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Information Technology in Biomedicine, IEEE Transactions on

Issue 4 • Date July 2009

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Displaying Results 1 - 25 of 36
  • Table of contents

    Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Information Technology in Biomedicine publication information

    Page(s): C2
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    Freely Available from IEEE
  • Table of contents

    Page(s): 413 - 414
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    Freely Available from IEEE
  • Guest Editorial: Introduction to the Special Section on Biomedical Informatics

    Page(s): 415 - 418
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (93 KB)  

    The five papers in this special section provide a snapshot of emerging technologies in biomedical informatics. The selected papers were originally presented at the International Special Topic Conference on Information Technology in Biomedicine, held in October 2006, in Ioannina, Epirus, Greece. View full abstract»

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  • Complementary DNA Microarray Image Processing Based on the Fuzzy Gaussian Mixture Model

    Page(s): 419 - 425
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (782 KB) |  | HTML iconHTML  

    The objective of this paper was to investigate the segmentation ability of the fuzzy Gaussian mixture model (FGMM) clustering algorithm, applied on complementary DNA (cDNA) images. Following a standard established procedure, a simulated microarray image of 1600 cells, each containing one spot, was produced. For further evaluation of the algorithm, three real microarray images were also used, each containing 6400 spots. For the task of locating spot borders and surrounding background (BG) in each cell, an automatic gridding process was developed and applied on microarray images. The FGMM and the Gaussian mixture model (GMM) algorithms were applied to each cell with the purpose of discriminating foreground (FG) from BG. The segmentation abilities of both algorithms were evaluated by means of the segmentation matching factor, coefficient of determination, and concordance correlation, in respect to the actual classes (FG-BG pixels) of the simulated spots. Pairwise correlation and mean absolute error of the real images among replicates were also calculated. The FGMM was found to perform better and with equal processing time, as compared to the GMM, rendering the FGMM algorithm an efficient alternative for segmenting cDNA microarray images. View full abstract»

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  • Ventricular Intramyocardial Electrograms and Their Expected Potential for Cardiac Risk Surveillance, Telemonitoring, and Therapy Management

    Page(s): 426 - 432
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (753 KB) |  | HTML iconHTML  

    Ventricular intramyocardial electrograms are recorded with electrodes directly from the heart either in intraventricular or epimyocardial position and may be acquired either from the spontaneously beating or from the paced heart. The morphology of these signals differs significantly from that of body surface ECG recordings. Although the morphology shows general characteristics, it additionally depends on different individual impacts. This problem of individual evaluation is briefly discussed. As an appropriate methodology for its solution, personalized referencing based on similarity averaging has been employed. A more general approach may be model-based signal interpretation, which is still under investigation. The preliminary results reveal a promising potential of intramyocardial electrograms for cardiac risk surveillance, e.g., for arrhythmia detection, recognition of rejection events in transplanted hearts, and assessment of hemodynamic performance. Employing implants with telemetric capabilities may render possible permanent and even continuous cardiac telemonitoring. Furthermore, the signals can be utilized for supporting therapy management, e.g., in patients with different kinds of cardiomyopathies. This paper shall demonstrate some preliminary results and discuss the expected potential. View full abstract»

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  • Assessment of Linear and Nonlinear Synchronization Measures for Analyzing EEG in a Mild Epileptic Paradigm

    Page(s): 433 - 441
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (459 KB) |  | HTML iconHTML  

    Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that produce no brain dysfunction. In this study, cognitive function is evaluated in children with mild epileptic seizures controlled with common antiepileptic drugs. Under this prism, we propose a concise technical framework of combining and validating both linear and nonlinear methods to efficiently evaluate (in terms of synchronization) neurophysiological activity during a visual cognitive task consisting of fractal pattern observation. We investigate six measures of quantifying synchronous oscillatory activity based on different underlying assumptions. These measures include the coherence computed with the traditional formula and an alternative evaluation of it that relies on autoregressive models, an information theoretic measure known as minimum description length, a robust phase coupling measure known as phase-locking value, a reliable way of assessing generalized synchronization in state-space and an unbiased alternative called synchronization likelihood. Assessment is performed in three stages; initially, the nonlinear methods are validated on coupled nonlinear oscillators under increasing noise interference; second, surrogate data testing is performed to assess the possible nonlinear channel interdependencies of the acquired EEGs by comparing the synchronization indexes under the null hypothesis of stationary, linear dynamics; and finally, synchronization on the actual data is measured. The results on the actual data suggest that there is a significant difference between normal controls and epileptics, mostly apparent in occipital-parietal lobes during fractal observation tests. View full abstract»

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  • A Pattern Similarity Scheme for Medical Image Retrieval

    Page(s): 442 - 450
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (383 KB) |  | HTML iconHTML  

    In this paper, we propose a novel scheme for efficient content-based medical image retrieval, formalized according to the PAtterns for Next generation DAtabase systems (PANDA) framework for pattern representation and management. The proposed scheme involves block-based low-level feature extraction from images followed by the clustering of the feature space to form higher-level, semantically meaningful patterns. The clustering of the feature space is realized by an expectation-maximization algorithm that uses an iterative approach to automatically determine the number of clusters. Then, the 2-component property of PANDA is exploited: the similarity between two clusters is estimated as a function of the similarity of both their structures and the measure components. Experiments were performed on a large set of reference radiographic images, using different kinds of features to encode the low-level image content. Through this experimentation, it is shown that the proposed scheme can be efficiently and effectively applied for medical image retrieval from large databases, providing unsupervised semantic interpretation of the results, which can be further extended by knowledge representation methodologies. View full abstract»

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  • A 3-D Collision Handling Algorithm for Surgery Simulation Based on Feedback Fuzzy Logic

    Page(s): 451 - 457
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (600 KB) |  | HTML iconHTML  

    In this paper, we propose a new method for collision handling between 3-D deformable (organs) and rigid (surgical tools) objects valid for nonstructured interaction scenes and, specifically, for laparoscopic surgery simulators. During the simulation step, vertices of the organ detected as collided must be accurately shifted out of the tool to elude a visual interpenetration. The proposed approach obtains the new position of each collided vertex of the organ taking into account both kinematic information of the surgical tool and geometric information of the organ surface that surrounds the vertex under analysis. Three parameters inferred from a fuzzy feedback system weigh the nature of the tool motion with respect to the organ. Experimental results show that the solution proposed in this paper is able to avoid the interpenetration among the multiple colliding points efficiently with physically and spatially coherent results. View full abstract»

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  • Wavelet-Based Compression With ROI Coding Support for Mobile Access to DICOM Images Over Heterogeneous Radio Networks

    Page(s): 458 - 466
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (891 KB) |  | HTML iconHTML  

    Most of the commercial medical image viewers do not provide scalability in image compression and/or region of interest (ROI) encoding/decoding. Furthermore, these viewers do not take into consideration the special requirements and needs of a heterogeneous radio setting that is constituted by different access technologies [e.g., general packet radio services (GPRS)/ universal mobile telecommunications system (UMTS), wireless local area network (WLAN), and digital video broadcasting (DVB-H)]. This paper discusses a medical application that contains a viewer for digital imaging and communications in medicine (DICOM) images as a core module. The proposed application enables scalable wavelet-based compression, retrieval, and decompression of DICOM medical images and also supports ROI coding/decoding. Furthermore, the presented application is appropriate for use by mobile devices activating in heterogeneous radio settings. In this context, performance issues regarding the usage of the proposed application in the case of a prototype heterogeneous system setup are also discussed. View full abstract»

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  • Achieving Clinical Statement Interoperability Using R-MIM and Archetype-Based Semantic Transformations

    Page(s): 467 - 477
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB) |  | HTML iconHTML  

    Effective use of electronic healthcare records (EHRs) has the potential to positively influence both the quality and the cost of health care. Consequently, sharing patient's EHRs is becoming a global priority in the healthcare information technology domain. This paper addresses the interoperability of EHR structure and content. It describes how two different EHR standards derived from the same reference information model (RIM) can be mapped to each other by using archetypes, refined message information model (R-MIM) derivations, and semantic tools. It is also demonstrated that well-defined R-MIM derivation rules help tracing the class properties back to their origins when the R-MIMs of two EHR standards are derived from the same RIM. Using well-defined rules also enable finding equivalences in the properties of the source and target EHRs. Yet an R-MIM still defines the concepts at the generic level. Archetypes (or templates), on the other hand, constrain an R-MIM to domain-specific concepts, and hence, provide finer granularity semantics. Therefore, while mapping clinical statements between EHRs, we also make use of the archetype semantics. Derivation statements are inferred from the Web Ontology Language definitions of the RIM, the R-MIMs, and the archetypes. Finally, we show how to transform Health Level Seven clinical statement instances to EHRcom clinical statement instances and vice versa by using the generated mapping definitions. View full abstract»

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  • Eigenvector Methods for Automated Detection of Electrocardiographic Changes in Partial Epileptic Patients

    Page(s): 478 - 485
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (215 KB) |  | HTML iconHTML  

    In this paper, the automated diagnostic systems trained on diverse and composite features were presented for detection of electrocardiographic changes in partial epileptic patients. In practical applications of pattern recognition, there are often diverse features extracted from raw data that require recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Two types (normal and partial epilepsy) of ECG beats (180 records from each class) were obtained from the Physiobank database. The multilayer perceptron neural network (MLPNN), combined neural network (CNN), mixture of experts (ME), and modified mixture of experts (MME) were tested and benchmarked for their performance on the classification of the studied ECG signals, which were trained on diverse or composite features. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The present research demonstrated that the MME trained on the diverse features achieved accuracy rates (total classification accuracy is 99.44%) that were higher than that of the other automated diagnostic systems. View full abstract»

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  • Automatic Detection System for Cough Sounds as a Symptom of Abnormal Health Condition

    Page(s): 486 - 493
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (746 KB) |  | HTML iconHTML  

    The problem of attending to the health of the aged who live alone has became an important issue in developed countries. One way of solving the problem is to check their health condition by a remote-monitoring technique and support them with well-timed treatment. The purpose of this study is to develop an automatic system that can monitor a health condition in real time using acoustical information and detect an abnormal symptom. In this study, cough sound was chosen as a representative acoustical symptom of abnormal health conditions. For the development of the system distinguishing a cough sound from other environmental sounds, a hybrid model was proposed that consists of an artificial neural network (ANN) model and a hidden Markov model (HMM). The ANN model used energy cepstral coefficients obtained by filter banks based on human auditory characteristics as input parameters representing a spectral feature of a sound signal. Subsequently, an output of this ANN model and a filtered envelope of the signal were used for making an input sequence for the HMM that deals with the temporal variation of the sound signal. Compared with the conventional HMM using Mel-frequency cepstral coefficients, the proposed hybrid model improved recognition rates on low SNR from 5 dB down to -10 dB. Finally, a preliminary prototype of the automatic detection system was simply illustrated. View full abstract»

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  • Realization of a Universal Patient Identifier for Electronic Medical Records Through Biometric Technology

    Page(s): 494 - 500
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (279 KB) |  | HTML iconHTML  

    The technology exists for the migration of healthcare data from its archaic paper-based system to an electronic one, and, once in digital form, to be transported anywhere in the world in a matter of seconds. The advent of universally accessible healthcare data has benefited all participants, but one of the outstanding problems that must be addressed is how the creation of a standardized nationwide electronic healthcare record system in the United States would uniquely identify and match a composite of an individual's recorded healthcare information to an identified individual patients out of approximately 300 million people to a 1:1 match. To date, a few solutions to this problem have been proposed that are limited in their effectiveness. We propose the use of biometric technology within our fingerprint, iris, retina scan, and DNA (FIRD) framework, which is a multiphase system whose primary phase is a multilayer consisting of these four types of biometric identifiers: 1) fingerprint; 2) iris; 3) retina scan; and 4) DNA. In addition, it also consists of additional phases of integration, consolidation, and data discrepancy functions to solve the unique association of a patient to their medical data distinctively. This would allow a patient to have real-time access to all of their recorded healthcare information electronically whenever it is necessary, securely with minimal effort, greater effectiveness, and ease. View full abstract»

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  • Refining an Automatic EDSS Scoring Expert System for Routine Clinical Use in Multiple Sclerosis

    Page(s): 501 - 511
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (567 KB) |  | HTML iconHTML  

    The expanded disability status scale (EDSS) has been the most widely used measure of disability in multiple sclerosis (MS) clinical trials. Although EDSS has the advantage of familiarity with respect to recent proposals, and remains the de facto standard, it is difficult to use consistently between evaluators. Automatic EDSS (AEDSS) is an expert system designed to overcome this problem. It constrains the neurologist to follow precise reasoning steps, enhancing EDSS reliability. In this paper, we show how a deep analysis of the neurological knowledge involved has been essential for adopting AEDSS in routine clinical use. We present an ontology for the EDSS domain and highlight the enhancements to AEDSS due to this additional knowledge. A validation experiment in four MS centers in Italy showed that AEDSS reduces interrater variability, and in many cases, is able to correct errors of neurologists. View full abstract»

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  • Heartbeat Time Series Classification With Support Vector Machines

    Page(s): 512 - 518
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (362 KB) |  | HTML iconHTML  

    In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease. View full abstract»

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  • Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Images

    Page(s): 519 - 527
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (629 KB) |  | HTML iconHTML  

    Thyroid nodules are solid or cystic lumps formed in the thyroid gland and may be caused by a variety of thyroid disorders. This paper presents a novel active contour model for precise delineation of thyroid nodules of various shapes according to their echogenicity and texture, as displayed in ultrasound (US) images. The proposed model, named joint echogenicity-texture (JET), is based on a modified Mumford-Shah functional that, in addition to regional image intensity, incorporates statistical texture information encoded by feature distributions. The distributions are aggregated within the functional through new log-likelihood goodness-of-fit terms. The JET model requires only a rough region of interest within the thyroid gland as input and automatically proceeds with precise delineation of the nodules, revealing their shape and size. The performance of the JET model was validated on a range of US images displaying hypoechoic and isoechoic nodules of various shapes. The quantification of the results shows that the JET model: 1) provides precise delineations of thyroid nodules as compared to ldquoground truthrdquo delineations obtained by experts and 2)copes with the limitations of the previous thyroid US delineation approaches as it is capable of delineating thyroid nodules regardless of their echogenicity or shape. View full abstract»

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  • A Modified Matched Filter With Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy

    Page(s): 528 - 534
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (786 KB) |  | HTML iconHTML  

    The early diagnosis of proliferative diabetic retinopathy (PDR), a common complication of diabetes that damages the retina, is crucial to the protection of the vision of diabetes sufferers. The onset of PDR is signaled by the appearance of neovascular net. Such neovascular nets might be identified using retinal vessel extraction techniques. The commonly used matched filter methods often produce false positive detections of neovascular nets due to their proneness to detect nonline edges as well as lines. In this paper, we propose a modified matched filter for retinal vessel extraction that applies a local vessel cross-section analysis using double-sided thresholding to reduce false responses to nonlinear edges. Our proposed modified matched filters demonstrated higher true positive rate and lesser false detection than existing matched-filter-based schemes in vessel extraction. View full abstract»

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  • A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images

    Page(s): 535 - 545
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (621 KB) |  | HTML iconHTML  

    Currently, there is an increasing interest for setting up medical systems that can screen a large number of people for sight threatening diseases, such as diabetic retinopathy. This paper presents a method for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques. The color retinal images are segmented using fuzzy c-means clustering following some preprocessing steps, i.e., color normalization and contrast enhancement. The entire segmented images establish a dataset of regions. To classify these segmented regions into exudates and nonexudates, a set of initial features such as color, size, edge strength, and texture are extracted. A genetic-based algorithm is used to rank the features and identify the subset that gives the best classification results. The selected feature vectors are then classified using a multilayer neural network classifier. The algorithm was implemented using a large image dataset consisting of 300 manually labeled retinal images, and could identify affected retinal images with 96.0% sensitivity while it recognized 94.6% of the normal images, i.e., the specificity. Moreover, the proposed scheme illustrated an accuracy including 93.5% sensitivity and 92.1% predictivity for identification of retinal exudates at the pixel level. View full abstract»

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  • Web-Based Multilayer Viewing Interface for Knee Cartilage

    Page(s): 546 - 553
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (813 KB) |  | HTML iconHTML  

    Many adults suffer from osteoarthritis (OA) with the majority of people over 65 showing radiographic evidence of the disease. To carry out effective diagnosis and treatment, it is necessary to understand the progression of cartilage loss and study the effectiveness of therapeutic interventions. Hence, it is important to have accurate, fast diagnosis of the disease. In this paper, we describe a Web-based user interface that enables the direct viewing of 2-D and 3-D image data from the visceral and tissue levels of the biological continuum (i.e., the continuum comprising systems, viscera, tissue, cells, proteins, and genes)-while preserving geometric integrity. This is achieved despite the fact that the data are from different modalities (i.e., magnetic resonance (MR) and light microscopy). The user interface was tested using image data acquired from a study of articular cartilage thickness in the porcine knee. The interface allows the clinician to view both MR and light microscopy images in an integrated manner-with the information linked geometrically. View full abstract»

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  • Discovering Genes-Diseases Associations From Specialized Literature Using the Grid

    Page(s): 554 - 560
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (476 KB) |  | HTML iconHTML  

    This paper proposes a novel method for text mining on the Grid, aimed at pointing out hidden relationships for hypothesis generation and suitable for semi-interactive querying. The method is based on unsupervised clustering and the outputs are visualized with contextual information. Grid implementation is crucial for feasibility. We demonstrate it with a mining run for discovering genes-diseases associations from bibliographic sources and annotated databases. The proposed methodology is in view of a Grid architecture specialized in bioinformatics mining tasks. Some performance considerations are provided. View full abstract»

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  • Enhancement of Multichannel Chromosome Classification Using a Region-Based Classifier and Vector Median Filtering

    Page(s): 561 - 570
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (843 KB) |  | HTML iconHTML  

    Multichannel chromosome image acquisition is used for cancer diagnosis and research on genetic disorders. This type of imaging, apart from aiding the cytogeneticist in several ways, facilitates the visual detection of chromosome abnormalities. However, chromosome misclassification errors result from different factors, such as uneven hybridization, spectral overlap among fluors, and biochemical noise. In this paper, we enhance the chromosome classification accuracy by making use of a region Bayes classifier that increases the classification accuracy when compared to the already developed pixel-by-pixel classifier and by incorporating the vector median filtering approach for filtering of the image. The method is evaluated using a publicly available database that contains 183 six-channel chromosome sets of images. The overall improvement on the chromosome classification accuracy is 9.99%, compared to the pixel-by-pixel classifier without filtering. This improvement in the chromosome classification accuracy would allow subtle deoxyribonucleic acid abnormalities to be identified easily. The efficiency of the method might further improve by using features extracted from each region and a more sophisticated classifier. View full abstract»

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  • Real-Time 3-D Ultrasound Scan Conversion Using a Multicore Processor

    Page(s): 571 - 574
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (223 KB) |  | HTML iconHTML  

    Real-time 3-D ultrasound scan conversion (SC) in software has not been practical due to its high computation and I/O data handling requirements. In this paper, we describe software-based 3-D SC with high volume rates using a multicore processor, Cell. We have implemented both 3-D SC approaches: 1) the separable 3-D SC where two 2-D coordinate transformations in orthogonal planes are performed in sequence and 2) the direct 3-D SC where the coordinate transformation is directly handled in 3-D. One Cell processor can scan-convert a 192 times 192 times 192 16-bit volume at 87.8 volumes/s with the separable 3-D SC algorithm and 28 volumes/s with the direct 3-D SC algorithm. View full abstract»

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  • Automatic Segmentation and Disentangling of Chromosomes in Q-Band Prometaphase Images

    Page(s): 575 - 581
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (314 KB) |  | HTML iconHTML  

    Karyotype analysis is a widespread procedure in cytogenetics to assess the possible presence of genetics defects. The procedure is lengthy and repetitive, so that an automatic analysis would greatly help the cytogeneticist routine work. Still, automatic segmentation and full disentangling of chromosomes are open issues. We propose an automatic procedure to obtain the separated chromosomes, which are then ready for a subsequent classification step. The segmentation is carried out by means of a space-variant thresholding scheme, which proved to be successful even in presence of hyper- or hypofluorescent regions in the image. Then, the tree of choices to resolve touching and overlapping chromosomes is recursively explored, choosing the best combination of cuts and overlaps based on geometric evidence and image information. We show the effectiveness of the proposed method on routine data acquired with different microscope-camera setup at different laboratories: from 162 images of 117 cells totaling 6683 chromosomes, 94% of the chromosomes were correctly segmented, solving 90% of the overlaps and 90% of the touchings. In order to provide the scientific community with a public dataset, the data used in this paper are available for public download. View full abstract»

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  • Providing Integrity and Authenticity in DICOM Images: A Novel Approach

    Page(s): 582 - 589
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB) |  | HTML iconHTML  

    The increasing adoption of information systems in healthcare has led to a scenario where patient information security is more and more being regarded as a critical issue. Allowing patient information to be in jeopardy may lead to irreparable damage, physically, morally, and socially to the patient, potentially shaking the credibility of the healthcare institution. Medical images play a crucial role in such context, given their importance in diagnosis, treatment, and research. Therefore, it is vital to take measures in order to prevent tampering and determine their provenance. This demands adoption of security mechanisms to assure information integrity and authenticity. There are a number of works done in this field, based on two major approaches: use of metadata and use of watermarking. However, there still are limitations for both approaches that must be properly addressed. This paper presents a new method using cryptographic means to improve trustworthiness of medical images, providing a stronger link between the image and the information on its integrity and authenticity, without compromising image quality to the end user. Use of Digital Imaging and Communications in Medicine structures is also an advantage for ease of development and deployment. 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