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Information Technology in Biomedicine, IEEE Transactions on

Issue 1 • Date March 2002

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Displaying Results 1 - 10 of 10
  • Editorial: information technology in biomedicine: maturational insights

    Page(s): 1 - 7
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    Freely Available from IEEE
  • Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review

    Page(s): 8 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (569 KB) |  | HTML iconHTML  

    The class of geometric deformable models, also known as level sets, has brought tremendous impact to medical imagery due to its capability of topology preservation and fast shape recovery. In an effort to facilitate a clear and full understanding of these powerful state-of-the-art applied mathematical tools, the paper is an attempt to explore these geometric methods, their implementations and integration of regularizers to improve the robustness of these topologically independent propagating curves/surfaces. The paper first presents the origination of level sets, followed by the taxonomy of level sets. We then derive the fundamental equation of curve/surface evolution and zero-level curves/surfaces. The paper then focuses on the first core class of level sets, known as "level sets without regularizers." This class presents five prototypes: gradient, edge, area-minimization, curvature-dependent and application driven. The next section is devoted to second core class of level sets, known as "level sets with regularizers." In this class, we present four kinds: clustering-based, Bayesian bidirectional classifier-based, shape-based and coupled constrained-based. An entire section is dedicated to optimization and quantification techniques for shape recovery when used in the level set framework. Finally, the paper concludes with 22 general merits and four demerits on level sets and the future of level sets in medical image segmentation. We present applications of level sets to complex shapes like the human cortex acquired via MRI for neurological image analysis. View full abstract»

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  • Iterative normalization of cDNA microarray data

    Page(s): 29 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (363 KB) |  | HTML iconHTML  

    Describes an approach to normalizing microarray expression data. The novel feature is to unify the tasks of estimating normalization coefficients and identifying the control gene set. Unification is realized by constructing a window function over the scatter plot defining the subset of constantly expressed genes and by affecting optimization using an iterative procedure. The structure of window function gates contributions to the control gene set used to estimate normalization coefficients. This window measures the consistency of the matched neighborhoods in the scatter plot and provides a means of rejecting control gene outliers. The recovery of normalizational regression and control gene selection are interleaved and are realized by applying coupled operations to the mean square error function. In this way, the two processes bootstrap one another. We evaluate the technique on real microarray data from breast cancer cell lines and complement the experiment with a data cluster visualization study. View full abstract»

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  • Fetal lung maturity analysis using ultrasound image features

    Page(s): 38 - 45
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (271 KB) |  | HTML iconHTML  

    This pilot study was carried out to find the feasibility of analyzing the maturity of the fetal lung using ultrasound images. Data were collected from normal pregnant women at intervals of two weeks from the gestation age of 24 to 38 weeks. Images were acquired at two centers located at different geographical locations. The total data acquired consisted of 750 images of immature and 250 images of mature class. A region of interest of 64×64 pixels was used for extracting the features. Various textural features were computed from the fetal lung and liver images. The ratios of fetal lung to liver feature values were investigated as possible indexes for classifying the images into those from mature (reduced pulmonary risk) and immature (possible pulmonary risk) lung. The features used are fractal dimension, lacunarity, and features derived from the histogram of the images. The following classifiers were used to classify the fetal lung images as belonging to mature or immature lung: nearest neighbor, k-nearest neighbor, modified k-nearest neighbor, multilayer perceptron, radial basis function network, and support vector machines. The classification accuracy obtained for the testing set ranges from 73% to 96%. View full abstract»

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  • A data-hiding technique with authentication, integration, and confidentiality for electronic patient records

    Page(s): 46 - 53
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (293 KB) |  | HTML iconHTML  

    A data-hiding technique called the "bipolar multiple-number base" was developed to provide capabilities of authentication, integration, and confidentiality for an electronic patient record (EPR) transmitted among hospitals through the Internet. The proposed technique is capable of hiding those EPR related data such as diagnostic reports, electrocardiogram, and digital signatures from doctors or a hospital into a mark image. The mark image could be the mark of a hospital used to identify the origin of an EPR. Those digital signatures from doctors and a hospital could be applied for the EPR authentication. Thus, different types of medical data can be integrated into the same mark image. The confidentiality is ultimately achieved by decrypting the EPR related data and digital signatures with an exact copy of the original mark image. The experimental results validate the integrity and the invisibility of the hidden EPR related data. This newly developed technique allows all of the hidden data to be separated and restored perfectly by authorized users. View full abstract»

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  • Fractal analysis in the detection of colonic cancer images

    Page(s): 54 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (221 KB) |  | HTML iconHTML  

    The aim of this study was to investigate the value of fractal dimension in separating normal and cancerous images, and to examine the relationship between fractal dimension and traditional texture analysis features. Forty-four normal images and 58 cancer images from sections of the colon were analyzed. A "leave-one-out" analysis approach was used to classify the samples into each group. With fractal analysis there was a highly significant difference between groups (p<0.0001). Correlation and entropy features showed greater differences between the groups (p<0.0001). Nevertheless, the addition of fractal analysis to the feature analysis improved the sensitivity from 90% to 95% and specificity from 86% to 93%. View full abstract»

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  • Model-based processing scheme for quantitative 4-D cardiac MRI analysis

    Page(s): 59 - 72
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    Presents an integrated model-based processing scheme for cardiac magnetic resonance imaging (MRI), embedded in an interactive computing environment suitable for quantitative cardiac analysis, which provides a set of functions for the extraction, modeling, and visualization of cardiac shape and deformation. The methods apply 4-D processing (three spatial and one temporal) to multiphase multislice MRI acquisitions and produce a continuous 4-D model of the myocardial surface deformation. The model is used to measure diagnostically useful parameters, such as wall motion, myocardial thickening, and myocardial mass measurements. The proposed model-based shape extraction method has the advantage of integrating local information into an overall representation and produces a robust description of cardiac cavities. A learning segmentation process that incorporates a generating-shrinking neural network is combined with a spatiotemporal parametric modeling method through functional basis decomposition. A multiscale approach is adopted, which uses at each step a coarse-scale model defined at the previous step in order to constrain the boundary detection. The main advantages of the proposed methods are efficiency, lack of uncertainty about convergence, and robustness to image artifacts. View full abstract»

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  • Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage

    Page(s): 73 - 85
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (467 KB) |  | HTML iconHTML  

    In the field of template-based medical image analysis, image registration and normalization are frequently used to evaluate and interpret data in a standard template or reference atlas space. Despite the large number of image-registration (warping) techniques developed recently in the literature, only a few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper, we introduce a new approach for analyzing image registration based on a selective-wavelet reconstruction technique using a frequency-adaptive wavelet shrinkage. We study four polynomial-based and two higher complexity nonaffine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging) and functional (positron emission tomography) data. Depending upon the aim of the image registration, we present several warp classification schemes. Our method uses a concise representation of the native and resliced (pre- and post-warp) data in compressed wavelet space to assess quality of registration. This technique is computationally inexpensive and utilizes the image compression, image enhancement, and denoising characteristics of the wavelet-based function representation, as well as the optimality properties of frequency-dependent wavelet shrinkage. View full abstract»

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  • Medical image compression by sampling DCT coefficients

    Page(s): 86 - 94
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1300 KB) |  | HTML iconHTML  

    Advanced medical imaging requires storage of large quantities of digitized clinical data. Due to the constrained bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, transform coding is one of the most effective strategies. Image data in the spatial domain is transformed into the spectral domain after the transformation to attain more compression gains. Based on the quantization strategy, coefficients of low amplitude in the transformed domain are discarded and significant coefficients are preserved to increase the compression ratio without inducing salient distortion. We use an adaptive sampling algorithm by calculating the difference area between correct points and predicted points to decide the significant coefficients. Recording or transmitting the significant coefficients instead of the whole coefficients achieves the goal of compression. On the decoder side, a linear equation is employed to reconstruct the coefficients between two sequent significant coefficients. Simulations are carried out to different medical images, which include sonogram, angiogram, computed tomography, and X-ray images. View full abstract»

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  • An open architecture patient monitoring system using standard technologies

    Page(s): 95 - 98
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    Computer-aided bedside patient monitoring is applied in areas where real-time vital function analysis takes place. Modern bedside monitoring requires not only the networking of bedside monitors with a central monitor but also other standard communication interfaces. In the paper, an approach to patient monitoring is introduced. A patient monitoring system was developed and implemented based on an existing industry standard communication network, using standard hardware components and software technologies. The open architecture system design offers scalability, standard interfaces, and flexible signal interpretation possibilities. View full abstract»

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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.

Full Aims & Scope

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