varunr14/newsummary huggingface dataset:
Eye blinks are one of the most influential artifact sources in electroencephalogram recorded from frontal channels and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data . In this paper a novel method to detect eye blink artifacts from a single channel frontal EEG signal was proposed by combining digital filters with a rule based decision system and its performance was validated using an EEG dataset recorded from 24 healthy participants . The proposed method has two main advantages over the conventional methods . First it uses single channel EEG data without the need for electrooculogram references . Therefore this method could be particularly useful in brain computer interface applications using headband type wearable EEG devices with a few frontal EEG channels . Second this method could estimate the ranges of eye blink artifacts accurately . Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods and thus the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods . The MATLAB package of our library source codes and sample data named Eyeblink Master is open for free download . 


Summary: A novel method to detect eye blink artifacts from a single channel frontal EEG signal was proposed. Overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased. An online experiment showed that our method is useful for headband type wearable EEG applications. A MATLAB package of our library and sample data is open for free download.
We introduce a new computational phonetic modeling framework for sign language recognition . This is based on dynamic static statistical subunits and provides sequentiality in an unsupervised manner without prior linguistic information . Subunit sequentiality refers to the decomposition of signs into two types of parts varying and non varying that are sequentially stacked across time . Our approach is inspired by the Movement Hold SL linguistic model that refers to such sequences . First we segment signs into intra sign primitives and classify each segment as dynamic or static i.e . movements and non movements . These segments are then clustered appropriately to construct a set of dynamic and static subunits . The dynamic static discrimination allows us employing different visual features for clustering the dynamic or static segments . Sequences of the generated subunits are used as sign pronunciations in a data driven lexicon . Based on this lexicon and the corresponding segmentation each subunit is statistically represented and trained on multimodal sign data as a hidden Markov model . In the proposed approach dynamic static sequentiality is incorporated in an unsupervised manner . Further handshape information is integrated in a parallel hidden Markov modeling scheme . The novel sign language modeling scheme is evaluated in recognition experiments on data from three corpora and two sign languages Boston University American SL which is employed pre segmented at the sign level Greek SL Lemmas and American SL Large Vocabulary Dictionary including both signer dependent and unseen signers testing . Results show consistent improvements when compared with other approaches demonstrating the importance of dynamic static structure in sub sign phonetic modeling . 


Summary: A phonetic modeling approach for unsupervised sequentiality of dynamic static SUs. Model based sign segmentation and subunit modeling with HMMs. Construction of a sign to subunits data driven lexicon. Comparison of different signers pronunciations and unseen signer pronunciation compensation. Evaluation on data from three corpora two sign languages and unseen signers.
Background and objective Cell migration differentiation proliferation and apoptosis are the main processes in tissue regeneration . Mesenchymal Stem Cells have the potential to differentiate into many cell phenotypes such as tissue or organ specific cells to perform special functions . Experimental observations illustrate that differentiation and proliferation of these cells can be regulated according to internal forces induced within their Extracellular Matrix . The process of how exactly they interpret and transduce these signals is not well understood . Methods A previously developed three dimensional computational model is here extended and employed to study how force free substrates and force induced substrate control cell differentiation and or proliferation during the mechanosensing process . Consistent with experimental observations it is assumed that cell internal deformation in correlation with the cell maturation state directly triggers cell differentiation and or proliferation . The Extracellular Matrix is modeled as Neo Hookean hyperelastic material assuming that cells are cultured within 3D nonlinear hydrogels . Results In agreement with well known experimental observations the findings here indicate that within neurogenic chondrogenic and osteogenic substrates Mesenchymal Stem Cells differentiation and proliferation can be precipitated by inducing the substrate with an internal force . Therefore cells require a longer time to grow and maturate within force free substrates than within force induced substrates . In the instance of Mesenchymal Stem Cells differentiation into a compatible phenotype the magnitude of the net traction force increases within chondrogenic and osteogenic substrates while it reduces within neurogenic substrates . This is consistent with experimental studies and numerical works recently published by the same authors . However in all cases the magnitude of the net traction force considerably increases at the instant of cell proliferation because of cell cell interaction . Conclusions The present model provides new perspectives to delineate the role of force induced substrates in remotely controlling the cell fate during cell matrix interaction which open the door for new tissue regeneration methodologies . 


Summary: A 3D model is developedto study how force induced substrate controls cell differentiation and or proliferation. Cells are cultured within 3D Neo Hookeanhyperelastic hydrogels with encapsulated magnetic nanoparticles. Internal forces can play an outstanding role in remotelycontrolling the lineage specification of MSCs and cell proliferation. A new perspective to delineate the role of force induced substrates in controlling the cell fate.
Conventional particle filtering based visual ego motion estimation or visual odometry often suffers from large local linearization errors in the case of abrupt camera motion . The main contribution of this paper is to present a novel particle filtering based visual ego motion estimation algorithm that is especially robust to the abrupt camera motion . The robustness to the abrupt camera motion is achieved by multi layered importance sampling via particle swarm optimization which iteratively moves particles to higher likelihood region without local linearization of the measurement equation . Furthermore we make the proposed visual ego motion estimation algorithm in real time by reformulating the conventional vector space PSO algorithm in consideration of the geometry of the special Euclidean group SE which is a Lie group representing the space of 3 D camera poses . The performance of our proposed algorithm is experimentally evaluated and compared with the local linearization and unscented particle filter based visual ego motion estimation algorithms on both simulated and real data sets . 


Summary: Novel particle filtering based visual ego motion estimation algorithm robust to abrupt camera motion. Multi layered importance sampling via particle swarm optimization PSO Reformulation of the conventional vector space PSO algorithm by geometric special Euclidean group SE 3 Efficient convergence of PSO and real time visual ego motion estimation performance
Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide . This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image . In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings . Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification . The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image . Accuracy of glaucoma identification achieved in this work is 94.7 and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification . 


Summary: In this work glaucoma identification is done using wavelet features of optic disk. Wavelet features are extracted from segmented and blood vessels removed optic disk. Several machine learning algorithms are used for prominent feature selection. Genetic algorithm is used to reduce the dimensionality of feature vector. Accuracy of glaucoma identification achieved in this work is 94.7 .
Automatic facial landmarking is a crucial prerequisite of many applications dedicated to face analysis. In this paper we describe a two step method. In a first step each landmark position in the image is predicted independently. To achieve fast and accurate localizations we implement detectors based on a two stage classifier and we use multiple kernel learning algorithms to combine multi scale features. In a second step to increase the robustness of the system we introduce spatial constraints between landmarks. To this end parameters of a deformable shape model are optimized using the first step outputs through a Gauss Newton algorithm. Extensive experiments have been carried out on different databases PIE LFPW Cohn Kanade Face Pix and BioID assessing the accuracy and the robustness of the proposed approach. They show that the proposed algorithm is not significantly affected by small rotations facial expressions or natural occlusions and can be favorably compared with the current state of the art landmarking systems. 


Summary: Multi Kernel Appearance Model is a new facial point detector. Multi Kernel SVM combines multi resolution features. A SVM cascade combines increasingly complex kernels to reduce the computational time. A shape model fitting introduces constraints between SVM detections.
Abnormal values of vital parameters such as hypotension or tachycardia may occur during anesthesia and may be detected by analyzing time series data collected during the procedure by the Anesthesia Information Management System . When crossed with other data from the Hospital Information System abnormal values of vital parameters have been linked with postoperative morbidity and mortality . However methods for the automatic detection of these events are poorly documented in the literature and differ between studies making it difficult to reproduce results . In this paper we propose a methodology for the automatic detection of abnormal values of vital parameters . This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data . Four examples illustrate the application of the algorithm after which it is applied to three vital signs to all 2014 anesthetic records at our institution . 


Summary: Methods for automatic detection of abnormal vital signs occurring during anesthesia are poorly documented in the literature. Existing methods are not reproducible between databases. We have developed a data model and an algorithm that allow automatic detection of abnormal values of vital parameters occurring during anesthesia. Predefined thresholds and various parameters such as time between measurements and time spent outside predefined thresholds provide adaptability to various clinical situations e.g. hypotension occurring after start of anesthesia. The relation between occurrence of abnormal values of vital parameters and mortality and length of stay may then be studied on a large and automated scale.
Background The simultaneous acquisition of electroencephalogram and functional magnetic resonance imaging provides both high temporal and spatial resolution when measuring brain activity . A real time analysis during a simultaneous EEG fMRI acquisition is essential when studying neurofeedback and conducting effective brain activity monitoring . However the ballistocardiogram artifacts which are induced by heartbeat related electrode movements in an MRI scanner severely contaminate the EEG signals and hinder a reliable real time analysis . New method The optimal basis sets method is an effective candidate for removing BCG artifacts in a traditional offline EEG fMRI analysis but has yet to be applied to a real time EEG fMRI analysis . Here a novel real time technique based on OBS method is proposed to remove BCG artifacts on a moment to moment basis . Real time electrocardiogram R peak detection procedure and sliding window OBS method were adopted . Results A series of simulated data was constructed to verify the feasibility of the rtOBS technique . Furthermore this method was applied to real EEG fMRI data to remove BCG artifacts . The results of both simulated data and real EEG fMRI data from eight healthy human subjects demonstrate the effectiveness of rtOBS in both the time and frequency domains . Comparison with existing methods A comparison between rtOBS and real time averaged artifact subtraction was conducted . The results suggest the efficacy and advantage of rtOBS in the real time removal of BCG artifacts . Conclusions In this study a novel real time OBS technique was proposed for the real time removal of BCG artifacts . The proposed method was tested using simulated data and applied to real simultaneous EEG fMRI data . The results suggest the effectiveness of this method . 


Summary: A real time OBS was proposed for the real time removal of ballistocardiogram artifacts. The real time OBS performed better than rtAAS in the real time removal of BCG artifacts. A real time analysis during a simultaneous EEG fMRI acquisition is essential to achieving neurofeedback.
Transfer function design is a key issue in direct volume rendering . Many sophisticated transfer functions have been proposed to visualize boundaries in volumetric data sets such as computed tomography and magnetic resonance imaging . However it is still conventionally challenging to reliably detect boundaries . Meanwhile the interactive strategy is complicated for new users or even experts . In this paper we first propose the human centric boundary extraction criteria and our boundary model . Based on the model we present a boundary visualization method through a what material you pick is what boundary you see approach . Users can pick out the material of interest to directly convey semantics . In addition the 3 D canny edge detection is utilized to ensure the good localization of boundaries . Furthermore we establish a point to material distance measure to guarantee the accuracy and integrity of boundaries . The proposed boundary visualization is intuitive and flexible for the exploration of volumetric data . 


Summary: Human centric boundary extraction criteria and new boundary model. A novel boundary visualization method though a what material you pick is what boundary you see approach. Point to material distance measure. A complete application.
Background and objective Integrative approaches for the study of biological systems have gained popularity in the realm of statistical genomics . For example The Cancer Genome Atlas has applied integrative clustering methodologies to various cancer types to determine molecular subtypes within a given cancer histology . In order to adequately compare integrative or systems biology type methods realistic and related datasets are needed to assess the methods . This involves simulating multiple types of omic data with realistic correlation between features of the same type and across data types . Methods We present the software application tool InterSIM for simulating multiple interrelated data types with realistic intra and inter relationships based on the DNA methylation mRNA gene expression and protein expression from the TCGA ovarian cancer study . Results The resulting simulated datasets can be used to assess and compare the operating characteristics of newly developed integrative bioinformatics methods to existing methods . Application of InterSIM is presented with an example of heatmaps of the simulated datasets . Conclusions InterSIM allows researchers to evaluate and test new integrative methods with realistically simulated interrelated genomic datasets . The software tool InterSIM is implemented in R and is freely available from CRAN . 


Summary: Integrative approaches for the study of biological systems have gained popularity.. There is lack of integrative simulation techniques to assess systems biology methods. InterSIM bridges this gap by simulating complex interrelated genomic data. InterSIM allows researchers to evaluate and test new integrative methods. The software tool InterSIM is implemented in R and is freely available from CRAN.
Current image matting methods based on color sampling use color to distinguish between foreground and background pixels . However they fail when the corresponding color distributions overlap . Other methods that define correlation between neighboring pixels based on color aim to propagate the opacity parameter from known pixels to unknown pixels . However strong edges of textured regions may block the propagation of . In this paper a new matting strategy is proposed that delivers an accurate matte by considering texture as a feature that can complement color even if the foreground and background color distributions overlap and the image is a complex one with highly textured regions . The texture feature is extracted in such a way as to increase distinction between foreground and background regions . An objective function containing color and texture components is optimized to find the best foreground and background pair among a set of candidate pairs . The effectiveness of proposed method is compared quantitatively as well as qualitatively with other matting methods by evaluating their results on a benchmark dataset and a set of complex images . The evaluations show that the proposed method presented the best among state of the art matting methods . 


Summary: The texture information is leveraged to complement color in image matting. The proposed method combines color and texture information in matting process. The proposed combined method presents the best among current matting methods. The proposed method has first ranks with respect to MSE and Gradient error. Several experiments are carried out to reveal potential power of texture in matting.
Background and objectives The automated analysis of indirect immunofluorescence images for Anti Nuclear Autoantibody testing is a fairly recent field that is receiving ever growing interest from the research community . ANA testing leverages on the categorization of intensity level and fluorescent pattern of IIF images of HEp 2 cells to perform a differential diagnosis of important autoimmune diseases . Nevertheless it suffers from tremendous lack of repeatability due to subjectivity in the visual interpretation of the images . The automatization of the analysis is seen as the only valid solution to this problem . Several works in literature address individual steps of the work flow nonetheless integrating such steps and assessing their effectiveness as a whole is still an open challenge . Methods We present a modular tool ANAlyte able to characterize a IIF image in terms of fluorescent intensity level and fluorescent pattern without any user interactions . For this purpose ANAlyte integrates the following Intensity Classifier module that categorizes the intensity level of the input slide based on multi scale contrast assessment Cell Segmenter module that splits the input slide into individual HEp 2 cells Pattern Classifier module that determines the fluorescent pattern of the slide based on the pattern of the individual cells . Results To demonstrate the accuracy and robustness of our tool we experimentally validated ANAlyte on two different public benchmarks of IIF HEp 2 images with rigorous leave one out cross validation strategy . We obtained overall accuracy of fluorescent intensity and pattern classification respectively around 85 and above 90 . We assessed all results by comparisons with some of the most representative state of the art works . Conclusions Unlike most of the other works in the recent literature ANAlyte aims at the automatization of all the major steps of ANA image analysis . Results on public benchmarks demonstrate that the tool can characterize HEp 2 slides in terms of intensity and fluorescent pattern with accuracy better or comparable with the state of the art techniques even when such techniques are run on manually segmented cells . Hence ANAlyte can be proposed as a valid solution to the problem of ANA testing automatization . 


Summary: We propose ANAlyte an automated tool for HEp 2 image analysis. ANAlyte is a valid solution to the problem of ANA test automatization. We integrate modules for the full characterization of HEp 2 slides. We propose a new technique for HEp 2 intensity characterization. Our tool is validated on two different public benchmarks of HEp 2 images.
Although direct volume rendering has become a commodity effective rendering of interesting features is still a challenge . In one of active DVR application fields the medicine radiologists have used DVR for the diagnosis of lesions or diseases that should be visualized distinguishably from other surrounding anatomical structures . One of most frequent and important radiologic tasks is the detection of lesions usually constrictions in complex tubular structures . In this paper we propose a 3D spatial field for the effective visualization of constricted tubular structures called as a stenosis map which stores the degree of constriction at each voxel . Constrictions within tubular structures are quantified by using newly proposed measures based on the localized structure analysis and classified with a proposed transfer function mapping the degree of constriction to color and opacity . We show the application results of our method to the visualization of coronary artery stenoses . We present performance evaluations using twenty eight clinical datasets demonstrating high accuracy and efficacy of our proposed method . The ability of our method to saliently visualize the constrictions within tubular structures and interactively adjust the visual appearance of the constrictions proves to deliver a substantial aid in radiologic practice . 


Summary: A new 3D spatial field for visualizing constricted tubular structures is proposed. Constrictions are quantified by new measures using the localized structure analysis. A new transfer function maps the degree of constriction to color and opacity. Our method provides intuitive adjustment of the visual appearance of constrictions. Our method can be applied to various constrictions with relevant parameters.