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https://www.readbyqxmd.com/read/28523350/comparison-of-a-radiomic-biomarker-with-volumetric-analysis-for-decoding-tumour-phenotypes-of-lung-adenocarcinoma-with-different-disease-specific-survival
#1
Mei Yuan, Yu-Dong Zhang, Xue-Hui Pu, Yan Zhong, Hai Li, Jiang-Fen Wu, Tong-Fu Yu
OBJECTIVES: To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. METHODS: This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified...
May 18, 2017: European Radiology
https://www.readbyqxmd.com/read/28474599/multi-feature-classifiers-for-burst-detection-in-single-eeg-channels-from-preterm-infants
#2
Xavier Navarro, Fabienne Poree, Mathieu Kuchenbuch, Mario Chavez, Alain Beuchée, Guy Carrault
<i>Objective</i>: The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA...
May 5, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28433412/quantitative-imaging-correlating-image-features-with-the-segmentation-accuracy-of-pet-based-tumor-contours-in-the-lung
#3
Perry B Johnson, Lori A Young, Narottam Lamichhane, Vivek Patel, Felix M Chinea, Fei Yang
The purpose of this study was to investigate the correlation between image features extracted from PET images and the accuracy of manually drawn lesion contours in the lung. Such correlations are interesting in that they could potentially be used in predictive models to help guide physician contouring. In this work, 26 synthetic PET datasets were created using an anthropomorphic phantom and Monte Carlo simulation. Manual contours of simulated lesions were provided by 10 physicians. Contour accuracy was quantified using five commonly used similarity metrics which were then correlated with several features extracted from the images...
May 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
https://www.readbyqxmd.com/read/28431822/detecting-bursts-in-the-eeg-of-very-and-extremely-premature-infants-using-a-multi-feature-approach
#4
John M O'Toole, Geraldine B Boylan, Rhodri O Lloyd, Robert M Goulding, Sampsa Vanhatalo, Nathan J Stevenson
AIM: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. METHODS: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features...
April 18, 2017: Medical Engineering & Physics
https://www.readbyqxmd.com/read/28391063/drugclust-a-machine-learning-approach-for-drugs-side-effects-prediction
#5
Giovanna Maria Dimitri, Pietro Lió
BACKGROUND: Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predictions, are crucial for understanding side effects. METHODS: In this paper we present DrugClust, a machine learning algorithm for drugs side effects prediction. DrugClust pipeline works as follows: first drugs are clustered with respect to their features and then side effects predictions are made, according to Bayesian scores...
March 30, 2017: Computational Biology and Chemistry
https://www.readbyqxmd.com/read/28333637/multi-scale-multi-feature-context-modeling-for-scene-recognition-in-the-semantic-manifold
#6
Xinhang Song, Shuqiang Jiang, Luis Herranz
Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category...
June 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28269092/detection-of-crackle-events-using-a-multi-feature-approach
#7
L Mendes, I M Vogiatzis, E Perantoni, E Kaimakamis, I Chouvarda, N Maglaveras, J Henriques, P Carvalho, R P Paiva
The automatic detection of adventitious lung sounds is a valuable tool to monitor respiratory diseases like chronic obstructive pulmonary disease. Crackles are adventitious and explosive respiratory sounds that are usually associated with the inflammation or infection of the small bronchi, bronchioles and alveoli. In this study a multi-feature approach is proposed for the detection of events, in the frame space, that contain one or more crackles. The performance of thirty-five features was tested. These features include thirty-one features usually used in the context of Music Information Retrieval, a wavelet based feature as well as the Teager energy and the entropy...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227313/detection-of-crackle-events-using-a-multi-feature-approach
#8
L Mendes, I M Vogiatzis, E Perantoni, E Kaimakamis, I Chouvarda, N Maglaveras, J Henriques, P Carvalho, R P Paiva, L Mendes, I M Vogiatzis, E Perantoni, E Kaimakamis, I Chouvarda, N Maglaveras, J Henriques, P Carvalho, R P Paiva, I Chouvarda, P Carvalho, L Mendes, E Perantoni, R P Paiva, J Henriques, I M Vogiatzis, N Maglaveras, E Kaimakamis
The automatic detection of adventitious lung sounds is a valuable tool to monitor respiratory diseases like chronic obstructive pulmonary disease. Crackles are adventitious and explosive respiratory sounds that are usually associated with the inflammation or infection of the small bronchi, bronchioles and alveoli. In this study a multi-feature approach is proposed for the detection of events, in the frame space, that contain one or more crackles. The performance of thirty-five features was tested. These features include thirty-one features usually used in the context of Music Information Retrieval, a wavelet based feature as well as the Teager energy and the entropy...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28215821/holistic-versus-feature-based-binding-in-the-medial-temporal-lobe
#9
Rebecca N van den Honert, Gregory McCarthy, Marcia K Johnson
A central question for cognitive neuroscience is how feature-combinations that give rise to episodic/source memories are encoded in the brain. Although there is much evidence that the hippocampus (HIP) is involved in feature binding, and some evidence that other brain regions are as well, there is relatively little evidence about the nature of the resulting representations in different brain regions. We used multivoxel pattern analysis (MVPA) to investigate how feature combinations might be represented, contrasting two possibilities, feature-based versus holistic...
January 23, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28114008/3d-catheter-shape-determination-for-endovascular-navigation-using-a-two-step-particle-filter-and-ultrasound-scanning
#10
Fang Chen, Jia Liu, Hongen Liao
In endovascular catheter interventions, the determination of the three-dimensional (3D) catheter shape can increase navigation information and help reduce trauma. This study describes a shape determination method for a flexible interventional catheter using ultrasound scanning and a two-step particle filter without X-ray fluoroscopy. First, we propose a multi-feature, multi-template particle filter algorithm for accurate catheter tracking from ultrasound images. Second, we model the mechanical behavior of the catheter and apply a particle filter shape optimization algorithm to refine the results from the first step...
March 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28113249/classification-of-the-excitation-location-of-snore-sounds-in-the-upper-airway-by-acoustic-multi-feature-analysis
#11
Kun Qian, Christoph Janott, Vedhas Pandit, Zixing Zhang, Clemens Heiser, Winfried Hohenhorst, Michael Herzog, Werner Hemmert, Bjoern Schuller
OBJECTIVE: Obstructive Sleep Apnea (OSA) is a serious chronic disease and a risk factor for cardiovascular diseases. Snoring is a typical symptom of OSA patients. Knowledge of the origin of obstruction and vibration within the upper airways is essential for a targeted surgical approach. Aim of this paper is to systematically compare different acoustic features, and classifiers for their performance in the classification of the excitation location of snore sounds. METHODS: Snore sounds from 40 male patients have been recorded during Drug-Induced Sleep Endoscopy, and categorized by ENT experts...
October 21, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/27958408/multi-stage-classification-method-oriented-to-aerial-image-based-on-low-rank-recovery-and-multi-feature-fusion-sparse-representation
#12
Xu Ma, Yongmei Cheng, Shuai Hao
Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary...
December 10, 2016: Applied Optics
https://www.readbyqxmd.com/read/27919417/multidirectional-wss-disturbances-in-stenotic-turbulent-flows-a-pre-and-post-intervention-study-in-an-aortic-coarctation
#13
Magnus Andersson, Jonas Lantz, Tino Ebbers, Matts Karlsson
Wall shear stress (WSS) disturbances are commonly expressed at sites of abnormal flow obstructions and may play an essential role in the pathogenesis of various vascular diseases. In laminar flows these disturbances have recently been assessed by the transverse wall shear stress (transWSS), which accounts for the WSS multidirectionality. Site-specific estimations of WSS disturbances in pulsatile transitional and turbulent type of flows are more challenging due to continuous and unpredictable changes in WSS behavior...
January 25, 2017: Journal of Biomechanics
https://www.readbyqxmd.com/read/27918414/wearable-sensor-based-human-activity-recognition-method-with-multi-features-extracted-from-hilbert-huang-transform
#14
Huile Xu, Jinyi Liu, Haibo Hu, Yi Zhang
Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity...
December 2, 2016: Sensors
https://www.readbyqxmd.com/read/27909886/automatic-snoring-sounds-detection-from-sleep-sounds-via-multi-features-analysis
#15
Can Wang, Jianxin Peng, Lijuan Song, Xiaowen Zhang
Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious respiratory disorder. Snoring is the most intuitively characteristic symptom of OSAHS. Recently, many studies have attempted to develop snore analysis technology for diagnosing OSAHS. The preliminary and essential step in such diagnosis is to automatically segment snoring sounds from original sleep sounds. This study presents an automatic snoring detection algorithm that detects potential snoring episodes using an adaptive effective-value threshold method, linear and nonlinear feature extraction using maximum power ratio, sum of positive/negative amplitudes, 500 Hz power ratio, spectral entropy (SE) and sample entropy (SampEn), and automatic snore/nonsnore classification using a support vector machine...
March 2017: Australasian Physical & Engineering Sciences in Medicine
https://www.readbyqxmd.com/read/27835647/network-receptive-field-modeling-reveals-extensive-integration-and-multi-feature-selectivity-in-auditory-cortical-neurons
#16
Nicol S Harper, Oliver Schoppe, Ben D B Willmore, Zhanfeng Cui, Jan W H Schnupp, Andrew J King
Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields...
November 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/27816860/unsupervised-boundary-delineation-of-spinal-neural-foramina-using-a-multi-feature-and-adaptive-spectral-segmentation
#17
Xiaoxu He, Heye Zhang, Mark Landis, Manas Sharma, James Warrington, Shuo Li
As a common disease in the elderly, neural foramina stenosis (NFS) brings a significantly negative impact on the quality of life due to its symptoms including pain, disability, fall risk and depression. Accurate boundary delineation is essential to the clinical diagnosis and treatment of NFS. However, existing clinical routine is extremely tedious and inefficient due to the requirement of physicians' intensively manual delineation. Automated delineation is highly needed but faces big challenges from the complexity and variability in neural foramina images...
October 29, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27810936/visual-prediction-error-spreads-across-object-features-in-human-visual-cortex
#18
Jiefeng Jiang, Christopher Summerfield, Tobias Egner
Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli...
December 14, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/27807833/network-oriented-approaches-to-anticancer-drug-response
#19
Paola Lecca, Angela Re
A long-standing paradigm in drug discovery has been the concept of designing maximally selective drugs to act on individual targets considered to underlie a disease of interest. Nonetheless, although some drugs have proven to be successful, many more potential drugs identified by the "one gene, one drug, one disease" approach have been found to be less effective than expected or to cause notable side effects. Advances in systems biology and high-throughput in-depth genomic profiling technologies along with an analysis of the successful and failed drugs uncovered that the prominent factor to determine drug sensitivity is the intrinsic robustness of the response of biological systems in the face of perturbations...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/27775900/classification-of-the-excitation-location-of-snore-sounds-in-the-upper-airway-by-acoustic-multi-feature-analysis
#20
Kun Qian, Christoph Janott, Vedhas Pandit, Zixing Zhang, Clemens Heiser, Winfried Hohenhorst, Michael Herzog, Werner Hemmert, Bjoern Schuller
OBJECTIVE: Obstructive Sleep Apnea (OSA) is a serious chronic disease and a risk factor for cardiovascular diseases. Snoring is a typical symptom of OSA patients. Knowledge of the origin of obstruction and vibration within the upper airways is essential for a targeted surgical approach. Aim of this paper is to systematically compare different acoustic features, and classifiers for their performance in the classification of the excitation location of snore sounds. METHODS: Snore sounds from 40 male patients have been recorded during Drug-Induced Sleep Endoscopy, and categorized by ENT experts...
October 21, 2016: IEEE Transactions on Bio-medical Engineering
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