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Information Technology in Biomedicine, IEEE Transactions on

Issue 1 • Date Jan. 2008

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  • Table of contents

    Publication Year: 2008 , Page(s): C1 - C4
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  • IEEE Transactions on Information Technology in Biomedicine publication information

    Publication Year: 2008 , Page(s): C2
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  • IT Applications for Pervasive, Personal, and Personalized Health

    Publication Year: 2008 , Page(s): 1 - 4
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (146 KB) |  | HTML iconHTML  

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  • A Note From the Incoming Editor-in-Chief

    Publication Year: 2008 , Page(s): 5 - 6
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  • 3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images

    Publication Year: 2008 , Page(s): 7 - 19
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (740 KB) |  | HTML iconHTML  

    Computed tomography (CT) is the most sensitive imaging technique for detecting lung nodules, and is now being evaluated as a screening tool for lung cancer in several large samples studies all over the world. In this report, we describe a semiautomatic method for 3-D segmentation of lung nodules in CT images for subsequent volume assessment. The distinguishing features of our algorithm are the following. 1) The user interaction process. It allows the introduction of the knowledge of the expert in a simple and reproducible manner. 2) The adoption of the geodesic distance in a multithreshold image representation. It allows the definition of a fusion--segregation process based on both gray-level similarity and objects shape. The algorithm was validated on low-dose CT scans of small nodule phantoms (mean diameter 5.3-11 mm) and in vivo lung nodules (mean diameter 5--9.8 mm) detected in the Italung-CT screening program for lung cancer. A further test on small lung nodules of Lung Image Database Consortium (LIDC) first data set was also performed. We observed a RMS error less than 6.6% in phantoms, and the correct outlining of the nodule contour was obtained in 82/95 lung nodules of Italung-CT and in 10/12 lung nodules of LIDC first data set. The achieved results support the use of the proposed algorithm for volume measurements of lung nodules examined with low-dose CT scanning technique. View full abstract»

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  • Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

    Publication Year: 2008 , Page(s): 20 - 26
    Cited by:  Papers (97)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (460 KB) |  | HTML iconHTML  

    Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings. View full abstract»

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  • 3-D Pain Drawings–-Mobile Data Collection Using a PDA

    Publication Year: 2008 , Page(s): 27 - 33
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (377 KB) |  | HTML iconHTML  

    A large number of the adult population suffers from some kind of back pain during their lifetime. Part of the process of diagnosing and treating such back pain is for a clinician to collect information as to the type and location of the pain that is being suffered. Traditional approaches to gathering and visualizing this pain data have relied on simple 2-D representations of the human body, where different types of sensation are recorded with various monochrome symbols. Although patients have been shown to prefer such drawings to traditional questionnaires, these pain drawings can be limited in their ability to accurately record pain. The work described in this paper proposes an alternative that uses a 3-D representation of the human body, which can be marked in color to visualize and record the pain data. This study has shown that the new approach is a promising development in this area of medical practice and has been positively received by patients and clinicians alike. View full abstract»

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  • A Cryptographic Key Management Solution for HIPAA Privacy/Security Regulations

    Publication Year: 2008 , Page(s): 34 - 41
    Cited by:  Papers (28)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (117 KB) |  | HTML iconHTML  

    The Health Insurance Portability and Accountability Act (HIPAA) privacy and security regulations are two crucial provisions in the protection of healthcare privacy. Privacy regulations create a principle to assure that patients have more control over their health information and set limits on the use and disclosure of health information. The security regulations stipulate the provisions implemented to guard data integrity, confidentiality, and availability. Undoubtedly, the cryptographic mechanisms are well defined to provide suitable solutions. In this paper, to comply with the HIPAA regulations, a flexible cryptographic key management solution is proposed to facilitate interoperations among the applied cryptographic mechanisms. In addition, case of consent exceptions intended to facilitate emergency applications and other possible exceptions can also be handled easily. View full abstract»

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  • Radial Basis Function Neural Networks Classification for the Recognition of Idiopathic Pulmonary Fibrosis in Microscopic Images

    Publication Year: 2008 , Page(s): 42 - 54
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1560 KB) |  | HTML iconHTML  

    This study investigates the potential of applying the radial basis function (RBF) neural network architecture for the classification of biological microscopic images displaying lung tissue sections with idiopathic pulmonary fibrosis. For the development of the RBF classifiers, the fuzzy means clustering algorithm is utilized. This method is based on a fuzzy partition of the input space and requires only a short amount of time to select both the structure and the parameters of the RBF classifier. The new technique was applied in lung sections acquired using a microscope and captured by a digital camera, at a magnification of 4times. Age-and sex-matched, 6-to 8-week-old mice (five for each time point and five as control) were used for the induction of pulmonary fibrosis (cf. bleomycin). Bleomycin administration initially induces lung inflammation that is followed by a progressive destruction of the normal lung architecture. The captured images correspond to 7,15, and 23 days after bleomycin or saline injection and bronchoalveolar lavage (BAL) has been performed to the mice sample. The images were analyzed and color features were extracted. A support vector machines (SVMs)-based classifier was also employed for the same problem. The resulting scores derived by visual assessment of the images by expert pathologists were compared with the RBF and SVM classification outcome. Overall, the RBF neural network had a slightly better performance than that of the SVM classifier, but both performed very well, matching to a great percentage the scoring of the experts. There are some erroneous predictions of the algorithm for the regions characterized as "ill" regions (i.e., some bronchia were wrongly classified as fibrotic areas); however, in general, the algorithm worked pretty fine in distinguishing pathologic from normal in most cases and for heterogeneous fibrotic foci, achieving high values in terms of specificity and sensitivity. View full abstract»

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  • A Novel Breast Tissue Density Classification Methodology

    Publication Year: 2008 , Page(s): 55 - 65
    Cited by:  Papers (46)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (342 KB) |  | HTML iconHTML  

    It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment. View full abstract»

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  • Miniature in vivo Robots for Remote and Harsh Environments

    Publication Year: 2008 , Page(s): 66 - 75
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (920 KB) |  | HTML iconHTML  

    Long-term human space exploration will require contingencies for emergency medical procedures including some capability to perform surgery. The ability to perform minimally invasive surgery (MIS) would be an important capability. The use of small incisions reduces surgical risk, but also eliminates the ability of the surgeon to view and touch the surgical environment directly. Robotic surgery, or telerobotic surgery, may provide emergency surgical care in remote or harsh environments such as space flight, or extremely forward environments such as battlefields. However, because current surgical robots are large and require extensive support personnel, their implementation has remained limited in forward environments, and they would be difficult, or impossible, to use in space flight or on battlefields. This paper presents experimental analysis of miniature fixed-base and mobile in vivo robots to support MIS surgery in remote and harsh environments. The objective is to develop wireless imaging and task-assisting robots that can be placed inside the abdominal cavity during surgery. Such robots will provide surgical task assistance and enable an on-site or remote surgeon to view the surgical environment from multiple angles. This approach is applicable to long-duration space flight, battlefield situations, and for traditional medical centers and other remote surgical locations. View full abstract»

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  • DefibViz: A Visualization Tool for the Assessment of Electrode Parameters on Transthoracic Defibrillation Thresholds

    Publication Year: 2008 , Page(s): 76 - 86
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1361 KB) |  | HTML iconHTML  

    DefibViz is a software application developed for defibrillation simulation and visualization. It exploits both surface techniques and methods for the interactive exploration of volumetric datasets for the analysis of transthoracic defibrillation simulation results. DefibViz has a graphical user interface for the specification of the shape, size, position, and applied voltage of a defibrillator's electrodes. An option is provided for using 3D slice plane widgets, which operate on the volumetric datasets, such that the distribution of the voltage gradient induced by an electric shock can be visually inspected in various tissues throughout the myocardium and torso. One goal of DefibViz is to enhance understanding of how electrode parameters relate to the change of the voltage gradient distribution throughout the heart, which may help lead to optimal defibrillator design. DefibViz is significant, in that, it is built by using an open-source graphics and visualization framework providing a platform for subsequent modifiability and extensibility. Moreover, it integrates simulation and visualization techniques, which previously required the running of several independent software executables, into an enhanced, seamless, and comprehensive software application. View full abstract»

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  • Performance Evaluation of Neural Network and Linear Predictors for Near-Lossless Compression of EEG Signals

    Publication Year: 2008 , Page(s): 87 - 93
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (718 KB) |  | HTML iconHTML  

    This paper presents a comparison of the performances of neural network and linear predictors for near-lossless compression of EEG signals. Three neural network predictors, namely, single-layer perceptron (SLP), multilayer perceptron (MLP), and Elman network (EN), and two linear predictors, namely, autoregressive model (AR) and finite-impulse response filter (FIR) are used. For all the predictors, uniform quantization is applied on the residue signals obtained as the difference between the original and the predicted values. The maximum allowable reconstruction error delta is varied to determine the theoretical bound delta0 for near-lossless compression and the corresponding bit rate rp. It is shown that among all the predictors, the SLP yields the best results in achieving the lowest values for delta0 and rp. The corresponding values of the fidelity parameters, namely, percent of root-mean-square difference, peak SNR and cross correlation are also determined. A compression efficiency of 82.8% is achieved using the SLP with a near-lossless bound delta0=3, with the diagnostic quality of the reconstructed EEG signal preserved. Thus, the proposed near-lossless scheme facilitates transmission of real time as well as offline EEG signals over network to remote interpretation center economically with less bandwidth utilization compared to other known lossless and near-lossless schemes. View full abstract»

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  • WS/PIDS: Standard Interoperable PIDS in Web Services Environments

    Publication Year: 2008 , Page(s): 94 - 99
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (248 KB) |  | HTML iconHTML  

    An electronic health record depends on the consistent handling of people's identities within and outside healthcare organizations. Currently, the Person Identification Service (PIDS), a CORBA specification, is the only well-researched standard that meets these needs. In this paper, we introduce WS/PIDS, a PIDS specification for Web Services (WS) that closely matches the original PIDS and improves on it by providing explicit support for medical multimedia attributes. WS/PIDS is currently supported by a test implementation, layered on top of a PIDS back-end, with Java- and NET-based, and Web clients. WS/PIDS is interoperable among platforms; it preserves PIDS semantics to a large extent, and it is intended to be fully compliant with established and emerging WS standards. The specification is open source and immediately usable in dynamic clinical systems participating in grid environments. WS/PIDS has been tested successfully with a comprehensive set of use cases, and it is being used in a clinical research setting. View full abstract»

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  • A Spine X-Ray Image Retrieval System Using Partial Shape Matching

    Publication Year: 2008 , Page(s): 100 - 108
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (511 KB) |  | HTML iconHTML  

    In recent years, there has been a rapid increase in the size and number of medical image collections. Thus, the development of appropriate methods for medical information retrieval is especially important. In a large collection of spine X-ray images, maintained by the National Library of Medicine, vertebral boundary shape has been determined to be relevant to pathology of interest. This paper presents an innovative partial shape matching (PSM) technique using dynamic programming (DP) for the retrieval of spine X-ray images. The improved version of this technique called corner-guided DP is introduced. It uses nine landmark boundary points for DP search and improves matching speed by approximately 10 times compared to traditional DP. The retrieval accuracy and processing speed of the retrieval system based on the new corner-guided PSM method are evaluated and included in this paper. View full abstract»

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  • Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images

    Publication Year: 2008 , Page(s): 109 - 117
    Cited by:  Papers (39)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (754 KB) |  | HTML iconHTML  

    High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems. View full abstract»

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  • A Sorting System for Hierarchical Grading of Diabetic Fundus Images: A Preliminary Study

    Publication Year: 2008 , Page(s): 118 - 130
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1142 KB) |  | HTML iconHTML  

    Diabetic retinopathy is a leading cause of blindness in developed countries. Diabetic patients can prevent severe visual loss by attending regular eye examinations and receiving timely treatments. In the United States, standard protocols have been developed and refined for years to provide better screening and evaluation procedures of the fundus images. Due to the emerging number of diabetic retinopathy cases, accurate and efficient evaluations of the fundus images have become a serious burden for the ophthalmologists or care providers. While diabetic retinopathy remains too complicated to call for an automatic diagnosis system, an efficient tool to facilitate the grading process with a limited number of personnel is in great demand. The current study is to develop a sorting system with a user-friendly interface, based upon the standardized early treatment diabetic retinopathy study (ETDRS) protocol, to assist the professional graders. The raw fundus images will be screened and assigned to different graders according to their skill levels and experiences. The developed hierarchical sorting process will greatly support the graders and enhance their efficiency and throughput. The proposed hybrid intelligent system with multilevel knowledge representation is used to construct this sorting system. A preliminary case study is conducted using only the features of the spot lesion group coupled with the ETDRS standard to demonstrate its feasibility and performance. The results obtained from the case study show a promising future. View full abstract»

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  • Order form for reprints

    Publication Year: 2008 , Page(s): 131
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  • Explore IEL IEEE's most comprehensive resource [advertisement]

    Publication Year: 2008 , Page(s): 132
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  • IEEE Transactions on Information Technology in Biomedicine Information for authors

    Publication Year: 2008 , Page(s): C3
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Aims & Scope

The IEEE Transactions on Information Technology in Biomedicine publishes basic and applied papers of information technology applications in health, healthcare and biomedicine.

 

This Transaction ceased publication in 2012. The current retitled publication is IEEE Journal of Biomedical and Health Informatics.

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Meet Our Editors

Editor-in-Chief
Yuan-ting Zhang
427, Ho Sin Hang Engineering Building, The Chinese
University of Hong Kong, Shatin, NT, Hong Kong
ytzhang@ee.cuhk.edu.hk
Phone:+852 2609-8458
Fax:+852 2609-5558