varunr14/newsummary huggingface dataset:
This paper provides a general formula for the dimension of spline space over general planar T meshes by using the smoothing cofactor conformality method . We introduce a new notion the diagonalizable T mesh where the dimension formula is only associated with the topological information of the T mesh . A necessary and sufficient condition for characterization of the diagonalizable T mesh is also provided . By this new notion we obtain some new dimension results for the spline spaces over T meshes . 


Summary: A general formula for the dimension of the spline space over the T mesh is provided. The diagonalizable T mesh is introduced. A necessary and sufficient condition to characterize the diagonalizable T meshes is proved. Some new dimension results for the spline space over T meshes that do not have a nested structure is provided.
We present a software system called Polyp Alert to assist the endoscopist find polyps by providing visual feedback during colonoscopy . Polyp Alert employs our previous edge cross section visual features and a rule based classifier to detect a polyp edge an edge along the contour of a polyp . The technique employs tracking of detected polyp edge to group a sequence of images covering the same polyp as one polyp shot . In our experiments the software correctly detected 97.7 of polyp shots on 53 randomly selected video files of entire colonoscopy procedures . However Polyp Alert incorrectly marked only 4.3 of a full length colonoscopy procedure as showing a polyp when they do not . The test data set consists of about 18h worth of video data from Olympus and Fujinon endoscopes . The technique is extensible to other brands of colonoscopes . Furthermore Polyp Alert can provide as high as ten feedbacks per second for a smooth display of feedback . The performance of our system is by far the most promising to potentially assist the endoscopist find more polyps in clinical practice during a routine screening colonoscopy . 


Summary: We surveyed recent research in polyp detection in screening colonoscopy. We introduced a fast polyp detection algorithm cable of running at 10 frames per second on an off the shelf workstation. It correctly detects 97.7 of polyp shots in our data set. We evaluated our technique on 53 video files of procedures performed using Olympus and Fujinon scopes. Real time polyp detection and feedback potentially help to reduce polyp miss rates to improve quality of care and quality of documentation. The proposed polyp detection algorithm is fast effective and potentially useful for improving quality of colonoscopy.
This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary . The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties i.e . mean and standard deviation and the reverse arrangements test . As a result of the proposed technique the first difference of EMG time series became more stationary compared to the original measured signal . Based on this finding the performance of time domain features extracted from raw and transformed EMG was investigated via an EMG classification problem on data from 18 subjects . The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis . On average the proposed differencing technique improved classification accuracies by 2 8 . 


Summary: The stationarity of surface EMG signals during upper limb motions is investigated. The differencing technique can transform EMG signals to become more stationary. The proposed differencing technique improves classification accuracies by 2 8 . Difference absolute mean value exhibits the best performance with an accuracy of 95.82 . EMG derivative features are recommended for use in the classification of dynamic motions.
We propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography . To detect vessel regions Hessian based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross sections perpendicular to the vessel direction . It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices . To improve computational efficiency an axial slice which contains part of three main coronary arteries is selected and regions of interest are extracted in the slice . Only for the voxels belonging to the ROIs the proposed geometric feature is calculated . With the seed points which are the centroids of the detected vessel regions and their vessel directions vessel tracking method can be used for artery extraction . Here a particle filtering based tracking algorithm is tested . Using 19 clinical CCTA datasets it is demonstrated that the proposed method detects seed points and can be used for full automatic coronary artery extraction . ROC curve analysis shows the advantages of the proposed method . 


Summary: The seed points are detected for automatic coronary artery tracking from CCTA. The computational efficiency is improved using prior knowledge of vessel structure. Using proposed method a particle filtering based tracking algorithm is tested. ROC curve analysis shows the advantage of the proposed method.
This paper presents a novel 3D shape descriptor which explicitly captures the local geometry and encodes it using an efficient representation. Our method consists of multiple evolving fronts which are realized by a set of growing spheres on the surface. At the core of this method is a simple intersection operator between the spheres and the shape s surface. Intersection curves yield a discrete sampling of the surface at different positions and scales. Our key idea is to define a shape descriptor that captures the continuous local geometry of the surface in an efficient and consistent representation by intersecting the surface with multiple spheres and transforming the intersection curve to frequency domain. To evaluate our descriptor we define shape similarity metric and perform shape matching on the SHREC11 non rigid benchmark and other classes. 


Summary: Descriptive and compact feature descriptor based on sphere intersection operator. Fast and meaningful feature detector based on the descriptor. Shape retrieval framework for evaluation against state of the art benchmarks.
Registration of pre clinical images to physical space is indispensable for computer assisted endoscopic interventions in operating rooms . Electromagnetically navigated endoscopic interventions are increasingly performed at current diagnoses and treatments . Such interventions use an electromagnetic tracker with a miniature sensor that is usually attached at an endoscope distal tip to real time track endoscope movements in a pre clinical image space . Spatial alignment between the electromagnetic tracker and pre clinical images must be performed to navigate the endoscope to target regions . This paper proposes an adaptive marker free registration method that uses a multiple point selection strategy . This method seeks to address an assumption that the endoscope is operated along the centerline of an intraluminal organ which is easily violated during interventions . We introduce an adaptive strategy that generates multiple points in terms of sensor measurements and endoscope tip center calibration . From these generated points we adaptively choose the optimal point which is the closest to its assigned the centerline of the hollow organ to perform registration . The experimental results demonstrate that our proposed adaptive strategy significantly reduced the target registration error from 5.32 to 2.59 mm in static phantoms validation as well as from at least 7.58mm to 4.71mm in dynamic phantom validation compared to current available methods . 


Summary: We proposed an adaptive mark free registration method for aligning electromagnetic trackers and pre operative images. An endoscope center calibration on the basis of a 2D printed model was presented. A multiple point strategy was proposed to address an assumption of moving endoscopes along the centerline of the intraluminal organ. The registration error was significantly reduced from 7.58 to 4.71mm under dynamic phantom validation.
With the aim of controlling drug resistant Plasmodium falciparum a computational attempt of designing novel adduct antimalarial drugs through the molecular docking method of combining chloroquine with five alkaloids individually is presented . These alkaloids were obtained from the medicinal plant Adhatoda vasica . From the obtained individual docking values of important derivatives of quinine and chloroquine as well as individual alkaloids and adduct agents of chloroquine with Adhatoda alkaloids as ligands it was discernible that the adduct agent 1 with chloroquine and adhatodine combination had the minimum energy of interaction as the docking score value of 11.144kcal mol against the target protein triosephosphate isomerase the key enzyme of glycolytic pathway . Drug resistance of P. falciparum is due to a mutation in the polypeptide of TIM . Moratorium of mutant TIM would disrupt the metabolism during the control of the drug resistant P. falciparum . This in silico work helped to locate the adduct agent 1 with chloroquine and adhatodine which could be taken up by pharmacology for further development of this compound as a new drug against drug resistant Plasmodium . 


Summary: Malaria is endemic in the majority of tropical countries and to control strains of Plasmodium falciparum resistant to chloroquine and quinine newer agents are needed. Today artemisinin based drugs are in use but artemisinin resistance in P. falciparum has been reported in several Asian countries. The molecular docking method of combining chloroquine with 5 alkaloids individually of the medicinal plant Adhatoda vasica is presented for the control drug resistant P. falciparum. This in silico work helped to locate the adduct agent 1with chloroquine and adhatodine for further use as a new drug against drug resistant Plasmodium.
Premature ventricular contraction is a common type of abnormal heartbeat . Without early diagnosis and proper treatment PVC may result in serious harms . Diagnosis of PVC is of great importance in goal directed treatment and preoperation prognosis . This paper proposes a novel diagnostic method for PVC based on Lyapunov exponents of electrocardiogram beats . The methodology consists of preprocessing feature extraction and classification integrated into the system . PVC beats can be classified and differentiated from other types of abnormal heartbeats by analyzing Lyapunov exponents and training a learning vector quantization neural network . Our algorithm can obtain a good diagnostic result with little features by using single lead ECG data . The sensitivity positive predictability and the overall accuracy of the automatic diagnosis of PVC is 90.26 92.31 and 98.90 respectively . The effectiveness of the new method is validated through extensive tests using data from MIT BIH database . The experimental results show that the proposed method is efficient and robust . 


Summary: Based on the non uniformity of RR interval the premature beats can be diagnosed. The features with Lyapunov exponents of ECG beat are extracted. LVQ neural network is used to identify PVC beats.
Setting The infection with Mycobacterium tuberculosis gives a delayed immune response measured by the tuberculine skin test . We present a new technique for evaluation based on automatic detection and measurement of skin temperature due to infrared emission . Design 34 subjects with suspected tuberculosis disease were examined with an IR thermal camera 48h after tuberculin skin injection . Results In 20 subjects IR analysis was positive for tuberculine test . Mean temperature of injection area was higher around 1 C for the positive group . Conclusion IR image analysis achieves similar estimation of tuberculin reaction as the visual evaluation based on higher temperature due to increased heat radiation from the skin lesion . 


Summary: First paper devoted to tuberculine reaction measurement based on thermal imaging. There is good agreement in area length of tuberculin reaction between visual and thermographic evaluation. Automatic generation of positive and negative results of the tuberculin skin test evaluated by infrared imaging.
Simulation based forecasting methods for a non Gaussian noncausal vector autoregressive model are proposed . In noncausal autoregressions the assumption of non Gaussianity is needed for reasons of identifiability . Unlike in conventional causal autoregressions the prediction problem in noncausal autoregressions is generally nonlinear implying that its analytical solution is unfeasible and therefore simulation or numerical methods are required in computing forecasts . It turns out that different special cases of the model call for different simulation procedures . Monte Carlo simulations demonstrate that gains in forecasting accuracy are achieved by using the correct noncausal VAR model instead of its conventional causal counterpart . In an empirical application a noncausal VAR model comprised of U.S. inflation and marginal cost turns out superior to the best fitting conventional causal VAR model in forecasting inflation . 


Summary: Forecasting methods for a noncausal VAR model are proposed. Due to the nonlinearity of the prediction problem simulation methods are required. Monte Carlo simulations are used to illustrate the methods. An empirical application to inflation forecasting is presented.
In this paper we propose a novel unsupervised algorithm for automatically segmenting a single 3D shape or co segmenting a family of 3D shapes using deep learning. The algorithm consists of three stages. In the first stage we pre decompose each 3D shape of interest into primitive patches to generate over segmentation and compute various signatures as low level shape features. In the second stage high level features are learned in an unsupervised style from the low level ones based on deep learning. Finally either segmentation or co segmentation results can be quickly reported by patch clustering in the high level feature space. The experimental results on the Princeton Segmentation Benchmark and the Shape COSEG Dataset exhibit superior segmentation performance of the proposed method over the previous state of the art approaches. 


Summary: We propose a novel segmentation and co segmentation approach for 3D shapes. We introduce deep learning into 3D shape segmentation and co segmentation. Our method is data driven but does not need a tedious labeling process. Our algorithm achieves better or comparable performance when compared with the state of the art methods.
Multiple statistics show that heart diseases are one of the main causes of mortality in our highly developed societies today . These diseases lead to a change of the physiology of the heart which gives useful information about characteristic and severity of the defect . A fast and reliable diagnosis is the base for successful therapy . As a first step towards recognition of such heart remodeling processes this work proposes a fully automatic processing pipeline for regional classification of the left ventricular wall in ultrasound images of small animals . The pipeline is based on state of the art methods from computer vision and pattern classification . The myocardial wall is segmented and its motion is estimated . A feature extraction using the segmented data is realized to automatically classify the image regions into normal and abnormal myocardial tissue . The performance of the proposed pipeline is evaluated and a comparison of common classification algorithms on ultrasound data of living mice before and after artificially induced myocardial infarction is given . It is shown that the results of this work reaching a maximum accuracy of 91.46 are an encouraging base for further investigation . 


Summary: A fully automatic processing pipeline is proposed for regional classification of the left ventricular wall in ultrasound images of small animals. The pipeline is implemented using state of the art methods from computer vision and pattern classification. Good performance of the processing pipeline is demonstrated on ultrasound data of living mice before and after artificially induced myocardial infarction.
This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes implemented in the semantic web for decision support tasks . Bayesian belief networks and fuzzy cognitive maps as dynamic influence graphs were applied to handle the task of medical knowledge formalization for decision support . In order to perform reasoning on these knowledge models a general purpose reasoning engine EYE with the necessary plug ins was developed in the semantic web . The two formal approaches constitute the proposed decision support system aiming to recognize the appropriate guidelines of a medical problem and to propose easily understandable course of actions to guide the practitioners . The urinary tract infection problem was selected as the proof of concept example to examine the proposed formalization techniques implemented in the semantic web . The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models . To assess the formal models performance 55 patient cases were extracted from a database and analyzed . The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web and gave a front end decision on antibiotics suggestion for UTI . 


Summary: Bayesian belief networks BBNs and fuzzy cognitive maps FCMs as dynamic influence graphs were applied to handle the task of medical knowledge formalization for decision support. Both approaches were implemented in semantic web framework. For reasoning on these knowledge models a general purpose reasoning engine EYE with the necessary plug ins was developed in the semantic web. To assess the formal models performance the UTI therapy problem was selected as a proof of concept example. The validation results showed that using probabilistic networks and fuzzy cognitive maps in semantic web framework is reliable and efficient for decision support tasks.
The histopathological examination of tissue specimens is necessary for the diagnosis and grading of colon cancer . However the process is subjective and leads to significant inter intra observer variation in diagnosis as it mainly relies on the visual assessment of histopathologists . Therefore a reliable computer aided technique which can automatically classify normal and malignant colon samples and determine grades of malignant samples is required . In this paper we propose a novel colon cancer diagnostic system which initially classifies colon biopsy images into normal and malignant classes and then automatically determines the grades of colon cancer for malignant images . To this end various novel structural descriptors which mathematically model and quantify the variation among the structure of normal colon tissues and malignant tissues of various cancer grades have been employed . Radial basis function kernel of support vector machines has been employed as classifier in order to classify grade colon samples based on these descriptors . The proposed system has been tested on 92 malignant and 82 normal colon biopsy images . The classification performance has been measured in terms of various performance measures and quite promising performance has been observed . Compared with previous techniques the proposed system has demonstrated better cancer detection and grading capability . Therefore the proposed CCD system can provide a reliable second opinion to the histopathologists . 


Summary: A novel colon cancer detection and grading system has been proposed. The system exploits structural variation among various colon tissue types. The system mathematically quantifies the variation in terms of some novel features. The shape of lumen is the most discriminating factor among different tissue types. RBF kernel of SVM shows promising results for classification of colon cancer data.
In this paper we devise a new algorithm for completing surface with missing geometry and topology founded upon the theory and techniques of sparse signal recovery . The key intuition is that any meaningful 3D shape represented as a discrete mesh almost certainly possesses a low dimensional intrinsic structure which can be expressed as a sparse representation in some transformed domains . Instead of estimating the missing geometry directly our novel method is to find this low dimensional representation which describes the entire original shape . More specifically we find that for many shapes the vertex coordinate function can be well approximated by a very sparse coefficient representation with respect to the dictionary comprising its Laplacian eigenfunctions and it is then possible to recover this sparse representation from partial measurements of the original shape . Taking advantage of the sparsity cue we advocate a novel variational approach for surface inpainting integrating data fidelity constraints on the shape domain with coefficient sparsity constraints on the transformed domain . Because of the powerful properties of Laplacian eigenbasis the inpainting results of our method tend to be smooth and globally coherent with the remaining shape . We demonstrate the performance of our new method via various examples in geometry restoration shape repair and hole filling . 


Summary: A surface inpainting framework based on representations in the transformed domain. Sparsity as a prior to estimate reconstruction coefficients. Laplacian eigenvectors are utilized for constructing reconstruction dictionary.
Background and objective The discrimination of Alzheimer s disease and its prodromal stage known as mild cognitive impairment from normal control is important for patients timely treatment . The simultaneous use of multi modality data has been demonstrated to be helpful for more accurate identification . The current study focused on extending a multi modality algorithm and evaluating the method by identifying AD MCI . Methods In this study sparse representation based classification a well developed method in pattern recognition and machine learning was extended to a multi modality classification framework named as weighted multi modality SRC . Data including three modalities of volumetric magnetic resonance imaging fluorodeoxyglucose positron emission tomography and florbetapir PET from the Alzheimer s disease Neuroimaging Initiative database were adopted for AD MCI classification . Results Adopting wmSRC the classification accuracy achieved 94.8 for AD vs. NC 74.5 for MCI vs. NC and 77.8 for progressive MCI vs. stable MCI superior to or comparable with the results of some other state of the art models in recent multi modality researches . Conclusions The wmSRC method is a promising tool for classification with multi modality data . It could be effective for identifying diseases from NC with neuroimaging data which could be helpful for the timely diagnosis and treatment of diseases . 


Summary: Multi modality classification on 113 AD 110 MCI patients and 117 normal controls. Originally single modality SRC was extended as a multi modality framework wmSRC . The wmSRC performed better than each single modality based SRC method. The wmSRC performed better or equally well compared to MKL RF and JRC.