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Information Technology in Biomedicine, IEEE Transactions on

Issue 3 • Date Sept. 2003

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Displaying Results 1 - 10 of 10
  • Computer-aided tumor detection in endoscopic video using color wavelet features

    Publication Year: 2003 , Page(s): 141 - 152
    Cited by:  Papers (82)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (858 KB) |  | HTML iconHTML  

    We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity. View full abstract»

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  • A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

    Publication Year: 2003 , Page(s): 153 - 162
    Cited by:  Papers (54)  |  Patents (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (655 KB) |  | HTML iconHTML  

    In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease". The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance. View full abstract»

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  • A novel approach to diagnose diabetes based on the fractal characteristics of retinal images

    Publication Year: 2003 , Page(s): 163 - 170
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (555 KB) |  | HTML iconHTML  

    A novel diagnostic scheme to develop quantitative indexes of diabetes is introduced in this paper. The fractal dimension of the vascular distribution is estimated because we discovered that the fractal dimension of a severe diabetic patient's retinal vascular distribution appears greater than that of a normal human's. The issue of how to yield an accurate fractal dimension is to use high-quality images. To achieve a better image-processing result, an appropriate image-processing algorithm is adopted in this paper. Another important fractal feature introduced in this paper is the measure of lacunarity, which describes the characteristics of fractals that have the same fractal dimension but different appearances. For those vascular distributions in the same fractal dimension, further classification can be made using the degree of lacunarity. In addition to the image-processing technique, the resolution of original image is also discussed here. In this paper, the influence of the image resolution upon the fractal dimension is explored. We found that a low-resolution image cannot yield an accurate fractal dimension. Therefore, an approach for examining the lower bound of image resolution is also proposed in this paper. As for the classification of diagnosis results, four different approaches are compared to achieve higher accuracy. In this study, the fractal dimension and the measure of lacunarity have shown their significance in the classification of diabetes and are adequate for use as quantitative indexes. View full abstract»

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  • Workflow-enabled distributed component-based information architecture for digital medical imaging enterprises

    Publication Year: 2003 , Page(s): 171 - 183
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2053 KB) |  | HTML iconHTML  

    Few information systems today offer a flexible means to define and manage the automated part of radiology processes, which provide clinical imaging services for the entire healthcare organization. Even fewer of them provide a coherent architecture that can easily cope with heterogeneity and inevitable local adaptation of applications and can integrate clinical and administrative information to aid better clinical, operational, and business decisions. We describe an innovative enterprise architecture of image information management systems to fill the needs. Such a system is based on the interplay of production workflow management, distributed object computing, Java and Web techniques, and in-depth domain knowledge in radiology operations. Our design adapts the approach of "4+1" architectural view. In this new architecture, PACS and RIS become one while the user interaction can be automated by customized workflow process. Clinical service applications are implemented as active components. They can be reasonably substituted by applications of local adaptations and can be multiplied for fault tolerance and load balancing. Furthermore, the workflow-enabled digital radiology system would provide powerful query and statistical functions for managing resources and improving productivity. This paper will potentially lead to a new direction of image information management. We illustrate the innovative design with examples taken from an implemented system. View full abstract»

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  • Fast JPEG 2000 decoder and its use in medical imaging

    Publication Year: 2003 , Page(s): 184 - 190
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (353 KB) |  | HTML iconHTML  

    Over the last decade, a picture archiving and communications system (PACS) has been accepted by an increasing number of clinical organizations. Today, PACS is considered as an essential image management and productivity enhancement tool. Image compression could further increase the attractiveness of PACS by reducing the time and cost in image transmission and storage as long as 1) image quality is not degraded and 2) compression and decompression can be done fast and inexpensively. Compared to JPEG, JPEG 2000 is a new image compression standard that has been designed to provide improved image quality at the expense of increased computation. Typically, the decompression time has a direct impact on the overall response time taken to display images after they are requested by the radiologist or referring clinician. We present a fast JPEG 2000 decoder running on a low-cost programmable processor. It can decode a losslessly compressed 2048×2048 CR image in 1.51 s. Using this kind of a decoder, performing JPEG 2000 decompression at the PACS display workstation right before images are displayed becomes viable. A response time of 2 s can be met with an effective transmission throughput between the central short-term archive and the workstation of 4.48 Mb/s in case of CT studies and 20.2 Mb/s for CR studies. We have found that JPEG 2000 decompression at the workstation is advantageous in that the desired response time can be obtained with slower communication channels compared to transmission of uncompressed images. View full abstract»

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  • Improving reliability of gene selection from microarray functional genomics data

    Publication Year: 2003 , Page(s): 191 - 196
    Cited by:  Papers (11)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (268 KB) |  | HTML iconHTML  

    Constructing a classifier based on microarray gene expression data has recently emerged as an important problem for cancer classification. Recent results have suggested the feasibility of constructing such a classifier with reasonable predictive accuracy under the circumstance where only a small number of cancer tissue samples of known type are available. Difficulty arises from the fact that each sample contains the expression data of a vast number of genes and these genes may interact with one another. Selection of a small number of critical genes is fundamental to correctly analyze the otherwise overwhelming data. It is essential to use a multivariate approach for capturing the correlated structure in the data. However, the curse of dimensionality leads to the concern about the reliability of selected genes. Here, we present a new gene selection method in which error and repeatability of selected genes are assessed within the context of M-fold cross-validation. In particular, we show that the method is able to identify source variables underlying data generation. View full abstract»

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  • 3-D snake for US in margin evaluation for malignant breast tumor excision using mammotome

    Publication Year: 2003 , Page(s): 197 - 201
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1405 KB) |  | HTML iconHTML  

    The goal of this paper is to use the three-dimensional (3-D) snake technique in 3-D ultrasound to obtain the tumor contour for the pre- and the post-operative malignant breast excision by the vacuum assisted biopsy instrument Mammotome. This technique of assessing the margin of two can help the physician to evaluate the effect of the surgery. By using the anisotropic diffusion filter, the noise and speckles can be reduced. Then the stick detection is adopted for enhancing the edge. Finally, the gradient vector flow (GVF) snake is used to obtain the tumor contour. These techniques are extended to the 3-D techniques to increase the accuracy and robust of segmentation results. We hope that this study can help physicians to improve the minimal invasive operation for a breast tumor. View full abstract»

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  • A contextual role-based access control authorization model for electronic patient record

    Publication Year: 2003 , Page(s): 202 - 207
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (264 KB) |  | HTML iconHTML  

    The design of proper models for authorization and access control for electronic patient record (EPR) is essential to a wide scale use of EPR in large health organizations. In this paper, we propose a contextual role-based access control authorization model aiming to increase the patient privacy and the confidentiality of patient data, whereas being flexible enough to consider specific cases. This model regulates user's access to EPR based on organizational roles. It supports a role-tree hierarchy with authorization inheritance; positive and negative authorizations; static and dynamic separation of duties based on weak and strong role conflicts. Contextual authorizations use environmental information available at access time, like user/patient relationship, in order to decide whether a user is allowed to access an EPR resource. This enables the specification of a more flexible and precise authorization policy, where permission is granted or denied according to the right and the need of the user to carry out a particular job function. View full abstract»

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  • Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules

    Publication Year: 2003 , Page(s): 208 - 217
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (498 KB) |  | HTML iconHTML  

    Identifying abdominal organs is one of the essential steps in visualizing organ structure to assist in teaching, clinical training, diagnosis, and medical image retrieval. However, due to partial volume effects, gray-level similarities of adjacent organs, contrast media affect, and the relatively high variations of organ position and shape, automatically identifying abdominal organs has always been a high challenging task. To conquer these difficulties, this paper proposes combining a multimodule contextual neural network and spatial fuzzy rules and fuzzy descriptors for automatically identifying abdominal organs from a series of CT image slices. The multimodule contextual neural network segments each image slice through a divide-and-conquer concept, embedded within multiple neural network modules, where the results obtained from each module are forwarded to other modules for integration, in which contextual constraints are enforced. With this approach, the difficulties arising from partial volume effects, gray-level similarities of adjacent organs, and contrast media affect can be reduced to the extreme. To address the issue of high variations in organ position and shape, spatial fuzzy rules and fuzzy descriptors are adopted, along with a contour modification scheme implementing consecutive organ region overlap constraints. This approach has been tested on 40 sets of abdominal CT images, where each set consists of about 40 image slices. We have found that 99% of the organ regions in the test images are correctly identified as its belonging organs, implying the high promise of the proposed method. View full abstract»

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  • Neural network-based assessment of prognostic markers and outcome prediction in bilharziasis-associated bladder cancer

    Publication Year: 2003 , Page(s): 218 - 224
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (285 KB) |  | HTML iconHTML  

    In this paper the potential value of two prognostic factors, namely, bilharziasis status and tumor histological type, is investigated in relation to their abilities to predict disease progression and outcome of patients with bladder cancer, using radial basis function (RBF) neural networks. The bladder cancer data set is described by eight clinical and pathological markers. Two outcomes are of interest: either a patient is alive and free of disease or the patient is dead within five years of diagnosis. Three hundred and twenty-one (321) patients are involved in this retrospective study, 83.5% of whom had been confirmed with bilharziasis history. Selected marker subsets are examined to improve the outcome predictive accuracy and to evaluate the effects of the assessed prognostic factors on such outcome. The highest predictive accuracy for patients with bladder adenocarcinoma, as obtained from the RBF network, is found to be 85% with one subset of markers. The predictive analysis shows that bilharziasis history and patients' histology type are both important prognostic factors in prediction and, for each histology type, different marker combinations with significant characteristics have been observed. 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