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Preview: Information Technology in Biomedicine, IEEE Transactions on - new TOC

Information Technology in Biomedicine, IEEE Transactions on - new TOC



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Table of Contents

Nov. 2012




Editorial: From "Information Technology in Biomedicine" to "Biomedical and Health Informatics"

Nov. 2012

The IEEE Transactions on Information Technology in Biomedicine (T-ITB) will be retitled as the IEEE Journal of Biomedical and Health Informatics (J-BHI) starting in January, 2013. The IEEE Transactions on Information Technology in Biomedicine was launched with four issues per year in 1997. After more than a decade of steady growth, the journal ventured into new challenges: open access of publication and rapid expansion in all health informatics-related fields. In this concluding issue of T-ITB the authors would like to take this opportunity to report briefly some historical retrospectives of journal with known statistics of it and to extend a special appreciation from the current editorial office to readers, authors, reviewers, associate editors, Engineering in Medicine and Biology Society (EMBS) relevant committees, IEEE publication staff, and especially two previous Editors-in-Chief for their great contributions to make the Transactions successful. The launching of the J-BHI as the new title of T-ITB is an outcome of the vision of the EMBS's 2011 Publications Committee. The title change and scope revision were accomplished through more than two years of team efforts by T-ITB Editorial Board, EMBS Executive Office, Publication Committee, and AdCom. In the next editorial to be published on January issue of J-BHI, we will discuss the rationale behind the title change and scope revision as well as the grand challenges in health informatics with future perspectives.



Guest EditorialMultimedia Services and Technologies for E-Health (MUST-EH)

Nov. 2012

The 11 papers in this special section focus on multimedia services and technologies for E-Health (MUST-EH).



Cross-Layer Design for Optimized Region of Interest of Ultrasound Video Data Over Mobile WiMAX

Nov. 2012

The application of advanced error concealment techniques applied as a postprocess to conceal lost video information in error-prone channels, such as the wireless channel, demands additional processing at the receiver. This increases the delivery delay and needs more computational power. However, in general, only a small region within medical video is of interest to the physician and thus if only this area is considered, the number of computations can be curtailed. In this paper, we present a technique whereby the region of interest specified by the physician is used to delimit the area where the more complex concealment techniques are applied. A cross-layer design approach in mobile worldwide interoperability for microwave access wireless communication environment is adopted in this paper to provide an optimized quality of experience in the region that matters most to the mobile physician while relaxing the requirements in the background, ensuring real-time delivery. Results show that a diagnostically acceptable peak signal-to-noise-ratio of about 36 dB can still be achieved within reasonable decoding time.



Data Interoperability and Multimedia Content Management in e-Health Systems

Nov. 2012

e-Health systems provide a collaborative platform for sharing patients' medical data typically stored in distributed autonomous healthcare data sources. Each autonomous source stores its medical and multimedia data without following any global structure. This causes heterogeneity in the underlying sources with respect to the data and storage structure. Therefore, a data interoperability mechanism is required for sharing the data among the heterogeneous sources. A proper metadata structure is also necessary to represent multimedia content in the sources to enable efficient query processing. Considering these needs, we present an interoperability solution for sharing data among heterogeneous data sources. We also propose a metadata management framework for medical multimedia content including X-ray, ECG, MRI, and ultrasound images. The framework identifies features, generates and represents metadata, and produces identifiers for the medical multimedia content to facilitate efficient query processing. The framework has been tested with various user queries and the accuracy of the query results evaluated by means of precision, recall, and user feedback methods. The results confirm the effectiveness of the proposed approach.



Virtual Caregiver: An Ambient-Aware Elderly Monitoring System

Nov. 2012

The growing number of elderly population at home and abroad necessitates improved approaches to elderly care provision. Elders, often with cognitive and physical impairment, need assistance in their activities of daily living (ADLs), which is usually provided by human caregivers (HCGs). As the demand for caregiver's assistance increases, the shortage of traditional care resources becomes obvious. In this paper, we present the Virtual Caregiver (ViCare) framework that supports a HCG to monitor continuously the elderly by being aware of their surroundings. The ViCare system attempts to understand the elderly persons' activities and contexts based on the data captured by the sensors placed in their environment and dynamically decides what services to provide them or whether there is a need to interrupt HCG depending on the type of activities. It not only minimizes the cognitive load of the HCG but also provides a seamless assistance to the elderly toward their improved health and well-being in their living environment. We conducted the experiments in an instrumented home environment and obtained positive results in terms of the satisfaction of the elderly, interaction event handling, caregiver's acceptance, and their engagement.



A Serious Game for Learning Ultrasound-Guided Needle Placement Skills

Nov. 2012

Ultrasound-guided needle placement is a key step in a lot of radiological intervention procedures such as biopsy, local anesthesia, and fluid drainage. To help training future intervention radiologists, we develop a serious game to teach the skills involved. We introduce novel techniques for realistic simulation and integrate game elements for active and effective learning. This game is designed in the context of needle placement training based on the some essential characteristics of serious games. Training scenarios are interactively generated via a block-based construction scheme. A novel example-based texture synthesis technique is proposed to simulate corresponding ultrasound images. Game levels are defined based on the difficulties of the generated scenarios. Interactive recommendation of desirable insertion paths is provided during the training as an adaptation mechanism. We also develop a fast physics-based approach to reproduce the shadowing effect of needles in ultrasound images. Game elements such as time-attack tasks, hints, and performance evaluation tools are also integrated in our system. Extensive experiments are performed to validate its feasibility for training.



Bio-Patch Design and Implementation Based on a Low-Power System-on-Chip and Paper-Based Inkjet Printing Technology

Nov. 2012

This paper presents the prototype implementation of a Bio-Patch using fully integrated low-power system-on-chip (SoC) sensor and paper-based inkjet printing technology. The SoC sensor is featured with programmable gain and bandwidth to accommodate a variety of biosignals. It is fabricated in a 0.18-μm standard CMOS technology, with a total power consumption of 20 μW from a 1.2 V supply. Both the electrodes and interconnections are implemented by printing conductive nanoparticle inks on a flexible photo paper substrate using inkjet printing technology. A Bio-Patch prototype is developed by integrating the SoC sensor, a soft battery, printed electrodes, and interconnections on a photo paper substrate. The Bio-Patch can work alone or operate along with other patches to establish a wired network for synchronous multiple-channel biosignals recording. The measurement results show that electrocardiogram and electromyogram are successfully measured in in vivo tests using the implemented Bio-Patch prototype.



Evaluation of Thermal and Nonthermal Effects of UHF RFID Exposure on Biological Drugs

Nov. 2012

The radio frequency identification (RFID) technology promises to improve several processes in the healthcare scenario, especially those related to the traceability of people and things. Unfortunately, there are still some barriers limiting the large-scale deployment of these innovative technologies in the healthcare field. Among these, the evaluation of potential thermal and nonthermal effects due to the exposure of biopharmaceutical products to electromagnetic fields is very challenging, but still slightly investigated. This paper aims to setup a controlled RF exposure environment, in order to reproduce a worst case exposure of pharmaceutical products to the electromagnetic fields generated by the UHF RFID devices placed along the supply chain. Radiated powers several times higher than recommended by current normative limits were applied (10 and 20 W). The electric field strength at the exposed sample location, used in tests, was as high as 100 V/m. Nonthermal effects were evaluated by chromatography techniques and in vitro assays. The results obtained for a particular case study, the ActrapidTM human insulin preparation, showed temperature increases lower than 0.5 °C and no significant changes in the structure and performance of the considered drug.



Equipment Location in Hospitals Using RFID-Based Positioning System

Nov. 2012

Throughout various complex processes within hospitals, context-aware services and applications can help to improve the quality of care and reduce costs. For example, sensors and radio frequency identification (RFID) technologies for e-health have been deployed to improve the flow of material, equipment, personal, and patient. Bed tracking, patient monitoring, real-time logistic analysis, and critical equipment tracking are famous applications of real-time location systems (RTLS) in hospitals. In fact, existing case studies show that RTLS can improve service quality and safety, and optimize emergency management and time critical processes. In this paper, we propose a robust system for position and orientation determination of equipment. Our system utilizes passive (RFID) technology mounted on flooring plates and several peripherals for sensor data interpretation. The system is implemented and tested through extensive experiments. The results show that our system's average positioning and orientation measurement outperforms existing systems in terms of accuracy. The details of the system as well as the experimental results are presented in this paper.



ECG-Cryptography and Authentication in Body Area Networks

Nov. 2012

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data commnications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.



3-D Streaming Supplying Partner Protocols for Mobile Collaborative Exergaming for Health

Nov. 2012

Childhood obesity is nowadays considered one of the major health problems that many societies suffer from today. The obesity epidemic leads to several life threatening conditions such as diabetes, heart disease, high blood pressure, and mental health problems like depression, anxiety, and loneliness just to mention a few. Several approaches, including physical exercises, strict diet, and exergames among others, have been adopted to address the obesity epidemic. Exergames are considered the innovative approach for fighting several health problems such as obesity, where a combination of “exercise” and 3-D “gaming” are proposed to incite kids to exercise as a team. Collaborative exergaming became even more popular given that it addresses the social side of the obesity epidemic, and it motivates kids to socialize with other kids. Traditional exergames are based on the client-server approach where the server is responsible for streaming the 3-D environment. However, this can lead to latency and server bottleneck if many clients participate in the exergame, which leads to the kids stopping exercising. Having an exergame application that does not suffer from networking problem such as delay, is very important given that it increases the exercise hours. In this paper, we propose a new trend of mobile collaborative exergaming applications that is based on the peer-to-peer architecture, as well as two supplying partner selection protocols that aim at selecting the suitable source responsible for streaming the relevant 3-D data. Our system, that we refer to as MOSAIC, is intended for mobile collaborative exergames that incite kids to move inside a large area, using thin mobile devices such as head-mounted devices, have physical exercises, and collaborate with other kids which in consequence address several health problems such as the obesity epidemic on the physical and social plans. Our proposed mobile collaborative exergame aims at inciting t- e kids to exercise as a team for a longer time by improving the quality of the streaming and reducing the delay. This is accomplished by our proposed supplying partner selection protocols that provide a quick discovery of multiple supplying partners, by minimizing the time required to acquire the data. The performance evaluation that we have obtained to evaluate our suite of protocols using a realistic set of exergame scenarios for obese kids is then presented and discussed.



A Knowledge Editing Service for Multisource Data Management in Remote Health Monitoring

Nov. 2012

Remote health monitoring (RHM) programmes are being increasingly developed to face the pervasive diffusion of chronic diseases. RHM strongly relies on Information and Communications Technologies (ICT) intelligent platforms devised to remotely acquire multisource data, process these according to specific domain knowledge, and support clinical decision making. However, since RHM domain is continuously evolving and the pertinent knowledge is not yet consolidated, there is a great demand for services and tools that allow the encoded knowledge to be modified and enriched. This paper presents a knowledge editing service (KES), which aims at enabling clinicians to insert novel knowledge, in a controlled fashion, into an ICT intelligent platform. The solution proposed is innovative since it addresses synergistically peculiar issues related to 1) RHM knowledge format; 2) controlled editing patterns; 3) knowledge verification; and 4) cooperative knowledge editing. None of the existing methods and systems for knowledge authoring tackles all these aspects at the same time. A prototype of the KES has been implemented and evaluated in real operational conditions.



Real-Time Mandibular Angle Reduction Surgical Simulation With Haptic Rendering

Nov. 2012

Mandibular angle reduction is a popular and efficient procedure widely used to alter the facial contour. The primary surgical instruments, the reciprocating saw, and the round burr, employed in the surgery have a common feature: operating at a high speed. Generally, inexperienced surgeons need a long-time practice to learn how to minimize the risks caused by the uncontrolled contacts and cutting motions in manipulation of instruments with high-speed reciprocation or rotation. A virtual reality-based surgical simulator for the mandibular angle reduction was designed and implemented on a compute unified device architecture (CUDA)-based platform in this paper. High-fidelity visual and haptic feedbacks are provided to enhance the perception in a realistic virtual surgical environment. The impulse-based haptic models were employed to simulate the contact forces and torques on the instruments. It provides convincing haptic sensation for surgeons to control the instruments under different reciprocation or rotation velocities. The real-time methods for bone removal and reconstruction during surgical procedures have been proposed to support realistic visual feedbacks. The simulated contact forces were verified by comparing against the actual force data measured through the constructed mechanical platform. An empirical study based on the patient-specific data was conducted to evaluate the ability of the proposed system in training surgeons with various experiences. The results confirm the validity of our simulator.



Distributed System for Cognitive Stimulation Over Interactive TV

Nov. 2012

This paper details the full design, implementation, and validation of an e-health service in order to improve the community health care services for patients with cognitive disorders. Specifically, the new service allows Parkinson's disease patients benefit from the possibility of doing cognitive stimulation therapy (CST) at home by using a familiar device such as a TV set. Its use instead of a PC could be a major advantage for some patients whose lack of familiarity with the use of a PC means that they can do therapy only in the presence of a therapist. For these patients this solution could bring about a great improvement in their autonomy. At the same time, this service provides therapists with the ability to conduct follow-up of therapy sessions via the web, benefiting from greater and easier control of the therapy exercises performed by patients and allowing them to customize new exercises in accordance with the particular needs of each patient. As a result, this kind of CST is considered to be a complement of other therapies oriented to the Parkinson patients. Furthermore, with small changes, the system could be useful for patients with a different cognitive disease such as Alzheimer's or mild cognitive impairment.



Blind Integrity Verification of Medical Images

Nov. 2012

This paper presents the first method of digital blind forensics within the medical imaging field with the objective to detect whether an image has been modified by some processing (e.g., filtering, lossy compression, and so on). It compares two image features: the histogram statistics of reorganized block-based discrete cosine transform coefficients, originally proposed for steganalysis purposes, and the histogram statistics of reorganized block-based Tchebichef moments. Both features serve as input of a set of support vector machine classifiers built in order to discriminate tampered images from original ones as well as to identify the nature of the global modification one image may have undergone. Performance evaluation, conducted in application to different medical image modalities, shows that these image features can help, independently or jointly, to blindly distinguish image processing or modifications with a detection rate greater than 70%. They also underline the complementarity of these features.



Multiparametric Decision Support System for the Prediction of Oral Cancer Reoccurrence

Nov. 2012

Oral squamous cell carcinoma (OSCC) constitutes the predominant neoplasm of the head and neck region, featuring particularly aggressive nature, associated with quite unfavorable prognosis. In this paper, we formulate a decision support system that integrates a multitude of heterogeneous data (clinical, imaging, and genomic), thus, framing all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses (local or metastatic) of the disease. The discrimination potential of each source of data is initially explored separately, and afterward the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse.



Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition

Nov. 2012

In this paper, we present a new method for classification of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) method. The intrinsic mode functions (IMFs) generated by EMD method can be considered as a set of amplitude and frequency modulated (AM-FM) signals. The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs. The two bandwidths, namely amplitude modulation bandwidth (BAM) and frequency modulation bandwidth (BFM), computed from the analytic IMFs, have been used as an input to least squares support vector machine (LS-SVM) for classifying seizure and nonseizure EEG signals. The proposed method for classification of EEG signals based on the bandwidth features (BAM and BFM) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010. The experimental results with the recorded EEG signals from a published dataset are included to show the effectiveness of the proposed method for EEG signal classification.



An Examination of the Motor Unit Number Index (MUNIX) in Muscles Paralyzed by Spinal Cord Injury

Nov. 2012

The objective of this study was to assess whether there is evidence of motor unit loss in muscles paralyzed by spinal cord injury (SCI), using a measurement called motor unit number index (MUNIX). The MUNIX technique was applied in SCI (n=12) and neurologically intact (n=12) subjects. The maximum M waves and voluntary surface electromyography (EMG) signals at different muscle contraction levels were recorded from the first dorsal interosseous (FDI) muscle in each subject. The MUNIX values were estimated using a mathematical model describing the relation between the surface EMG signal and the ideal motor unit number count derived from the M wave and surface EMG measurements. We recorded a significant decrease in both maximum M wave amplitude and in estimated MUNIX values in paralyzed FDI muscles, as compared with neurologically intact muscles. Across all subjects, the maximum M wave amplitude was 8.3±4.4 mV for the paralyzed muscles and 14.4±2.0 mV for the neurologically intact muscles (p <; 0.0001). These measurements, when combined with voluntary EMG recordings, resulted in a mean MUNIX value of 112±71 for the paralyzed muscles, much lower than the mean MUNIX value of 228±49 for the neurologically intact muscles (p <; 0.00001). A motor unit size index was also calculated using the maximum M wave recording and the MUNIX values. We found that paralyzed muscles showed a mean motor unit size index value of 80.7±17.7 μV, significantly higher than the mean value of 64.9±10.1 μV obtained from neurologically intact muscles (p <; 0.001). The MUNIX method used in this study offers several practical benefits compared with the traditional motor unit number estimation technique because it is noninvasive, induces minimal discomfort due to electrical nerve stimulation, and can be performed quickly. The findings from this study help understand the complicated determinants of SCI induced muscle weakness an- provide further evidence of motoneuron degeneration after a spinal injury.



Increasing the Signal-to-Noise Ratio by Using Vertically Stacked Phased Array Coils for Low-Field Magnetic Resonance Imaging

Nov. 2012

A new method is introduced to increase the signal-to-noise ratio (SNR) in low-field magnetic resonance imaging (MRI) systems by using a vertically stacked phased coil array. It is shown theoretically that the SNR is increased with the square root of the number of coils in the array if the array signals are properly combined to remove the mutual coupling effect. Based on this, a number of vertically stacked phased coil arrays have been designed and characterized by a numerical simulation method. The performance of these arrays confirms the significant increase of SNR by increasing the number of coils in the arrays. This provides a simple and efficient method to improve the SNR for low-field MRI systems.



A Feasibility Study of Enhancing Independent Task Performance for People with Cognitive Impairments Through the Use of a Handheld Location-Based Prompting System

Nov. 2012

An autonomous task-prompting system is presented to increase workplace and life independence for people with cognitive impairments such as traumatic brain injury, intellectual disability, schizophrenia, and down syndrome. This paper describes an approach to providing distributed cognition support of work engagement for persons with cognitive disabilities. In the pilot study, a prototype was built and tested in a community-based rehabilitation program involving preservice food preparation training of eight participants with cognitive impairments. The results show improvement in helping with task engagement is statistically significant compared to the oral-instruction method. A follow-up comparative study with two participants evaluated the shadow-team approach against the proposed system. Although the number of participants was few, the participants were studied in depth and the findings were very promising. The results in the autonomous task prompting without staff intervention indicate that the performance is statistically as good as the shadow-team approach. Our findings suggest that acquisition of job skills may be facilitated by the proposed system in conjunction with operant conditioning strategies.



A Distributed Trust Evaluation Model and Its Application Scenarios for Medical Sensor Networks

Nov. 2012

The development of medical sensor networks (MSNs) is imperative for e-healthcare, but security remains a formidable challenge yet to be resolved. Traditional cryptographic mechanisms do not suffice given the unique characteristics of MSNs, and the fact that MSNs are susceptible to a variety of node misbehaviors. In such situations, the security and performance of MSNs depend on the cooperative and trust nature of the distributed nodes, and it is important for each node to evaluate the trustworthiness of other nodes. In this paper, we identify the unique features of MSNs and introduce relevant node behaviors, such as transmission rate and leaving time, into trust evaluation to detect malicious nodes. We then propose an application-independent and distributed trust evaluation model for MSNs. The trust management is carried out through the use of simple cryptographic techniques. Simulation results demonstrate that the proposed model can be used to effectively identify malicious behaviors and thereby exclude malicious nodes. This paper also reports the experimental results of the Collection Tree Protocol with the addition of our proposed model in a network of TelosB motes, which show that the network performance can be significantly improved in practice. Further, some suggestions are given on how to employ such a trust evaluation model in some application scenarios.



System Identification and Closed-Loop Control of End-Tidal CO (image) in Mechanically Ventilated Patients

Nov. 2012

This paper presents a systematic approach to system identification and closed-loop control of end-tidal carbon dioxide partial pressure (PETCO2) in mechanically ventilated patients. An empirical model consisting of a linear dynamic system followed by an affine transform is proposed to derive a low-order and high-fidelity representation that can reproduce the positive and inversely proportional dynamic input-output relationship between PETCO2 and minute ventilation in mechanically ventilated patients. The predictive capability of the empirical model was evaluated using experimental respiratory data collected from 18 mechanically ventilated human subjects. The model predicted PETCO2 response accurately with a root-mean-squared error of 0.22 ± 0.16 mmHg and a coefficient of determination r2 of 0.81 ± 0.18 (mean ± SD) when a second-order rational transfer function was used as its linear dynamic component. Using the proposed model, a closed-loop control method for PETCO2 based on a proportional-integral (PI) compensator was proposed by systematic analysis of the system root locus. For the 18 mechanically ventilated patient models identified, the PI compensator exhibited acceptable closed-loop response with a settling time of 1.27 ± 0.20 min and a negligible overshoot (0.51 ± 1.17%), in addition to zero steady-state PETCO2 set point tracking. The physiologic implication of the proposed empirical model was analyzed by comparing it with the traditional multicompartmental model widely used in pharmacological modeling.



Enhancement of Initial Equivalency for Protein Structure Alignment Based on Encoded Local Structures

Nov. 2012

Most alignment algorithms find an initial equivalent residue pair followed by an iterative optimization process to explore better near-optimal alignments in the surrounding solution space of the initial alignment. It plays a decisive role in determining the alignment quality since a poor initial alignment may make the final alignment trapped in an undesirable local optimum even with an iterative optimization. We proposed a vector-based alignment algorithm with a new initial alignment approach accounting for local structure features called MIRAGE-align. The new idea is to enhance the quality of the initial alignment based on encoded local structural alphabets to identify the protein structure pair whose sequence identity falls in or below twilight zone. The statistical analysis of alignment quality based on match index and computation time demonstrated that MIRAGE-align algorithm outperformed four previously published algorithms, i.e., the residue-based algorithm, the vector-based algorithm, TM-align, and Fr-TM-align. MIRAGE-align yields a better estimate of initial solution to enhance the quality of initial alignment and enable the employment of a noniterative optimization process to achieve a better alignment.



Optical Tracking-Based Model Surgery for Orthognathic Surgical Planning Using a Quantifying Evaluation Method

Nov. 2012

Traditional cephalometry with a cast-mounted articulator is a useful and well-established tool for orthognathic surgery planning. However, 2-D planning with dental casts cannot provide comprehensive information on facial bone conditions, especially with regards to symmetry. To plan and predict postsurgical facial symmetry and occlusions, this paper uses an optical navigation system to track the movement of the upper and lower dental models in model surgery. The corresponding movement and the new position of the jawbones are demonstrated in the computer and the symmetry status can be evaluated. Surgical splints can be fabricated from the virtual models and used in surgery. The procedure provides more realistic predictions, which can assist surgeons to better control postsurgical facial harmony.



Image Analysis and Length Estimation of Biomolecules Using AFM

Nov. 2012

There are many examples of problems in pattern analysis for which it is often possible to obtain systematic characterizations, if in addition a small number of useful features or parameters of the image are known a priori or can be estimated reasonably well. Often, the relevant features of a particular pattern analysis problem are easy to enumerate, as when statistical structures of the patterns are well understood from the knowledge of the domain. We study a problem from molecular image analysis, where such a domain-dependent understanding may be lacking to some degree and the features must be inferred via machine-learning techniques. In this paper, we propose a rigorous, fully automated technique for this problem. We are motivated by an application of atomic force microscopy (AFM) image processing needed to solve a central problem in molecular biology, aimed at obtaining the complete transcription profile of a single cell, a snapshot that shows which genes are being expressed and to what degree. Reed et al. (“Single molecule transcription profiling with AFM,” Nanotechnology, vol. 18, no. 4, 2007) showed that the transcription profiling problem reduces to making high-precision measurements of biomolecule backbone lengths, correct to within 20-25 bp (6-7.5 nm). Here, we present an image processing and length estimation pipeline using AFM that comes close to achieving these measurement tolerances. In particular, we develop a biased length estimator on trained coefficients of a simple linear regression model, biweighted by a Beaton-Tukey function, whose feature universe is constrained by James-Stein shrinkage to avoid overfitting. In terms of extensibility and addressing the model selection problem, this formulation subsumes the models we studied.



Implantable Ultralow Pulmonary Pressure Monitoring System for Fetal Surgery

Nov. 2012

Congenital pulmonary hypoplasia is a devastating condition affecting fetal and newborn pulmonary physiology, resulting in great morbidity and mortality. The fetal lung develops in a fluid-filled environment. In this paper, we describe a novel, implantable pressure sensing and recording device which we use to study the pressures present in the fetal pulmonary tree throughout gestation. The system achieves 0.18 cm H 2O resolution and can record for 21 days continuously at 256 Hz. Sample tracings of in vivo fetal lamb recordings are shown.



Embedded Ubiquitous Services on Hospital Information Systems

Nov. 2012

Hospital information systems (HIS) have turned a hospital into a gigantic computer with huge computational power, huge storage, and wired/wireless local area network. On the other hand, a modern medical device, such as echograph, is a computer system with several functional units connected by an internal network named a bus. Therefore, we can embed such a medical device into the HIS by simply replacing the bus with the local area network. This paper designed and developed two embedded systems, a ubiquitous echograph system, and a networked digital camera. Evaluations of the developed systems clearly show that the proposed approach, embedding existing clinical systems into HIS, drastically changes productivity in the clinical field. Once a clinical system becomes a pluggable unit for a gigantic computer system, HIS, the combination of multiple embedded systems with application software designed under deep consideration about clinical processes may lead to the emergence of disruptive innovation in the clinical field.



Fuzzy Logic-Based Prognostic Score for Outcome Prediction in Esophageal Cancer

Nov. 2012

Given the poor prognosis of esophageal cancer and the invasiveness of combined modality treatment, improved prognostic scoring systems are needed. We developed a fuzzy logic-based system to improve the predictive performance of a risk score based on the serum concentrations of C-reactive protein (CRP) and albumin in a cohort of 271 patients with esophageal cancer before radiotherapy. Univariate and multivariate survival analyses were employed to validate the independent prognostic value of the fuzzy risk score. To further compare the predictive performance of the fuzzy risk score with other prognostic scoring systems, time-dependent receiver operating characteristic curve analysis was used. Application of fuzzy logic to the serum values of CRP and albumin increased predictive performance for one-year overall survival (AUC = 0.773) compared with that of a single marker (AUC = 0.743 and 0.700 for CRP and albumin, respectively), where the AUC denotes the area under curve. This fuzzy logic-based approach also performed consistently better than the Glasgow prognostic score (AUC = 0.745). Thus, application of fuzzy logic to the analysis of serum markers can more accurately predict the outcome for patients with esophageal cancer.



A Two-Class Approach to the Detection of Physiological Deterioration in Patient Vital Signs, With Clinical Label Refinement

Nov. 2012

Hospital patient outcomes can be improved by the early identification of physiological deterioration. Automatic methods of detecting patient deterioration in vital-sign data typically attempt to identify deviations from assumed “normal” physiological conditions, which is a one-class approach to classification. This paper investigates the use of a two-class approach, in which “abnormal” physiology is modeled explicitly. The success of such a method relies on the accuracy of data labels provided by clinical experts, which may be incomplete (due to large dataset size) or imprecise (due to clinical labels covering intervals, rather than each data point within those intervals). We propose a novel method of refining clinical labels such that the two-class classification approach may be adopted for identifying patient deterioration. We demonstrate the effectiveness of the proposed methods using a large dataset acquired in a 24-bed hospital step-down unit.



Computer-Aided Diagnosis of Melanoma Using Border- and Wavelet-Based Texture Analysis

Nov. 2012

This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimized selection and integration of features derived from textural, border-based, and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundary-series model of the lesion border and analyzing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimized selection of features is achieved by using the gain-ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, support vector machine, random forest, logistic model tree, and hidden naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation, and test image sets. The system achieves an accuracy of 91.26% and area under curve value of 0.937, when 23 features are used. Other important findings include 1) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and 2) higher contribution of texture features than border-based features in the optimized feature set.



Irregular Breathing Classification From Multiple Patient Datasets Using Neural Networks

Nov. 2012

Complicated breathing behaviors including uncertain and irregular patterns can affect the accuracy of predicting respiratory motion for precise radiation dose delivery. So far investigations on irregular breathing patterns have been limited to respiratory monitoring of only extreme inspiration and expiration. Using breathing traces acquired on a Cyberknife treatment facility, we retrospectively categorized breathing data into several classes based on the extracted feature metrics derived from breathing data of multiple patients. The novelty of this paper is that the classifier using neural networks can provide clinical merit for the statistical quantitative modeling of irregular breathing motion based on a regular ratio representing how many regular/irregular patterns exist within an observation period. We propose a new approach to detect irregular breathing patterns using neural networks, where the reconstruction error can be used to build the distribution model for each breathing class. The proposed irregular breathing classification used a regular ratio to decide whether or not the current breathing patterns were regular. The sensitivity, specificity, and receiver operating characteristiccurve of the proposed irregular breathing pattern detector was analyzed. The experimental results of 448 patients' breathing patterns validated the proposed irregular breathing classifier.



A Resource-Efficient Planning for Pressure Ulcer Prevention

Nov. 2012

Pressure ulcer is a critical problem for bed-ridden and wheelchair-bound patients, diabetics, and the elderly. Patients need to be regularly repositioned to prevent excessive pressure on a single area of body, which can lead to ulcers. Pressure ulcers are extremely costly to treat and may lead to several other health problems, including death. The current standard for prevention is to reposition at-risk patients every 2 h. Even if it is done properly, a fixed schedule is not sufficient to prevent all ulcers. Moreover, it may result in nurses being overworked by turning some patients too frequently. In this paper, we present an algorithm for finding a nurse-effort optimal repositioning schedule that prevents pressure ulcer formation for a finite planning horizon. Our proposed algorithm uses data from a commercial pressure mat assembled on the bed's surface and provides a sequence of next positions and the time of repositioning for each patient.



A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

Nov. 2012

We propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain postprocessing. Information from ellipse fitting and a projection histogram along the axes of the ellipse is used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.



Knowledge Discovery in Medical Systems Using Differential Diagnosis, LAMSTAR, and (image) -NN

Nov. 2012

Medical data are an ever-growing source of information generated from the hospitals in the form of patient records. When mined properly, the information hidden in these records is a huge resource bank for medical research. As of now, these data are mostly used only for clinical work. These data often contain hidden patterns and relationships, which can lead to better diagnosis, better medicines, better treatment, and overall, a platform to better understand the mechanisms governing almost all aspects of the medical domain. Unfortunately, discovery of these hidden patterns and relationships often goes unexploited. However, there is on-going research in medical diagnosis which can predict the diseases of the heart, lungs, and various tumours based on the past data collected from the patients. They are mostly limited to domain-specific systems that predict diseases restricted to their area of operation like heart, brain, and various other domains. These are not applicable to the whole medical dataset. The system proposed in this paper uses this vast storage of information so that diagnosis based on these historical data can be made. It focuses on computing the probability of occurrence of a particular ailment from the medical data by mining it using a unique algorithm which increases accuracy of such diagnosis by combining the key points of neural networks, Large Memory Storage, and Retrieval, k-NN, and differential diagnosis all integrated into one single algorithm. The system uses a service-oriented architecture wherein the system components of diagnosis, information portal, and other miscellaneous services are provided. This algorithm can be used in solving a few common problems that are encountered in automated diagnosis these days, which include diagnosis of multiple diseases showing similar symptoms, diagnosis of a person suffering from multiple diseases, receiving faster and more accurate second opinion, and faster identification of trends present in the- medical records.



A Service-Oriented Distributed Semantic Mediator: Integrating Multiscale Biomedical Information

Nov. 2012

Biomedical research continuously generates large amounts of heterogeneous and multimodal data spread over multiple data sources. These data, if appropriately shared and exploited, could dramatically improve the research practice itself, and ultimately the quality of health care delivered. This paper presents DIstributed Semantic MEDiator (DISMED), an open source semantic mediator that provides a unified view of a federated environment of multiscale biomedical data sources. DISMED is a Web-based software application to query and retrieve information distributed over a set of registered data sources, using semantic technologies. It also offers a user-friendly interface specifically designed to simplify the usage of these technologies by nonexpert users. Although the architecture of the software mediator is generic and domain independent, in the context of this paper, DISMED has been evaluated for managing biomedical environments and facilitating research with respect to the handling of scientific data distributed in multiple heterogeneous data sources. As part of this contribution, a quantitative evaluation framework has been developed. It consist of a benchmarking scenario and the definition of five realistic use-cases. This framework, created entirely with public datasets, has been used to compare the performance of DISMED against other available mediators. It is also available to the scientific community in order to evaluate progress in the domain of semantic mediation, in a systematic and comparable manner. The results show an average improvement in the execution time by DISMED of 55% compared to the second best alternative in four out of the five use-cases of the experimental evaluation.



WiiPD—Objective Home Assessment of Parkinson's Disease Using the Nintendo Wii Remote

Nov. 2012

Current clinical methods for the assessment of Parkinson's disease (PD) suffer from inconvenience, infrequency, and subjectivity. WiiPD is an approach for the objective home-based assessment of PD which utilizes the intuitive and sensor-rich Nintendo Wii remote. Combined with an electronic patient diary, a suite of minigames, a metric analyzer, and a visualization engine, we propose that this system can complement existing clinical practice by providing objective metrics gathered frequently over extended periods of time. In this paper, we detail the approach and introduce a series of metrics deemed capable of quantifying the severity of tremor and bradykinesia in those with PD. The system has been tested on a 71-year-old participant with PD over a period of 15 days, a 72-year-old control user without PD, and a group of eight young adults. Results indicate a clear correlation between patient self-rating scores of tremor severity and metric values obtained, in addition to clear differences in metrics obtained from each user group. These results suggest that this approach is capable of indicating the presence and severity of the motor symptoms of PD that affect arm motor control.



Prostate Cancer Localization Using Multiparametric MRI based on Semisupervised Techniques With Automated Seed Initialization

Nov. 2012

In this paper, we propose a novel and efficient semisupervised technique for automated prostate cancer localization using multiparametric magnetic resonance imaging (MRI). This method can be used in guiding biopsy, surgery, and therapy. We systematically present a new segmentation technique by developing a multiparametric graph-based random walker (RW) algorithm with automated seed initialization to perform prostate cancer segmentation using multiparametric MRI. RW algorithm has proved to be accurate and fast in segmentation applications; however, it requires a set of (user provided) seed points in order to perform segmentation. In this study, we first developed a novel RW method, which can be used with multiparametric MR images and then devised alternative methods that can determine seed points in an automated manner using discriminative classifiers such as support vector machines (SVM). Proposed RW method with automated seed initialization is able to produce improved segmentation results by assigning more weights to the images with more discriminative power. We applied the proposed method to a multiparametric dataset obtained from biopsy confirmed prostate cancer patients. Proposed method produces a sensitivity/specificity rate of 0.76 and 0.86, respectively. Both visual, quantitative as well as statistical results are presented to show the significant performance improvements. Fisher sign test is used to demonstrate the statistical significance of our results by achieving p-values less than 0.05. This method outperforms available RW- and SVM-based methods by achieving a high-specificity rate, while not reducing sensitivity.



An Intelligent Scoring System and Its Application to Cardiac Arrest Prediction

Nov. 2012

Traditional risk score prediction is based on vital signs and clinical assessment. In this paper, we present an intelligent scoring system for the prediction of cardiac arrest within 72 h. The patient population is represented by a set of feature vectors, from which risk scores are derived based on geometric distance calculation and support vector machine. Each feature vector is a combination of heart rate variability (HRV) parameters and vital signs. Performance evaluation is conducted on the leave-one-out cross-validation framework, and receiver operating characteristic, sensitivity, specificity, positive predictive value, and negative predictive value are reported. Experimental results reveal that the proposed scoring system not only achieves satisfactory performance on determining the risk of cardiac arrest within 72 h but also has the ability to generate continuous risk scores rather than a simple binary decision by a traditional classifier. Furthermore, the proposed scoring system works well for both balanced and imbalanced datasets, and the combination of HRV parameters and vital signs shows superiority in prediction to using HRV parameters only or vital signs only.



Accurate Coronary Centerline Extraction, Caliber Estimation, and Catheter Detection in Angiographies

Nov. 2012

Segmentation of coronary arteries in X-ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities, which allows physicians rapid access to different medical imaging information from computed tomography (CT) scans or magnetic resonance imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multiscale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection. We evaluate the method performance on three datasets coming from different imaging systems. The method performs as good as the expert observer with respect to centerline detection and caliber estimation. Moreover, the method discriminates between arteries and catheter with an accuracy of 96.5%, sensitivity of 72%, and precision of 97.4%.



Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy

Nov. 2012

Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content - clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media.



List of Editors and Reviewers

Nov. 2012

Lists the reviewers who contributed to IEEE Transactions on Information Technology in Biomedicine.



Open Access

Nov. 2012




2012 Index IEEE Transactions on Information Technology in Biomedicine Vol. 16

Nov. 2012

This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.



IEEE Transactions on Information Technology in Biomedicine information for authors

Nov. 2012

Provides instructions and guidelines to prospective authors who wish to submit manuscripts.



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Nov. 2012