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State of the art paper

Ratna Saha, Mariusz Bajger, Gobert Lee
Accurate detection and segmentation of cell nucleus is the precursor step towards computer aided analysis of Pap smear images. This is a challenging and complex task due to degree of overlap, inconsistent staining and poor contrast. In this paper, a novel nucleus segmentation method is proposed by incorporating a circular shape function in fuzzy clustering. The proposed method was evaluated quantitatively and qualitatively using the Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014 challenge dataset comprised of 945 overlapping Pap smear images...
April 14, 2017: Computers in Biology and Medicine
John A Onofrey, Lawrence H Staib, Saradwata Sarkar, Rajesh Venkataraman, Cayce B Nawaf, Preston C Sprenkle, Xenophon Papademetris
Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland...
April 12, 2017: Medical Image Analysis
Steven Kinio, James Mills
Thrombogenesis (blood clot formation) is a major barrier to the development of biomedical devices that interface with blood. Although state-of-the-art chemically and pharmacologically mediated clot mitigation strategies are effective, some limitations of such approaches include depletion of active agents, or adverse reactions in patients. Increased clotting protein adsorption and platelet adhesion, which occurs when artificial surfaces are exposed to blood, results in enhanced clot formation on artificial surfaces...
April 21, 2017: Electrophoresis
Jathin Bandari, Charles B Wessel, Bruce L Jacobs
PURPOSE OF REVIEW: Comparative effectiveness research plays a vital role in healthcare delivery by guiding evidence-based practices. We performed a state-of-the-art review of comparative effectiveness research in the urology literature for 2016, utilizing a systematic approach. Seven high-impact papers are reviewed in detail. RECENT FINDINGS: Across the breadth of urology, there were several important studies in comparative effectiveness research, of which we will highlight two randomized controlled trials and five observational trials: radiotherapy, prostatectomy, and active monitoring have equivalent mortality outcomes in patients with localized prostate cancer; the ideal modality of patient education is yet to be determined, and written education has minimal effect on patient perception of prostate specific antigen screening; robotic prostatectomy is associated with higher perioperative complication rates on a population basis; racial disparities exist in incontinence rates after treatment for localized prostate cancer, but not in irritative, bowel, or sexual function; androgen deprivation therapy is associated with higher fracture, peripheral artery disease, and cardiac-related complications than bilateral orchiectomy; robotic and open cystectomy offer comparable cancer-specific mortality and perioperative outcomes; and bonuses for low-cost hospitals can inadvertently reward low-quality hospitals...
April 19, 2017: Current Opinion in Urology
Doaa Alantary, Samuel Yalkowsky
The General Solubility Equation (GSE) is the state of the art method for estimating the aqueous solubilities of organic compounds. It is an extremely simple equation that expresses aqueous solubility as a function of only two inputs: the octanol-water partition coefficient calculated by readily available softwares like clogp and acdlogp, and the commonly known melting point of the solute. Recently, Bahadori, et al. (2016) proposed that their genetic algorithm support vector machine is a "better" predictor...
April 20, 2017: Pharmaceutical Development and Technology
Hongwei Hu, Bo Ma, Jianbing Shen, Ling Shao
In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions...
April 12, 2017: IEEE Transactions on Neural Networks and Learning Systems
Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Yi Da Xu, Xiangfeng Luo
Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP)...
April 12, 2017: IEEE Transactions on Neural Networks and Learning Systems
Yao Sui, Guanghui Wang, Li Zhang
This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering...
April 13, 2017: IEEE Transactions on Cybernetics
Kim Schouten, Onne van der Weijde, Flavius Frasincar, Rommert Dekker
Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods...
April 14, 2017: IEEE Transactions on Cybernetics
Xin Luo, MengChu Zhou, Shuai Li, YunNi Xia, Zhu-Hong You, QingSheng Zhu, Hareton Leung
Generating highly accurate predictions for missing quality-of-service (QoS) data is an important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in dealing with it. However, they are based on first-order solvers that cannot well address their target problem that is inherently bilinear and nonconvex, thereby leaving a significant opportunity for accuracy improvement. This paper proposes to incorporate an efficient second-order solver into them to raise their accuracy. To do so, we adopt the principle of Hessian-free optimization and successfully avoid the direct manipulation of a Hessian matrix, by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector...
April 14, 2017: IEEE Transactions on Cybernetics
Yu Zhou, Sam Kwong, Hainan Guo, Xiao Zhang, Qingfu Zhang
Sparse signal reconstruction can be regarded as a problem of locating the nonzero entries of the signal. In presence of measurement noise, conventional methods such as l₁ norm relaxation methods and greedy algorithms, have shown their weakness in finding the nonzero entries accurately. In order to reduce the impact of noise and better locate the nonzero entries, in this paper, we propose a two-phase algorithm which works in a coarse-to-fine manner. In phase 1, a decomposition-based multiobjective evolutionary algorithm is applied to generate a group of robust solutions by optimizing l₁ norm of the solutions...
April 14, 2017: IEEE Transactions on Cybernetics
Pietro Arico, Gianluca Borghini, Gianluca Di Flumeri, Stefano Bonelli, Alessia Golfetti, Ilenia Graziani, Simone Pozzi, Jean-Paul Imbert, Geraud Granger, Railane Benhacene, Dirk Schaefer, Fabio Babiloni
This article provides the reader a focused and organised review of the research progresses on neurophysiological indicators, also called "neurometrics", to show how neurometrics could effectively address some of the most important Human Factors (HFs) needs in the Air Traffic Management (ATM) field. The state of the art on the most involved HFs and related cognitive processes (e.g. mental workload, cognitive training) is presented together with examples of possible applications in the current and future ATM scenarios, in order to better understand and highlight the available opportunities of such neuroscientific applications...
April 12, 2017: IEEE Reviews in Biomedical Engineering
Xiwen Yao, Junwei Han, Dingwen Zhang, Feiping Nie
With the goal of discovering the common and salient objects from the given image group, co-saliency detection has received tremendous research interest in recent years. However, as most of the existing co-saliency detection methods are performed based on the assumption that all the images in the given image group should contain co-salient objects in only one category, they can hardly be applied in practice, particularly for the large-scale image set obtained from the internet. To address this problem, this paper revisits the co-saliency detection task and advances its development into a new phase, where the problem setting is generalized to allow the image group to contain objects in arbitrary number of categories and the algorithms need to simultaneously detect multi-class co-salient objects from such complex data...
April 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Rashid Mehmood, Saeed El-Ashram, Rongfang Bie, Hussain Dawood, Anton Kos
Clustering is an unsupervised approach to classify elements based on their similarity, and it is used to find the intrinsic patterns of data. There are enormous applications of clustering in bioinformatics, pattern recognition, and astronomy. This paper presents a clustering approach based on the idea that density wise single or multiple connected regions make a cluster, in which density maxima point represents the center of the corresponding density region. More precisely, our approach firstly finds the local density regions and subsequently merges the density connected regions to form the meaningful clusters...
April 19, 2017: Scientific Reports
Wei Zhou, Chengdong Wu, Dali Chen, Zhenzhu Wang, Yugen Yi, Wenyou Du
Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs...
2017: Computational and Mathematical Methods in Medicine
Sang-Il Oh, Hang-Bong Kang
Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame...
April 18, 2017: Sensors
Cristhian A Aguilera, Angel D Sappa, Cristhian Aguilera, Ricardo Toledo
This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum...
April 15, 2017: Sensors
Simone Benatti, Bojan Milosevic, Elisabetta Farella, Emanuele Gruppioni, Luca Benini
Poliarticulated prosthetic hands represent a powerful tool to restore functionality and improve quality of life for upper limb amputees. Such devices offer, on the same wearable node, sensing and actuation capabilities, which are not equally supported by natural interaction and control strategies. The control in state-of-the-art solutions is still performed mainly through complex encoding of gestures in bursts of contractions of the residual forearm muscles, resulting in a non-intuitive Human-Machine Interface (HMI)...
April 15, 2017: Sensors
Michael Fenton, David Lynch, Stepan Kucera, Holger Claussen, Michael O'Neill
Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs...
April 6, 2017: IEEE Transactions on Cybernetics
Yue Huang, Han Zheng, Chi Liu, Xinghao Ding, Gustavo Rohde
Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficient volumes of data for each pathology study in a professional manner, which results in limitations in real-world applications...
April 6, 2017: IEEE Journal of Biomedical and Health Informatics
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