varunr14/newsummary huggingface dataset:
X ray micro tomography is increasingly used for the quantitative analysis of the volumes of features within the 3D images . As with any measurement there will be error and uncertainty associated with these measurements . In this paper a method for quantifying both the systematic and random components of this error in the measured volume is presented . The systematic error is the offset between the actual and measured volume which is consistent between different measurements and can therefore be eliminated by appropriate calibration . In XMT measurements this is often caused by an inappropriate threshold value . The random error is not associated with any systematic offset in the measured volume and could be caused for instance by variations in the location of the specific object relative to the voxel grid . It can be eliminated by repeated measurements . It was found that both the systematic and random components of the error are a strong function of the size of the object measured relative to the voxel size . The relative error in the volume was found to follow approximately a power law relationship with the volume of the object but with an exponent that implied unexpectedly that the relative error was proportional to the radius of the object for small objects though the exponent did imply that the relative error was approximately proportional to the surface area of the object for larger objects . In an example application involving the size of mineral grains in an ore sample the uncertainty associated with the random error in the volume is larger than the object itself for objects smaller than about 8 voxels and is greater than 10 for any object smaller than about 260 voxels . A methodology is presented for reducing the random error by combining the results from either multiple scans of the same object or scans of multiple similar objects with an uncertainty of less than 5 requiring 12 objects of 100 voxels or 600 objects of 4 voxels . As the systematic error in a measurement can not be eliminated by combining the results from multiple measurements this paper introduces a procedure for using volume standards to reduce the systematic error especially for smaller objects where the relative error is larger . 


Summary: Methodology for quantifying random and systematic errors in microCT images presented. Unexpected power law scaling for error in small particle volume as threshold changes. Random component of error in volume insensitive to threshold value.
In online systems of videos music or books users behaviors are disclosed to the recommender systems to learn their interests . Such a disclosure raises a serious concern in the public for the leak of users privacy . Meanwhile some algorithms are proposed to obfuscate users historical behavior records to protect users privacy at the cost of degradation of recommendation accuracy . It is a common belief that such tradeoff is inevitable . In this paper however we break this pessimistic belief based on the fact that people s interests are not necessarily limited to items which are geared to a certain gender age or profession . Based on this idea we propose a recommendation friendly privacy preserving framework by introducing a privacy preserving module between a recommender system and user side . For instance to obfuscate a female user s gender information the privacy preserving module adds a set of extra factitious ratings of movies not watched by the given user . These added movies are selected to be those mostly watched by male viewers but interesting the given female user . Extensive experiments show that our algorithm obfuscates users privacy information e.g . gender efficiently but also maintains or even improves recommendation accuracy . 


Summary: Highlight Preserve privacy without loss of recommendation accuracy. Add factitious ratings to blur the privacy but intensify interests. Propose a new similarity metric of ratings.
Virtual colon flattening is a minimally invasive viewing mode used to detect colorectal polyps on the colonic inner surface in virtual colonoscopy . Compared with conventional colonoscopy inspecting a flattened colonic inner surface is faster and results in fewer uninspected regions . Unfortunately the deformation distortions of flattened colonic inner surface impede the performance of VF . Conventionally the deformation distortions can be corrected by using the colonic inner surface . However colonic curvatures and haustral folds make correcting deformation distortions using only the colonic inner surface difficult . Therefore we propose a VF method that is based on the colonic outer surface . The proposed method includes two novel algorithms namely the colonic outer surface extraction algorithm and the colonic outer surface based distortion correction algorithm . Sixty scans involving 77 annotated polyps were used for the validation . The flattened colons were independently inspected by three operators and then compared with three existing VF methods . The correct detection rates of the proposed method and the three existing methods were 79.6 67.1 71.9 and 72.7 respectively and the false positives per scan were 0.16 0.32 0.21 and 0.26 respectively . The experimental results demonstrate that our proposed method has better performance than existing methods that are based on the colonic inner surface . 


Summary: We do virtual colon flattening based on colonic outer surface. We conduct both of quantitative and visual evaluations to the results. Using colonic outer surface can improve the outcome of virtual colon flattening.
This paper proposes new combined methods to classify normal and epileptic seizure EEG signals using wavelet transform phase space reconstruction and Euclidean distance based on a neural network with weighted fuzzy membership functions . WT PSR ED and statistical methods that include frequency distributions and variation were implemented to extract 24 initial features to use as inputs . Of the 24 initial features 4 minimum features with the highest accuracy were selected using a non overlap area distribution measurement method supported by the NEWFM . These 4 minimum features were used as inputs for the NEWFM and this resulted in performance sensitivity specificity and accuracy of 96.33 100 and 98.17 respectively . In addition the area under Receiver Operating Characteristic curve was used to measure the performances of NEWFM both without and with feature selections . 


Summary: The wavelet transform the phase space reconstruction and the Euclidean distances. Four minimum features to classify normal and epileptic seizure signals in EEG signals. The area under Receiver Operating Characteristic ROC curve was used.
This research examines the precision of an adaptive neuro fuzzy computing technique in estimating the anti obesity property of a potent medicinal plant in a clinical dietary intervention . Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti obesity properties estimation in terms of reduction in body mass index body fat percentage and body weight loss there are still disadvantages of the models like very demanding in terms of calculation time . Since it is a very crucial problem in this paper a process was constructed which simulates the anti obesity activities of caraway a traditional medicine on obese women with adaptive neuro fuzzy inference method . The ANFIS results are compared with the support vector regression results using root mean square error and coefficient of determination . The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach . The following statistical characteristics are obtained for BMI loss estimation RMSE 0.032118 and R 0.9964 in ANFIS testing and RMSE 0.47287 and R 0.361 in SVR testing . For fat loss estimation RMSE 0.23787 and R 0.8599 in ANFIS testing and RMSE 0.32822 and R 0.7814 in SVR testing . For weight loss estimation RMSE 0.00000035601 and R 1 in ANFIS testing and RMSE 0.17192 and R 0.6607 in SVR testing . Because of that it can be applied for practical purposes . 


Summary: Adaptive neuro fuzzy computing technique in estimating the anti obesity. Property of a potent medicinal plant in a clinical dietary intervention. Reduction in body mass index BMI body fat percentage and body weight loss. Anti obesity activities of caraway Carum carvi a traditional medicine on obese women.
Breast cancer continues to be a significant public health problem in the world . Early detection is the key for improving breast cancer prognosis . Mammogram breast X ray is considered the most reliable method in early detection of breast cancer . However it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening . Micro calcification clusters and masses are the two most important signs for the breast cancer and their automated detection is very valuable for early breast cancer diagnosis . The main objective is to discuss the computer aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three steps procedure beginning with enhancement by using Histogram equalization and Morphological Enhancement followed by segmentation based on Otsu s threshold the region of interest for the identification of micro calcifications and mass lesions and at last classification stage which classify between normal and micro calcifications patterns and then classify between benign and malignant micro calcifications . In classification stage three methods were used the voting K Nearest Neighbor classifier with prediction accuracy of 73 Support Vector Machine classifier with prediction accuracy of 83 and Artificial Neural Network classifier with prediction accuracy of 77 . 


Summary: Noise and background removed using histogram equalization and morphological filtering. Local thresholding and Otsu s technique were used to segment masses from the background. Feature extraction of mammograms images was proposed using Gray Level co occurrence matrix GLCM . Three classifications techniques of mammogram images were applied.
Current electrocardiogram signal quality assessment studies have aimed to provide a two level classification clean or noisy . However clinical usage demands more specific noise level classification for varying applications . This work outlines a five level ECG signal quality classification algorithm . A total of 13 signal quality metrics were derived from segments of ECG waveforms which were labeled by experts . A support vector machine was trained to perform the classification and tested on a simulated dataset and was validated using data from the MIT BIH arrhythmia database . The simulated training and test datasets were created by selecting clean segments of the ECG in the 2011 PhysioNet Computing in Cardiology Challenge database and adding three types of real ECG noise at different signal to noise ratio levels from the MIT BIH Noise Stress Test Database . The MITDB was re annotated for five levels of signal quality . Different combinations of the 13 metrics were trained and tested on the simulated datasets and the best combination that produced the highest classification accuracy was selected and validated on the MITDB . Performance was assessed using classification accuracy and a single class overlap accuracy which assumes that an individual type classified into an adjacent class is acceptable . An Ac of 80.26 and an OAc of 98.60 on the test set were obtained by selecting 10 metrics while 57.26 and 94.23 were the numbers for the unseen MITDB validation data without retraining . By performing the fivefold cross validation an Ac of 88.07 0.32 and OAc of 99.34 0.07 were gained on the validation fold of MITDB . 


Summary: A five level ECG signal quality classification algorithm using a machine learning approach was proposed. 13 signal quality metrics were derived from segments of ECG waveforms. The algorithm was trained and tested on a simulated dataset and was validated using data from the MIT BIH arrhythmia database. A classification accuracy of 80.26 and a single class overlap accuracy of 98.60 on the test set were obtained by selecting 10 metrics model. A classification accuracy of 88.07 0.32 and a single class overlap accuracy of 99.34 0.07 were gained on the validation fold of MITDB.
In this work the direct transfer of nanopatterns into titanium is demonstrated . The nanofeatures are imprinted at room temperature using diamond stamps in a single step . We also show that the imprint properties of the titanium surface can be altered by anodisation yielding a significant reduction in the required imprint force for pattern transfer . The anodisation process is also utilised for curved titanium surfaces where a reduced imprint force is preferable to avoid sample deformation and damage . We finally demonstrate that our process can be applied directly to titanium rods . 


Summary: Direct nanopatterning of titanium using nanopatterned diamond based stamps. Quantify nanopillar matrix imprint depth with regards to feature size and density. An account of a novel method of reducing the imprint load required to emboss titanium. TEM and EELS analysis following our load reduction treatment. The first demonstration of nanopatterning curved bulk titanium.
The detection of change points in heterogeneous sequences is a statistical challenge with applications across a wide variety of fields . In bioinformatics a vast amount of methodology exists to identify an ideal set of change points for detecting Copy Number Variation . While considerable efficient algorithms are currently available for finding the best segmentation of the data in CNV relatively few approaches consider the important problem of assessing the uncertainty of the change point location . Asymptotic and stochastic approaches exist but often require additional model assumptions to speed up the computations while exact methods generally have quadratic complexity which may be intractable for large data sets of tens of thousands points or more . A hidden Markov model with constraints specifically chosen to correspond to a segment based change point model provides an exact method for obtaining the posterior distribution of change points with linear complexity . The methods are implemented in the R package postCP which uses the results of a given change point detection algorithm to estimate the probability that each observation is a change point . The results include an implementation of postCP on a publicly available CNV data set 120 . Due to its frequentist framework postCP obtains less conservative confidence intervals than previously published Bayesian methods but with linear complexity instead of quadratic . Simulations showed that postCP provided comparable loss to a Bayesian MCMC method when estimating posterior means specifically when assessing larger scale changes while being more computationally efficient . On another high resolution CNV data set 14 241 the implementation processed information in less than one second on a mid range laptop computer . 


Summary: We describe a method to assess uncertainty in a set of prespecified change points. It obtains exact estimates of posterior probability of locations without resampling. A constrained hidden Markov model estimates probabilities in linear time. Methods are implemented in the R package postCP available on CRAN. Simulations showed comparable loss to Bayesian implementation in estimating means.
Today more and more biological laboratories use 3D cell cultures and tissues grown in vitro as a 3D model of in vivo tumours and metastases . In the last decades it has been extensively established that multicellular spheroids represent an efficient model to validate effects of drugs and treatments for human care applications . However a lack of methods for quantitative analysis limits the usage of spheroids as models for routine experiments . Several methods have been proposed in literature to perform high throughput experiments employing spheroids by automatically computing different morphological parameters such as diameter volume and sphericity . Nevertheless these systems are typically grounded on expensive automated technologies that make the suggested solutions affordable only for a limited subset of laboratories frequently performing high content screening analysis . In this work we propose AnaSP an open source software suitable for automatically estimating several morphological parameters of spheroids by simply analyzing brightfield images acquired with a standard widefield microscope also not endowed with a motorized stage . The experiments performed proved sensitivity and precision of the segmentation method proposed and excellent reliability of AnaSP to compute several morphological parameters of spheroids imaged in different conditions . AnaSP is distributed as an open source software tool . Its modular architecture and graphical user interface make it attractive also for researchers who do not work in areas of computer vision and suitable for both high content screenings and occasional spheroid based experiments . 


Summary: We present a new software suite to analyze brightfield images of spheroids. AnaSP estimates several morphological parameters in a very limited time. We proved the high accuracy of the segmentation method proposed. Both AnaSP source code and a standalone executable version are freely available. The GUI developed makes AnaSP effective even without expertise in computer vision.
Purpose Clinical pathways fall under the process perspective of health care quality . For care providers clinical pathways can be compared to improve health care quality . The objective of this study was to design a convenient physician order set comparison system based on claim records from the National Health Insurance Research Database of Taiwan . Methods Data were retrieved from the NHIRD for the period of 2003 2007 for frequent physician order sets found in hospital surgical hernia repair inpatient claim records . The derived frequent physician order sets were divided into five frequency thresholds 80 85 90 95 and 100 . A consistency index was defined and calculated to understand each care providers adherence to clinical pathways . In addition the average count of physician orders average amount of cost Charlson comorbidity index and recurrence rate were calculated these variables were considered in frequent physician order sets comparison . Results Records for 3262 patients from 257 hospitals were retrieved . The frequent physician order sets of various frequency thresholds Charlson comorbidities and recurrence rates were extracted and computed for comparison among hospitals . A recurrence rate threshold of 2 was established to separate low and high quality of herniorrhaphy at each hospital . Univariable analysis showed that low recurrence rate was associated with high consistency index few surgeons at each hospital and non medical center facility type . A multivariable Cox regression analysis indicated an association of low recurrence rates with consistency index only . Conclusions The proposed system leveraged the claim records to generate frequent physician order sets at hospitals thus solving the difficulty in obtaining clinical pathway data . This allows medical professionals and management to conveniently and effectively compare and query similarities and differences in clinical pathways among hospitals . 


Summary: A system that provides a convenient way for physicians to retrieve and compare clinical pathways among health care providers about herniorrhaphy. The frequent physician order sets were derived from the National Health Database. A higher consistency index leads to lower recurrence rates.
Pattern classification methods assign an object to one of several predefined classes categories based on features extracted from observed attributes of the object . When L discriminatory features for the pattern can be accurately determined the pattern classification problem presents no difficulty . However precise identification of the relevant features for a classification algorithm to be able to categorize real world patterns without errors is generally infeasible . In this case the pattern classification problem is often cast as devising a classifier that minimizes the misclassification rate . One way of doing this is to consider both the pattern attributes and its class label as random variables estimate the posterior class probabilities for a given pattern and then assign the pattern to the class category for which the posterior class probability value estimated is maximum . More often than not the form of the posterior class probabilities is unknown . The so called Parzen Window approach is widely employed to estimate class conditional probability densities for a given pattern . These probability densities can then be utilized to estimate the appropriate posterior class probabilities for that pattern . However the Parzen Window scheme can become computationally impractical when the size of the training dataset is in the tens of thousands and L is also large . Over the years various schemes have been suggested to ameliorate the computational drawback of the Parzen Window approach but the problem still remains outstanding and unresolved . In this paper we revisit the Parzen Window technique and introduce a novel approach that may circumvent the aforementioned computational bottleneck . The current paper presents the mathematical aspect of our idea . Practical realizations of the proposed scheme will be given elsewhere . 


Summary: We revisit the Parzen Window approach widely employed in pattern recognition. The Parzen Window approach can suffer from a severe computational bottleneck. This manuscript introduces a new scheme to ameliorate this computational drawback.
The deposits of fat on the surroundings of the heart are correlated to several health risk factors such as atherosclerosis carotid stiffness coronary artery calcification atrial fibrillation and many others . These deposits vary unrelated to obesity which reinforces its direct segmentation for further quantification . However manual segmentation of these fats has not been widely deployed in clinical practice due to the required human workload and consequential high cost of physicians and technicians . In this work we propose a unified method for an autonomous segmentation and quantification of two types of cardiac fats . The segmented fats are termed epicardial and mediastinal and stand apart from each other by the pericardium . Much effort was devoted to achieve minimal user intervention . The proposed methodology mainly comprises registration and classification algorithms to perform the desired segmentation . We compare the performance of several classification algorithms on this task including neural networks probabilistic models and decision tree algorithms . Experimental results of the proposed methodology have shown that the mean accuracy regarding both epicardial and mediastinal fats is 98.5 with a mean true positive rate of 98.0 . In average the Dice similarity index was equal to 97.6 . 


Summary: Proposing an accurate intersubject registration for cardiac CT images. Proposing and analyzing a hybrid similarity measure that was applied within the registration procedure. Corroborating on the appliance of classification algorithms for image segmentation. Analyzing the performance and accuracy of various classifiers for the problem. Proposing a unified and fully automatic segmentation method for both epicardial and mediastinal fats on cardiac CT images.