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Biomedical Engineering, IEEE Transactions on

Issue 6 • Date June 2010

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  • Table of contents

    Publication Year: 2010 , Page(s): C1
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  • IEEE Transactions on Biomedical Engineering publication information

    Publication Year: 2010 , Page(s): C2
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  • Table of contents

    Publication Year: 2010 , Page(s): 1253 - 1254
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  • Mathematical Modeling of CSF Pulsatile Hydrodynamics Based on Fluid–Solid Interaction

    Publication Year: 2010 , Page(s): 1255 - 1263
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (530 KB) |  | HTML iconHTML  

    Intracranial pressure (ICP) is derived from cerebral blood pressure and cerebrospinal fluid (CSF) circulatory dynamics and can be affected in the course of many diseases. Computer analysis of the ICP time pattern plays a crucial role in the diagnosis and treatment of those diseases. This study proposes the application of Linninger et al.'s [IEEE Trans. Biomed. Eng. , vol. 52, no. 4, pp. 557-565, Apr. 2005] fluid-solid interaction model of CSF hydrodynamic in ventricular system based on our clinical data from a group of patients with brain parenchyma tumor. The clinical experiments include the arterial blood pressure (ABP), venous blood pressure, and ICP in the subarachnoid space (SAS). These data were used as inputs to the model that predicts the intracranial dynamic phenomena. In addition, the model has been modified by considering CSF pulsatile production rate as the major factor of CSF motion. The approximations of ventricle enlargement, CSF pressure distribution in the ventricular system and CSF velocity magnitude in the aqueduct and foramina were obtained in this study. The observation of reversal flow in the CSF flow pattern due to brain tissue compression is another finding in our investigation. Based on the experimental results, no existence of large transmural pressure differences were found in the brain system. The measured pressure drop in the ventricular system was less than 5 Pa. Moreover, the CSF flow pattern, ICP distribution, and velocity magnitude were in good agreement with the published models and CINE (phase-contrast magnetic resonance imaging) experiments, respectively. View full abstract»

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  • A Self-Propelled Inflatable Earthworm-Like Endoscope Actuated by Single Supply Line

    Publication Year: 2010 , Page(s): 1264 - 1272
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (700 KB) |  | HTML iconHTML  

    Design of a self-propelling endoscope has been of interest for decades, as it allows for simplified medical examination techniques and improved patient comfort, together with advanced analysis capacity. In this paper, we describe the development of a fully automatic, multiple-balloon system achieving peristaltic locomotion, controlled by a single supply channel. The system employs the nonlinear pressure-radius characteristics of elastic balloons to simultaneously control numerous balloons with a constant inlet pressure. The balloons are connected in series and the flow is controlled by small orifices, which delay the flow between them. The proposed multiple-balloon system requires no moving parts, no electronics, and relies on dynamics of the fluid flow between serially interconnected inflatable balloons. The entire system is made of disposable silicone and is plastic-modeled by injection molding. Additionally, the cost of such a system is expected to be low and suitable for numerous biomedical applications as it can be easily scaled down due to the need for only one supply line. Mathematical modeling, and simulation and experimental results of a system prototype are presented in this paper. Experimental results in the straight cylinder show close correlation to simulated system. View full abstract»

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  • Large-Scale Propagation of Ultrasound in a 3-D Breast Model Based on High-Resolution MRI Data

    Publication Year: 2010 , Page(s): 1273 - 1284
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1773 KB) |  | HTML iconHTML  

    A 40 × 35 × 25-mm3 specimen of human breast consisting mostly of fat and connective tissue was imaged using a 3-T magnetic resonance scanner. The resolutions in the image plane and in the orthogonal direction were 130 μm and 150 μm, respectively. Initial processing to prepare the data for segmentation consisted of contrast inversion, interpolation, and noise reduction. Noise reduction used a multilevel bidirectional median filter to preserve edges. The volume of data was segmented into regions of fat and connective tissue by using a combination of local and global thresholding. Local thresholding was performed to preserve fine detail, while global thresholding was performed to minimize the interclass variance between voxels classified as background and voxels classified as object. After smoothing the data to avoid aliasing artifacts, the segmented data volume was visualized using isosurfaces. The isosurfaces were enhanced using transparency, lighting, shading, reflectance, and animation. Computations of pulse propagation through the model illustrate its utility for the study of ultrasound aberration. The results show the feasibility of using the described combination of methods to demonstrate tissue morphology in a form that provides insight about the way ultrasound beams are aberrated in three dimensions by tissue. View full abstract»

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  • A Fast and Efficient Method to Compensate for Brain Shift for Tumor Resection Therapies Measured Between Preoperative and Postoperative Tomograms

    Publication Year: 2010 , Page(s): 1285 - 1296
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (671 KB) |  | HTML iconHTML  

    In this paper, an efficient paradigm is presented to correct for brain shift during tumor resection therapies. For this study, high resolution preoperative (pre-op) and postoperative (post-op) MR images were acquired for eight in vivo patients, and surface/subsurface shift was identified by manual identification of homologous points between the pre-op and immediate post-op tomograms. Cortical surface deformation data were then used to drive an inverse problem framework. The manually identified subsurface deformations served as a comparison toward validation. The proposed framework recaptured 85% of the mean subsurface shift. This translated to a subsurface shift error of 0.4 ± 0.4 mm for a measured shift of 3.1 ± 0.6 mm. The patient's pre-op tomograms were also deformed volumetrically using displacements predicted by the model. Results presented allow a preliminary evaluation of correction both quantitatively and visually. While intraoperative (intra-op) MR imaging data would be optimal, the extent of shift measured from pre- to post-op MR was comparable to clinical conditions. This study demonstrates the accuracy of the proposed framework in predicting full-volume displacements from sparse shift measurements. It also shows that the proposed framework can be extended and used to update pre-op images on a time scale that is compatible with surgery. View full abstract»

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  • Using Point Process Models to Compare Neural Spiking Activity in the Subthalamic Nucleus of Parkinson's Patients and a Healthy Primate

    Publication Year: 2010 , Page(s): 1297 - 1305
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (527 KB) |  | HTML iconHTML  

    Placement of deep brain stimulating electrodes in the subthalamic nucleus (STN) to treat Parkinson's disease (PD) also allows the recording of single neuron spiking activity. Analyses of these unique data offer an important opportunity to better understand the pathophysiology of PD. Despite the point process nature of PD neural spiking activity, point process methods are rarely used to analyze these recordings. We develop a point process representation of PD neural spiking activity using a generalized linear model to describe long- and short-term temporal dependencies in the spiking activity of 28 STN neurons from seven PD patients and 35 neurons from one healthy primate (surrogate control) recorded, while the subjects executed a directed-hand movement task. We used the point process model to characterize each neuron's bursting, oscillatory, and directional tuning properties during key periods in the task trial. Relative to the control neurons, the PD neurons showed increased bursting, increased 10-30 Hz oscillations, and increased fluctuations in directional tuning. These features, which traditional methods failed to capture accurately, were efficiently summarized in a single model in the point process analysis of each neuron. The point process framework suggests a useful approach for developing quantitative neural correlates that may be related directly to the movement and behavioral disorders characteristic of PD. View full abstract»

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  • Electronic Cleansing for Computed Tomography (CT) Colonography Using a Scale-Invariant Three-Material Model

    Publication Year: 2010 , Page(s): 1306 - 1317
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1055 KB) |  | HTML iconHTML  

    A well-known reading pitfall in computed tomography (CT) colonography is posed by artifacts at T-junctions, i.e., locations where air-fluid levels interface with the colon wall. This paper presents a scale-invariant method to determine material fractions in voxels near such T-junctions. The proposed electronic cleansing method particularly improves the segmentation at those locations. The algorithm takes a vector of Gaussian derivatives as input features. The measured features are made invariant to the orientation-dependent apparent scale of the data and normalized in a way to obtain equal noise variance. A so-called parachute model is introduced that maps Gaussian derivatives onto material fractions near T-junctions. Projection of the noisy derivatives onto the model yields improved estimates of the true, underlying feature values. The method is shown to render an accurate representation of the object boundary without artifacts near junctions. Therefore, it enhances the reading of CT colonography in a 3-D display mode. View full abstract»

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  • Application of Covariate Shift Adaptation Techniques in Brain–Computer Interfaces

    Publication Year: 2010 , Page(s): 1318 - 1324
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (350 KB) |  | HTML iconHTML  

    A phenomenon often found in session-to-session transfers of brain-computer interfaces (BCIs) is nonstationarity. It can be caused by fatigue and changing attention level of the user, differing electrode placements, varying impedances, among other reasons. Covariate shift adaptation is an effective method that can adapt to the testing sessions without the need for labeling the testing session data. The method was applied on a BCI Competition III dataset. Results showed that covariate shift adaptation compares favorably with methods used in the BCI competition in coping with nonstationarities. Specifically, bagging combined with covariate shift helped to increase stability, when applied to the competition dataset. An online experiment also proved the effectiveness of bagged-covariate shift method. Thus, it can be summarized that covariate shift adaptation is helpful to realize adaptive BCI systems. View full abstract»

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  • Double-Edge Detection of Radiographic Lumbar Vertebrae Images Using Pressurized Open DGVF Snakes

    Publication Year: 2010 , Page(s): 1325 - 1334
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (934 KB) |  | HTML iconHTML  

    The detection of double edges in X-ray images of lumbar vertebrae is of prime importance in the assessment of vertebral injury or collapse that may be caused by osteoporosis and other spine pathology. In addition, if the above double-edge detection process is conducted within an automatic framework, it would not only facilitate inexpensive and fast means of obtaining objective morphometric measurements on the spine, but also remove the human subjectivity involved in the morphometric analysis. This paper proposes a novel force-formulation scheme, termed as pressurized open directional gradient vector flow snakes, to discriminate and detect the superior and inferior double edges present in the radiographic images of the lumbar vertebrae. As part of the validation process, this algorithm is applied to a set of 100 lumbar images and the detection results are quantified using analyst-generated ground truth. The promising nature of the detection results bears testimony to the efficacy of the proposed approach. View full abstract»

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  • Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework

    Publication Year: 2010 , Page(s): 1335 - 1347
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (725 KB) |  | HTML iconHTML  

    Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-Gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings. View full abstract»

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  • Thyroid Segmentation and Volume Estimation in Ultrasound Images

    Publication Year: 2010 , Page(s): 1348 - 1357
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (953 KB) |  | HTML iconHTML  

    Physicians usually diagnose the pathology of the thyroid gland by its volume. However, even if the thyroid glands are found and the shapes are hand-marked from ultrasound (US) images, most physicians still depend on computed tomography (CT) images, which are expensive to obtain, for precise measurements of the volume of the thyroid gland. This approach relies heavily on the experience of the physicians and is very time consuming. Patients are exposed to high radiation when obtaining CT images. In contrast, US imaging does not require ionizing radiation and is relatively inexpensive. US imaging is thus one of the most commonly used auxiliary tools in clinical diagnosis. The present study proposes a complete solution to estimate the volume of the thyroid gland directly from US images. The radial basis function neural network is used to classify blocks of the thyroid gland. The integral region is acquired by applying a specific-region-growing method to potential points of interest. The parameters for evaluating the thyroid volume are estimated using a particle swarm optimization algorithm. Experimental results of the thyroid region segmentation and volume estimation in US images show that the proposed approach is very promising. View full abstract»

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  • Array-Gain Constraint Minimum-Norm Spatial Filter With Recursively Updated Gram Matrix For Biomagnetic Source Imaging

    Publication Year: 2010 , Page(s): 1358 - 1365
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (667 KB) |  | HTML iconHTML  

    This paper proposes a novel spatial filter for biomagnetic source imaging. The proposed spatial filter is derived based on a modified version of the minimum-norm spatial filter and is designed to have a performance close to that of the adaptive minimum-variance spatial filter through the use of an estimated covariance matrix. In this method, the theoretical form of the measurement covariance matrix is estimated as an updated gram matrix in a recursive procedure. Since the proposed method does not use the sample covariance matrix, it is free of the well-known weaknesses of the minimum-variance spatial filter, namely, the proposed spatial filter does not require a large number of time samples, and it can even be applied to single-time-sample data. It is also robust to source correlation. We have validated the method's effectiveness by our computer simulations as well as through experiments using auditory-evoked magnetoencephalographic data. View full abstract»

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  • Heart Rate Variability on 7-Day Holter Monitoring Using a Bootstrap Rhythmometric Procedure

    Publication Year: 2010 , Page(s): 1366 - 1376
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1014 KB) |  | HTML iconHTML  

    Heart rate variability (HRV) markers have been widely used to characterize the autonomous regulation state of the heart from 24-h Holter monitoring, but long-term evolution of HRV indexes is mostly unknown. A dataset of 7-day Holter recordings of 22 patients with congestive heart failure was studied. A rhythmometric procedure was designed to characterize the infradian, circadian, and ultradian components for each patient, as well as circadian and ultradian fluctuations. Furthermore, a bootstrap test yielded automatically the rhythmometric model for each patient. We analyzed the temporal evolution of relevant time-domain (AVNN, SDNN, and NN50), frequency-domain (LF, HF, HFn, and LF/HF), and nonlinear (α1 and SampEn) HRV indexes. Circadian components were the most significant for all HRV indexes, but the infradian ones were also strongly present in NN50, HFn, LF/HF, α1, and SampEn indexes. Among ultradian components that one corresponding to 12 h, was the most relevant. Long-term monitoring of HRV conveys new potentially relevant rhythmometric information, which can be analyzed by using the proposed automatic procedure. View full abstract»

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  • Motion Artifact Removal for Functional Near Infrared Spectroscopy: A Comparison of Methods

    Publication Year: 2010 , Page(s): 1377 - 1387
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (645 KB) |  | HTML iconHTML  

    Near infrared spectroscopy (NIRS) is rapidly gaining popularity for functional brain imaging. It is well suited to studies of patients or children; however, in these populations particularly, motion artifacts can present a problem. Here, we propose the use of imaging channels with negligible distance between light source and detector to detect subject motion, without the need for an additional motion sensor. Datasets containing deliberate motion artifacts were obtained from three subjects. Motion artifacts could be detected in the signal from the co-located channels with a minimum sensitivity of 0.75 and specificity of 0.98. Five techniques for removing motion artifact from the functional signals were compared, namely two-input recursive least squares (RLS) adaptive filtering, wavelet-based filtering, independent component analysis (ICA), and two-channel and multiple-channel regression. In most datasets, the median change in SNR across all channels was the greatest using ICA or multiple-channel regression. RLS adaptive filtering produced the smallest increase in SNR. Where sharp spikes were present, wavelet filtering produced the largest SNR increase. ICA and multiple-channel regression are promising ways to reduce motion artifact in functional NIRS without requiring time-consuming manual techniques. View full abstract»

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  • Analysis of Fractionated Atrial Fibrillation Electrograms by Wavelet Decomposition

    Publication Year: 2010 , Page(s): 1388 - 1398
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (867 KB) |  | HTML iconHTML  

    This study introduces the use of wavelet decomposition of unipolar fibrillation electrograms for the automatic detection of local activation times during complex atrial fibrillation (AF). The purpose of this study was to evaluate this technique in patients with structural heart disease and longstanding persistent AF. In 46 patients undergoing cardiac surgery, unipolar fibrillation electrograms were recorded from the right atrium, using a mapping array of 244 electrodes. In 25 patients with normal sinus rhythm, AF was induced by rapid pacing, whereas 21 patients were in persistent AF. In patients with longstanding AF, the atrial electrograms showed a high degree of fractionation. In each patient, 12 s of AF were analyzed by wavelet transformation (15 scales). The finest scales (1-7) were used to reconstruct a “local” fibrillation electrogram, whereas with the coarse scales (9-15), a far-field signal was generated. With these local and far-field electrograms, the “primary” fibrillation potentials, due to wave propagation underneath the electrode, could be distinguished from double potentials and multiple components generated by remote wavefronts. Wavelet transformation resulted in AF histograms with a closely Gaussian distribution and the automatically generated activation maps showed a good resemblance with fibrillation maps obtained by laborious manual editing. A special chaining algorithm was developed to detect multiple components in fractionated electrograms. The degree of fractionation showed a positive correlation with the complexity of fibrillation, thus providing an objective quantification of the degree of electrical dissociation of the atria. Wavelet transformation can be a useful technique to detect the primary potentials and quantify the degree of fractionation of fibrillation electrograms. This could enable real-time mapping of complex cases of human AF and classification of the underlying electropathological substrate. View full abstract»

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  • Wavelet-Based ECG Data Compression System With Linear Quality Control Scheme

    Publication Year: 2010 , Page(s): 1399 - 1409
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (971 KB) |  | HTML iconHTML  

    Maintaining reconstructed signals at a desired level of quality is crucial for lossy ECG data compression. Wavelet-based approaches using a recursive decomposition process are unsuitable for real-time ECG signal recoding and commonly obtain a nonlinear compression performance with distortion sensitive to quantization error. The sensitive response is caused without compromising the influences of word-length-growth (WLG) effect and unfavorable for the reconstruction quality control of ECG data compression. In this paper, the 1-D reversible round-off nonrecursive discrete periodic wavelet transform is applied to overcome the WLG magnification effect in terms of the mechanisms of error propagation resistance and significant normalization of octave coefficients. The two mechanisms enable the design of a multivariable quantization scheme that can obtain a compression performance with the approximate characteristics of linear distortion. The quantization scheme can be controlled with a single control variable. Based on the linear compression performance, a linear quantization scale prediction model is presented for guaranteeing reconstruction quality. Following the use of the MIT-BIH arrhythmia database, the experimental results show that the proposed system, with lower computational complexity, can obtain much better reconstruction quality control than other wavelet-based methods. View full abstract»

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  • Orthogonal Fuzzy Neighborhood Discriminant Analysis for Multifunction Myoelectric Hand Control

    Publication Year: 2010 , Page(s): 1410 - 1419
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (411 KB) |  | HTML iconHTML  

    Developing accurate and powerful electromyogram (EMG) driven prostheses controllers that can provide the amputees with effective control on their artificial limbs, has been the focus of a great deal of research in the past few years. One of the major challenges in such research is extracting an informative subset of features that can best discriminate between the different forearm movements. In this paper, a new dimensionality reduction method, referred to as orthogonal fuzzy neighborhood discriminant analysis (OFNDA), is proposed as a response to such a challenge. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed OFNDA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The proposed OFNDA is validated on EMG datasets collected from seven subjects performing a range of 5 to 10 classes of forearm movements. Practical results indicate the significance of OFNDA in comparison to many other feature projection methods (including locality preserving and uncorrelated variants of discriminant analysis) with accuracies ranging from 97.66% to 87.84% for 5 to 10 classes of movements, respectively, using only two EMG electrodes. View full abstract»

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  • A Novel Metric for Bone Marrow Cells Chromosome Pairing

    Publication Year: 2010 , Page(s): 1420 - 1429
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (596 KB) |  | HTML iconHTML  

    Karyotyping is a set of procedures, in the scope of the cytogenetics, that produces a visual representation of the 46 chromosomes observed during the metaphase step of the cellular division, called mitosis, paired and arranged in decreasing order of size. Automatic pairing of bone marrow cells is a difficult task because these chromosomes appear distorted, overlapped, and their images are usually blurred with undefined edges and low level of detail. In this paper, a new metric is proposed to compare this type of chromosome images toward the design of an automatic pairing algorithm for leukemia diagnostic purposes. Besides the features used in the traditional karyotyping procedures, a new feature, based on mutual information , is proposed to increase the discriminate power of the G-banding pattern dissimilarity between chromosomes and improve the performance of the classifier. The pairing algorithm is formulated as a combinatorial optimization problem where the distances between homologous chromosomes are minimized and the distances between nonhomologous ones are maximized. The optimization task is solved by using an integer programming approach. A new bone marrow chromosome dataset-Lisbon-K1 (LK1) chromosome dataset with 9200 chromosomes---was build for this study. These chromosomes have much lower quality than the classic Copenhagen, Edinburgh, and Philadelphia datasets, and its classification and pairing is therefore more difficult. Experiments using real images from the LK1 and Grisan et al. datasets based on a leave-one-out cross-validation strategy are performed to test and validate the pairing algorithm. View full abstract»

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  • Intuitionistic Fuzzy Segmentation of Medical Images

    Publication Year: 2010 , Page(s): 1430 - 1436
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (621 KB) |  | HTML iconHTML  

    This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates, mammary, etc. The medical images are not properly illuminated, and segmentation in that case becomes very difficult. A novel image segmentation approach using intuitionistic fuzzy set theory and a new membership function is proposed using restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. Local thresholding is applied to threshold medical images. The results showed a much better performance on poor contrast medical images, where almost all the blood vessels and blood cells are visible properly. There are several fuzzy and intuitionistic fuzzy thresholding methods, but these methods are not related to the medical images. To make a comparison with the proposed method with other thresholding methods, the method is compared with six nonfuzzy, fuzzy, and intuitionistic fuzzy methods. View full abstract»

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  • A Flow Quantification Method Using Fluid Dynamics Regularization and MR Tagging

    Publication Year: 2010 , Page(s): 1437 - 1445
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (662 KB) |  | HTML iconHTML  

    This paper presents a new method for improved flow analysis and quantification using MRI. The method incorporates fluid dynamics to regularize the flow quantification from tagged MR images. Specifically, the flow quantification is formulated as a minimization problem based on the following: 1) the Navier-Stokes equation governing the fluid dynamics; 2) the flow continuity equation and boundary conditions; and 3) the data consistency constraint. The minimization is carried out using a genetic algorithm. This method is tested using both computer simulations and MR flow experiments. The results are evaluated using flow vector fields from the computational fluid dynamics software package as a reference, which show that the new method can achieve more realistic and accurate flow quantifications than the conventional method. View full abstract»

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  • Assessing and Compensating for Zero-Lag Correlation Effects in Time-Lagged Granger Causality Analysis of fMRI

    Publication Year: 2010 , Page(s): 1446 - 1456
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1269 KB) |  | HTML iconHTML  

    Effective connectivity in brain networks can be studied using Granger causality analysis, which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this paper showed that the zero-lag correlation does “leak” into estimates of time-lagged causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task. View full abstract»

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  • Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging

    Publication Year: 2010 , Page(s): 1457 - 1466
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (406 KB) |  | HTML iconHTML  

    Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent “pure” facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners. View full abstract»

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  • 3-D Visualization of Acute RF Ablation Lesions Using MRI for the Simultaneous Determination of the Patterns of Necrosis and Edema

    Publication Year: 2010 , Page(s): 1467 - 1475
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (855 KB) |  | HTML iconHTML  

    Catheter ablation using RF energy is a common treatment for atrial arrhythmias. Although this treatment provides a potential cure, currently, there remains a high proportion of patients returning for repeat ablations. Electrophysiologists have little information to verify that a lesion has been created in the myocardium. Temporary electrical block can be created from edema, which will subside. MRI can visualize acute and chronic ablation lesions using delayed-enhancement techniques. However, the ablation patterns cannot be determined from 2-D images alone. Using the combination of T2-weighted and delayed-enhancement MRI, ablation lesions can be characterized in terms of necrosis and edema. A novel 3-D visualization technique is presented that projects the image intensity due the lesions onto a 3-D cardiac surface, allowing the complete, simultaneous visualization of the delayed-enhancement and T2 -weighted ablation patterns. Results show successful visualization of ablation patterns in 18 patients, and an application of this technique is presented in which electroanatomical mapping systems can be validated by overlaying the acquired ablation points onto the cardiac surfaces and assessing the correlation with the lesion maps. View full abstract»

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Aims & Scope

IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.

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Meet Our Editors

Editor-in-Chief
Bin He
Department of Biomedical Engineering