedom (SSF). It describes the space environment and identifies the types of damage for which to search. This paper provides an overview of the Remote Surface Inspection System that was developed to conduct proof-of-concept demonstrations and to perform experiments in a laboratory environment. Specifically, the paper describes three technology areas: (1) manipulator control for sensor placement; (2) automated non-contact inspection to detect and classify flaws; and (3) an operator interface to command the system interactively and receive raw or processed sensor data. Initial findings for the automated and human visual inspection tests are reported.

Remote surface inspection system

NASA Technical Reports Server (NTRS)

Hayati, S.; Balaram, J.; Seraji, H.; Kim, W. S.; Tso, K.; Prasad, V.

1993-01-01

This paper reports on an on-going research and development effort in remote surface inspection of space platforms such as the Space Station Freedom (SSF). It describes the space environment and identifies the types of damage for which to search. This paper provides an overview of the Remote Surface Inspection System that was developed to conduct proof-of-concept demonstrations and to perform experiments in a laboratory environment. Specifically, the paper describes three technology areas: (1) manipulator control for sensor placement; (2) automated non-contact inspection to detect and classify flaws; and (3) an operator interface to command the system interactively and receive raw or processed sensor data. Initial findings for the automated and human visual inspection tests are reported.

Combining Passive Microwave Rain Rate Retrieval with Visible and Infrared Cloud Classification.

NASA Astrophysics Data System (ADS)

Miller, Shawn William

The relation between cloud type and rain rate has been investigated here from different approaches. Previous studies and intercomparisons have indicated that no single passive microwave rain rate algorithm is an optimal choice for all types of precipitating systems. Motivated by the upcoming Tropical Rainfall Measuring Mission (TRMM), an algorithm which combines visible and infrared cloud classification with passive microwave rain rate estimation was developed and analyzed in a preliminary manner using data from the Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response Experiment (TOGA-COARE). Overall correlation with radar rain rate measurements across five case studies showed substantial improvement in the combined algorithm approach when compared to the use of any single microwave algorithm. An automated neural network cloud classifier for use over both land and ocean was independently developed and tested on Advanced Very High Resolution Radiometer (AVHRR) data. The global classifier achieved strict accuracy for 82% of the test samples, while a more localized version achieved strict accuracy for 89% of its own test set. These numbers provide hope for the eventual development of a global automated cloud classifier for use throughout the tropics and the temperate zones. The localized classifier was used in conjunction with gridded 15-minute averaged radar rain rates at 8km resolution produced from the current operational network of National Weather Service (NWS) radars, to investigate the relation between cloud type and rain rate over three regions of the continental United States and adjacent waters. The results indicate a substantially lower amount of available moisture in the Front Range of the Rocky Mountains than in the Midwest or in the eastern Gulf of Mexico.

75 FR 67985 - Government-Owned Inventions; Availability for Licensing

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-04

... spectrum data, recalibrates and scales the normalized MRS spectrum data, and then renormalizes the scaled MRS spectrum data. The resulting preprocessed MRS data is used to assist in identifying abnormalities in tissues shown in MRS scans. Raw MRS spectrum data and scaling the raw MRS spectrum data is...

Non-destructive monitoring of creaming of oil-in-water emulsion-based formulations using magnetic resonance imaging.

PubMed

Onuki, Yoshinori; Horita, Akihiro; Kuribayashi, Hideto; Okuno, Yoshihide; Obata, Yasuko; Takayama, Kozo

2014-07-01

A non-destructive method for monitoring creaming of emulsion-based formulations is in great demand because it allows us to understand fully their instability mechanisms. This study was aimed at demonstrating the usefulness of magnetic resonance (MR) techniques, including MR imaging (MRI) and MR spectroscopy (MRS), for evaluating the physicochemical stability of emulsion-based formulations. Emulsions that are applicable as the base of practical skin creams were used as test samples. Substantial creaming was developed by centrifugation, which was then monitored by MRI. The creaming oil droplet layer and aqueous phase were clearly distinguished by quantitative MRI by measuring T1 and the apparent diffusion coefficient. Components in a selected volume in the emulsions could be analyzed using MRS. Then, model emulsions having different hydrophilic-lipophilic balance (HLB) values were tested, and the optimal HLB value for a stable dispersion was determined. In addition, the MRI examination enables the detection of creaming occurring in a polyethylene tube, which is commonly used for commercial products, without losing any image quality. These findings strongly indicate that MR techniques are powerful tools to evaluate the physicochemical stability of emulsion-based formulations. This study will make a great contribution to the development and quality control of emulsion-based formulations.

Medical Representatives' Intention to Use Information Technology in Pharmaceutical Marketing.

PubMed

Kwak, Eun-Seon; Chang, Hyejung

2016-10-01

Electronic detailing (e-detailing), the use of electronic devices to facilitate sales presentations to physicians, has been adopted and expanded in the pharmaceutical industry. To maximize the potential outcome of e-detailing, it is important to understand medical representatives (MRs)' behavior and attitude to e-detailing. This study investigates how information technology devices such as laptop computers and tablet PCs are utilized in pharmaceutical marketing, and it analyzes the factors influencing MRs' intention to use devices. This study has adopted and modified the theory of Roger's diffusion of innovation model and the technology acceptance model. To test the model empirically, a questionnaire survey was conducted with 221 MRs who were working in three multinational or eleven domestic pharmaceutical companies in Korea. Overall, 28% and 35% of MRs experienced using laptop computers and tablet PCs in pharmaceutical marketing, respectively. However, the rates were different across different groups of MRs, categorized by age, education level, position, and career. The results showed that MRs' intention to use information technology devices was significantly influenced by perceived usefulness in general. Perceived ease of use, organizational and individual innovativeness, and several MR characteristics were also found to have significant impacts. This study provides timely information about e-detailing devices to marketing managers and policy makers in the pharmaceutical industry for successful marketing strategy development by understanding the needs of MRs' intention to use information technology. Further in-depth study should be conducted to understand obstacles and limitations and to improve the strategies for better marketing tools.

WordBytes: Exploring an Intermediate Constraint Format for Rapid Classification of Student Answers on Constructed Response Assessments

ERIC Educational Resources Information Center

Kim, Kerry J.; Meir, Eli; Pope, Denise S.; Wendel, Daniel

2017-01-01

Computerized classification of student answers offers the possibility of instant feedback and improved learning. Open response (OR) questions provide greater insight into student thinking and understanding than more constrained multiple choice (MC) questions, but development of automated classifiers is more difficult, often requiring training a…

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Automatic brain tumor detection in MRI: methodology and statistical validation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert

2005-04-01

Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

An explosives detection system for airline security using coherent x-ray scattering technology

NASA Astrophysics Data System (ADS)

Madden, Robert W.; Mahdavieh, Jacob; Smith, Richard C.; Subramanian, Ravi

2008-08-01

L-3 Communications Security and Detection Systems (SDS) has developed a new system for automated alarm resolution in airline baggage Explosive Detection Systems (EDS) based on coherent x-ray scattering spectroscopy. The capabilities of the system were demonstrated in tests with concealed explosives at the Transportation Security Laboratory and airline passenger baggage at Orlando International Airport. The system uses x-ray image information to identify suspicious objects and performs targeted diffraction measurements to classify them. This extra layer of detection capability affords a significant reduction in the rate of false alarm objects that must presently be resolved by opening passenger bags for hand inspection.

Testing Saliency Parameters for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Pandya, Sagar

2012-01-01

A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.

Detecting tympanostomy tubes from otoscopic images via offline and online training.

PubMed

Wang, Xin; Valdez, Tulio A; Bi, Jinbo

2015-06-01

Tympanostomy tube placement has been commonly used nowadays as a surgical treatment for otitis media. Following the placement, regular scheduled follow-ups for checking the status of the tympanostomy tubes are important during the treatment. The complexity of performing the follow up care mainly lies on identifying the presence and patency of the tympanostomy tube. An automated tube detection program will largely reduce the care costs and enhance the clinical efficiency of the ear nose and throat specialists and general practitioners. In this paper, we develop a computer vision system that is able to automatically detect a tympanostomy tube in an otoscopic image of the ear drum. The system comprises an offline classifier training process followed by a real-time refinement stage performed at the point of care. The offline training process constructs a three-layer cascaded classifier with each layer reflecting specific characteristics of the tube. The real-time refinement process enables the end users to interact and adjust the system over time based on their otoscopic images and patient care. The support vector machine (SVM) algorithm has been applied to train all of the classifiers. Empirical evaluation of the proposed system on both high quality hospital images and low quality internet images demonstrates the effectiveness of the system. The offline classifier trained using 215 images could achieve a 90% accuracy in terms of classifying otoscopic images with and without a tympanostomy tube, and then the real-time refinement process could improve the classification accuracy by 3-5% based on additional 20 images. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of a Rigid One-Meter-Side and Cooled Coil Sensor at 77 K for Magnetic Resonance Sounding to Detect Subsurface Water Sources.

PubMed

Lin, Jun; Du, Guanfeng; Zhang, Jian; Yi, Xiaofeng; Jiang, Chuandong; Lin, Tingting

2017-06-12

Magnetic resonance sounding (MRS) using the Earth's magnetic field is a noninvasive and on-site geophysical technique providing quantitative characteristics of aquifers in the subsurface. When the MRS technology is applied in a mine or tunnel for advance detecting the source of water that may cause disastrous accident, spatial constraints limit the size of coil sensor and thus lower the detection capability. In this paper, a coil sensor for detecting the weak MRS signal is designed and the signal to noise (SNR) for the coil sensor is analyzed and optimized. The coil sensor has a rigid structure and square size of 1 m for deploying in a narrow underground space and is cooled at a low temperature of 77 K for improving the SNR. A theoretical calculation and an experimental test in an electromagnetically shielded room (EMSR) show that the optimal design of coil sensor consists of an 80-turn coil and a low-current-noise preamplifier AD745. It has a field sensitivity of 0.17 fT / Hz in the EMSR at 77 K, which is superior to the low temperature Superconducting Quantum Interference Device (LT SQUID) that is the latest application in MRS and the cooled coil with a diameter of 9 cm when detecting the laboratory NMR signal in kHz range. In the field experiment above the Taipingchi Reservoir near Changchun in China, the cooled coil sensor (CCS) developed in this paper has successfully obtained a valid weak MRS signal in high noise environment. The field results showed that the quality of measured MRS signal at 77 K is significantly superior to that at 298 K and the SNR is improved up to three times. This property of CCS makes the MRS instrument more convenient and reliable in a constricted space underground engineering environment (e.g., a mine or a tunnel).

Development of a Rigid One-Meter-Side and Cooled Coil Sensor at 77 K for Magnetic Resonance Sounding to Detect Subsurface Water Sources

PubMed Central

Lin, Jun; Du, Guanfeng; Zhang, Jian; Yi, Xiaofeng; Jiang, Chuandong; Lin, Tingting

2017-01-01

Magnetic resonance sounding (MRS) using the Earth’s magnetic field is a noninvasive and on-site geophysical technique providing quantitative characteristics of aquifers in the subsurface. When the MRS technology is applied in a mine or tunnel for advance detecting the source of water that may cause disastrous accident, spatial constraints limit the size of coil sensor and thus lower the detection capability. In this paper, a coil sensor for detecting the weak MRS signal is designed and the signal to noise (SNR) for the coil sensor is analyzed and optimized. The coil sensor has a rigid structure and square size of 1 m for deploying in a narrow underground space and is cooled at a low temperature of 77 K for improving the SNR. A theoretical calculation and an experimental test in an electromagnetically shielded room (EMSR) show that the optimal design of coil sensor consists of an 80-turn coil and a low-current-noise preamplifier AD745. It has a field sensitivity of 0.17 fT/Hz in the EMSR at 77 K, which is superior to the low temperature Superconducting Quantum Interference Device (LT SQUID) that is the latest application in MRS and the cooled coil with a diameter of 9 cm when detecting the laboratory NMR signal in kHz range. In the field experiment above the Taipingchi Reservoir near Changchun in China, the cooled coil sensor (CCS) developed in this paper has successfully obtained a valid weak MRS signal in high noise environment. The field results showed that the quality of measured MRS signal at 77 K is significantly superior to that at 298 K and the SNR is improved up to three times. This property of CCS makes the MRS instrument more convenient and reliable in a constricted space underground engineering environment (e.g., a mine or a tunnel). PMID:28604621

A method for classification of transient events in EEG recordings: application to epilepsy diagnosis.

PubMed

Tzallas, A T; Karvelis, P S; Katsis, C D; Fotiadis, D I; Giannopoulos, S; Konitsiotis, S

2006-01-01

The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Toward automatic phenotyping of retinal images from genetically determined mono- and dizygotic twins using amplitude modulation-frequency modulation methods

NASA Astrophysics Data System (ADS)

Soliz, P.; Davis, B.; Murray, V.; Pattichis, M.; Barriga, S.; Russell, S.

2010-03-01

This paper presents an image processing technique for automatically categorize age-related macular degeneration (AMD) phenotypes from retinal images. Ultimately, an automated approach will be much more precise and consistent in phenotyping of retinal diseases, such as AMD. We have applied the automated phenotyping to retina images from a cohort of mono- and dizygotic twins. The application of this technology will allow one to perform more quantitative studies that will lead to a better understanding of the genetic and environmental factors associated with diseases such as AMD. A method for classifying retinal images based on features derived from the application of amplitude-modulation frequency-modulation (AM-FM) methods is presented. Retinal images from identical and fraternal twins who presented with AMD were processed to determine whether AM-FM could be used to differentiate between the two types of twins. Results of the automatic classifier agreed with the findings of other researchers in explaining the variation of the disease between the related twins. AM-FM features classified 72% of the twins correctly. Visual grading found that genetics could explain between 46% and 71% of the variance.

Bioimage informatics approach to automated meibomian gland analysis in infrared images of meibography

PubMed Central

Celik, Turgay; Lee, Hwee Kuan; Petznick, Andrea; Tong, Louis

2013-01-01

Background Infrared (IR) meibography is an imaging technique to capture the Meibomian glands in the eyelids. These ocular surface structures are responsible for producing the lipid layer of the tear film which helps to reduce tear evaporation. In a normal healthy eye, the glands have similar morphological features in terms of spatial width, in-plane elongation, length. On the other hand, eyes with Meibomian gland dysfunction show visible structural irregularities that help in the diagnosis and prognosis of the disease. However, currently there is no universally accepted algorithm for detection of these image features which will be clinically useful. We aim to develop a method of automated gland segmentation which allows images to be classified. Methods A set of 131 meibography images were acquired from patients from the Singapore National Eye Center. We used a method of automated gland segmentation using Gabor wavelets. Features of the imaged glands including orientation, width, length and curvature were extracted and the IR images enhanced. The images were classified as ‘healthy’, ‘intermediate’ or ‘unhealthy’, through the use of a support vector machine classifier (SVM). Half the images were used for training the SVM and the other half for validation. Independently of this procedure, the meibographs were classified by an expert clinician into the same 3 grades. Results The algorithm correctly detected 94% and 98% of mid-line pixels of gland and inter-gland regions, respectively, on healthy images. On intermediate images, correct detection rates of 92% and 97% of mid-line pixels of gland and inter-gland regions were achieved respectively. The true positive rate of detecting healthy images was 86%, and for intermediate images, 74%. The corresponding false positive rates were 15% and 31% respectively. Using the SVM, the proposed method has 88% accuracy in classifying images into the 3 classes. The classification of images into healthy and unhealthy classes achieved a 100% accuracy, but 7/38 intermediate images were incorrectly classified. Conclusions This technique of image analysis in meibography can help clinicians to interpret the degree of gland destruction in patients with dry eye and meibomian gland dysfunction.

Iterative Repair Planning for Spacecraft Operations Using the Aspen System

NASA Technical Reports Server (NTRS)

Rabideau, G.; Knight, R.; Chien, S.; Fukunaga, A.; Govindjee, A.

2000-01-01

This paper describes the Automated Scheduling and Planning Environment (ASPEN). ASPEN encodes complex spacecraft knowledge of operability constraints, flight rules, spacecraft hardware, science experiments and operations procedures to allow for automated generation of low level spacecraft sequences. Using a technique called iterative repair, ASPEN classifies constraint violations (i.e., conflicts) and attempts to repair each by performing a planning or scheduling operation. It must reason about which conflict to resolve first and what repair method to try for the given conflict. ASPEN is currently being utilized in the development of automated planner/scheduler systems for several spacecraft, including the UFO-1 naval communications satellite and the Citizen Explorer (CX1) satellite, as well as for planetary rover operations and antenna ground systems automation. This paper focuses on the algorithm and search strategies employed by ASPEN to resolve spacecraft operations constraints, as well as the data structures for representing these constraints.

Differential Item Functioning of the Psychological Domain of the Menopause Rating Scale.

PubMed

Monterrosa-Castro, Alvaro; Portela-Buelvas, Katherin; Oviedo, Heidi C; Herazo, Edwin; Campo-Arias, Adalberto

2016-01-01

Introduction. Quality of life could be quantified with the Menopause Rating Scale (MRS), which evaluates the severity of somatic, psychological, and urogenital symptoms in menopause. However, differential item functioning (DIF) analysis has not been applied previously. Objective . To establish the DIF of the psychological domain of the MRS in Colombian women. Methods . 4,009 women aged between 40 and 59 years, who participated in the CAVIMEC (Calidad de Vida en la Menopausia y Etnias Colombianas) project, were included. Average age was 49.0 ± 5.9 years. Women were classified in mestizo, Afro-Colombian, and indigenous. The results were presented as averages and standard deviation ( X ± SD). A p value <0.001 was considered statistically significant. Results . In mestizo women, the highest X ± SD were obtained in physical and mental exhaustion (PME) (0.86 ± 0.93) and the lowest ones in anxiety (0.44 ± 0.79). In Afro-Colombian women, an average score of 0.99 ± 1.07 for PME and 0.63 ± 0.88 for anxiety was gotten. Indigenous women obtained an increased average score for PME (1.33 ± 0.93). The lowest score was evidenced in depressive mood (0.50 ± 0.81), which is different from other Colombian women ( p < 0.001). Conclusions . The psychological items of the MRS show differential functioning according to the ethnic group, which may induce systematic error in the measurement of the construct.

Differential Item Functioning of the Psychological Domain of the Menopause Rating Scale

PubMed Central

Portela-Buelvas, Katherin; Oviedo, Heidi C.; Herazo, Edwin; Campo-Arias, Adalberto

2016-01-01

Introduction. Quality of life could be quantified with the Menopause Rating Scale (MRS), which evaluates the severity of somatic, psychological, and urogenital symptoms in menopause. However, differential item functioning (DIF) analysis has not been applied previously. Objective. To establish the DIF of the psychological domain of the MRS in Colombian women. Methods. 4,009 women aged between 40 and 59 years, who participated in the CAVIMEC (Calidad de Vida en la Menopausia y Etnias Colombianas) project, were included. Average age was 49.0 ± 5.9 years. Women were classified in mestizo, Afro-Colombian, and indigenous. The results were presented as averages and standard deviation (X ± SD). A p value <0.001 was considered statistically significant. Results. In mestizo women, the highest X ± SD were obtained in physical and mental exhaustion (PME) (0.86 ± 0.93) and the lowest ones in anxiety (0.44 ± 0.79). In Afro-Colombian women, an average score of 0.99 ± 1.07 for PME and 0.63 ± 0.88 for anxiety was gotten. Indigenous women obtained an increased average score for PME (1.33 ± 0.93). The lowest score was evidenced in depressive mood (0.50 ± 0.81), which is different from other Colombian women (p < 0.001). Conclusions. The psychological items of the MRS show differential functioning according to the ethnic group, which may induce systematic error in the measurement of the construct. PMID:27847825

Nonalcoholic fatty liver disease (NAFLD) in the Veterans Administration population: development and validation of an algorithm for NAFLD using automated data.

PubMed

Husain, N; Blais, P; Kramer, J; Kowalkowski, M; Richardson, P; El-Serag, H B; Kanwal, F

2014-10-01

In practice, nonalcoholic fatty liver disease (NAFLD) is diagnosed based on elevated liver enzymes and confirmatory liver biopsy or abdominal imaging. Neither method is feasible in identifying individuals with NAFLD in a large-scale healthcare system. To develop and validate an algorithm to identify patients with NAFLD using automated data. Using the Veterans Administration Corporate Data Warehouse, we identified patients who had persistent ALT elevation (≥2 values ≥40 IU/mL ≥6 months apart) and did not have evidence of hepatitis B, hepatitis C or excessive alcohol use. We conducted a structured chart review of 450 patients classified as NAFLD and 150 patients who were classified as non-NAFLD by the database algorithm, and subsequently refined the database algorithm. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) for the initial database definition of NAFLD were 78.4% (95% CI: 70.0-86.8%), 74.5% (95% CI: 68.1-80.9%), 64.1% (95% CI: 56.4-71.7%) and 85.6% (95% CI: 79.4-91.8%), respectively. Reclassifying patients as having NAFLD if they had two elevated ALTs that were at least 6 months apart but within 2 years of each other, increased the specificity and PPV of the algorithm to 92.4% (95% CI: 88.8-96.0%) and 80.8% (95% CI: 72.5-89.0%), respectively. However, the sensitivity and NPV decreased to 55.0% (95% CI: 46.1-63.9%) and 78.0% (95% CI: 72.1-83.8%), respectively. Predictive algorithms using automated data can be used to identify patients with NAFLD, determine prevalence of NAFLD at the system-wide level, and may help select a target population for future clinical studies in veterans with NAFLD. © 2014 John Wiley & Sons Ltd.

Detecting falls with wearable sensors using machine learning techniques.

PubMed

Özdemir, Ahmet Turan; Barshan, Billur

2014-06-18

Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

Functional outcome of microsurgical clipping compared to endovascular coiling.

PubMed

Premananda, R M; Ramesh, N; Hillol, K P

2012-12-01

Endovascular coiling has been used increasingly as an alternative to neurosurgical clipping for treating subarachnoid hemorrhage secondary to aneurysm rupture. In a retrospective cohort review on the treatment methods of aneurysm rupture in Hospital Kuala Lumpur over the period of five years (2005-2009) a total of 268 patients were treated. These patients were broadly categorized into two groups based on their treatment mode for ruptured aneurysms. Statistical analysis was determined using Chi- Square tests to study these associations. In our study, 67.5% of patients presented with Good World Federation of Neurosurgical Societies (WFNS) grade (WFNS1-2) while 32.5% patients presented with Poor WFNS prior to intervention. In our outcome, it was noted that 60.4% had good functional outcome (mRS grade 0-2) as compared to 39.6% patients who had poor mRS(modified rankin scale) outcome (mRS 3-6). In the good WFNS group, 76% of patients in clipping group had a good mRS outcome while, 86.5% patients in coiling group had good mRS outcome (p=0.114). In poor WFNS presentation, it was noted that in 77.3% patients in clipping group, had poor mRS outcome. Similarly with poor WFNS presentation, 83.3% of patient in coiling group had poor outcome. (p=1.00). Hence when we control the WFNS group, there was no significant association between treatment group (clipping and coiling) and mRS outcome at 6 months. The outcome of patient is determined by initial clinical presentation (WFNS grade) and influenced by requirement of Extraventricular drain (EVD) in presence of hydrocephalus, CSF infection and pneumonia. Therefore the decision regarding treatment option needs to be individualized based on the presentation of the patient.

An Integrated MRI and MRS Approach to Evaluation of Multiple Sclerosis with Cognitive Impairment

NASA Astrophysics Data System (ADS)

Liang, Zhengrong; Li, Lihong; Lu, Hongbing; Huang, Wei; Tudorica, Alina; Krupp, Lauren

Magnetic resonance imaging and spectroscopy (MRI/MRS) plays a unique role in multiple sclerosis (MS) evaluation, because of its ability to provide both high image contrast and significant chemical change among brain tissues. The image contrast renders the possibility of quantifying the tissue volumetric and texture variations, e.g., cerebral atrophy and progressing speed, reflecting the ongoing destructive pathologic processes. Any chemical change reflects an early sign of pathological alteration, e.g., decreased N-acetyl aspartate (NAA) in lesions and normal appearing white matter, related to axonal damage or dysfunction. Both MRI and MRS encounter partial volume (PV) effect, which compromises the quantitative capability, especially for MRS. This work aims to develop a statistical framework to segment the tissue mixtures inside each image element, eliminating theoretically the PV effect, and apply the framework to the evaluation of MS with cognitive impairment. The quantitative measures from MRI/MRS neuroimaging are strongly correlated with the qualitative neuropsychological scores of Brief Repeatable Battery (BRB) test on cognitive impairment, demonstrating the usefulness of the PV image segmentation framework in this clinically significant problem.

Army Science Board (ASB) 1983 Summer Study on the Future Development Goal

DTIC Science & Technology

1983-11-01

PARTICIPATED WITH ASB DR. ROGER 0. BOURKE JPL MR. ARTHUR C. CHRISTMAN TRADOC MAJ MICHAEL M. FERGUSON TRADOC DR. EDGAR M. JOHNSON ARI BG ROBERT B...MARGARET POTTER ASS MS. SHARON L. BERTONI ODCSOPS MS. BRENDA E. CALLAHAN OASAIRDA) MRS. SHARON J. LAYTON ODCSOPS MRS. MARY G. QUELLEIrE ODCSRDA I

Development of common neural representations for distinct numerical problems

PubMed Central

Chang, Ting-Ting; Rosenberg-Lee, Miriam; Metcalfe, Arron W. S.; Chen, Tianwen; Menon, Vinod

2015-01-01

How the brain develops representations for abstract cognitive problems is a major unaddressed question in neuroscience. Here we tackle this fundamental question using arithmetic problem solving, a cognitive domain important for the development of mathematical reasoning. We first examined whether adults demonstrate common neural representations for addition and subtraction problems, two complementary arithmetic operations that manipulate the same quantities. We then examined how the common neural representations for the two problem types change with development. Whole-brain multivoxel representational similarity (MRS) analysis was conducted to examine common coding of addition and subtraction problems in children and adults. We found that adults exhibited significant levels of MRS between the two problem types, not only in the intra-parietal sulcus (IPS) region of the posterior parietal cortex (PPC), but also in ventral temporal-occipital, anterior temporal and dorsolateral prefrontal cortices. Relative to adults, children showed significantly reduced levels of MRS in these same regions. In contrast, no brain areas showed significantly greater MRS between problem types in children. Our findings provide novel evidence that the emergence of arithmetic problem solving skills from childhood to adulthood is characterized by maturation of common neural representations between distinct numerical operations, and involve distributed brain regions important for representing and manipulating numerical quantity. More broadly, our findings demonstrate that representational analysis provides a powerful approach for uncovering fundamental mechanisms by which children develop proficiencies that are a hallmark of human cognition. PMID:26160287

An automated approach for mapping persistent ice and snow cover over high latitude regions

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors.

Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

PubMed

Whiteside, David; Cant, Olivia; Connolly, Molly; Reid, Machar

2017-10-01

Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players' heavy travel schedules. To develop an automated stroke-classification system to help quantify hitting load in tennis. Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals. Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types. With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.

A translational platform for prototyping closed-loop neuromodulation systems

PubMed Central

Afshar, Pedram; Khambhati, Ankit; Stanslaski, Scott; Carlson, David; Jensen, Randy; Linde, Dave; Dani, Siddharth; Lazarewicz, Maciej; Cong, Peng; Giftakis, Jon; Stypulkowski, Paul; Denison, Tim

2013-01-01

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders. PMID:23346048

A translational platform for prototyping closed-loop neuromodulation systems.

PubMed

Afshar, Pedram; Khambhati, Ankit; Stanslaski, Scott; Carlson, David; Jensen, Randy; Linde, Dave; Dani, Siddharth; Lazarewicz, Maciej; Cong, Peng; Giftakis, Jon; Stypulkowski, Paul; Denison, Tim

2012-01-01

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders.

Human cell structure-driven model construction for predicting protein subcellular location from biological images.

PubMed

Shao, Wei; Liu, Mingxia; Zhang, Daoqiang

2016-01-01

The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting. We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method. The dataset and code can be downloaded from https://github.com/shaoweinuaa/. dqzhang@nuaa.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem

NASA Astrophysics Data System (ADS)

Zhang, Caiyun

2015-06-01

Accurate mapping and effective monitoring of benthic habitat in the Florida Keys are critical in developing management strategies for this valuable coral reef ecosystem. For this study, a framework was designed for automated benthic habitat mapping by combining multiple data sources (hyperspectral, aerial photography, and bathymetry data) and four contemporary imagery processing techniques (data fusion, Object-based Image Analysis (OBIA), machine learning, and ensemble analysis). In the framework, 1-m digital aerial photograph was first merged with 17-m hyperspectral imagery and 10-m bathymetry data using a pixel/feature-level fusion strategy. The fused dataset was then preclassified by three machine learning algorithms (Random Forest, Support Vector Machines, and k-Nearest Neighbor). Final object-based habitat maps were produced through ensemble analysis of outcomes from three classifiers. The framework was tested for classifying a group-level (3-class) and code-level (9-class) habitats in a portion of the Florida Keys. Informative and accurate habitat maps were achieved with an overall accuracy of 88.5% and 83.5% for the group-level and code-level classifications, respectively.

Magnetic resonance spectroscopy (MRS) of vertebral column – an additional tool for evaluation of aggressiveness of vertebral haemangioma like lesion

PubMed Central

Jeromel, Miran; Podobnik, Janez

2014-01-01

Background Most vertebral haemangioma are asymptomatic and discovered incidentally. Sometimes the symptomatic lesions present with radiological signs of aggressiveness and their appearance resemble other aggressive lesions (e.g. solitary plasmacytoma). Case report. We present a patient with large symptomatic aggressive haemangioma like lesion in 12th thoracic vertebra in which a magnetic resonance spectroscopy (MRS) was used to analyse fat content within the lesion. The lesion in affected vertebrae showed low fat content with 33% of fat fraction (%FF). The fat content in non-affected (1st lumbar) vertebra was as expected for patient’s age (68%). Based on MRS data, the lesion was characterized as an aggressive haemangioma. The diagnosis was confirmed with biopsy, performed during the treatment – percutaneous vertebroplasty. Conclusions The presented case shows that MRS can be used as an additional tool for evaluation of aggressiveness of vertebral haemangioma like lesions. PMID:24991203

Acute urinary retention due to benign inflammatory nervous diseases.

PubMed

Sakakibara, Ryuji; Yamanishi, Tomonori; Uchiyama, Tomoyuki; Hattori, Takamichi

2006-08-01

Both neurologists and urologists might encounter patients with acute urinary retention due to benign inflammatory nervous diseases. Based on the mechanism of urinary retention, these disorders can be divided into two subgroups: disorders of the peripheral nervous system (e.g., sacral herpes) or the central nervous system (e.g., meningitis-retention syndrome [MRS]). Laboratory abnormalities include increased herpes virus titers in sacral herpes, and increased myelin basic protein in the cerebrospinal fluid (CSF) in some cases with MRS. Urodynamic abnormality in both conditions is detrusor areflexia; the putative mechanism of it is direct involvement of the pelvic nerves in sacral herpes; and acute spinal shock in MRS. There are few cases with CSF abnormality alone. Although these cases have a benign course, management of the acute urinary retention is necessary to avoid bladder injury due to overdistension. Clinical features of sacral herpes or MRS differ markedly from those of the original "Elsberg syndrome" cases.

Misclassification of OSA severity with automated scoring of home sleep recordings.

PubMed

Aurora, R Nisha; Swartz, Rachel; Punjabi, Naresh M

2015-03-01

The advent of home sleep testing has allowed for the development of an ambulatory care model for OSA that most health-care providers can easily deploy. Although automated algorithms that accompany home sleep monitors can identify and classify disordered breathing events, it is unclear whether manual scoring followed by expert review of home sleep recordings is of any value. Thus, this study examined the agreement between automated and manual scoring of home sleep recordings. Two type 3 monitors (ApneaLink Plus [ResMed] and Embletta [Embla Systems]) were examined in distinct study samples. Data from manual and automated scoring were available for 200 subjects. Two thresholds for oxygen desaturation (≥ 3% and ≥ 4%) were used to define disordered breathing events. Agreement between manual and automated scoring was examined using Pearson correlation coefficients and Bland-Altman analyses. Automated scoring consistently underscored disordered breathing events compared with manual scoring for both sleep monitors irrespective of whether a ≥ 3% or ≥ 4% oxygen desaturation threshold was used to define the apnea-hypopnea index (AHI). For the ApneaLink Plus monitor, Bland-Altman analyses revealed an average AHI difference between manual and automated scoring of 6.1 (95% CI, 4.9-7.3) and 4.6 (95% CI, 3.5-5.6) events/h for the ≥ 3% and ≥ 4% oxygen desaturation thresholds, respectively. Similarly for the Embletta monitor, the average difference between manual and automated scoring was 5.3 (95% CI, 3.2-7.3) and 8.4 (95% CI, 7.2-9.6) events/h, respectively. Although agreement between automated and manual scoring of home sleep recordings varies based on the device used, modest agreement was observed between the two approaches. However, manual review of home sleep test recordings can decrease the misclassification of OSA severity, particularly for those with mild disease. ClinicalTrials.gov; No.: NCT01503164; www.clinicaltrials.gov.

Misclassification of OSA Severity With Automated Scoring of Home Sleep Recordings

PubMed Central

Aurora, R. Nisha; Swartz, Rachel

2015-01-01

BACKGROUND: The advent of home sleep testing has allowed for the development of an ambulatory care model for OSA that most health-care providers can easily deploy. Although automated algorithms that accompany home sleep monitors can identify and classify disordered breathing events, it is unclear whether manual scoring followed by expert review of home sleep recordings is of any value. Thus, this study examined the agreement between automated and manual scoring of home sleep recordings. METHODS: Two type 3 monitors (ApneaLink Plus [ResMed] and Embletta [Embla Systems]) were examined in distinct study samples. Data from manual and automated scoring were available for 200 subjects. Two thresholds for oxygen desaturation (≥ 3% and ≥ 4%) were used to define disordered breathing events. Agreement between manual and automated scoring was examined using Pearson correlation coefficients and Bland-Altman analyses. RESULTS: Automated scoring consistently underscored disordered breathing events compared with manual scoring for both sleep monitors irrespective of whether a ≥ 3% or ≥ 4% oxygen desaturation threshold was used to define the apnea-hypopnea index (AHI). For the ApneaLink Plus monitor, Bland-Altman analyses revealed an average AHI difference between manual and automated scoring of 6.1 (95% CI, 4.9-7.3) and 4.6 (95% CI, 3.5-5.6) events/h for the ≥ 3% and ≥ 4% oxygen desaturation thresholds, respectively. Similarly for the Embletta monitor, the average difference between manual and automated scoring was 5.3 (95% CI, 3.2-7.3) and 8.4 (95% CI, 7.2-9.6) events/h, respectively. CONCLUSIONS: Although agreement between automated and manual scoring of home sleep recordings varies based on the device used, modest agreement was observed between the two approaches. However, manual review of home sleep test recordings can decrease the misclassification of OSA severity, particularly for those with mild disease. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT01503164; www.clinicaltrials.gov PMID:25411804

Using multiclass classification to automate the identification of patient safety incident reports by type and severity.

PubMed

Wang, Ying; Coiera, Enrico; Runciman, William; Magrabi, Farah

2017-06-12

Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incident reports in a timely manner is limited by the sheer volumes of data collected. In this study, we aim to evaluate the feasibility of using multiclass classification to automate the identification of patient safety incidents in hospitals. Text based classifiers were applied to identify 10 incident types and 4 severity levels. Using the one-versus-one (OvsO) and one-versus-all (OvsA) ensemble strategies, we evaluated regularized logistic regression, linear support vector machine (SVM) and SVM with a radial-basis function (RBF) kernel. Classifiers were trained and tested with "balanced" datasets (n_ Type  = 2860, n_ SeverityLevel  = 1160) from a state-wide incident reporting system. Testing was also undertaken with imbalanced "stratified" datasets (n_ Type  = 6000, n_ SeverityLevel =5950) from the state-wide system and an independent hospital reporting system. Classifier performance was evaluated using a confusion matrix, as well as F-score, precision and recall. The most effective combination was a OvsO ensemble of binary SVM RBF classifiers with binary count feature extraction. For incident type, classifiers performed well on balanced and stratified datasets (F-score: 78.3, 73.9%), but were worse on independent datasets (68.5%). Reports about falls, medications, pressure injury, aggression and blood products were identified with high recall and precision. "Documentation" was the hardest type to identify. For severity level, F-score for severity assessment code (SAC) 1 (extreme risk) was 87.3 and 64% for SAC4 (low risk) on balanced data. With stratified data, high recall was achieved for SAC1 (82.8-84%) but precision was poor (6.8-11.2%). High risk incidents (SAC2) were confused with medium risk incidents (SAC3). Binary classifier ensembles appear to be a feasible method for identifying incidents by type and severity level. Automated identification should enable safety problems to be detected and addressed in a more timely manner. Multi-label classifiers may be necessary for reports that relate to more than one incident type.

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

The EB factory project. II. Validation with the Kepler field in preparation for K2 and TESS

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

Parvizi, Mahmoud; Paegert, Martin; Stassun, Keivan G., E-mail: mahmoud.parvizi@vanderbilt.edu

Large repositories of high precision light curve data, such as the Kepler data set, provide the opportunity to identify astrophysically important eclipsing binary (EB) systems in large quantities. However, the rate of classical “by eye” human analysis restricts complete and efficient mining of EBs from these data using classical techniques. To prepare for mining EBs from the upcoming K2 mission as well as other current missions, we developed an automated end-to-end computational pipeline—the Eclipsing Binary Factory (EBF)—that automatically identifies EBs and classifies them into morphological types. The EBF has been previously tested on ground-based light curves. To assess the performancemore » of the EBF in the context of space-based data, we apply the EBF to the full set of light curves in the Kepler “Q3” Data Release. We compare the EBs identified from this automated approach against the human generated Kepler EB Catalog of ∼2600 EBs. When we require EB classification with ⩾90% confidence, we find that the EBF correctly identifies and classifies eclipsing contact (EC), eclipsing semi-detached (ESD), and eclipsing detached (ED) systems with a false positive rate of only 4%, 4%, and 8%, while complete to 64%, 46%, and 32%, respectively. When classification confidence is relaxed, the EBF identifies and classifies ECs, ESDs, and EDs with a slightly higher false positive rate of 6%, 16%, and 8%, while much more complete to 86%, 74%, and 62%, respectively. Through our processing of the entire Kepler “Q3” data set, we also identify 68 new candidate EBs that may have been missed by the human generated Kepler EB Catalog. We discuss the EBF's potential application to light curve classification for periodic variable stars more generally for current and upcoming surveys like K2 and the Transiting Exoplanet Survey Satellite.« less

The Eb Factory Project. Ii. Validation With the Kepler Field in Preparation for K2 and Tess

NASA Astrophysics Data System (ADS)

Parvizi, Mahmoud; Paegert, Martin; Stassun, Keivan G.

2014-12-01

Large repositories of high precision light curve data, such as the Kepler data set, provide the opportunity to identify astrophysically important eclipsing binary (EB) systems in large quantities. However, the rate of classical “by eye” human analysis restricts complete and efficient mining of EBs from these data using classical techniques. To prepare for mining EBs from the upcoming K2 mission as well as other current missions, we developed an automated end-to-end computational pipeline—the Eclipsing Binary Factory (EBF)—that automatically identifies EBs and classifies them into morphological types. The EBF has been previously tested on ground-based light curves. To assess the performance of the EBF in the context of space-based data, we apply the EBF to the full set of light curves in the Kepler “Q3” Data Release. We compare the EBs identified from this automated approach against the human generated Kepler EB Catalog of ˜ 2600 EBs. When we require EB classification with ≥slant 90% confidence, we find that the EBF correctly identifies and classifies eclipsing contact (EC), eclipsing semi-detached (ESD), and eclipsing detached (ED) systems with a false positive rate of only 4%, 4%, and 8%, while complete to 64%, 46%, and 32%, respectively. When classification confidence is relaxed, the EBF identifies and classifies ECs, ESDs, and EDs with a slightly higher false positive rate of 6%, 16%, and 8%, while much more complete to 86%, 74%, and 62%, respectively. Through our processing of the entire Kepler “Q3” data set, we also identify 68 new candidate EBs that may have been missed by the human generated Kepler EB Catalog. We discuss the EBF's potential application to light curve classification for periodic variable stars more generally for current and upcoming surveys like K2 and the Transiting Exoplanet Survey Satellite.

The Thermodynamic Structure of Arctic Coastal Fog Occurring During the Melt Season over East Greenland

NASA Astrophysics Data System (ADS)

Gilson, Gaëlle F.; Jiskoot, Hester; Cassano, John J.; Gultepe, Ismail; James, Timothy D.

2018-05-01

An automated method to classify Arctic fog into distinct thermodynamic profiles using historic in-situ surface and upper-air observations is presented. This classification is applied to low-resolution Integrated Global Radiosonde Archive (IGRA) soundings and high-resolution Arctic Summer Cloud Ocean Study (ASCOS) soundings in low- and high-Arctic coastal and pack-ice environments. Results allow investigation of fog macrophysical properties and processes in coastal East Greenland during melt seasons 1980-2012. Integrated with fog observations from three synoptic weather stations, 422 IGRA soundings are classified into six fog thermodynamic types based on surface saturation ratio, type of temperature inversion, fog-top height relative to inversion-base height and stability using the virtual potential temperature gradient. Between 65-80% of fog observations occur with a low-level inversion, and statically neutral or unstable surface layers occur frequently. Thermodynamic classification is sensitive to the assigned dew-point depression threshold, but categorization is robust. Despite differences in the vertical resolution of radiosonde observations, IGRA and ASCOS soundings yield the same six fog classes, with fog-class distribution varying with latitude and environmental conditions. High-Arctic fog frequently resides within an elevated inversion layer, whereas low-Arctic fog is more often restricted to the mixed layer. Using supplementary time-lapse images, ASCOS microwave radiometer retrievals and airmass back-trajectories, we hypothesize that the thermodynamic classes represent different stages of advection fog formation, development, and dissipation, including stratus-base lowering and fog lifting. This automated extraction of thermodynamic boundary-layer and inversion structure can be applied to radiosonde observations worldwide to better evaluate fog conditions that affect transportation and lead to improvements in numerical models.

Retinal sensitivity in subjects with type 2 diabetes mellitus: Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetics Study (SN-DREAMS II, Report No. 4).

PubMed

Gella, Laxmi; Raman, Rajiv; Kulothungan, Vaitheeswaran; Saumya Pal, Swakshyar; Ganesan, Suganeswari; Sharma, Tarun

2016-06-01

To evaluate retinal sensitivity (RS) in subjects with diabetes in a population-based study and to elucidate associated risk factors for abnormal RS. A subset of 357 subjects from Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetics Study-II was included in this study. All subjects underwent detailed ophthalmic evaluation including microperimetry and spectral domain optical coherence tomography. The prevalence of abnormal mean retinal sensitivity (MRS) was 89.1%. MRS was significantly reduced in subjects with diabetes but no retinopathy when compared with non-diabetic subjects. MRS was reduced in moderate non-proliferative diabetic retinopathy (DR) and macular oedema (ME) at 8° (p=0.04, p=0.01, respectively) and in ME at 10° (p=0.009) and 12° (p=0.036) compared with no DR. Significant negative correlation was found between MRS and best corrected visual acuity, duration of diabetes, glycosylated haemoglobin and central foveal thickness. Increased retinal thickness remained a significant risk factor (OR, 1.02; p=0.044) for abnormal MRS. Altered inner retinal layers and foveal contour were associated with reduced MRS among subjects with DR and presence of epiretinal membrane, altered foveal contour and altered retinal pigment epithelium were associated with reduced MRS. Reduced RS in those subjects with diabetes but no retinopathy suggests the early neuronal damage in type 2 diabetes mellitus. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Residual stress measurement in a metal microdevice by micro Raman spectroscopy

NASA Astrophysics Data System (ADS)

Song, Chang; Du, Liqun; Qi, Leijie; Li, Yu; Li, Xiaojun; Li, Yuanqi

2017-10-01

Large residual stress induced during the electroforming process cannot be ignored to fabricate reliable metal microdevices. Accurate measurement is the basis for studying the residual stress. Influenced by the topological feature size of micron scale in the metal microdevice, residual stress in it can hardly be measured by common methods. In this manuscript, a methodology is proposed to measure the residual stress in the metal microdevice using micro Raman spectroscopy (MRS). To estimate the residual stress in metal materials, micron sized β-SiC particles were mixed in the electroforming solution for codeposition. First, the calculated expression relating the Raman shifts to the induced biaxial stress for β-SiC was derived based on the theory of phonon deformation potentials and Hooke’s law. Corresponding micro electroforming experiments were performed and the residual stress in Ni-SiC composite layer was both measured by x-ray diffraction (XRD) and MRS methods. Then, the validity of the MRS measurements was verified by comparing with the residual stress measured by XRD method. The reliability of the MRS method was further validated by the statistical student’s t-test. The MRS measurements were found to have no systematic error in comparison with the XRD measurements, which confirm that the residual stresses measured by the MRS method are reliable. Besides that, the MRS method, by which the residual stress in a micro inertial switch was measured, has been confirmed to be a convincing experiment tool for estimating the residual stress in metal microdevice with micron order topological feature size.

Spectroscopic magnetic resonance imaging of the brain: voxel localisation and tissue segmentation in the follow up of brain tumour.

PubMed

Poloni, Guy; Bastianello, S; Vultaggio, Angela; Pozzi, S; Maccabelli, Gloria; Germani, Giancarlo; Chiarati, Patrizia; Pichiecchio, Anna

2008-01-01

The field of application of magnetic resonance spectroscopy (MRS) in biomedical research is expanding all the time and providing opportunities to investigate tissue metabolism and function. The data derived can be integrated with the information on tissue structure gained from conventional and non-conventional magnetic resonance imaging (MRI) techniques. Clinical MRS is also strongly expected to play an important role as a diagnostic tool. Essential for the future success of MRS as a clinical and research tool in biomedical sciences, both in vivo and in vitro, is the development of an accurate, biochemically relevant and physically consistent and reliable data analysis standard. Stable and well established analysis algorithms, in both the time and the frequency domain, are already available, as is free commercial software for implementing them. In this study, we propose an automatic algorithm that takes into account anatomical localisation, relative concentrations of white matter, grey matter, cerebrospinal fluid and signal abnormalities and inter-scan patient movement. The endpoint is the collection of a series of covariates that could be implemented in a multivariate analysis of covariance (MANCOVA) of the MRS data, as a tool for dealing with differences that may be ascribed to the anatomical variability of the subjects, to inaccuracies in the localisation of the voxel or slab, or to movement, rather than to the pathology under investigation. The aim was to develop an analysis procedure that can be consistently and reliably applied in the follow up of brain tumour. In this study, we demonstrate that the inclusion of such variables in the data analysis of quantitative MRS is fundamentally important (especially in view of the reduced accuracy typical of MRS measures compared to other MRI techniques), reducing the occurrence of false positives.

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery.

PubMed

Belgiu, Mariana; Dr Guţ, Lucian

2014-10-01

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing 'optimal segmentation'. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.

Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

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

Adal, Kedir M.; Sidebe, Desire; Ali, Sharib

2014-01-07

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using onlymore » few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.« less

Using SAR Interferograms and Coherence Images for Object-Based Delineation of Unstable Slopes

NASA Astrophysics Data System (ADS)

Friedl, Barbara; Holbling, Daniel

2015-05-01

This study uses synthetic aperture radar (SAR) interferometric products for the semi-automated identification and delineation of unstable slopes and active landslides. Single-pair interferograms and coherence images are therefore segmented and classified in an object-based image analysis (OBIA) framework. The rule-based classification approach has been applied to landslide-prone areas located in Taiwan and Southern Germany. The semi-automatically obtained results were validated against landslide polygons derived from manual interpretation.

Self-organizing ontology of biochemically relevant small molecules

PubMed Central

2012-01-01

Background The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest. Results To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development. Conclusions We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development. PMID:22221313

Automated anatomical labeling method for abdominal arteries extracted from 3D abdominal CT images

NASA Astrophysics Data System (ADS)

Oda, Masahiro; Hoang, Bui Huy; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Mori, Kensaku

2012-02-01

This paper presents an automated anatomical labeling method of abdominal arteries. In abdominal surgery, understanding of blood vessel structure concerning with a target organ is very important. Branching pattern of blood vessels differs among individuals. It is required to develop a system that can assist understanding of a blood vessel structure and anatomical names of blood vessels of a patient. Previous anatomical labbeling methods for abdominal arteries deal with either of the upper or lower abdominal arteries. In this paper, we present an automated anatomical labeling method of both of the upper and lower abdominal arteries extracted from CT images. We obtain a tree structure of artery regions and calculate feature values for each branch. These feature values include the diameter, curvature, direction, and running vectors of a branch. Target arteries of this method are grouped based on branching conditions. The following processes are separately applied for each group. We compute candidate artery names by using classifiers that are trained to output artery names. A correction process of the candidate anatomical names based on the rule of majority is applied to determine final names. We applied the proposed method to 23 cases of 3D abdominal CT images. Experimental results showed that the proposed method is able to perform nomenclature of entire major abdominal arteries. The recall and the precision rates of labeling are 79.01% and 80.41%, respectively.

Synthetic training sets for the development of discriminant functions for the detection of volatile organic compounds from passive infrared remote sensing data.

PubMed

Wan, Boyong; Small, Gary W

2011-01-21

A novel synthetic data generation methodology is described for use in the development of pattern recognition classifiers that are employed for the automated detection of volatile organic compounds (VOCs) during infrared remote sensing measurements. The approach used is passive Fourier transform infrared spectrometry implemented in a downward-looking mode on an aircraft platform. A key issue in developing this methodology in practice is the need for example data that can be used to train the classifiers. To replace the time-consuming and costly collection of training data in the field, this work implements a strategy for taking laboratory analyte spectra and superimposing them on background spectra collected from the air. The resulting synthetic spectra can be used to train the classifiers. This methodology is tested by developing classifiers for ethanol and methanol, two prevalent VOCs in wide industrial use. The classifiers are successfully tested with data collected from the aircraft during controlled releases of ethanol and during a methanol release from an industrial facility. For both ethanol and methanol, missed detections in the aircraft data are in the range of 4 to 5%, with false positive detections ranging from 0.1 to 0.3%.

Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.

PubMed

Wang, Kang; Jayadev, Chaitra; Nittala, Muneeswar G; Velaga, Swetha B; Ramachandra, Chaithanya A; Bhaskaranand, Malavika; Bhat, Sandeep; Solanki, Kaushal; Sadda, SriniVas R

2018-03-01

We examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images. Patients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed. The automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1-93.9/80.4-89.4) with a 50.0%/53.6% specificity (95% CI 31.7-72.8/36.5-71.4) for detecting referral-warranted retinopathy at the patient/eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819-0.922/0.804-0.894). Diabetic retinopathy (DR) lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification.

PubMed

Wang, Lei; Pedersen, Peder C; Agu, Emmanuel; Strong, Diane M; Tulu, Bengisu

2017-09-01

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

Robust and Elastic Lunar and Martian Structures from 3D-Printed Regolith Inks

NASA Astrophysics Data System (ADS)

Jakus, Adam E.; Koube, Katie D.; Geisendorfer, Nicholas R.; Shah, Ramille N.

2017-03-01

Here, we present a comprehensive approach for creating robust, elastic, designer Lunar and Martian regolith simulant (LRS and MRS, respectively) architectures using ambient condition, extrusion-based 3D-printing of regolith simulant inks. The LRS and MRS powders are characterized by distinct, highly inhomogeneous morphologies and sizes, where LRS powder particles are highly irregular and jagged and MRS powder particles are rough, but primarily rounded. The inks are synthesized via simple mixing of evaporant, surfactant, and plasticizer solvents, polylactic-co-glycolic acid (30% by solids volume), and regolith simulant powders (70% by solids volume). Both LRS and MRS inks exhibit similar rheological and 3D-printing characteristics, and can be 3D-printed at linear deposition rates of 1-150 mm/s using 300 μm to 1.4 cm-diameter nozzles. The resulting LRS and MRS 3D-printed materials exhibit similar, but distinct internal and external microstructures and material porosity (~20-40%). These microstructures contribute to the rubber-like quasi-static and cyclic mechanical properties of both materials, with young’s moduli ranging from 1.8 to 13.2 MPa and extension to failure exceeding 250% over a range of strain rates (10-1-102 min-1). Finally, we discuss the potential for LRS and MRS ink components to be reclaimed and recycled, as well as be synthesized in resource-limited, extraterrestrial environments.

Magnetic resonance spectroscopy in mild cognitive impairment: systematic review and meta-analysis.

PubMed

Tumati, Shankar; Martens, Sander; Aleman, André

2013-12-01

Research using proton magnetic resonance spectroscopy (MRS) can potentially elucidate metabolite changes representing early degeneration in Mild Cognitive Impairment (MCI), an early stage of dementia. We integrated the published literature using meta-analysis to identify patterns of metabolite changes in MCI. 29 MRS studies (with a total of 607 MCI patients and 862 healthy controls) were classified according to brain regions. Hedges' g was used as effect size in a random effects model. N-Acetyl Aspartate (NAA) measures were consistently reduced in posterior cingulate (PC), hippocampus, and the paratrigonal white matter (PWM). Creatine (Cr) concentration was reduced in the hippocampus and PWM. Choline (Cho) concentration was reduced in the hippocampus while Cho/Cr ratio was raised in the PC. Myo-inositol (mI) concentration was raised in the PC and mI/Cr ratio was raised in the hippocampus. NAA/mI ratio was reduced in the PC. NAA may be the most reliable marker of brain dysfunction in MCI though mI, Cho, and Cr may also contribute towards this. Copyright © 2013 Elsevier Ltd. All rights reserved.

Real-time ultrasonic weld evaluation system

NASA Astrophysics Data System (ADS)

Katragadda, Gopichand; Nair, Satish; Liu, Harry; Brown, Lawrence M.

1996-11-01

Ultrasonic testing techniques are currently used as an alternative to radiography for detecting, classifying,and sizing weld defects, and for evaluating weld quality. Typically, ultrasonic weld inspections are performed manually, which require significant operator expertise and time. Thus, in recent years, the emphasis is to develop automated methods to aid or replace operators in critical weld inspections where inspection time, reliability, and operator safety are major issues. During this period, significant advances wee made in the areas of weld defect classification and sizing. Very few of these methods, however have found their way into the market, largely due to the lack of an integrated approach enabling real-time implementation. Also, not much research effort was directed in improving weld acceptance criteria. This paper presents an integrated system utilizing state-of-the-art techniques for a complete automation of the weld inspection procedure. The modules discussed include transducer tracking, classification, sizing, and weld acceptance criteria. Transducer tracking was studied by experimentally evaluating sonic and optical position tracking techniques. Details for this evaluation are presented. Classification is obtained using a multi-layer perceptron. Results from different feature extraction schemes, including a new method based on a combination of time and frequency-domain signal representations are given. Algorithms developed to automate defect registration and sizing are discussed. A fuzzy-logic acceptance criteria for weld acceptance is presented describing how this scheme provides improved robustness compared to the traditional flow-diagram standards.

Automated classification of Acid Rock Drainage potential from Corescan drill core imagery

NASA Astrophysics Data System (ADS)

Cracknell, M. J.; Jackson, L.; Parbhakar-Fox, A.; Savinova, K.

2017-12-01

Classification of the acid forming potential of waste rock is important for managing environmental hazards associated with mining operations. Current methods for the classification of acid rock drainage (ARD) potential usually involve labour intensive and subjective assessment of drill core and/or hand specimens. Manual methods are subject to operator bias, human error and the amount of material that can be assessed within a given time frame is limited. The automated classification of ARD potential documented here is based on the ARD Index developed by Parbhakar-Fox et al. (2011). This ARD Index involves the combination of five indicators: A - sulphide content; B - sulphide alteration; C - sulphide morphology; D - primary neutraliser content; and E - sulphide mineral association. Several components of the ARD Index require accurate identification of sulphide minerals. This is achieved by classifying Corescan Red-Green-Blue true colour images into the presence or absence of sulphide minerals using supervised classification. Subsequently, sulphide classification images are processed and combined with Corescan SWIR-based mineral classifications to obtain information on sulphide content, indices representing sulphide textures (disseminated versus massive and degree of veining), and spatially associated minerals. This information is combined to calculate ARD Index indicator values that feed into the classification of ARD potential. Automated ARD potential classifications of drill core samples associated with a porphyry Cu-Au deposit are compared to manually derived classifications and those obtained by standard static geochemical testing and X-ray diffractometry analyses. Results indicate a high degree of similarity between automated and manual ARD potential classifications. Major differences between approaches are observed in sulphide and neutraliser mineral percentages, likely due to the subjective nature of manual estimates of mineral content. The automated approach presented here for the classification of ARD potential offers rapid, repeatable and accurate outcomes comparable to manually derived classifications. Methods for automated ARD classifications from digital drill core data represent a step-change for geoenvironmental management practices in the mining industry.

Medical Representatives' Intention to Use Information Technology in Pharmaceutical Marketing

PubMed Central

Kwak, Eun-Seon

2016-01-01

Objectives Electronic detailing (e-detailing), the use of electronic devices to facilitate sales presentations to physicians, has been adopted and expanded in the pharmaceutical industry. To maximize the potential outcome of e-detailing, it is important to understand medical representatives (MRs)' behavior and attitude to e-detailing. This study investigates how information technology devices such as laptop computers and tablet PCs are utilized in pharmaceutical marketing, and it analyzes the factors influencing MRs' intention to use devices. Methods This study has adopted and modified the theory of Roger's diffusion of innovation model and the technology acceptance model. To test the model empirically, a questionnaire survey was conducted with 221 MRs who were working in three multinational or eleven domestic pharmaceutical companies in Korea. Results Overall, 28% and 35% of MRs experienced using laptop computers and tablet PCs in pharmaceutical marketing, respectively. However, the rates were different across different groups of MRs, categorized by age, education level, position, and career. The results showed that MRs' intention to use information technology devices was significantly influenced by perceived usefulness in general. Perceived ease of use, organizational and individual innovativeness, and several MR characteristics were also found to have significant impacts. Conclusions This study provides timely information about e-detailing devices to marketing managers and policy makers in the pharmaceutical industry for successful marketing strategy development by understanding the needs of MRs' intention to use information technology. Further in-depth study should be conducted to understand obstacles and limitations and to improve the strategies for better marketing tools. PMID:27895967

Hybrid region merging method for segmentation of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Xueliang; Xiao, Pengfeng; Feng, Xuezhi; Wang, Jiangeng; Wang, Zuo

2014-12-01

Image segmentation remains a challenging problem for object-based image analysis. In this paper, a hybrid region merging (HRM) method is proposed to segment high-resolution remote sensing images. HRM integrates the advantages of global-oriented and local-oriented region merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region, which provides an elegant way to avoid the problem of starting point assignment and to enhance the optimization ability for local-oriented region merging. During the region growing procedure, the merging iterations are constrained within the local vicinity, so that the segmentation is accelerated and can reflect the local context, as compared with the global-oriented method. A set of high-resolution remote sensing images is used to test the effectiveness of the HRM method, and three region-based remote sensing image segmentation methods are adopted for comparison, including the hierarchical stepwise optimization (HSWO) method, the local-mutual best region merging (LMM) method, and the multiresolution segmentation (MRS) method embedded in eCognition Developer software. Both the supervised evaluation and visual assessment show that HRM performs better than HSWO and LMM by combining both their advantages. The segmentation results of HRM and MRS are visually comparable, but HRM can describe objects as single regions better than MRS, and the supervised and unsupervised evaluation results further prove the superiority of HRM.

A multicenter hospital-based diagnosis study of automated breast ultrasound system in detecting breast cancer among Chinese women.

PubMed

Zhang, Xi; Lin, Xi; Tan, Yanjuan; Zhu, Ying; Wang, Hui; Feng, Ruimei; Tang, Guoxue; Zhou, Xiang; Li, Anhua; Qiao, Youlin

2018-04-01

The automated breast ultrasound system (ABUS) is a potential method for breast cancer detection; however, its diagnostic performance remains unclear. We conducted a hospital-based multicenter diagnostic study to evaluate the clinical performance of the ABUS for breast cancer detection by comparing it to handheld ultrasound (HHUS) and mammography (MG). Eligible participants underwent HHUS and ABUS testing; women aged 40-69 years additionally underwent MG. Images were interpreted using the Breast Imaging Reporting and Data System (BI-RADS). Women in the BI-RADS categories 1-2 were considered negative. Women classified as BI-RADS 3 underwent magnetic resonance imaging to distinguish true- and false-negative results. Core aspiration or surgical biopsy was performed in women classified as BI-RADS 4-5, followed by a pathological diagnosis. Kappa values and agreement rates were calculated between ABUS, HHUS and MG. A total of 1,973 women were included in the final analysis. Of these, 1,353 (68.6%) and 620 (31.4%) were classified as BI-RADS categories 1-3 and 4-5, respectively. In the older age group, the agreement rate and Kappa value between the ABUS and HHUS were 94.0% and 0.860 (P<0.001), respectively; they were 89.2% and 0.735 (P<0.001) between the ABUS and MG, respectively. Regarding consistency between imaging and pathology results, 78.6% of women classified as BI-RADS 4-5 based on the ABUS were diagnosed with precancerous lesions or cancer; which was 7.2% higher than that of women based on HHUS. For BI-RADS 1-2, the false-negative rates of the ABUS and HHUS were almost identical and were much lower than those of MG. We observed a good diagnostic reliability for the ABUS. Considering its performance for breast cancer detection in women with high-density breasts and its lower operator dependence, the ABUS is a promising option for breast cancer detection in China.

Adopting a Patient-Centered Approach to Primary Outcome Analysis of Acute Stroke Trials by Use of a Utility-Weighted Modified Rankin Scale

PubMed Central

Chaisinanunkul, Napasri; Adeoye, Opeolu; Lewis, Roger J.; Grotta, James C.; Broderick, Joseph; Jovin, Tudor G.; Nogueira, Raul G.; Elm, Jordan; Graves, Todd; Berry, Scott; Lees, Kennedy R.; Barreto, Andrew D.; Saver, Jeffrey L.

2015-01-01

Background and Purpose Although the modified Rankin Scale (mRS) is the most commonly employed primary endpoint in acute stroke trials, its power is limited when analyzed in dichotomized fashion and its indication of effect size challenging to interpret when analyzed ordinally. Weighting the seven Rankin levels by utilities may improve scale interpretability while preserving statistical power. Methods A utility weighted mRS (UW-mRS) was derived by averaging values from time-tradeoff (patient centered) and person-tradeoff (clinician centered) studies. The UW-mRS, standard ordinal mRS, and dichotomized mRS were applied to 11 trials or meta-analyses of acute stroke treatments, including lytic, endovascular reperfusion, blood pressure moderation, and hemicraniectomy interventions. Results Utility values were: mRS 0–1.0; mRS 1 - 0.91; mRS 2 - 0.76; mRS 3 - 0.65; mRS 4 - 0.33; mRS 5 & 6 - 0. For trials with unidirectional treatment effects, the UW-mRS paralleled the ordinal mRS and outperformed dichotomous mRS analyses. Both the UW-mRS and the ordinal mRS were statistically significant in six of eight unidirectional effect trials, while dichotomous analyses were statistically significant in two to four of eight. In bidirectional effect trials, both the UW-mRS and ordinal tests captured the divergent treatment effects by showing neutral results whereas some dichotomized analyses showed positive results. Mean utility differences in trials with statistically significant positive results ranged from 0.026 to 0.249. Conclusion A utility-weighted mRS performs similarly to the standard ordinal mRS in detecting treatment effects in actual stroke trials and ensures the quantitative outcome is a valid reflection of patient-centered benefits. PMID:26138130

Accuracy and the Effect of Possible Subject-Based Confounders of Magnitude-Based MRI for Estimating Hepatic Proton Density Fat Fraction in Adults, Using MR Spectroscopy as Reference

PubMed Central

Heba, Elhamy R.; Desai, Ajinkya; Zand, Kevin A.; Hamilton, Gavin; Wolfson, Tanya; Schlein, Alexandra N.; Gamst, Anthony; Loomba, Rohit; Sirlin, Claude B.; Middleton, Michael S.

2016-01-01

Purpose To determine the accuracy and the effect of possible subject-based confounders of magnitude-based magnetic resonance imaging (MRI) for estimating hepatic proton density fat fraction (PDFF) for different numbers of echoes in adults with known or suspected nonalcoholic fatty liver disease, using MR spectroscopy (MRS) as a reference. Materials and Methods In this retrospective analysis of 506 adults, hepatic PDFF was estimated by unenhanced 3.0T MRI, using right-lobe MRS as reference. Regions of interest placed on source images and on six-echo parametric PDFF maps were colocalized to MRS voxel location. Accuracy using different numbers of echoes was assessed by regression and Bland–Altman analysis; slope, intercept, average bias, and R2 were calculated. The effect of age, sex, and body mass index (BMI) on hepatic PDFF accuracy was investigated using multivariate linear regression analyses. Results MRI closely agreed with MRS for all tested methods. For three- to six-echo methods, slope, regression intercept, average bias, and R2 were 1.01–0.99, 0.11–0.62%, 0.24–0.56%, and 0.981–0.982, respectively. Slope was closest to unity for the five-echo method. The two-echo method was least accurate, underestimating PDFF by an average of 2.93%, compared to an average of 0.23–0.69% for the other methods. Statistically significant but clinically nonmeaningful effects on PDFF error were found for subject BMI (P range: 0.0016 to 0.0783), male sex (P range: 0.015 to 0.037), and no statistically significant effect was found for subject age (P range: 0.18–0.24). Conclusion Hepatic magnitude-based MRI PDFF estimates using three, four, five, and six echoes, and six-echo parametric maps are accurate compared to reference MRS values, and that accuracy is not meaningfully confounded by age, sex, or BMI. PMID:26201284

Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples.

PubMed

Zhou, Zhi; Pons, Marie Noëlle; Raskin, Lutgarde; Zilles, Julie L

2007-05-01

When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4',6'-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.

Automated detection of heuristics and biases among pathologists in a computer-based system.

PubMed

Crowley, Rebecca S; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-08-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

Remote surface inspection system. [of large space platforms

NASA Technical Reports Server (NTRS)

Hayati, Samad; Balaram, J.; Seraji, Homayoun; Kim, Won S.; Tso, Kam S.

1993-01-01

This paper reports on an on-going research and development effort in remote surface inspection of space platforms such as the Space Station Freedom (SSF). It describes the space environment and identifies the types of damage for which to search. This paper provides an overview of the Remote Surface Inspection System that was developed to conduct proof-of-concept demonstrations and to perform experiments in a laboratory environment. Specifically, the paper describes three technology areas: (1) manipulator control for sensor placement; (2) automated non-contact inspection to detect and classify flaws; and (3) an operator interface to command the system interactively and receive raw or processed sensor data. Initial findings for the automated and human visual inspection tests are reported.

PREFACE: E-MRS 2012 Spring Meeting, Symposium M: More than Moore: Novel materials approaches for functionalized Silicon based Microelectronics

NASA Astrophysics Data System (ADS)

Wenger, Christian; Fompeyrine, Jean; Vallée, Christophe; Locquet, Jean-Pierre

2012-12-01

More than Moore explores a new area of Silicon based microelectronics, which reaches beyond the boundaries of conventional semiconductor applications. Creating new functionality to semiconductor circuits, More than Moore focuses on motivating new technological possibilities. In the past decades, the main stream of microelectronics progresses was mainly powered by Moore's law, with two focused development arenas, namely, IC miniaturization down to nano scale, and SoC based system integration. While the microelectronics community continues to invent new solutions around the world to keep Moore's law alive, there is increasing momentum for the development of 'More than Moore' technologies which are based on silicon technologies but do not simply scale with Moore's law. Typical examples are RF, Power/HV, Passives, Sensor/Actuator/MEMS or Bio-chips. The More than Moore strategy is driven by the increasing social needs for high level heterogeneous system integration including non-digital functions, the necessity to speed up innovative product creation and to broaden the product portfolio of wafer fabs, and the limiting cost and time factors of advanced SoC development. It is believed that More than Moore will add value to society on top of and beyond advanced CMOS with fast increasing marketing potentials. Important key challenges for the realization of the 'More than Moore' strategy are: perspective materials for future THz devices materials systems for embedded sensors and actuators perspective materials for epitaxial approaches material systems for embedded innovative memory technologies development of new materials with customized characteristics The Hot topics covered by the symposium M (More than Moore: Novel materials approaches for functionalized Silicon based Microelectronics) at E-MRS 2012 Spring Meeting, 14-18 May 2012 have been: development of functional ceramics thin films New dielectric materials for advanced microelectronics bio- and CMOS compatible material systems piezoelectric films and nanostructures Atomic Layer Deposition (ALD) of oxides and nitrides characterization and metrology of very thin oxide layers We would like to take this opportunity to thank the Scientific Committee and Local Committee for bringing together a coherent and high quality Symposium at E-MRS 2012 Spring Meeting. Christian Wenger, Jean Fompeyrine, Christophe Vallée and Jean-Pierre Locquet Organizing Committee of Symposium M September 2012

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, R.; Beaudet, P.

1982-01-01

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

Effect of Emergency Medical Services Use on Hospital Outcomes of Acute Hemorrhagic Stroke.

PubMed

Kim, Sola; Shin, Sang Do; Ro, Young Sun; Song, Kyoung Jun; Lee, Yu Jin; Lee, Eui Jung; Ahn, Ki Ok; Kim, Taeyun; Hong, Ki Jeong; Kim, Yu Jin

2016-01-01

It is unclear whether the use of emergency medical services (EMS) is associated with enhanced survival and decreased disability after hemorrhagic stroke and whether the effect size of EMS use differs according to the length of stay (LOS) in emergency department (ED). Adult patients (19 years and older) with acute hemorrhagic stroke who survived to admission at 29 hospitals between 2008 and 2011 were analyzed, excluding those who had symptom-to-ED arrival time of 3 h or greater, received thrombolysis or craniotomy before inter-hospital transfer, or had experienced cardiac arrest, had unknown information about ambulance use and outcomes. Exposure variable was EMS use. Endpoints were survival at discharge and worsened modified Rankin Scale (W-MRS) defined as 3 or greater points difference between pre- and post-event MRS. Adjusted odds ratios (AORs) with 95% confidence intervals (95% CIs) for the outcomes were calculated, including potential confounders (demographic, socioeconomic status, clinical parameter, comorbidity, behavior, and time of event) in the final model and stratifying patients by inter-hospital transfer and by time interval from symptom to ED arrival (S2D). ED LOS, classified into short (<120 min) and long (≥120 min), was added to the final model for testing of the interaction model. A total of 2,095 hemorrhagic strokes were analyzed in which 75.6% were transported by EMS. For outcome measures, 17.4% and 41.4% were dead and had worsened MRS, respectively. AORs (95% CIs) of EMS were 0.67 (0.51-0.89) for death and 0.74 (0.59-0.92) for W-MRS in all patients. The effect size of EMS, however, was different according to LOS in ED. AORs (95% CIs) for death were 0.74 (0.54-1.01) in short LOS and 0.60 (0.44-0.83) in long LOS group. AORs (95% CIs) for W-MRS were 0.76 (0.60-0.97) in short LOS and 0.68 (0.52-0.88) in long LOS group. EMS transport was associated with lower hospital mortality and disability after acute hemorrhagic stroke. Effect size of EMS use for mortality was significant in patients with long ED LOS. Key  words: emergency medical service; hemorrhagic stroke; mortality; disability.

Confounding by Pre-Morbid Functional Status in Studies of Apparent Sex Differences in Severity and Outcome of Stroke

PubMed Central

Coulombe, Janie; Li, Linxin; Ganesh, Aravind; Silver, Louise; Rothwell, Peter M.

2017-01-01

Background and Purpose— Several studies have reported unexplained worse outcomes after stroke in women but none included the full spectrum of symptomatic ischemic cerebrovascular events while adjusting for prior handicap. Methods— Using a prospective population-based incident cohort of all transient ischemic attack/stroke (OXVASC [Oxford Vascular Study]) recruited between April 2002 and March 2014, we compared pre-morbid and post-event modified Rankin Scale score (mRS) in women and men and change in mRS score 1 month, 6 months, 1 year, and 5 years after stroke. Baseline stroke-related neurological impairment was measured with the National Institutes of Health Stroke Scale. Results— Among 2553 patients (50.6% women) with a first transient ischemic attack/ischemic stroke, women had a worse handicap 1 month after ischemic stroke (age-adjusted odds ratio for mRS score, 1.35; 95% confidence interval, 1.12–1.63). However, women also had a higher pre-morbid mRS score compared with men (age-adjusted odds ratio, 1.58; 95% confidence interval, 1.36–1.84). There was no difference in stroke severity when adjusting for age and pre-morbid mRS (odds ratio, 1.10; 95% confidence interval, 0.90–1.35) and no difference in the pre-/poststroke change in mRS at 1 month (age-adjusted odds ratio, 1.00; 95% confidence interval, 0.82–1.21), 6 months, 1 year, and 5 years. Women had a lower mortality rate, and there was no sex difference in risk of recurrent stroke. Conclusions— We found no evidence of a worse outcome of stroke in women when adjusting for age and pre-morbid mRS. Failure to account for sex differences in pre-morbid handicap could explain contradictory findings in previous studies. Properties of the mRS may also contribute to these inconsistencies. PMID:28798261

Confounding by Pre-Morbid Functional Status in Studies of Apparent Sex Differences in Severity and Outcome of Stroke.

PubMed

Renoux, Christel; Coulombe, Janie; Li, Linxin; Ganesh, Aravind; Silver, Louise; Rothwell, Peter M

2017-10-01

Several studies have reported unexplained worse outcomes after stroke in women but none included the full spectrum of symptomatic ischemic cerebrovascular events while adjusting for prior handicap. Using a prospective population-based incident cohort of all transient ischemic attack/stroke (OXVASC [Oxford Vascular Study]) recruited between April 2002 and March 2014, we compared pre-morbid and post-event modified Rankin Scale score (mRS) in women and men and change in mRS score 1 month, 6 months, 1 year, and 5 years after stroke. Baseline stroke-related neurological impairment was measured with the National Institutes of Health Stroke Scale. Among 2553 patients (50.6% women) with a first transient ischemic attack/ischemic stroke, women had a worse handicap 1 month after ischemic stroke (age-adjusted odds ratio for mRS score, 1.35; 95% confidence interval, 1.12-1.63). However, women also had a higher pre-morbid mRS score compared with men (age-adjusted odds ratio, 1.58; 95% confidence interval, 1.36-1.84). There was no difference in stroke severity when adjusting for age and pre-morbid mRS (odds ratio, 1.10; 95% confidence interval, 0.90-1.35) and no difference in the pre-/poststroke change in mRS at 1 month (age-adjusted odds ratio, 1.00; 95% confidence interval, 0.82-1.21), 6 months, 1 year, and 5 years. Women had a lower mortality rate, and there was no sex difference in risk of recurrent stroke. We found no evidence of a worse outcome of stroke in women when adjusting for age and pre-morbid mRS. Failure to account for sex differences in pre-morbid handicap could explain contradictory findings in previous studies. Properties of the mRS may also contribute to these inconsistencies. Copyright © 2017 The Author(s).

Classification of forensic autopsy reports through conceptual graph-based document representation model.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2018-06-01

Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD. Copyright © 2018 Elsevier Inc. All rights reserved.

SCOPe: Manual Curation and Artifact Removal in the Structural Classification of Proteins - extended Database.

PubMed

Chandonia, John-Marc; Fox, Naomi K; Brenner, Steven E

2017-02-03

SCOPe (Structural Classification of Proteins-extended, http://scop.berkeley.edu) is a database of relationships between protein structures that extends the Structural Classification of Proteins (SCOP) database. SCOP is an expert-curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. SCOPe classifies the majority of protein structures released since SCOP development concluded in 2009, using a combination of manual curation and highly precise automated tools, aiming to have the same accuracy as fully hand-curated SCOP releases. SCOPe also incorporates and updates the ASTRAL compendium, which provides several databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. SCOPe continues high-quality manual classification of new superfamilies, a key feature of SCOP. Artifacts such as expression tags are now separated into their own class, in order to distinguish them from the homology-based annotations in the remainder of the SCOPe hierarchy. SCOPe 2.06 contains 77,439 Protein Data Bank entries, double the 38,221 structures classified in SCOP. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Classification of bifurcations regions in IVOCT images using support vector machine and artificial neural network models

NASA Astrophysics Data System (ADS)

Porto, C. D. N.; Costa Filho, C. F. F.; Macedo, M. M. G.; Gutierrez, M. A.; Costa, M. G. F.

2017-03-01

Studies in intravascular optical coherence tomography (IV-OCT) have demonstrated the importance of coronary bifurcation regions in intravascular medical imaging analysis, as plaques are more likely to accumulate in this region leading to coronary disease. A typical IV-OCT pullback acquires hundreds of frames, thus developing an automated tool to classify the OCT frames as bifurcation or non-bifurcation can be an important step to speed up OCT pullbacks analysis and assist automated methods for atherosclerotic plaque quantification. In this work, we evaluate the performance of two state-of-the-art classifiers, SVM and Neural Networks in the bifurcation classification task. The study included IV-OCT frames from 9 patients. In order to improve classification performance, we trained and tested the SVM with different parameters by means of a grid search and different stop criteria were applied to the Neural Network classifier: mean square error, early stop and regularization. Different sets of features were tested, using feature selection techniques: PCA, LDA and scalar feature selection with correlation. Training and test were performed in sets with a maximum of 1460 OCT frames. We quantified our results in terms of false positive rate, true positive rate, accuracy, specificity, precision, false alarm, f-measure and area under ROC curve. Neural networks obtained the best classification accuracy, 98.83%, overcoming the results found in literature. Our methods appear to offer a robust and reliable automated classification of OCT frames that might assist physicians indicating potential frames to analyze. Methods for improving neural networks generalization have increased the classification performance.

ORACLE Stroke Study: Opinion Regarding Acceptable Outcome Following Decompressive Hemicraniectomy for Ischemic Stroke.

PubMed

Honeybul, Stephen; Ho, Kwok M; Blacker, David W

2016-08-01

There continues to be considerable interest in the use of decompressive hemicraniectomy in the management of malignant cerebral artery infarction; however, concerns remain about long-term outcome. To assess opinion on consent and acceptable outcome among a wide range of healthcare workers. Seven hundred seventy-three healthcare workers at the 2 major public neurosurgical centers in Western Australia participated. Participants were asked to record their opinion on consent and acceptable outcome based on the modified Rankin Score (mRS). The evidence for clinical efficacy of the procedure was presented, and participants were then asked to reconsider their initial responses. Of the 773 participants included in the study, 407 (52.7%) initially felt that they would provide consent for a decompressive craniectomy as a lifesaving procedure, but only a minority of them considered an mRS score of 4 or 5 an acceptable outcome (for mRS score ≤4, n = 67, 8.7%; for mRS score = 4, n = 57, 7.4%). After the introduction of the concept of the disability paradox and the evidence for the clinical efficacy of decompressive craniectomy, more participants were unwilling to accept decompressive craniectomy (18.1% vs 37.8%), but at the same time, more were willing to accept an mRS score ≤4 as an acceptable outcome (for mRS score ≤4, n = 92, 11.9%; for mRS score = 4, n = 79, 10.2%). Most participants felt survival with dependency to be unacceptable. However, many would be willing to provide consent for surgery in the hopes that they may survive with some degree of independence. DESTINY, Decompressive Surgery for the Treatment of Malignant Infarction of the Middle Cerebral ArterymRS, modified Rankin Scale.

A novel metric that quantifies risk stratification for evaluating diagnostic tests: The example of evaluating cervical-cancer screening tests across populations.

PubMed

Katki, Hormuzd A; Schiffman, Mark

2018-05-01

Our work involves assessing whether new biomarkers might be useful for cervical-cancer screening across populations with different disease prevalences and biomarker distributions. When comparing across populations, we show that standard diagnostic accuracy statistics (predictive values, risk-differences, Youden's index and Area Under the Curve (AUC)) can easily be misinterpreted. We introduce an intuitively simple statistic for a 2 × 2 table, Mean Risk Stratification (MRS): the average change in risk (pre-test vs. post-test) revealed for tested individuals. High MRS implies better risk separation achieved by testing. MRS has 3 key advantages for comparing test performance across populations with different disease prevalences and biomarker distributions. First, MRS demonstrates that conventional predictive values and the risk-difference do not measure risk-stratification because they do not account for test-positivity rates. Second, Youden's index and AUC measure only multiplicative relative gains in risk-stratification: AUC = 0.6 achieves only 20% of maximum risk-stratification (AUC = 0.9 achieves 80%). Third, large relative gains in risk-stratification might not imply large absolute gains if disease is rare, demonstrating a "high-bar" to justify population-based screening for rare diseases such as cancer. We illustrate MRS by our experience comparing the performance of cervical-cancer screening tests in China vs. the USA. The test with the worst AUC = 0.72 in China (visual inspection with acetic acid) provides twice the risk-stratification (i.e. MRS) of the test with best AUC = 0.83 in the USA (human papillomavirus and Pap cotesting) because China has three times more cervical precancer/cancer. MRS could be routinely calculated to better understand the clinical/public-health implications of standard diagnostic accuracy statistics. Published by Elsevier Inc.

Automated identification of best-quality coronary artery segments from multiple-phase coronary CT angiography (cCTA) for vessel analysis

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Chughtai, Aamer; Wei, Jun; Kazerooni, Ella A.

2016-03-01

We are developing an automated method to identify the best quality segment among the corresponding segments in multiple-phase cCTA. The coronary artery trees are automatically extracted from different cCTA phases using our multi-scale vessel segmentation and tracking method. An automated registration method is then used to align the multiple-phase artery trees. The corresponding coronary artery segments are identified in the registered vessel trees and are straightened by curved planar reformation (CPR). Four features are extracted from each segment in each phase as quality indicators in the original CT volume and the straightened CPR volume. Each quality indicator is used as a voting classifier to vote the corresponding segments. A newly designed weighted voting ensemble (WVE) classifier is finally used to determine the best-quality coronary segment. An observer preference study is conducted with three readers to visually rate the quality of the vessels in 1 to 6 rankings. Six and 10 cCTA cases are used as training and test set in this preliminary study. For the 10 test cases, the agreement between automatically identified best-quality (AI-BQ) segments and radiologist's top 2 rankings is 79.7%, and between AI-BQ and the other two readers are 74.8% and 83.7%, respectively. The results demonstrated that the performance of our automated method was comparable to those of experienced readers for identification of the best-quality coronary segments.

Longitudinal 1H MR spectroscopy of rat forebrain from infancy to adulthood reveals adolescence as a distinctive phase of neurometabolite development

PubMed Central

Morgan, Jonathan J.; Kleven, Gale A.; Tulbert, Christina D.; Olson, John; Horita, David A.; Ronca, April E.

2013-01-01

The present study represents the first longitudinal, within-subject 1H MRS investigation of the developing rat brain spanning infancy, adolescence, and early adulthood. We obtained neurometabolite profiles from a voxel located in a central location of the forebrain, centered on the striatum, with smaller contributions for cortex, thalamus, and hypothalamus, on postnatal days 7, 35, and 60. Water-scaled metabolite signals were corrected for T1 effects and quantified using the automated processing software LCModel, yielding molal concentrations. Our findings indicate age-related concentration changes in N-acetylaspartate + N-acetylaspartylglutamate, myo-inositol, glutamate + glutamine, taurine, creatine + phosphocreatine, and glycerophosphocholine + phosphocholine. Using a repeated measures design and analysis, we identified significant neurodevelopment change across all three developmental ages and identified adolescence as a distinctive phase in normative neurometabolic brain development. Between postnatal days 35 and 60, changes were observed in concentrations of N-acetylaspartate + N-acetylaspartylglutamate, glutamate + glutamine, and glycerophosphocholine + phosphocholine observed between postnatal days 35 and 60. Our data replicate past studies of early neurometabolite development and, for the first time, link maturational profiles in the same subjects across infancy, adolescence, and adulthood. PMID:23322706

Sexuality, Lung Cancer, and the Older Adult: An Unlikely Trio?

PubMed Central

Williams, Anna Cathy; Reckamp, Karen; Freeman, Bonnie; Sidhu, Rupinder; Grant, Marcia

2013-01-01

Case Study  Mrs. L. is a 60-year-old retired female teacher with stage IIIA squamous cell carcinoma of the lung, status postchemoradiation. She recently developed radiation pneumonitis, which was managed conservatively, and she did not require steroids. Mrs. L. has noted some progression of her underlying dyspnea. She is monitoring her oxygen saturation at home, and most of the time it is in the range of 94% to 96%. On one occasion only, her oxygen dropped to 88% and rapidly improved to the mid-90s. Her cough has improved for the past 4 to 6 weeks. She denies sputum production, congestion, or fever. Mrs. L. does not require a walker and uses a wheelchair only for long distances. She has occasional, slight dysphagia. A recent CT scan shows stable disease, and she is to return to the clinic in 2 months for restaging and possible further chemotherapy. Mrs. L. and her husband have been married for 33 years, and they have been very close. Until recently, they have continued to be sexually active and very intimate with each other. Since Mrs. L.’s diagnosis, and during treatment, the couple have become extremely stressed and psychologically spent. The act of sexual intercourse has ceased, yet they have attempted to remain close and maintain open communication. In addition to Mrs. L.’s increasing dyspnea, she has also suffered a great deal of fatigue and depression, along with alopecia and vaginal atrophy, due to the chemotherapy and radiation treatments. Both Mr. and Mrs. L. are very distressed over the change in their sexual lives. Mr. L. has mentioned that he now feels more like a "nursemaid" than a husband or lover. Mrs. L. has made concerted efforts to maintain intimacy with her husband, but her fatigue is profound. She has taken to sleeping in the living room, sitting up on the couch, as it relieves her dyspnea to some degree. PMID:25032012

Ontology-Based Data Integration of Open Source Electronic Medical Record and Data Capture Systems

ERIC Educational Resources Information Center

Guidry, Alicia F.

2013-01-01

In low-resource settings, the prioritization of clinical care funding is often determined by immediate health priorities. As a result, investment directed towards the development of standards for clinical data representation and exchange are rare and accordingly, data management systems are often redundant. Open-source systems such as OpenMRS and…

Automated object-based classification of topography from SRTM data

PubMed Central

Drăguţ, Lucian; Eisank, Clemens

2012-01-01

We introduce an object-based method to automatically classify topography from SRTM data. The new method relies on the concept of decomposing land-surface complexity into more homogeneous domains. An elevation layer is automatically segmented and classified at three scale levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation is performed at these appropriate scales. Objects resulting from segmentation are partitioned into sub-domains based on thresholds given by the mean values of elevation and standard deviation of elevation respectively. Results resemble reasonably patterns of existing global and regional classifications, displaying a level of detail close to manually drawn maps. Statistical evaluation indicates that most of classes satisfy the regionalization requirements of maximizing internal homogeneity while minimizing external homogeneity. Most objects have boundaries matching natural discontinuities at regional level. The method is simple and fully automated. The input data consist of only one layer, which does not need any pre-processing. Both segmentation and classification rely on only two parameters: elevation and standard deviation of elevation. The methodology is implemented as a customized process for the eCognition® software, available as online download. The results are embedded in a web application with functionalities of visualization and download. PMID:22485060

Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope

NASA Astrophysics Data System (ADS)

Asiedu, Mercy Nyamewaa; Simhal, Anish; Lam, Christopher T.; Mueller, Jenna; Chaudhary, Usamah; Schmitt, John W.; Sapiro, Guillermo; Ramanujam, Nimmi

2018-02-01

The world health organization recommends visual inspection with acetic acid (VIA) and/or Lugol's Iodine (VILI) for cervical cancer screening in low-resource settings. Human interpretation of diagnostic indicators for visual inspection is qualitative, subjective, and has high inter-observer discordance, which could lead both to adverse outcomes for the patient and unnecessary follow-ups. In this work, we a simple method for automatic feature extraction and classification for Lugol's Iodine cervigrams acquired with a low-cost, miniature, digital colposcope. Algorithms to preprocess expert physician-labelled cervigrams and to extract simple but powerful color-based features are introduced. The features are used to train a support vector machine model to classify cervigrams based on expert physician labels. The selected framework achieved a sensitivity, specificity, and accuracy of 89.2%, 66.7% and 80.6% with majority diagnosis of the expert physicians in discriminating cervical intraepithelial neoplasia (CIN +) relative to normal tissues. The proposed classifier also achieved an area under the curve of 84 when trained with majority diagnosis of the expert physicians. The results suggest that utilizing simple color-based features may enable unbiased automation of VILI cervigrams, opening the door to a full system of low-cost data acquisition complemented with automatic interpretation.

Automated object-based classification of topography from SRTM data

NASA Astrophysics Data System (ADS)

Drăguţ, Lucian; Eisank, Clemens

2012-03-01

We introduce an object-based method to automatically classify topography from SRTM data. The new method relies on the concept of decomposing land-surface complexity into more homogeneous domains. An elevation layer is automatically segmented and classified at three scale levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation is performed at these appropriate scales. Objects resulting from segmentation are partitioned into sub-domains based on thresholds given by the mean values of elevation and standard deviation of elevation respectively. Results resemble reasonably patterns of existing global and regional classifications, displaying a level of detail close to manually drawn maps. Statistical evaluation indicates that most of classes satisfy the regionalization requirements of maximizing internal homogeneity while minimizing external homogeneity. Most objects have boundaries matching natural discontinuities at regional level. The method is simple and fully automated. The input data consist of only one layer, which does not need any pre-processing. Both segmentation and classification rely on only two parameters: elevation and standard deviation of elevation. The methodology is implemented as a customized process for the eCognition® software, available as online download. The results are embedded in a web application with functionalities of visualization and download.

Automated measurement of retinal vascular tortuosity.

PubMed Central

Hart, W. E.; Goldbaum, M.; Côté, B.; Kube, P.; Nelson, M. R.

1997-01-01

Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classification tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classification rate of 91% for the first problem and 95% on the second problem, which confirms that they capture much of the ophthalmologists' notion of tortuosity. Images Figure 1 PMID:9357668

Quantitative X-ray Map Analyser (Q-XRMA): A new GIS-based statistical approach to Mineral Image Analysis

NASA Astrophysics Data System (ADS)

Ortolano, Gaetano; Visalli, Roberto; Godard, Gaston; Cirrincione, Rosolino

2018-06-01

We present a new ArcGIS®-based tool developed in the Python programming language for calibrating EDS/WDS X-ray element maps, with the aim of acquiring quantitative information of petrological interest. The calibration procedure is based on a multiple linear regression technique that takes into account interdependence among elements and is constrained by the stoichiometry of minerals. The procedure requires an appropriate number of spot analyses for use as internal standards and provides several test indexes for a rapid check of calibration accuracy. The code is based on an earlier image-processing tool designed primarily for classifying minerals in X-ray element maps; the original Python code has now been enhanced to yield calibrated maps of mineral end-members or the chemical parameters of each classified mineral. The semi-automated procedure can be used to extract a dataset that is automatically stored within queryable tables. As a case study, the software was applied to an amphibolite-facies garnet-bearing micaschist. The calibrated images obtained for both anhydrous (i.e., garnet and plagioclase) and hydrous (i.e., biotite) phases show a good fit with corresponding electron microprobe analyses. This new GIS-based tool package can thus find useful application in petrology and materials science research. Moreover, the huge quantity of data extracted opens new opportunities for the development of a thin-section microchemical database that, using a GIS platform, can be linked with other major global geoscience databases.

Differences in open versus laparoscopic gastric bypass mortality risk using the Obesity Surgery Mortality Risk Score (OS-MRS).

PubMed

Brolin, Robert E; Cody, Ronald P; Marcella, Stephen W

2015-01-01

The Obesity Surgery Mortality Risk Score (OS-MRS) was developed to ascertain preoperative mortality risk of patients having bariatric surgery. To date there has not been a comparison between open and laparoscopic operations using the OS-MRS. To determine whether there are differences in mortality risk between open and laparoscopic Roux-en-Y Gastric Bypass (RYGB) using the OS-MRS. Three university-affiliated hospitals. The 90-day mortality of 2467 consecutive patients who had primary open (1574) or laparoscopic (893) RYGB performed by one surgeon was determined. Univariate and multivariate analysis using 5 OS-MRS risk factors including body mass index (BMI) gender, age>45, presence of hypertension and preoperative deep vein thrombosis (DVT) risk was performed in each group. Each patient was placed in 1 of 3 OS-MRS risk classes based on the number of risks: A (0-1), B (2-3), and C (4-5). Preoperative BMI and DVT risk factors were significantly greater in the open group (OG). Preoperative age was significantly greater in the laparoscopic group (LG). There were significantly more class B and C patients in LG. Ninety-day mortality rates for OG and LG patients were 1.0% and .9%, respectively. Pulmonary embolism was the most common cause of death. All deaths in LG occurred during first 4 years of that experience. Mortality rate by class was A = .1%; B = 1.5%; C = 2.3%. The difference in mortality between class B and C patients was not significant. Univariate analysis in the OG indicated that BMI, age, gender, and DVT risk were significant predictors of mortality. In the LG only BMI and DVT were significant predictors of death. Presence of hypertension was not a significant predictor in either group. Multivariate analysis excluding hypertension found that age was predictive of mortality in the OG while BMI (P = .057) and gender (P = .065) approached statistical significance. Conversely, only BMI was predictive of mortality in the LG with age approaching significance (P = .058). In multivariate analysis DVT risk was not predictive of mortality in either group. There are significant differences in the predictive value of the OS-MRS between open and laparoscopic RYGB. Although laparoscopic patients were significantly older versus the open patients, age was not predictive of mortality after laparoscopic RYGB. BMI trended toward increased mortality risk in both groups. Changes in technique and protocol likely contributed toward no mortality during the last 6 years of our laparoscopic experience. Copyright © 2015 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research.

PubMed

Ababneh, Sufyan Y; Prescott, Jeff W; Gurcan, Metin N

2011-08-01

In this paper, a new, fully automated, content-based system is proposed for knee bone segmentation from magnetic resonance images (MRI). The purpose of the bone segmentation is to support the discovery and characterization of imaging biomarkers for the incidence and progression of osteoarthritis, a debilitating joint disease, which affects a large portion of the aging population. The segmentation algorithm includes a novel content-based, two-pass disjoint block discovery mechanism, which is designed to support automation, segmentation initialization, and post-processing. The block discovery is achieved by classifying the image content to bone and background blocks according to their similarity to the categories in the training data collected from typical bone structures. The classified blocks are then used to design an efficient graph-cut based segmentation algorithm. This algorithm requires constructing a graph using image pixel data followed by applying a maximum-flow algorithm which generates a minimum graph-cut that corresponds to an initial image segmentation. Content-based refinements and morphological operations are then applied to obtain the final segmentation. The proposed segmentation technique does not require any user interaction and can distinguish between bone and highly similar adjacent structures, such as fat tissues with high accuracy. The performance of the proposed system is evaluated by testing it on 376 MR images from the Osteoarthritis Initiative (OAI) database. This database included a selection of single images containing the femur and tibia from 200 subjects with varying levels of osteoarthritis severity. Additionally, a full three-dimensional segmentation of the bones from ten subjects with 14 slices each, and synthetic images with background having intensity and spatial characteristics similar to those of bone are used to assess the robustness and consistency of the developed algorithm. The results show an automatic bone detection rate of 0.99 and an average segmentation accuracy of 0.95 using the Dice similarity index. Copyright © 2011 Elsevier B.V. All rights reserved.

Adopting a Patient-Centered Approach to Primary Outcome Analysis of Acute Stroke Trials Using a Utility-Weighted Modified Rankin Scale.

PubMed

Chaisinanunkul, Napasri; Adeoye, Opeolu; Lewis, Roger J; Grotta, James C; Broderick, Joseph; Jovin, Tudor G; Nogueira, Raul G; Elm, Jordan J; Graves, Todd; Berry, Scott; Lees, Kennedy R; Barreto, Andrew D; Saver, Jeffrey L

2015-08-01

Although the modified Rankin Scale (mRS) is the most commonly used primary end point in acute stroke trials, its power is limited when analyzed in dichotomized fashion and its indication of effect size challenging to interpret when analyzed ordinally. Weighting the 7 Rankin levels by utilities may improve scale interpretability while preserving statistical power. A utility-weighted mRS (UW-mRS) was derived by averaging values from time-tradeoff (patient centered) and person-tradeoff (clinician centered) studies. The UW-mRS, standard ordinal mRS, and dichotomized mRS were applied to 11 trials or meta-analyses of acute stroke treatments, including lytic, endovascular reperfusion, blood pressure moderation, and hemicraniectomy interventions. Utility values were 1.0 for mRS level 0; 0.91 for mRS level 1; 0.76 for mRS level 2; 0.65 for mRS level 3; 0.33 for mRS level 4; 0 for mRS level 5; and 0 for mRS level 6. For trials with unidirectional treatment effects, the UW-mRS paralleled the ordinal mRS and outperformed dichotomous mRS analyses. Both the UW-mRS and the ordinal mRS were statistically significant in 6 of 8 unidirectional effect trials, whereas dichotomous analyses were statistically significant in 2 to 4 of 8. In bidirectional effect trials, both the UW-mRS and ordinal tests captured the divergent treatment effects by showing neutral results, whereas some dichotomized analyses showed positive results. Mean utility differences in trials with statistically significant positive results ranged from 0.026 to 0.249. A UW-mRS performs similar to the standard ordinal mRS in detecting treatment effects in actual stroke trials and ensures the quantitative outcome is a valid reflection of patient-centered benefits. © 2015 American Heart Association, Inc.

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

Evaluation of cell count and classification capabilities in body fluids using a fully automated Sysmex XN equipped with high-sensitive Analysis (hsA) mode and DI-60 hematology analyzer system.

PubMed

Takemura, Hiroyuki; Ai, Tomohiko; Kimura, Konobu; Nagasaka, Kaori; Takahashi, Toshihiro; Tsuchiya, Koji; Yang, Haeun; Konishi, Aya; Uchihashi, Kinya; Horii, Takashi; Tabe, Yoko; Ohsaka, Akimichi

2018-01-01

The XN series automated hematology analyzer has been equipped with a body fluid (BF) mode to count and differentiate leukocytes in BF samples including cerebrospinal fluid (CSF). However, its diagnostic accuracy is not reliable for CSF samples with low cell concentration at the border between normal and pathologic level. To overcome this limitation, a new flow cytometry-based technology, termed "high sensitive analysis (hsA) mode," has been developed. In addition, the XN series analyzer has been equipped with the automated digital cell imaging analyzer DI-60 to classify cell morphology including normal leukocytes differential and abnormal malignant cells detection. Using various BF samples, we evaluated the performance of the XN-hsA mode and DI-60 compared to manual microscopic examination. The reproducibility of the XN-hsA mode showed good results in samples with low cell densities (coefficient of variation; % CV: 7.8% for 6 cells/μL). The linearity of the XN-hsA mode was established up to 938 cells/μL. The cell number obtained using the XN-hsA mode correlated highly with the corresponding microscopic examination. Good correlation was also observed between the DI-60 analyses and manual microscopic classification for all leukocyte types, except monocytes. In conclusion, the combined use of cell counting with the XN-hsA mode and automated morphological analyses using the DI-60 mode is potentially useful for the automated analysis of BF cells.

Maturity assessment of harumanis mango using thermal camera sensor

NASA Astrophysics Data System (ADS)

Sa'ad, F. S. A.; Shakaff, A. Y. Md.; Zakaria, A.; Abdullah, A. H.; Ibrahim, M. F.

2017-03-01

The perceived quality of fruits, such as mangoes, is greatly dependent on many parameters such as ripeness, shape, size, and is influenced by other factors such as harvesting time. Unfortunately, a manual fruit grading has several drawbacks such as subjectivity, tediousness and inconsistency. By automating the procedure, as well as developing new classification technique, it may solve these problems. This paper presents the novel work on the using Infrared as a Tool in Quality Monitoring of Harumanis Mangoes. The histogram of infrared image was used to distinguish and classify the level of ripeness of the fruits based on the colour spectrum by week. The approach proposed thermal data was able to achieve 90.5% correct classification.

[Cross-sectional study of the variability of work-related stress among post-graduate medical residents at the main University Polyclinic of Sicily].

PubMed

Costantino, Claudio; Albeggiani, Valentina; Bonfante, Maria Stefania; Monte, Caterina; Lo Cascio, Nunzio; Mazzucco, Walter

2015-02-10

Among health care workers (HCWs), work-related stress is one of the main topics in risk assessment and prevention at the workplace. Post-graduate medical residents (MRs) are a group of HCWs comparable to medical doctors in terms of occupational exposure and occurrence of work-related stress syndromes. Risk assessment of work-related stress among MRs attending the major University Hospital of Sicily. A cross-sectional survey via an anonymous and self-administered questionnaire. 45% of clinical MRs and 37% of surgical MRs had access to compensatory rest days against 92% of MRs of the services area (p<0.001). A work attendance recording system for MRs was available in 80% of the postgraduate medical schools of the services area, in 60% of the clinical postgraduate schools and in 50% of the surgical postgraduate schools (p<0.001). MRs of the postgraduate surgical schools reported having access to work breaks (41%) with less frequency compared to clinical (60%) and services MRs (74%) (p<0.001). Both clinical (47%) and surgical MRs (47%) were more exposed to work-related stress than MRs of the services area (27%) (p<0.001). The survey demonstrated excess exposure to work-related stress for all the considered variables in MRs of the surgical area, compared with MRs of clinical and services areas. It is strongly recommended to provide specific training programmes aimed at managing the MRs' risk of exposure to work-related stress, focusing both on the workers and the work environment.

Automated microaneurysm detection in diabetic retinopathy using curvelet transform

NASA Astrophysics Data System (ADS)

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

2016-10-01

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

Automated microaneurysm detection in diabetic retinopathy using curvelet transform.

PubMed

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

2016-10-01

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

PubMed

Mourao-Miranda, J; Reinders, A A T S; Rocha-Rego, V; Lappin, J; Rondina, J; Morgan, C; Morgan, K D; Fearon, P; Jones, P B; Doody, G A; Murray, R M; Kapur, S; Dazzan, P

2012-05-01

To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study

PubMed Central

Mourao-Miranda, J.; Reinders, A. A. T. S.; Rocha-Rego, V.; Lappin, J.; Rondina, J.; Morgan, C.; Morgan, K. D.; Fearon, P.; Jones, P. B.; Doody, G. A.; Murray, R. M.; Kapur, S.; Dazzan, P.

2012-01-01

Background To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. Method One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. Results At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). Conclusions We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data. PMID:22059690

Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan

NASA Astrophysics Data System (ADS)

Demharter, Timo; Michel, Ulrich; Ehlers, Manfred; Reinartz, Peter

2011-11-01

Given the importance of Change Detection, especially in the field of crisis management, this paper discusses the advantage of object-based Change Detection. This project and the used methods give an opportunity to coordinate relief actions strategically. The principal objective of this project was to develop an algorithm which allows to detect rapidly damaged and destroyed buildings in the area of Abu Suruj. This Sudanese village is located in West-Darfur and has become the victim of civil war. The software eCognition Developer was used to per-form an object-based Change Detection on two panchromatic Quickbird 2 images from two different time slots. The first image shows the area before, the second image shows the area after the massacres in this region. Seeking a classification for the huts of the Sudanese town Abu Suruj was reached by first segmenting the huts and then classifying them on the basis of geo-metrical and brightness-related values. The huts were classified as "new", "destroyed" and "preserved" with the help of a automated algorithm. Finally the results were presented in the form of a map which displays the different conditions of the huts. The accuracy of the project is validated by an accuracy assessment resulting in an Overall Classification Accuracy of 90.50 percent. These change detection results allow aid organizations to provide quick and efficient help where it is needed the most.

Four-and-one-half years' experience in monitoring of reproducibility of an MR spectroscopy system--application of in vitro results to interpretation of in vivo data.

PubMed

Skorupa, Agnieszka; Wicher, Magdalena; Banasik, Tomasz; Jamroz, Ewa; Paprocka, Justyna; Kiełtyka, Aleksandra; Sokół, Maria; Konopka, Marek

2014-05-08

The primary purpose of this work was to assess long-term in vitro reproducibility of metabolite levels measured using 1H MRS (proton magnetic resonance spectroscopy). The secondary purpose was to use the in vitro results for interpretation of 1H MRS in vivo spectra acquired from patients diagnosed with Canavan disease. 1H MRS measurements were performed in the period from April 2006 to September 2010. 118 short and 116 long echo spectra were acquired from a stable phantom during this period. Change-point analysis of the in vitro N-acetylaspartate levels was exploited in the computation of fT factor (ratio of the actual to the reference N-acetylaspartate level normalized by the reciprocity principle). This coefficient was utilized in the interpretation of in vivo spectra analyzed using absolute reference technique. The monitored time period was divided into six time intervals based on short echo in vitro data (seven time intervals based on long echo in vitro data) characterized by fT coefficient ranging from 0.97 to 1.09 (based on short echo data) and from 1.0 to 1.11 (based on long echo data). Application of this coefficient to interpretation of in vivo spectra confirmed increased N-acetylaspartate level in Canavan disease. Long-term monitoring of an MRS system reproducibility, allowing for absolute referencing of metabolite levels, facilitates interpretation of metabolic changes in white matter disorders.

Quantitative analysis of hepatic fat fraction by single-breath-holding MR spectroscopy with T₂ correction: phantom and clinical study with histologic assessment.

PubMed

Hayashi, Norio; Miyati, Tosiaki; Minami, Takashi; Takeshita, Yumie; Ryu, Yasuji; Matsuda, Tsuyoshi; Ohno, Naoki; Hamaguchi, Takashi; Kato, Kenichiro; Takamura, Toshinari; Matsui, Osamu

2013-01-01

The focus of this study was on the investigation of the accuracy of the fat fraction of the liver by use of single-breath-holding magnetic resonance spectroscopy (MRS) with T (2) correction. Single-voxel proton MRS was performed with several TE values, and the fat fraction was determined with and without T (2) correction. MRS was also performed with use of the point-resolved spectroscopy sequence in single breath holding. The T (2) values of both water and fat were determined separately at the same time, and the effect of T (2) on the fat fraction was corrected. In addition, MRS-based fat fractions were compared with the degree of hepatic steatosis (HS) by liver biopsy in human subjects. With T (2) correction, the MRI-derived fat fractions were in good agreement with the fat fractions in all phantoms, but the fat fractions were overestimated without T (2) correction. R (2) values were in good agreement with the preset iron concentrations in the phantoms. The MRI-derived fat fraction was well correlated with the degree of HS. Iron deposited in the liver affects the signal strength when proton MRS is used for detection of the fat signal in the liver. However, the fat signal can be evaluated more accurately when the T (2) correction is applied. Breath-holding MRS minimizes the respiratory motion, and it can be more accurate in the quantification of the hepatic fat fraction.

The "focus on aneurysm" principle: Classification and surgical principles of management of concurrent arterial aneurysm with arteriovenous malformation causing intracranial hemorrhage.

PubMed

Jha, Vikas; Behari, Sanjay; Jaiswal, Awadhesh K; Bhaisora, Kamlesh Singh; Shende, Yogesh P; Phadke, Rajendra V

2016-01-01

Concurrent arterial aneurysms (AAs) occurring in 2.7-16.7% patients harboring an arteriovenous malformation (AVM) aggravate the risk of intracranial hemorrhage. We evaluate the variations of aneurysms simultaneously coexisting with AVMs. A classification-based management strategy and an abbreviated nomenclature that describes their radiological features is also proposed. Tertiary care academic institute. Test of significance applied to determine the factors causing rebleeding in the groups of patients with concurrent AVM and aneurysm and those with only AVMs. Sixteen patients (5 with subarachnoid hemorrhage and 11 with intracerebral/intraventricular hemorrhage; 10 with low flow [LF] and 6 with high flow [HF] AVMs) underwent radiological assessment of Spetzler Martin (SM) grading and flow status of AA + AVM. Their modified Rankin's score (mRS) at admission was compared with their follow-up (F/U) score. Pre-operative mRS was 0 in 5, 2 in 6, 3 in 1, 4 in 3 and 5 in 1; and, SM grade I in 5, II in 3, III in 3, IV in 4 and V in 1 patients, respectively. AA associated AVMs were classified as: (I) Flow-related proximal (n = 2); (II) flow-related distal (n = 3); (III) intranidal (n = 5); (IV) extra-intranidal (n = 2); (V) remote major ipsilateral (n = 1); (VI) remote major contralateral (n = 1); (VII) deep perforator related (n = 1); (VIII) superficial (n = 1); and (IX) distal (n = 0). Their treatment strategy included: Flow related AA, SM I-III LF AVM: aneurysm clipping with AVM excision; nidal-extranidal AA, SM I-III LF AVM: Excision or embolization of both AA + AVM; nidal-extranidal and perforator-related AA, SM IV-V HF AVM: Only endovascular embolization or radiosurgery. Surgical decision-making for remote AA took into account their ipsilateral/contralateral filling status and vessel dominance; and, for AA associated with SM III HF AVM, it varied in each patient based on diffuseness of AVM nidus, flow across arteriovenous fistula and eloquence of cortex. Follow up (F/U) (23.29 months; range: 1.5-69 months) mRS scores were 0 in 12, 2 in 2, 3 in 1 and 6 in 1 patients, respectively. Patients with intracranial AVMs should be screened for concurrent AAs. Further grading, management protocols and prognostication should particularly "focus on the aneurysm."

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

NASA Astrophysics Data System (ADS)

Sun, Yankui; Li, Shan; Sun, Zhongyang

2017-01-01

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

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

PubMed

Sun, Yankui; Li, Shan; Sun, Zhongyang

2017-01-01

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

Accuracy of PDFF estimation by magnitude-based and complex-based MRI in children with MR spectroscopy as a reference.

PubMed

Haufe, William M; Wolfson, Tanya; Hooker, Catherine A; Hooker, Jonathan C; Covarrubias, Yesenia; Schlein, Alex N; Hamilton, Gavin; Middleton, Michael S; Angeles, Jorge E; Hernando, Diego; Reeder, Scott B; Schwimmer, Jeffrey B; Sirlin, Claude B

2017-12-01

To assess and compare the accuracy of magnitude-based magnetic resonance imaging (MRI-M) and complex-based MRI (MRI-C) for estimating hepatic proton density fat fraction (PDFF) in children, using MR spectroscopy (MRS) as the reference standard. A secondary aim was to assess the agreement between MRI-M and MRI-C. This was a HIPAA-compliant, retrospective analysis of data collected in children enrolled in prospective, Institutional Review Board (IRB)-approved studies between 2012 and 2014. Informed consent was obtained from 200 children (ages 8-19 years) who subsequently underwent 3T MR exams that included MRI-M, MRI-C, and T 1 -independent, T 2 -corrected, single-voxel stimulated echo acquisition mode (STEAM) MRS. Both MRI methods acquired six echoes at low flip angles. T2*-corrected PDFF parametric maps were generated. PDFF values were recorded from regions of interest (ROIs) drawn on the maps in each of the nine Couinaud segments and three ROIs colocalized to the MRS voxel location. Regression analyses assessing agreement with MRS were performed to evaluate the accuracy of each MRI method, and Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to assess agreement between the MRI methods. MRI-M and MRI-C PDFF were accurate relative to the colocalized MRS reference standard, with regression intercepts of 0.63% and -0.07%, slopes of 0.998 and 0.975, and proportion-of-explained-variance values (R 2 ) of 0.982 and 0.979, respectively. For individual Couinaud segments and for the whole liver averages, Bland-Altman biases between MRI-M and MRI-C were small (ranging from 0.04 to 1.11%) and ICCs were high (≥0.978). Both MRI-M and MRI-C accurately estimated hepatic PDFF in children, and high intermethod agreement was observed. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1641-1647. © 2017 International Society for Magnetic Resonance in Medicine.

Multispectral Resource Sampler - An experimental satellite sensor for the mid-1980s

NASA Technical Reports Server (NTRS)

Schnetzler, C. C.; Thompson, L. L.

1979-01-01

An experimental pushbroom scan sensor, the Multispectral Resource Sampler (MRS), being developed by NASA for a future earth orbiting flight is presented. This sensor will provide new earth survey capabilities beyond those of current sensor systems, with a ground resolution of 15 m over a swath width of 15 km in four bands. The four arrays are aligned on a common focal surface requiring no beamsplitters, thus causing a spatial separation on the ground which requires computer processing to register the bands. Along track pointing permits stereo coverage at variable base/height ratios and atmospheric correction experiments, while across track pointing will provide repeat coverage, from a Landsat-type orbit, of every 1 to 3 days. The MRS can be used for experiments in crop discrimination and status, rock discrimination, land use classification, and forestry.

A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments

Treesearch

Jeffrey S. Evans; Andrew T. Hudak

2007-01-01

One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground...

Biosensing Technologies for Mycobacterium tuberculosis Detection: Status and New Developments

PubMed Central

Zhou, Lixia; He, Xiaoxiao; He, Dinggeng; Wang, Kemin; Qin, Dilan

2011-01-01

Biosensing technologies promise to improve Mycobacterium tuberculosis (M. tuberculosis) detection and management in clinical diagnosis, food analysis, bioprocess, and environmental monitoring. A variety of portable, rapid, and sensitive biosensors with immediate “on-the-spot” interpretation have been developed for M. tuberculosis detection based on different biological elements recognition systems and basic signal transducer principles. Here, we present a synopsis of current developments of biosensing technologies for M. tuberculosis detection, which are classified on the basis of basic signal transducer principles, including piezoelectric quartz crystal biosensors, electrochemical biosensors, and magnetoelastic biosensors. Special attention is paid to the methods for improving the framework and analytical parameters of the biosensors, including sensitivity and analysis time as well as automation of analysis procedures. Challenges and perspectives of biosensing technologies development for M. tuberculosis detection are also discussed in the final part of this paper. PMID:21437177

Automated Detection of Electroencephalography Artifacts in Human, Rodent and Canine Subjects using Machine Learning.

PubMed

Levitt, Joshua; Nitenson, Adam; Koyama, Suguru; Heijmans, Lonne; Curry, James; Ross, Jason T; Kamerling, Steven; Saab, Carl Y

2018-06-23

Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. Comparison with Existing Methods: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra. Copyright © 2018. Published by Elsevier B.V.

Synthetic Aperture Radar (SAR)-based paddy rice monitoring system: Development and application in key rice producing areas in Tropical Asia

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Quicho, E.; Collivignarelli, F.; Maunahan, A.; Gatti, L.; Romuga, G. C.

2017-01-01

Reliable and regular rice information is essential part of many countries’ national accounting process but the existing system may not be sufficient to meet the information demand in the context of food security and policy. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland paddy rice, especially in tropical region where pervasive cloud cover in the rainy seasons limits the use of optical imagery. This study uses multi-temporal X-band and C-band SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations across Tropical Asia and assimilate the information into ORYZA Crop Growth Simulation model (CGSM) to generate high resolution yield maps. The resulting cultivated rice area maps had classification accuracies above 85% and yield estimates were within 81-93% agreement against district level reported yields. The study sites capture much of the diversity in water management, crop establishment and rice maturity durations and the study demonstrates the feasibility of rice detection, yield monitoring, and damage assessment in case of climate disaster at national and supra-national scales using multi-temporal SAR imagery combined with CGSM and automated methods.

Single-cell printer: automated, on demand, and label free.

PubMed

Gross, Andre; Schöndube, Jonas; Niekrawitz, Sonja; Streule, Wolfgang; Riegger, Lutz; Zengerle, Roland; Koltay, Peter

2013-12-01

Within the past years, single-cell analysis has developed into a key topic in cell biology to study cellular functions that are not accessible by investigation of larger cell populations. Engineering approaches aiming to access single cells to extract information about their physiology, phenotype, and genotype at the single-cell level are going manifold ways, meanwhile allowing separation, sorting, culturing, and analysis of individual cells. Based on our earlier research toward inkjet-like printing of single cells, this article presents further characterization results obtained with a fully automated prototype instrument for printing of single living cells in a noncontact inkjet-like manner. The presented technology is based on a transparent microfluidic drop-on-demand dispenser chip coupled with a camera-assisted automatic detection system. Cells inside the chip are detected and classified with this detection system before they are expelled from the nozzle confined in microdroplets, thus enabling a "one cell per droplet" printing mode. To demonstrate the prototype instrument's suitability for biological and biomedical applications, basic experiments such as printing of single-bead and cell arrays as well as deposition and culture of single cells in microwell plates are presented. Printing efficiencies greater than 80% and viability rates about 90% were achieved.

Enhancement of gesture recognition for contactless interface using a personalized classifier in the operating room.

PubMed

Cho, Yongwon; Lee, Areum; Park, Jongha; Ko, Bemseok; Kim, Namkug

2018-07-01

Contactless operating room (OR) interfaces are important for computer-aided surgery, and have been developed to decrease the risk of contamination during surgical procedures. In this study, we used Leap Motion™, with a personalized automated classifier, to enhance the accuracy of gesture recognition for contactless interfaces. This software was trained and tested on a personal basis that means the training of gesture per a user. We used 30 features including finger and hand data, which were computed, selected, and fed into a multiclass support vector machine (SVM), and Naïve Bayes classifiers and to predict and train five types of gestures including hover, grab, click, one peak, and two peaks. Overall accuracy of the five gestures was 99.58% ± 0.06, and 98.74% ± 3.64 on a personal basis using SVM and Naïve Bayes classifiers, respectively. We compared gesture accuracy across the entire dataset and used SVM and Naïve Bayes classifiers to examine the strength of personal basis training. We developed and enhanced non-contact interfaces with gesture recognition to enhance OR control systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Practical protocols for fast histopathology by Fourier transform infrared spectroscopic imaging

NASA Astrophysics Data System (ADS)

Keith, Frances N.; Reddy, Rohith K.; Bhargava, Rohit

2008-02-01

Fourier transform infrared (FT-IR) spectroscopic imaging is an emerging technique that combines the molecular selectivity of spectroscopy with the spatial specificity of optical microscopy. We demonstrate a new concept in obtaining high fidelity data using commercial array detectors coupled to a microscope and Michelson interferometer. Next, we apply the developed technique to rapidly provide automated histopathologic information for breast cancer. Traditionally, disease diagnoses are based on optical examinations of stained tissue and involve a skilled recognition of morphological patterns of specific cell types (histopathology). Consequently, histopathologic determinations are a time consuming, subjective process with innate intra- and inter-operator variability. Utilizing endogenous molecular contrast inherent in vibrational spectra, specially designed tissue microarrays and pattern recognition of specific biochemical features, we report an integrated algorithm for automated classifications. The developed protocol is objective, statistically significant and, being compatible with current tissue processing procedures, holds potential for routine clinical diagnoses. We first demonstrate that the classification of tissue type (histology) can be accomplished in a manner that is robust and rigorous. Since data quality and classifier performance are linked, we quantify the relationship through our analysis model. Last, we demonstrate the application of the minimum noise fraction (MNF) transform to improve tissue segmentation.

Automated analysis and reannotation of subcellular locations in confocal images from the Human Protein Atlas.

PubMed

Li, Jieyue; Newberg, Justin Y; Uhlén, Mathias; Lundberg, Emma; Murphy, Robert F

2012-01-01

The Human Protein Atlas contains immunofluorescence images showing subcellular locations for thousands of proteins. These are currently annotated by visual inspection. In this paper, we describe automated approaches to analyze the images and their use to improve annotation. We began by training classifiers to recognize the annotated patterns. By ranking proteins according to the confidence of the classifier, we generated a list of proteins that were strong candidates for reexamination. In parallel, we applied hierarchical clustering to group proteins and identified proteins whose annotations were inconsistent with the remainder of the proteins in their cluster. These proteins were reexamined by the original annotators, and a significant fraction had their annotations changed. The results demonstrate that automated approaches can provide an important complement to visual annotation.

Proof-of-the-Concept Study on Mathematically Optimized Magnetic Resonance Spectroscopy for Breast Cancer Diagnostics.

PubMed

Belkić, Dževad; Belkić, Karen

2015-06-01

Magnetic resonance (MR)-based modalities aid breast cancer detection without exposure to ionizing radiation. Magnetic resonance imaging is very sensitive but costly and insufficiently specific. Molecular imaging through magnetic resonance spectroscopy (MRS) can provide information about key metabolites. Here, the measured/encoded time signals cannot be interpreted directly, necessitating mathematics for mapping to the more manageable frequency domain. Conventional applications of MRS are hampered by data analysis via the fast Fourier transform (FFT) and postprocessing by fitting techniques. Most in vivo MRS studies on breast cancer rely upon estimations of total choline (tCHO). These have yielded only incremental improvements in diagnostic accuracy. In vitro studies reveal richer metabolic information for identifying breast cancer, particularly in closely overlapping components of tCHO. Among these are phosphocholine (PC), a marker of malignant transformation of the breast. The FFT cannot assess these congested spectral components. This can be done by the fast Padé transform (FPT), a high-resolution, quantification-equipped method, which we presently apply to noisy MRS time signals consistent with those encoded in breast cancer. The FPT unequivocally and robustly extracted the concentrations of all physical metabolites, including PC. In sharp contrast, the FFT produced a rough envelope spectrum with a few distorted peaks and key metabolites absent altogether. As such, the FFT has poor resolution for these typical MRS time signals from breast cancer. Hence, based on Fourier-estimated envelope spectra, tCHO estimates are unreliable. Using even truncated time signals, the FPT clearly distinguishes noise from true metabolites whose concentrations are accurately extracted. The high resolution of the FPT translates directly into shortened examination time of the patient. These capabilities strongly suggest that by applying the FPT to time signals encoded in vivo from the breast, MRS will, at last, fulfill its potential to become a clinically reliable, cost-effective method for breast cancer detection, including screening/surveillance. © The Author(s) 2014.

Automated separation of merged Langerhans islets

NASA Astrophysics Data System (ADS)

Švihlík, Jan; Kybic, Jan; Habart, David

2016-03-01

This paper deals with separation of merged Langerhans islets in segmentations in order to evaluate correct histogram of islet diameters. A distribution of islet diameters is useful for determining the feasibility of islet transplantation in diabetes. First, the merged islets at training segmentations are manually separated by medical experts. Based on the single islets, the merged islets are identified and the SVM classifier is trained on both classes (merged/single islets). The testing segmentations were over-segmented using watershed transform and the most probable back merging of islets were found using trained SVM classifier. Finally, the optimized segmentation is compared with ground truth segmentation (correctly separated islets).

Texture analysis of pulmonary parenchyma in normal and emphysematous lung

NASA Astrophysics Data System (ADS)

Uppaluri, Renuka; Mitsa, Theophano; Hoffman, Eric A.; McLennan, Geoffrey; Sonka, Milan

1996-04-01

Tissue characterization using texture analysis is gaining increasing importance in medical imaging. We present a completely automated method for discriminating between normal and emphysematous regions from CT images. This method involves extracting seventeen features which are based on statistical, hybrid and fractal texture models. The best subset of features is derived from the training set using the divergence technique. A minimum distance classifier is used to classify the samples into one of the two classes--normal and emphysema. Sensitivity and specificity and accuracy values achieved were 80% or greater in most cases proving that texture analysis holds great promise in identifying emphysema.

Metabolic assessment of the action of targeted cancer therapeutics using magnetic resonance spectroscopy

PubMed Central

Beloueche-Babari, M; Chung, Y-L; Al-Saffar, N M S; Falck-Miniotis, M; Leach, M O

2009-01-01

Developing rational targeted cancer drugs requires the implementation of pharmacodynamic (PD), preferably non-invasive, biomarkers to aid response assessment and patient follow-up. Magnetic resonance spectroscopy (MRS) allows the non-invasive study of tumour metabolism. We describe the MRS-detectable PD biomarkers resulting from the action of targeted therapeutics, and discuss their biological significance and future translation into clinical use. PMID:19935796

Psychometric Properties of Spanish Adaptation of the PDD-MRS Scale in Adults with Intellectual Developmental Disorders: The EVTEA-DI Scale

ERIC Educational Resources Information Center

Cortés, Maria José; Orejuela, Carmen; Castellví, Gemma; Folch, Annabel; Rovira, Lluís; Salvador-Carulla, Luis; Irazábal, Marcia; Muñoz, Silvia; Haro, Josep Maria; Vilella, Elisabet; Martínez-Leal, Rafael

2018-01-01

Strategies for the early detection of autism spectrum disorders (ASD) in people with intellectual developmental disorder (IDD) are urgently needed, but few specific tools have been developed. The present study examines the psychometric properties of the EVTEA-DI, a Spanish adaptation of the PDD-MRS, in a large randomized sample of 979 adults with…

Automated grouping of action potentials of human embryonic stem cell-derived cardiomyocytes.

PubMed

Gorospe, Giann; Zhu, Renjun; Millrod, Michal A; Zambidis, Elias T; Tung, Leslie; Vidal, Rene

2014-09-01

Methods for obtaining cardiomyocytes from human embryonic stem cells (hESCs) are improving at a significant rate. However, the characterization of these cardiomyocytes (CMs) is evolving at a relatively slower rate. In particular, there is still uncertainty in classifying the phenotype (ventricular-like, atrial-like, nodal-like, etc.) of an hESC-derived cardiomyocyte (hESC-CM). While previous studies identified the phenotype of a CM based on electrophysiological features of its action potential, the criteria for classification were typically subjective and differed across studies. In this paper, we use techniques from signal processing and machine learning to develop an automated approach to discriminate the electrophysiological differences between hESC-CMs. Specifically, we propose a spectral grouping-based algorithm to separate a population of CMs into distinct groups based on the similarity of their action potential shapes. We applied this method to a dataset of optical maps of cardiac cell clusters dissected from human embryoid bodies. While some of the nine cell clusters in the dataset are presented with just one phenotype, the majority of the cell clusters are presented with multiple phenotypes. The proposed algorithm is generally applicable to other action potential datasets and could prove useful in investigating the purification of specific types of CMs from an electrophysiological perspective.

Automated Grouping of Action Potentials of Human Embryonic Stem Cell-Derived Cardiomyocytes

PubMed Central

Gorospe, Giann; Zhu, Renjun; Millrod, Michal A.; Zambidis, Elias T.; Tung, Leslie; Vidal, René

2015-01-01

Methods for obtaining cardiomyocytes from human embryonic stem cells (hESCs) are improving at a significant rate. However, the characterization of these cardiomyocytes is evolving at a relatively slower rate. In particular, there is still uncertainty in classifying the phenotype (ventricular-like, atrial-like, nodal-like, etc.) of an hESC-derived cardiomyocyte (hESC-CM). While previous studies identified the phenotype of a cardiomyocyte based on electrophysiological features of its action potential, the criteria for classification were typically subjective and differed across studies. In this paper, we use techniques from signal processing and machine learning to develop an automated approach to discriminate the electrophysiological differences between hESC-CMs. Specifically, we propose a spectral grouping-based algorithm to separate a population of cardiomyocytes into distinct groups based on the similarity of their action potential shapes. We applied this method to a dataset of optical maps of cardiac cell clusters dissected from human embryoid bodies (hEBs). While some of the 9 cell clusters in the dataset presented with just one phenotype, the majority of the cell clusters presented with multiple phenotypes. The proposed algorithm is generally applicable to other action potential datasets and could prove useful in investigating the purification of specific types of cardiomyocytes from an electrophysiological perspective. PMID:25148658

Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms.

PubMed

Hurnanen, Tero; Lehtonen, Eero; Tadi, Mojtaba Jafari; Kuusela, Tom; Kiviniemi, Tuomas; Saraste, Antti; Vasankari, Tuija; Airaksinen, Juhani; Koivisto, Tero; Pankaala, Mikko

2017-09-01

In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of [Formula: see text] and an average true negative rate of [Formula: see text] for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.

System Diagnostic Builder - A rule generation tool for expert systems that do intelligent data evaluation. [applied to Shuttle Mission Simulator

NASA Technical Reports Server (NTRS)

Nieten, Joseph; Burke, Roger

1993-01-01

Consideration is given to the System Diagnostic Builder (SDB), an automated knowledge acquisition tool using state-of-the-art AI technologies. The SDB employs an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert. Thus, data are captured from the subject system, classified, and used to drive the rule generation process. These rule bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The knowledge bases captured from the Shuttle Mission Simulator can be used as black box simulations by the Intelligent Computer Aided Training devices. The SDB can also be used to construct knowledge bases for the process control industry, such as chemical production or oil and gas production.

32 CFR 2001.50 - Telecommunications automated information systems and network security.

Code of Federal Regulations, 2014 CFR

2014-07-01

... and network security. 2001.50 Section 2001.50 National Defense Other Regulations Relating to National Defense INFORMATION SECURITY OVERSIGHT OFFICE, NATIONAL ARCHIVES AND RECORDS ADMINISTRATION CLASSIFIED... network security. Each agency head shall ensure that classified information electronically accessed...

32 CFR 2001.50 - Telecommunications automated information systems and network security.

Code of Federal Regulations, 2013 CFR

2013-07-01

... and network security. 2001.50 Section 2001.50 National Defense Other Regulations Relating to National Defense INFORMATION SECURITY OVERSIGHT OFFICE, NATIONAL ARCHIVES AND RECORDS ADMINISTRATION CLASSIFIED... network security. Each agency head shall ensure that classified information electronically accessed...

32 CFR 2001.50 - Telecommunications automated information systems and network security.

Code of Federal Regulations, 2012 CFR

2012-07-01

... and network security. 2001.50 Section 2001.50 National Defense Other Regulations Relating to National Defense INFORMATION SECURITY OVERSIGHT OFFICE, NATIONAL ARCHIVES AND RECORDS ADMINISTRATION CLASSIFIED... network security. Each agency head shall ensure that classified information electronically accessed...

Development of FARICH detector for particle identification system at accelerators

NASA Astrophysics Data System (ADS)

Finogeev, D. A.; Kurepin, A. B.; Razin, V. I.; Reshetin, A. I.; Usenko, E. A.; Barnyakov, A. Yu.; Barnyakov, M. Yu.; Bobrovnikov, V. S.; Buzykaev, A. R.; Kasyanenko, P. V.; Kononov, S. A.; Kravchenko, E. A.; Kuyanov, I. A.; Onuchin, A. P.; Ovtin, I. V.; Podgornov, N. A.; Talyshev, A. A.; Danilyuk, A. F.

2018-01-01

Aerogel has been successfully used as a radiator in Cherenkov detectors. In 2004, a multilayer aerogel providing Cherenkov ring focusing was proposed and produced. FARICH (Focusing Aerogel Rich Imaging CHerenkov) detectors such as ARICH for Belle-II (KEK, Japan), Forward RICH for PANDA detector (FAIR, Germany), and FARICH for the Super Charm-Tau factory project (BINP, Novosibirsk) have been developed based on this aerogel. Prototypes of FARICH detector based on MRS APD and Philips DPC photosensors were developed and tested in the framework of this project. An angular resolution for Cherenkov rings of 3.6 mrad was achieved.

Experiments on automatic classification of tissue malignancy in the field of digital pathology

NASA Astrophysics Data System (ADS)

Pereira, J.; Barata, R.; Furtado, Pedro

2017-06-01

Automated analysis of histological images helps diagnose and further classify breast cancer. Totally automated approaches can be used to pinpoint images for further analysis by the medical doctor. But tissue images are especially challenging for either manual or automated approaches, due to mixed patterns and textures, where malignant regions are sometimes difficult to detect unless they are in very advanced stages. Some of the major challenges are related to irregular and very diffuse patterns, as well as difficulty to define winning features and classifier models. Although it is also hard to segment correctly into regions, due to the diffuse nature, it is still crucial to take low-level features over individualized regions instead of the whole image, and to select those with the best outcomes. In this paper we report on our experiments building a region classifier with a simple subspace division and a feature selection model that improves results over image-wide and/or limited feature sets. Experimental results show modest accuracy for a set of classifiers applied over the whole image, while the conjunction of image division, per-region low-level extraction of features and selection of features, together with the use of a neural network classifier achieved the best levels of accuracy for the dataset and settings we used in the experiments. Future work involves deep learning techniques, adding structures semantics and embedding the approach as a tumor finding helper in a practical Medical Imaging Application.

Centralized automated quality assurance for large scale health care systems. A pilot method for some aspects of dental radiography.

PubMed

Benn, D K; Minden, N J; Pettigrew, J C; Shim, M

1994-08-01

President Clinton's Health Security Act proposes the formation of large scale health plans with improved quality assurance. Dental radiography consumes 4% ($1.2 billion in 1990) of total dental expenditure yet regular systematic office quality assurance is not performed. A pilot automated method is described for assessing density of exposed film and fogging of unexposed processed film. A workstation and camera were used to input intraoral radiographs. Test images were produced from a phantom jaw with increasing exposure times. Two radiologists subjectively classified the images as too light, acceptable, or too dark. A computer program automatically classified global grey level histograms from the test images as too light, acceptable, or too dark. The program correctly classified 95% of 88 clinical films. Optical density of unexposed film in the range 0.15 to 0.52 measured by computer was reliable to better than 0.01. Further work is needed to see if comprehensive centralized automated radiographic quality assurance systems with feedback to dentists are feasible, are able to improve quality, and are significantly cheaper than conventional clerical methods.

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

14. Photocopy of 1872 photograph by Eadweard Muybridge in Stanford ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

14. Photocopy of 1872 photograph by Eadweard Muybridge in Stanford University Archives, PC 6. SEWING ROOM ('BIRD ROOM').LEFT TO RIGHT, ANNA MARIA LATHROP (MRS. STANFORD'S SISTER), MRS. JANE ANN (DYER) LATHROP (MRS. STANFORD'S MOTHER), ELIZABETH PHILLIPS (MRS. JOSIAH) STANFORD (GOV. STANFORD'S MOTHER), JANE LATHROP (MRS. LELAND) STANFORD AND HER SON, LELAND, JR. - Leland Stanford House, 800 N Street, Sacramento, Sacramento County, CA

Petri net modelling of buffers in automated manufacturing systems.

PubMed

Zhou, M; Dicesare, F

1996-01-01

This paper presents Petri net models of buffers and a methodology by which buffers can be included in a system without introducing deadlocks or overflows. The context is automated manufacturing. The buffers and models are classified as random order or order preserved (first-in-first-out or last-in-first-out), single-input-single-output or multiple-input-multiple-output, part type and/or space distinguishable or indistinguishable, and bounded or safe. Theoretical results for the development of Petri net models which include buffer modules are developed. This theory provides the conditions under which the system properties of boundedness, liveness, and reversibility are preserved. The results are illustrated through two manufacturing system examples: a multiple machine and multiple buffer production line and an automatic storage and retrieval system in the context of flexible manufacturing.

32 CFR 2001.50 - Telecommunications automated information systems and network security.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 6 2011-07-01 2011-07-01 false Telecommunications automated information systems and network security. 2001.50 Section 2001.50 National Defense Other Regulations Relating to National... network security. Each agency head shall ensure that classified information electronically accessed...

32 CFR 2001.50 - Telecommunications automated information systems and network security.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Telecommunications automated information systems and network security. 2001.50 Section 2001.50 National Defense Other Regulations Relating to National... network security. Each agency head shall ensure that classified information electronically accessed...

MR-Based Assessment of Bone Marrow Fat in Osteoporosis, Diabetes, and Obesity

PubMed Central

Cordes, Christian; Baum, Thomas; Dieckmeyer, Michael; Ruschke, Stefan; Diefenbach, Maximilian N.; Hauner, Hans; Kirschke, Jan S.; Karampinos, Dimitrios C.

2016-01-01

Bone consists of the mineralized component (i.e., cortex and trabeculae) and the non-mineralized component (i.e., bone marrow). Most of the routine clinical bone imaging uses X-ray-based techniques and focuses on the mineralized component. However, bone marrow adiposity has been also shown to have a strong linkage with bone health. Specifically, multiple previous studies have demonstrated a negative association between bone marrow fat fraction (BMFF) and bone mineral density. Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are ideal imaging techniques for non-invasively investigating the properties of bone marrow fat. In the present work, we first review the most important MRI and MRS methods for assessing properties of bone marrow fat, including methodologies for measuring BMFF and bone marrow fatty acid composition parameters. Previous MRI and MRS studies measuring BMFF and fat unsaturation in the context of osteoporosis are then reviewed. Finally, previous studies investigating the relationship between bone marrow fat, other fat depots, and bone health in patients with obesity and type 2 diabetes are presented. In summary, MRI and MRS are powerful non-invasive techniques for measuring properties of bone marrow fat in osteoporosis, obesity, and type 2 diabetes and can assist in future studies investigating the pathophysiology of bone changes in the above clinical scenarios. PMID:27445977

Recommendations, evaluation and validation of a semi-automated, fluorescent-based scoring protocol for micronucleus testing in human cells.

PubMed

Seager, Anna L; Shah, Ume-Kulsoom; Brüsehafer, Katja; Wills, John; Manshian, Bella; Chapman, Katherine E; Thomas, Adam D; Scott, Andrew D; Doherty, Ann T; Doak, Shareen H; Johnson, George E; Jenkins, Gareth J S

2014-05-01

Micronucleus (MN) induction is an established cytogenetic end point for evaluating structural and numerical chromosomal alterations in genotoxicity testing. A semi-automated scoring protocol for the assessment of MN preparations from human cell lines and a 3D skin cell model has been developed and validated. Following exposure to a range of test agents, slides were stained with 4'-6-diamidino-2-phenylindole (DAPI) and scanned by use of the MicroNuc module of metafer 4, after the development of a modified classifier for selecting MN in binucleate cells. A common difficulty observed with automated systems is an artefactual output of high false positives, in the case of the metafer system this is mainly due to the loss of cytoplasmic boundaries during slide preparation. Slide quality is paramount to obtain accurate results. We show here that to avoid elevated artefactual-positive MN outputs, diffuse cell density and low-intensity nuclear staining are critical. Comparisons between visual (Giemsa stained) and automated (DAPI stained) MN frequencies and dose-response curves were highly correlated (R (2) = 0.70 for hydrogen peroxide, R (2) = 0.98 for menadione, R (2) = 0.99 for mitomycin C, R (2) = 0.89 for potassium bromate and R (2) = 0.68 for quantum dots), indicating the system is adequate to produce biologically relevant and reliable results. Metafer offers many advantages over conventional scoring including increased output and statistical power, and reduced scoring subjectivity, labour and costs. Further, the metafer system is easily adaptable for use with a range of different cells, both suspension and adherent human cell lines. Awareness of the points raised here reduces the automatic positive errors flagged and drastically reduces slide scoring time, making metafer an ideal candidate for genotoxic biomonitoring and population studies and regulatory genotoxic testing.

Scanning electron microscope automatic defect classification of process induced defects

NASA Astrophysics Data System (ADS)

Wolfe, Scott; McGarvey, Steve

2017-03-01

With the integration of high speed Scanning Electron Microscope (SEM) based Automated Defect Redetection (ADR) in both high volume semiconductor manufacturing and Research and Development (R and D), the need for reliable SEM Automated Defect Classification (ADC) has grown tremendously in the past few years. In many high volume manufacturing facilities and R and D operations, defect inspection is performed on EBeam (EB), Bright Field (BF) or Dark Field (DF) defect inspection equipment. A comma separated value (CSV) file is created by both the patterned and non-patterned defect inspection tools. The defect inspection result file contains a list of the inspection anomalies detected during the inspection tools' examination of each structure, or the examination of an entire wafers surface for non-patterned applications. This file is imported into the Defect Review Scanning Electron Microscope (DRSEM). Following the defect inspection result file import, the DRSEM automatically moves the wafer to each defect coordinate and performs ADR. During ADR the DRSEM operates in a reference mode, capturing a SEM image at the exact position of the anomalies coordinates and capturing a SEM image of a reference location in the center of the wafer. A Defect reference image is created based on the Reference image minus the Defect image. The exact coordinates of the defect is calculated based on the calculated defect position and the anomalies stage coordinate calculated when the high magnification SEM defect image is captured. The captured SEM image is processed through either DRSEM ADC binning, exporting to a Yield Analysis System (YAS), or a combination of both. Process Engineers, Yield Analysis Engineers or Failure Analysis Engineers will manually review the captured images to insure that either the YAS defect binning is accurately classifying the defects or that the DRSEM defect binning is accurately classifying the defects. This paper is an exploration of the feasibility of the utilization of a Hitachi RS4000 Defect Review SEM to perform Automatic Defect Classification with the objective of the total automated classification accuracy being greater than human based defect classification binning when the defects do not require multiple process step knowledge for accurate classification. The implementation of DRSEM ADC has the potential to improve the response time between defect detection and defect classification. Faster defect classification will allow for rapid response to yield anomalies that will ultimately reduce the wafer and/or the die yield.

Mapping landslide source and transport areas in VHR images with Object-Based Analysis and Support Vector Machines

NASA Astrophysics Data System (ADS)

Heleno, Sandra; Matias, Magda; Pina, Pedro

2015-04-01

Visual interpretation of satellite imagery remains extremely demanding in terms of resources and time, especially when dealing with numerous multi-scale landslides affecting wide areas, such as is the case of rainfall-induced shallow landslides. Applying automated methods can contribute to more efficient landslide mapping and updating of existing inventories, and in recent years the number and variety of approaches is rapidly increasing. Very High Resolution (VHR) images, acquired by space-borne sensors with sub-metric precision, such as Ikonos, Quickbird, Geoeye and Worldview, are increasingly being considered as the best option for landslide mapping, but these new levels of spatial detail also present new challenges to state of the art image analysis tools, asking for automated methods specifically suited to map landslide events on VHR optical images. In this work we develop and test a methodology for semi-automatic landslide recognition and mapping of landslide source and transport areas. The method combines object-based image analysis and a Support Vector Machine supervised learning algorithm, and was tested using a GeoEye-1 multispectral image, sensed 3 days after a damaging landslide event in Madeira Island, together with a pre-event LiDAR DEM. Our approach has proved successful in the recognition of landslides on a 15 Km2-wide study area, with 81 out of 85 landslides detected in its validation regions. The classifier also showed reasonable performance (false positive rate 60% and false positive rate below 36% in both validation regions) in the internal mapping of landslide source and transport areas, in particular in the sunnier east-facing slopes. In the less illuminated areas the classifier is still able to accurately map the source areas, but performs poorly in the mapping of landslide transport areas.

Non-invasive investigation of the saturated/unsaturated zone with magnetic resonance sounding - a field example at the testsite Fuhrberger Feld near Hannover, Germany

NASA Astrophysics Data System (ADS)

Costabel, S.; Noell, U.; Ganz, C.

2012-04-01

Magnetic resonance sounding (MRS) is a non-invasive geophysical method for groundwater prospection that uses the principle of nuclear magnetic resonance (NMR) in the Earth's magnetic field. Its unique property distinct from other hydrogeophysical methods is the direct sensitivity to the amount of water, i.e. to the amount of 1H nuclei in the subsurface. Because MRS is normally used to investigate the water content of the saturated zone and to characterize aquifer structures, the standard application is optimized for 1D-measurements in depths from several to several tens of meters. However, our investigations show that MRS has also the potential to contribute substantially to the study of groundwater recharge if the sensitivity of the method for the unsaturated zone and for the transition to the saturated zone is increased by using a modified measurement setup and adjusted interpretation schemes. We conducted MRS test measurements with the focus on the very shallow subsurface in the range of some few decimeters down to the groundwater table in a depth of 3 m. The test site is located in the area Fuhrberger Feld about 30 km north-east of Hannover, Germany, which comprises an unconfined sandy aquifer of 20 to 30-m thickness. Previous studies have discovered the soil physical characteristics of the site with tension infiltrometer measurements and tracer irrigation experiments in the field, as well as with water retention measurements in the laboratory. In addition, several infiltration experiments with dye tracer were conducted and monitored with electrical resistivity tomography (ERT), tensiometers and TDR devices. For the MRS measurements at the testsite, a serious challenge was the intense electromagnetic noise consisting of large spiky radio signals and harmonic components, respectively. A special combination of new processing techniques was developed to isolate and interpret the NMR signals with amplitudes of approximately 5 to 14 nV. The standard inversion of the MRS data shows the ground water table at the correct depth and furthermore, increased residual water in the topsoil, which is verified by the water retention measurements in the lab. However, the amount of water at shallow depth down to 30 cm is difficult to quantify and to allocate exactly in depth due to the limited resolution properties of the method in this depth range. A new inversion scheme that parameterizes the capillary fringe using the van-Genuchten model was applied to the data. These results are in good agreement with the laboratory measurements. In order to develop MRS as a method for monitoring groundwater recharge processes, we combine hydraulic simulations and MRS forward modeling. Our numerical experiments suggest that the common MRS measurement scheme must be modified to enable faster repetitions, i.e., to resolve fast infiltration processes accordingly in time. For such modifications one must accept losses in the spatial resolution of the method. Compared to non-invasive ERT measurements with a 2D or 3D resolution in the decimeter range, the resolution properties of MRS are much worse. However, the direct sensitivity of the MRS method to the water content is an important benefit, whereas the quantification of water with ERT methods remains a serious problem. Therefore, we anticipate therefore that combining both methods could be the key for non-invasive monitoring of groundwater recharge in the future.

20 CFR 416.1166a - How we deem income to you from your sponsor if you are an alien.

Code of Federal Regulations, 2010 CFR

2010-04-01

.... Mr. and Mrs. Smith are an alien couple who have no income and who have been sponsored by Mr. Hart. Mr... $660. This amount must be deemed independently to Mr. and Mrs. Smith. Mr. and Mrs. Smith would qualify... ($660 each to Mr. and Mrs. Smith) deemed income is unearned income to Mr. and Mrs. Smith and is subject...

Comparison of LCModel and SAGE in Analysis of Brain Metabolite Concentrations-A study of Patients with Mild Cognitive Impairment.

PubMed

Shih, Chiu-Ming; Lai, Jui-Jen; Chang, Chin-Ching; Chen, Cheng-Sheng; Yeh, Yi-Chun; Jaw, Twei-Shiun; Hsu, Jui-Sheng; Li, Chun-Wei

2017-03-15

The purpose of this study was to compare brain metabolite concentration ratios determined by LCModel and Spectroscopy Analysis by General Electric (SAGE) quantitative methods to elucidate the advantages and disadvantages of each method. A total of 10 healthy volunteers and 10 patients with mild cognitive impairment (MCI) were recruited in this study. A point-resolved spectroscopy (PRESS) sequence was used to obtain the brain magnetic resonance spectroscopy (MRS) spectra of the volunteers and patients, as well as the General Electric (GE) MRS-HD-sphere phantom. The brain metabolite concentration ratios were estimated based on the peak area obtained from both LCModel and SAGE software. Three brain regions were sampled for each volunteer or patient, and 20 replicates were acquired at different times for the phantom analysis. The metabolite ratios of the GE phantom were estimated to be myo-inositol (mI)/creatine (Cr): 0.70 ± 0.01, choline (Cho)/Cr: 0.37 ± 0.00, N-acetylaspartate (NAA)/Cr: 1.26 ± 0.02, and NAA/mI: 1.81 ± 0.04 by LCModel, and mI/Cr: 0.88 ± 0.15, Cho/Cr: 0.35 ± 0.01, NAA/Cr: 1.33 ± 0.03, and NAA/mI: 1.55 ± 0.26 by SAGE. In the healthy volunteers and MCI patients, the ratios of mI/Cr and Cho/Cr estimated by LCModel were higher than those estimated by SAGE. In contrast, the ratio of NAA/Cr estimated by LCModel was lower than that estimated by SAGE. Both methods were acceptable in estimating brain metabolite concentration ratios. However, LCModel was marginally more accurate than SAGE because of its full automation, basis set, and user independency.

Geophysical investigation of a freshwater lens on the island of Langeoog, Germany - Insights from combined HEM, TEM and MRS data

NASA Astrophysics Data System (ADS)

Costabel, Stephan; Siemon, Bernhard; Houben, Georg; Günther, Thomas

2017-01-01

A multi-method geophysical survey, including helicopter-borne electromagnetics (HEM), transient electromagnetics (TEM), and magnetic resonance sounding (MRS), was conducted to investigate a freshwater lens on the North Sea island of Langeoog, Germany. The HEM survey covers the entire island and gives an overview of the extent of three freshwater lenses that reach depths of up to 45 m. Ground-based TEM and MRS were conducted particularly on the managed western lens to verify the HEM results and to complement the lithological information from existing boreholes. The results of HEM and TEM are in good agreement. Salt- and freshwater-bearing sediments can, as expected, clearly be distinguished due to their individual resistivity ranges. In the resistivity data, a large transition zone between fresh- and saltwater with a thickness of up to 20 m is identified, the existence of which is verified by borehole logging and sampling. Regarding lithological characterisation of the subsurface, the MRS method provides more accurate and reliable results than HEM and TEM. Using a lithological index derived from MRS water content and relaxation time, thin aquitard structures as well as fine and coarse sand aquifers can be distinguished. Complementing the existing borehole data with the lithology information estimated from MRS, we generate a map showing the occurrence of aquitard structures, which significantly improves the hydrogeological model of the island. Moreover, we demonstrate that the estimates of groundwater conductivity in the sand aquifers from geophysical data are in agreement with the fluid conductivity measured in the boreholes.

Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours.

PubMed

Wilson, Martin; Cummins, Carole L; Macpherson, Lesley; Sun, Yu; Natarajan, Kal; Grundy, Richard G; Arvanitis, Theodoros N; Kauppinen, Risto A; Peet, Andrew C

2013-01-01

Brain tumours cause the highest mortality and morbidity rate of all childhood tumour groups and new methods are required to improve clinical management. (1)H magnetic resonance spectroscopy (MRS) allows non-invasive concentration measurements of small molecules present in tumour tissue, providing clinically useful imaging biomarkers. The primary aim of this study was to investigate whether MRS detectable molecules can predict the survival of paediatric brain tumour patients. Short echo time (30ms) single voxel (1)H MRS was performed on children attending Birmingham Children's Hospital with a suspected brain tumour and 115 patients were included in the survival analysis. Patients were followed-up for a median period of 35 months and Cox-Regression was used to establish the prognostic value of individual MRS detectable molecules. A multivariate model of survival was also investigated to improve prognostic power. Lipids and scyllo-inositol predicted poor survival whilst glutamine and N-acetyl aspartate predicted improved survival (p<0.05). A multivariate model of survival based on three MRS biomarkers predicted survival with a similar accuracy to histologic grading (p<5e-5). A negative correlation between lipids and glutamine was found, suggesting a functional link between these molecules. MRS detectable biomolecules have been identified that predict survival of paediatric brain tumour patients across a range of tumour types. The evaluation of these biomarkers in large prospective studies of specific tumour types should be undertaken. The correlation between lipids and glutamine provides new insight into paediatric brain tumour metabolism that may present novel targets for therapy. Copyright © 2012 Elsevier Ltd. All rights reserved.