Showing papers in "Computers in Biology and Medicine in 2016"

TL;DR: Sepsis can be predicted at least three hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today.

TL;DR: The experimental results prove the proposed DICOM cryptosystem has achieved a desirable amount of protection for real time medical image security applications.

TL;DR: It is found that the newly investigated features are more robust than existing features and show better recognition accuracy even in low signal-to-noise ratios (SNRs).

TL;DR: Endoscope analysis on peristaltic blood flow of Sisko fluid having Titanium magneto-nanoparticles through a uniform tube has been analyzed and many interesting results are depicted that provide further study on different blood flow problems.

TL;DR: Detailed two-phase flow modeling of airflow, transport and deposition of micro-particles in a realistic tracheobronchial airway geometry based on CT scan images under various breathing conditions suggested that inertial impaction is the dominant deposition mechanism in tracheo-breathing airways.

TL;DR: SAAE is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the EEG-based emotion recognition field and shows the effectiveness of the proposed method relative to state-of-the-art methods.

TL;DR: From the results, it is observed that the proposed methodology provides comparable accuracy with the methods existing in the literature at reduced computational cost due to the lesser number of features selected for the classification.

TL;DR: A new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification is proposed.

TL;DR: This paper introduces a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events and shows the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features.

TL;DR: This work is proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method and develops a liver disease index (LDI), which can significantly help the radiologists to discriminateFLD and Cirrhosis in their routine liver screening.

TL;DR: The dynamics of unsteady flow in the human large airways during a rapid inhalation were investigated using highly detailed large-scale computational fluid dynamics on a subject-specific geometry, indicating the potential of large- scale simulations to further understanding of airway physiological mechanics, which is essential to guide clinical diagnosis.

TL;DR: The aim of this study is to compare the five leading keypoint descriptors on BAA, and, in doing so, presenting a generic approach for a specific task.

TL;DR: Pain Buddy appears to be a promising tool to improve pain and symptom management in children undergoing cancer treatment and is recommended for a randomized controlled trial to assess the efficacy of this innovative treatment.

TL;DR: In silico analysis showed that (GGGGS)3 linker confers the best structure and stability for the target fusion protein, which can potentially act as an oral vaccine candidate against malaria.

TL;DR: A wheezing recognition algorithm from recorded respiratory sounds with a Smartphone placed near the mouth valuable in contact-free sound recording, thus valuable in the pediatric population.

TL;DR: This paper proposes a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel and proposes a spatial adjacent histogram strategy to encode the micro-structures for image representation.

TL;DR: The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise to show better performance on the denoising and the QRS detection.

TL;DR: A patient non-specific strategy for seizure detection based on Stationary Wavelet Transform of EEG signals is developed and a new set of features is proposed based on an average process.

TL;DR: Comparisons between the DFFS-based approach and state-of-art methods on BCI competition IV data set 2b have been conducted, which have shown the superiority of the proposed algorithm.

TL;DR: The results indicate the usefulness of the proposed texture features for distinguishing between benign and malignant lesions and the superiority of the radial patterns compared with the conventional rotation invariant patterns.

TL;DR: Wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images using supervised classifiers are reviewed.

TL;DR: A novel Retinal Risk Index (RRI) is developed using two significant features to distinguish two classes using single number to reduce eye screening time in polyclinics or community-based mass screening.

TL;DR: This study presents a comprehensive review of both clinical and automated image analysis based approaches to quantify liver fat and evaluate fatty liver diseases from different medical imaging modalities.

TL;DR: Novel features to detect micro-patterns of echocardiography images in order to determine the severity of Mitral Regurgitation (MR) are introduced.

TL;DR: This model describes the whole image as an undirected probabilistic graphical model and was developed using an automatic label-map mechanism for determining nuclear, cytoplasmic and background regions and the proposed gap-search algorithm is much more faster than pixel-based and superpixel-based algorithms.

TL;DR: The unsupervised approach proposed is promising and well suited for a semi-automatic labelling of the extracted relations, and has been tested on a fairly large data set of clinical records in Italian.

TL;DR: An algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies is proposed, which will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset, with minimal operator involvement, and at low cost.

TL;DR: A comprehensive approach to better understand nasal spray mechanism and evaluate its performance for existing nasal delivery practices is elaborates and can assist the pharmaceutical industry to improve the current design of nasal drug delivery device and ultimately benefit more patients through optimized medications delivery.

TL;DR: Clinical data and numerical simulations are presented to demonstrate that reflected pressure waves could participate as one of the causes of the dicrotic notch and to explore the mechanisms behind this phenomenon by using a numerical model based on integrated axisymmetric Navier-Stokes equations to compute the hemodynamic flow.

TL;DR: The proposed classification methods based on shape, texture, and statistical features have provided high accuracy and may assist in the diagnosis of VCFs.