Breast image registration techniques: a survey.

Breast cancer is the most common type of cancer in women worldwide. Image registration plays an important role in breast cancer detection. This paper gives an overview of the current state-of-the-art in the breast image registration techniques. For the intramodality registration techniques, X-ray, MRI, and ultrasound are the primary focuses of interest. Intermodality techniques will cover the combination of different modalities. Validation of breast registration methods is also discussed.

Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals.

The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the r-r interval, etc., may help to identify the nature of disease afflicting the heart. However, human observer cannot directly monitor these subtle details and it is difficult to evaluate the cardiac health using ECG alone. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. In this study, a computer based analysis and display of the heterogeneous signals for the detection of life threatening states is demonstrated using fuzzy logic based data fusion. And to evaluate the severity of the disease a new parameter, deterioration index is proposed and results are tabulated for various cases.

Non-linear analysis of EEG signals at various sleep stages.

Application of non-linear dynamics methods to the physiological sciences demonstrated that non-linear models are useful for understanding complex physiological phenomena such as abrupt transitions and chaotic behavior. Sleep stages and sustained fluctuations of autonomic functions such as temperature, blood pressure, electroencephalogram (EEG), etc., can be described as a chaotic process. The EEG signals are highly subjective and the information about the various states may appear at random in the time scale. Therefore, EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. The sleep data analysis is carried out using non-linear parameters: correlation dimension, fractal dimension, largest Lyapunov entropy, approximate entropy, Hurst exponent, phase space plot and recurrence plots. These non-linear parameters quantify the cortical function at different sleep stages and the results are tabulated.

Plaque imaging using ultrasound, magnetic resonance and computer tomography: a review.

Different classifications have been proposed in the literature for the characterization of atherosclerotic plaque morphology, resulting in considerable confusion. For example plaques containing medium of high level uniform echoes were classified as homogeneous by others and correspond closely to dense and calcified plaques, other types. This survey is to understand different types of plaque when imaged using ultrasound and MR.

Understanding the Social Implications of ICT in Medicine and Health: The Role of Professional Societies.

In past times, engineers and other ICT professionals could normally function exclusively within an environment of purely technical dimensions. This sphere could be easily delineated from those involving policy, political or social questions. Consequently, these professions could well be characterized as generally isolated from mainstream society, engendering a condition that Zussman (1985) has described as a "technical rationality that is the engineer's stock-in-trade requir[ing] the calculation of means for the realization of given ends. But it requir[ing] no broad insight into those ends or their consequences". This condition has often led to a perceived technical mindset that according to Florman (1976), draws upon "the comfort that comes with the total absorption in a mechanical environment. The world becomes reduced and manageable, controlled and unchaotic".In a relatively short period of time, ICT has been radically transformed in both its capabilities and reach. Specifically, within the context of this event, the permeation of digital technologies into nearly every aspect of bioengineering and healthcare delivery have broken down the borders between technological pursuits and the larger dynamics of society. This has in turn has produced, according to Williams (2000) a discipline that has "evolved into an open-ended Profession of Everything in a world where technology shades into science, into art, and into management, with no strong institutions to define an overarching mission". Within ICT, H.C. von Baeyer (2003) affirms this status in noting "the frustration of engineers who have at their disposal a variety of methods for measuring the amount of information in a message, but to none deal with its meaning".

Economic impact of telemedicine: a survey.

The economic evaluation of telemedicine has faced difficulties, both in terms of the effectiveness and cost-benefit analyses. The main challenges that lie ahead for economic assessment are: (a) technological changes; (b) sustainability of applications; (c) availability of outcomes and other patient data; (d) generalisability of evaluation results. These challenges have lead to an unsatisfactory modeling of cost analysis of teleradiology systems versus non-teleradiology (visiting radiology services) applications.This paper presents the analysis on the impact of telemedicine on health care. It particularly emphasizes a model for teleradiology cost systems. We study and compare cost analysis of teleradiology system versus non-teleradiology systems. Finally, a model is presented which is made viable for computing the number of patients needed to demonstrate the viability of the telemedicine systems.We conclude the following: (a) that large number of patients is needed to validate the economic impact of telemedicine services; (b) cultural change in USA will bring most prominent effect in improving health care thereby bringing health care costs down. This when combined with improving cost effective technology like telemedicine services will bring the overall health care costs down.

Health informatics: a roadmap for autism knowledge sharing.

With the prevalence of diagnosed autism on the rise, increased efforts are needed to support surveillance, research, and case management. Challenges to collect, analyze and share typical and unique patient information and observations are magnified by expanding provider caseloads, delays in treatment and patient office visits, and lack of sharable data. This paper outlines recommended principles and approaches for utilizing state-of-the-art information systems technology and population-based registries to facilitate collection, analysis, and reporting of autism patient data. Such a platform will increase treatment options and registry information to facilitate diagnosis, treatment and research of this disorder.

Cardiac State Diagnosis using Adaptive Neuro-Fuzzy Technique.

Heart rate signals may either contain indicators of a current disease or even warnings about impending diseases. However, to manually study and pinpoint heart abnormalities in voluminous data is strenuous and time consuming. Here, an adaptive neuro-fuzzy network is used to classify heart abnormalities in ten different cardiac states and shown to be effective.

Cardiac Health Diagnosis using Wavelet Transformation and Phase Space Plots.

Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. This paper presents the continuous time wavelet analysis of heart rate variability signal for disease identification. Phase space plots of heart rate signal for a chosen embedding dimension are compared with the wavelet analysis patterns.

Status of mobile computing in health care: an evidence study.

Information technology (IT) has played an impacting role in helping healthcare professionals increasingly recognize the value of IT in clinical medicine, disease management, treatment strategies and other related areas. Of the numerous IT applications currently pursued, the use of telemedicine is a major force in addressing the healthcare delivery issues. As evidenced by a number of studies, telemedicine has come to play a significant mode of delivery and treatment, especially in the remote rural areas. Despite the growing interest, the R&D advances and the vast number of telemedicine applications currently ongoing, there still seem to be only a few quantitative studies characterizing the economic aspects of telemedicine and how cost-effective such solutions are. Of particular interest to the healthcare community is the effectiveness of the emerging mobile communications in telemedicine especially in home healthcare environment. In this paper we report on a profile of the current status of the use of mobile devices based on an extensive literature search of 213 articles. In particular we have focused our research on reports of the cost effectiveness of mobile telemedicine applications with respect to improved quality of care and medical error reduction. Preliminary results are reported in the paper, with an extended discussion to be provided in the final conference presentation, based on outcomes obtained from a more robust data analysis.

Healthcare compunetics.

Changes in life expectancy, healthy life expectancy and health seeking behaviour are having an impact on the demand for care. Such changes could occur across the whole population, or for specific groups. Changes for specific groups will be particularly affected by policy initiatives, while both these and wider changes will be affected by people's levels of engagement with their health and the health service itself. Levels of education, income and media coverage of health issues are also important. These factors could also encourage an increase in people caring for themselves and their families or community. People are now expecting a patient-centred service with safe high quality treatment, comfortable accommodation services, fast access and an integrated joined-up system. The uptake of integrated Information and Communication technologies (ICT) will be crucial. Healthcare Compunetics, the combination of computing and networking customised for medical and care, will provide the common policy and framework for combined multi-disciplinary research, development, implementation and usage.

Information technology and behavioral medicine: impact on autism treatment & research.

Families, clinicians and researchers involved with varying neurological disorders face amazing challenges to understand, treat, and assist the people they are serving. Autism brings unique challenges and serves as an important model for the application of important concepts in information technology and telemedicine. The rising incidence of autism with limited professional resources has led to more consideration for using information technology and related specialties to link families and professionals, and to implement strategies which implement information technology to improve the outcomes for individuals with autism and their families. These are reviewed in context of the unique health, education, and the research issues facing those dealing with autism.

White and black blood volumetric angiographic filtering: ellipsoidal scale-space approach.

Prefiltering is a critical step in three-dimensional (3-D) segmentation of the blood vessel and its display (see the recent book by Suri et al.). This paper presents a scale-space approach for filtering the white blood and black blood angiographic volumes and its implementation issues. The raw MR angiographic volume is first converted to isotropic volume followed by 3-D higher order separable Gaussian derivative convolution with known scales to generate edge volume. The edge volume is then run by the directional processor at each voxel where the eigenvalues of the 3-D ellipsoid are computed. The vessel score per voxel is then estimated based on these three eigenvalues which suppress the nonvasculature and background structures yielding the filtered volume. The filtered volume is ray-cast to generate the maximum intensity projection images for display. The performance of the system is evaluated by computing the mean, variance, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) images. The system is run over 20 patient studies from different areas of the body such as the brain, abdomen, kidney, knee, and ankle. The computer program takes around 150 s of processing time per study for a data size of 512 x 512 x 194, which includes the complete performance evaluation. We also compare our strategy with the recently published MR filtering algorithms by Alexander et al. and Sun et al.

Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review.

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, this 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. This 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.

A review on MR vascular image processing algorithms: acquisition and prefiltering: part I.

Vascular segmentation has recently been given much attention. This review paper has two parts. Part I focuses on the physics of magnetic resonance angiography (MRA) generation and prefiltering techniques applied to MRA data sets. Part II of the review focuses on the vessel segmentation algorithms. The first section of this paper introduces the five different sets of receive coils used with the MRI system for magnetic resonance angiography data acquisition. This section then presents the five different types of the most popular data acquisition techniques: time-of-flight (TOF), phase-contrast, contrast-enhanced, black-blood, T2-weighted, and T2*-weighted, along with their pros and cons. Section II of this paper focuses on prefiltering algorithms for MRA data sets. This is necessary for removing the background nonvascular structures in the MRA data sets. Finally, the paper concludes with a clinical discussion on the challenges and the future of the data acquisition and the automated filtering algorithms.