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 Biomedical Engineering, IEEE Transactions on   [SJR: 1.201]   [H-I: 138]   [32 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 0018-9294    Published by IEEE  [191 journals]
• IEEE Engineering in Medicine and Biology Society
• Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• IEEE Transactions on Biomedical Engineering (T-BME)
• Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• IEEE Transactions on Biomedical Engineering Handling Editors
• Abstract: Provides a listing of current committee members and society officers.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Performance Assessment of a Custom, Portable, and Low-Cost
Brain–Computer Interface Platform
• Authors: Colin M. McCrimmon;Jonathan Lee Fu;Ming Wang;Lucas Silva Lopes;Po T. Wang;Alireza Karimi-Bidhendi;Charles Y. Liu;Payam Heydari;Zoran Nenadic;An Hong Do;
Pages: 2313 - 2320
Abstract: Objective: Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. Methods: The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states. Results: The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated (ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different. Conclusion: The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. Significance: Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• A Versatile Noise Performance Metric for Electrical Impedance Tomography
Algorithms
• Authors: Fabian Braun;Martin Proença;Josep Solà;Jean-Philippe Thiran;Andy Adler;
Pages: 2321 - 2330
Abstract: Electrical impedance tomography (EIT) is an emerging technology for real-time monitoring of patients under mechanical ventilation. EIT has the potential to offer continuous medical monitoring while being noninvasive, radiation free, and low cost. Due to their ill-posedness, image reconstruction typically uses regularization, which implies a hyperparameter controlling the tradeoff between noise rejection and resolution or other accuracies. In order to compare reconstruction algorithms, it is common to choose hyperparameter values such that the reconstructed images have equal noise performance (NP), i.e., the amount of measurement noise reflected in the images. For EIT many methods have been suggested, but none work well when the data originate from different measurement setups, such as for different electrode positions or measurement patterns. To address this issue, we propose a new NP metric based on the average signal-to-noise ratio in the image domain. The approach is validated for EIT using simulation experiments on a human thorax model and measurements on a resistor phantom. Results show that the approach is robust to the measurement configuration (i.e., number and position of electrodes, skip pattern) and the reconstruction algorithm used. We propose this novel approach as a way to select optimized measurement configurations and algorithms.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Design and Characterization of an Exoskeleton for Perturbing the Knee
During Gait
• Authors: Michael R. Tucker;Camila Shirota;Olivier Lambercy;James S. Sulzer;Roger Gassert;
Pages: 2331 - 2343
Abstract: Objective: An improved understanding of mechanical impedance modulation in human joints would provide insights about the neuromechanics underlying functional movements. Experimental estimation of impedance requires specialized tools with highly reproducible perturbation dynamics and reliable measurement capabilities. This paper presents the design and mechanical characterization of the ETH Knee Perturbator: an actuated exoskeleton for perturbing the knee during gait. Methods: A novel wearable perturbation device was developed based on specific experimental objectives. Bench-top tests validated the device's torque limiting capability and characterized the time delays of the on-board clutch. Further tests demonstrated the device's ability to perform system identification on passive loads with static initial conditions. Finally, the ability of the device to consistently perturb human gait was evaluated through a pilot study on three unimpaired subjects. Results: The ETH Knee Perturbator is capable of identifying mass-spring systems within 15% accuracy, accounting for over 95% of the variance in the observed torque in 10 out of 16 cases. Five-degree extension and flexion perturbations were executed on human subjects with an onset timing precision of 2.52% of swing phase duration and a rise time of 36.5 ms. Conclusion: The ETH Knee Perturbator can deliver safe, precisely timed, and controlled perturbations, which is a prerequisite for the estimation of knee joint impedance during gait. Significance: Tools such as this can enhance models of neuromuscular control, which may improve rehabilitative outcomes following impairments affecting gait and advance the design and control of assistive devices.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Automated Classification of Breast Cancer Stroma Maturity From
Histological Images
• Authors: Sara Reis;Patrycja Gazinska;John H. Hipwell;Thomy Mertzanidou;Kalnisha Naidoo;Norman Williams;Sarah Pinder;David J. Hawkes;
Pages: 2344 - 2352
Abstract: Objective: The tumor microenvironment plays a crucial role in regulating tumor progression by a number of different mechanisms, in particular, the remodeling of collagen fibers in tumor-associated stroma, which has been reported to be related to patient survival. The underlying motivation of this work is that remodeling of collagen fibers gives rise to observable patterns in hematoxylin and eosin (H&E) stained slides from clinical cases of invasive breast carcinoma that the pathologist can label as mature or immature stroma. The aim of this paper is to categorise and automatically classify stromal regions according to their maturity and show that this classification agrees with that of skilled observers, hence providing a repeatable and quantitative measure for prognostic studies. Methods: We use multiscale basic image features and local binary patterns, in combination with a random decision trees classifier for classification of breast cancer stroma regions-of-interest (ROI). Results: We present results from a cohort of 55 patients with analysis of 169 ROI. Our multiscale approach achieved a classification accuracy of 84%. Conclusion: This work demonstrates the ability of texture-based image analysis to differentiate breast cancer stroma maturity in clinically acquired H&E-stained slides at least as well as skilled observers.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Wearable Vector Electrical Bioimpedance System to Assess Knee Joint Health
• Authors: Sinan Hersek;Hakan Töreyin;Caitlin N. Teague;Mindy L. Millard-Stafford;Hyeon-Ki Jeong;Miheer M. Bavare;Paul Wolkoff;Michael N. Sawka;Omer T. Inan;
Pages: 2353 - 2360
Abstract: Objective: We designed and validated a portable electrical bioimpedance (EBI) system to quantify knee joint health. Methods: Five separate experiments were performed to demonstrate the: 1) ability of the EBI system to assess knee injury and recovery; 2) interday variability of knee EBI measurements; 3) sensitivity of the system to small changes in interstitial fluid volume; 4) reducing the error of EBI measurements using acceleration signals; and 5) use of the system with dry electrodes integrated to a wearable knee wrap. Results: 1) The absolute difference in resistance ( R) and reactance (X) from the left to the right knee was able to distinguish injured and healthy knees (p
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• A Hidden Markov Model for Seismocardiography
• Authors: Johan Wahlström;Isaac Skog;Peter Händel;Farzad Khosrow-khavar;Kouhyar Tavakolian;Phyllis K. Stein;Arye Nehorai;
Pages: 2361 - 2372
Abstract: We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Detecting Clinically Meaningful Shape Clusters in Medical Image Data:
Metrics Analysis for Hierarchical Clustering Applied to Healthy and
Pathological Aortic Arches
• Authors: Jan L. Bruse;Maria A. Zuluaga;Abbas Khushnood;Kristin McLeod;Hopewell N. Ntsinjana;Tain-Yen Hsia;Maxime Sermesant;Xavier Pennec;Andrew M. Taylor;Silvia Schievano;
Pages: 2373 - 2383
Abstract: Objective: Today's growing medical image databases call for novel processing tools to structure the bulk of data and extract clinically relevant information. Unsupervised hierarchical clustering may reveal clusters within anatomical shape data of patient populations as required for modern precision medicine strategies. Few studies have applied hierarchical clustering techniques to three-dimensional patient shape data and results depend heavily on the chosen clustering distance metrics and linkage functions. In this study, we sought to assess clustering classification performance of various distance/linkage combinations and of different types of input data to obtain clinically meaningful shape clusters. Methods: We present a processing pipeline combining automatic segmentation, statistical shape modeling, and agglomerative hierarchical clustering to automatically subdivide a set of 60 aortic arch anatomical models into healthy controls, two groups affected by congenital heart disease, and their respective subgroups as defined by clinical diagnosis. Results were compared with traditional morphometrics and principal component analysis of shape features. Results: Our pipeline achieved automatic division of input shape data according to primary clinical diagnosis with high F-score (0.902 ± 0.042) and Matthews correlation coefficient (0.851 ± 0.064) using the correlation/weighted distance/linkage combination. Meaningful subgroups within the three patient groups were obtained and benchmark scores for automatic segmentation and classification performance are reported. Conclusion: Clustering results vary depending on the distance/linkage combination used to divide the data. Yet, clinically relevant shape clusters and subgroups could be found with high specificity and low misclassification rates. Significance: Detecting disease-specific clusters within medical image data could improve image-based risk assessment, treatment planning, and-medical device development in complex disease.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Real-Time Assessment of Mechanical Tissue Trauma in Surgery
• Authors: James H. Chandler;Faisal Mushtaq;Benjamin Moxley-Wyles;Nicholas P. West;Gregory W. Taylor;Peter R. Culmer;
Pages: 2384 - 2393
Abstract: Objective: This work presents a method to assess and prevent tissue trauma in real-time during surgery. Background: Tissue trauma occurs routinely during laparoscopic surgery with potentially severe consequences. As such, it is crucial that a surgeon is able to regulate the pressure exerted by surgical instruments. We propose a novel method to assess the onset of tissue trauma by considering the mechanical response of tissue as it is loaded in real-time. Methods: We conducted a parametric study using a lab-based grasping model and differing load conditions. Mechanical stress-time data were analyzed to characterize the tissue response to grasps. Qualitative and quantitative histological analyses were performed to inspect damage characteristics of the tissue under different load conditions. These were correlated against the mechanical measures to identify the nature of trauma onset with respect to our predictive metric. Results: Results showed increasing tissue trauma with load and a strong correlation with the mechanical response of the tissue. Load rate and load history also showed a clear effect on tissue response. The proposed method for trauma assessment was effective in identifying damage. The metric can be normalized with respect to loading rate and history, making it feasible in the unconstrained environment of intraoperative surgery. Significance: This work demonstrates that tissue trauma can be predicted using mechanical measures in real-time. Applying this technique to laparoscopic tools has the potential to reduce unnecessary tissue trauma and its associated complications by indicating through user feedback or actively regulating the mechanical impact of surgical instruments.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Smart Gait-Aid Glasses for Parkinson's Disease Patients
• Authors: DaeHan Ahn;Hyerim Chung;Ho-Won Lee;Kyunghun Kang;Pan-Woo Ko;Nam Sung Kim;Taejoon Park;
Pages: 2394 - 2402
Abstract: Parkinson's disease (PD) is a chronic progressive disease caused by loss of dopaminergic neurons in the substantia nigra, degenerating the nervous system of a patient over time. Freezing of gait (FOG), which is a form of akinesia, is a symptom of PD. Meanwhile, recent studies show that the gait of PD patients experiencing FOG can be significantly improved by providing the regular visual or auditory patterns for the patients. In this paper, we propose a gait-aid system built upon smart glasses. Our system continuously monitors the gait and so on of a PD patient to detect FOG, and upon detection of FOG it projects visual patterns on the glasses as if the patterns were actually on the floor. Conducting experiments involving ten PD patients, we demonstrate that our system achieves the accuracy of 92.86% in detecting FOG episodes and that it improves the gait speed and stride length of PD patients by 15.3 ~ 37.2% and 18.7 ~ 31.7%, respectively.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Efficient OCT Volume Reconstruction From Slitlamp Microscopes
• Authors: Stefanos Apostolopoulos;Raphael Sznitman;
Pages: 2403 - 2410
Abstract: Since its introduction 25 years ago, Optical Coherence Tomography (OCT) has contributed tremendously to diagnostic and monitoring capabilities of pathologies in the field of ophthalmology. Despite rapid progress in hardware and software technology however, the price of OCT devices has remained high, limiting their use in private practice, and in screening examinations. In this paper, we present a slitlamp-integrated OCT device, built with off-the-shelf components, which can generate high-quality volumetric images of the posterior eye segment. To do so, we present a novel strategy for 3D image reconstruction in this challenging domain that allows us for state-of-the-art OCT volumes to be generated at fast speeds. The result is an OCT device that can match current systems in clinical practice, at a significantly lower cost.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• ECG-Based Classification of Resuscitation Cardiac Rhythms for
Retrospective Data Analysis
• Authors: Ali Bahrami Rad;Trygve Eftestøl;Kjersti Engan;Unai Irusta;Jan Terje Kvaløy;Jo Kramer-Johansen;Lars Wik;Aggelos K. Katsaggelos;
Pages: 2411 - 2418
Abstract: Objective: There is a need to monitor the heart rhythm in resuscitation to improve treatment quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse-generating rhythm (PR). Manual annotation of rhythms is time-consuming and infeasible for large datasets. Our objective was to develop ECG-based algorithms for the retrospective and automatic classification of resuscitation cardiac rhythms. Methods: The dataset consisted of 1631 3-s ECG segments with clinical rhythm annotations, obtained from 298 out-of-hospital cardiac arrest patients. In total, 47 wavelet- and time-domain-based features were computed from the ECG. Features were selected using a wrapper-based feature selection architecture. Classifiers based on Bayesian decision theory, k-nearest neighbor, k-local hyperplane distance nearest neighbor, artificial neural network (ANN), and ensemble of decision trees were studied. Results: The best results were obtained for ANN classifier with Bayesian regularization backpropagation training algorithm with 14 features, which forms the proposed algorithm. The overall accuracy for the proposed algorithm was 78.5%. The sensitivities (and positive-predictive-values) for AS, PEA, PR, VF, and VT were 88.7% (91.0%), 68.9% (70.4%), 65.9% (69.0%), 86.2% (83.8%), and 78.8% (72.9%), respectively. Conclusions: The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low. Significance: We have made an important step toward making classification of resuscitation rhythms more efficient in the sense of minimal feedback from human experts.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive
Oscillators
• Authors: Enhao Zheng;Silvia Manca;Tingfang Yan;Andrea Parri;Nicola Vitiello;Qining Wang;
Pages: 2419 - 2430
Abstract: This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact capacitive sensing and adaptive oscillators (AOs). The capacitive sensing system is designed with two sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-mean-square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on capacitive sensing and AOs is a promising alternative for the control of exoskeleton/orthosis.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• A MEMS Condenser Microphone-Based Intracochlear Acoustic Receiver
• Authors: Flurin Pfiffner;Lukas Prochazka;Dominik Péus;Ivo Dobrev;Adrian Dalbert;Jae Hoon Sim;Rahel Kesterke;Joris Walraevens;Francesca Harris;Christof Röösli;Dominik Obrist;Alexander Huber;
Pages: 2431 - 2438
Abstract: Goal: Intracochlear sound pressure (ICSP) measurements are limited by the small dimensions of the human inner ear and the requirements imposed by the liquid medium. A robust intracochlear acoustic receiver (ICAR) for repeated use with a simple data acquisition system that provides the required high sensitivity and small dimensions does not yet exist. The work described in this report aims to fill this gap and presents a new microelectromechanical systems (MEMS) condenser microphone (CMIC)-based ICAR concept suitable for ICSP measurements in human temporal bones. Methods: The ICAR head consisted of a passive protective diaphragm (PD) sealing the MEMS CMIC against the liquid medium, enabling insertion into the inner ear. The components of the MEMS CMIC-based ICAR were expressed by a lumped element model (LEM) and compared to the performance of successfully fabricated ICARs. Results: Good agreement was achieved between the LEM and the measurements with different sizes of the PD. The ICSP measurements in a human cadaver temporal bone yielded data in agreement with the literature. Conclusion: Our results confirm that the presented MEMS CMIC-based ICAR is a promising technology for measuring ICSP in human temporal bones in the audible frequency range. Significance: A sensor for evaluation of the biomechanical hearing process by quantification of ICSP is presented. The concept has potential as an acoustic receiver in totally implantable cochlear implants.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Position and Orientation Insensitive Wireless Power Transmission for
EnerCage-Homecage System
• Authors: Yaoyao Jia;S. Abdollah Mirbozorgi;Zheyuan Wang;Chia-Chun Hsu;Teresa E. Madsen;Donald Rainnie;Maysam Ghovanloo;
Pages: 2439 - 2449
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Single-Shot ${\text{T}}_{{2}}$ Mapping Through OverLapping-Echo Detachment
(OLED) Planar Imaging
• Authors: Congbo Cai;Yiqing Zeng;Yuchuan Zhuang;Shuhui Cai;Lin Chen;Xinghao Ding;Lijun Bao;Jianhui Zhong;Zhong Chen;
Pages: 2450 - 2461
Abstract: Objective: Develop a reliable single-shot T2 mapping method with extra robustness to motion and the potential for real-time dynamic and quantitative MR imaging. Methods: A single-shot T2 mapping sequence was proposed based on spin-echo planar imaging acquisition scheme. Two overlapped echo signals with different T2 weighting were obtained simultaneously by using two small flip-angle excitation pulses and corresponding echo-shifting gradients. A detachment algorithm based on structure similarity constraint was proposed to separate the two echo signals. T2 mapping was obtained from the two separated echo signals. Results: The robustness and efficiency of the method were demonstrated through simulation, phantom experiments, and human brain measurements. Conclusion: Reliable T2 mapping can be obtained within milliseconds even under continuous head motion. Significance: Reliable T2 mapping was achieved with a single shot for the first time. The proposed method will facilitate real-time dynamic and quantitative MR imaging.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Robust Estimation of Sparse Narrowband Spectra from Neuronal Spiking Data
• Authors: Sina Miran;Patrick L. Purdon;Emery N. Brown;Behtash Babadi;
Pages: 2462 - 2474
Abstract: Objective: Characterizing the spectral properties of neuronal responses is an important problem in computational neuroscience, as it provides insight into the spectral organization of the underlying functional neural processes. Although spectral analysis techniques are widely used in the analysis of noninvasive neural recordings such as EEG, their application to spiking data is limited due to the binary and nonlinear nature of neuronal spiking. In this paper, we address the problem of estimating the power spectral density of the neural covariate driving the spiking statistics of a neuronal population from binary observations. Methods: We consider a neuronal ensemble spiking according to Bernoulli statistics, for which the conditional intensity function is given by the logistic map of a harmonic second-order stationary process with sparse narrowband spectra. By employing sparsity-promoting priors, we compute the maximum a posteriori estimate of the power spectral density of the process from the binary spiking observations. Furthermore, we construct confidence intervals for these estimates by an efficient posterior sampling procedure. Results: We provide simulation studies which reveal that our method outperforms the existing methods for extracting the frequency content of spiking data. Application of our method to clinically recorded spiking data from a patient under general anesthesia reveals a striking resemblance between our estimated power spectral density and that of the local field potential signal. This result corroborates existing findings regarding the salient role of the local field potential as a major neural covariate of rhythmic cortical spiking activity under anesthesia. Conclusion: Our technique allows us to analyze the harmonic structure of spiking activity in a robust fashion, independently of the local field potentials, and without any prior assumption of the spectral spread and content of the underlying neural processes. Significance: Other than i-s usage in the spectral analysis of neuronal spiking data, our technique can be applied to a wide variety of binary data, such as heart beat data, in order to obtain a robust spectral representation.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• Automatic Nuclear Segmentation Using Multiscale Radial Line Scanning With
Dynamic Programming
• Authors: Hongming Xu;Cheng Lu;Richard Berendt;Naresh Jha;Mrinal Mandal;
Pages: 2475 - 2485
Abstract: In the diagnosis of various cancers by analyzing histological images, automatic nuclear segmentation is an important step. However, nuclear segmentation is a difficult problem because of overlapping nuclei, inhomogeneous staining, and presence of noisy pixels and other tissue components. In this paper, we present an automatic technique for nuclear segmentation in skin histological images. The proposed technique first applies a bank of generalized Laplacian of Gaussian kernels to detect nuclear seeds. Based on the detected nuclear seeds, a multiscale radial line scanning method combined with dynamic programming is applied to extract a set of candidate nuclear boundaries. The gradient, intensity, and shape information are then integrated to determine the optimal boundary for each nucleus in the image. Nuclear overlap limitation is finally imposed based on a Dice coefficient measure such that the obtained nuclear contours do not severely intersect with each other. Experiments have been thoroughly performed on two datasets with H&E and Ki-67 stained images, which show that the proposed technique is superior to conventional schemes of nuclear segmentation.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• A Subspace Approach to Spectral Quantification for MR Spectroscopic
Imaging
• Authors: Yudu Li;Fan Lam;Bryan Clifford;Zhi-Pei Liang;
Pages: 2486 - 2489
Abstract: Objective: To provide a new approach to spectral quantification for magnetic resonance spectroscopic imaging (MRSI), incorporating both spatial and spectral priors. Methods: A novel signal model is proposed, which represents the spectral distributions of each molecule as a subspace and the entire spectrum as a union of subspaces. Based on this model, the spectral quantification can be solved in two steps: 1) subspace estimation based on the empirical distributions of the spectral parameters estimated using spectral priors; and 2) parameter estimation for the union-of-subspaces model incorporating spatial priors. Results: The proposed method has been evaluated using both simulated and experimental data, producing impressive results. Conclusion: The proposed union-of-subspaces representation of spatiospectral functions provides an effective computational framework for solving the MRSI spectral quantification problem with spatiospectral constraints. Significance: The proposed approach transforms how the MRSI spectral quantification problem is solved and enables efficient and effective use of spatiospectral priors to improve parameter estimation. The resulting algorithm is expected to be useful for a wide range of quantitative metabolic imaging studies using MRSI.
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• BHI-2018 IEEE International Conference on Biomedical and Health
Informatics
• Pages: 2490 - 2490
Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

• BSN 2018 Body Sensor Networks Conference
• Pages: 2491 - 2491
Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers
PubDate: Oct. 2017
Issue No: Vol. 64, No. 10 (2017)

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