Computer Methods and Programs in Biomedicine

Editorial Board

2012-06-01

EPILEPSIAE – A European epilepsy database

2012-06-01

Abstract: With a worldwide prevalence of about 1%, epilepsy is one of the most common serious brain diseases with profound physical, psychological and, social consequences. Characteristic symptoms are seizures caused by abnormally synchronized neuronal activity that can lead to temporary impairments of motor functions, perception, speech, memory or, consciousness.The possibility to predict the occurrence of epileptic seizures by monitoring the electroencephalographic activity (EEG) is considered one of the most promising options to establish new therapeutic strategies for the considerable fraction of patients with currently insufficiently controlled seizures.Here, a database is presented which is part of an EU-funded project “EPILEPSIAE” aiming at the development of seizure prediction algorithms which can monitor the EEG for seizure precursors. High-quality, long-term continuous EEG data, enriched with clinical metadata, which so far have not been available, are managed in this database as a joint effort of epilepsy centers in Portugal (Coimbra), France (Paris) and Germany (Freiburg).The architecture and the underlying schema are here reported for this database. It was designed for an efficient organization, access and search of the data of 300 epilepsy patients, including high quality long-term EEG recordings, obtained with scalp and intracranial electrodes, as well as derived features and supplementary clinical and imaging data. The organization of this European database will allow for accessibility by a wide spectrum of research groups and may serve as a model for similar databases planned for the future.

Interconnectivity analysis of supercritical CO2-foamed scaffolds

2012-06-01

Abstract: This paper describes a computer algorithm for the determination of the interconnectivity of the pore space inside scaffolds used for tissue engineering. To validate the algorithm and its computer implementation, the algorithm was applied to a computer-generated scaffold consisting of a set of overlapping spherical pores, for which the interconnectivity was calculated exactly. The algorithm was then applied to micro-computed X-ray tomography images of supercritical CO2-foamed scaffolds made from poly(lactic-co-glycolic acid) (PLGA), whereby the effect of using different weight average molecular weight polymer on the interconnectivity was investigated.

Automating the tracking of lymph nodes in follow-up studies of thoracic CT images

Peicong Yu, Kenneth Sheah, Chueh Loo Poh — 2012-06-01

Abstract: The study of lymph node features over time is of great clinical significance. Tracking of the same lymph node in CT images over time is done manually in the current clinical practice, which is tedious and lack of consistency. In this paper, we propose a search scheme to automate the process. Regions of interest (ROIs) are located by mapping the center point of lymph node based on the transformation found in the rigid registration. Similarity values between ROI of the template image and ROIs of repository images are compared, the highest of which decides the best match. Our method generated a success rate of 82% in determining the corresponding image in follow-up scan with the same lymph node as in baseline. The location of the lymph node in the corresponding image is tracked and estimated by mapping the lymph node center at baseline image using the transformation obtained from both affine and free-form deformation (FFD) registration. FFD performs better than affine registration in tracking the lymph node location. All lymph nodes in our study are tracked successfully by the suggested points which fall within the boundary of the same node in the corresponding follow-up images using FFD registration.

User-centered requirements engineering in health information systems: A study in the hemophilia field

Leonor Teixeira, Carlos Ferreira, Beatriz Sousa Santos — 2012-06-01

Abstract: The use of sophisticated information and communication technologies (ICTs) in the health care domain is a way to improve the quality of services. However, there are also hazards associated with the introduction of ICTs in this domain and a great number of projects have failed due to the lack of systematic consideration of human and other non-technology issues throughout the design or implementation process, particularly in the requirements engineering process.This paper presents the methodological approach followed in the design process of a web-based information system (WbIS) for managing the clinical information in hemophilia care, which integrates the values and practices of user-centered design (UCD) activities into the principles of software engineering, particularly in the phase of requirements engineering (RE). This process followed a paradigm that combines a grounded theory for data collection with an evolutionary design based on constant development and refinement of the generic domain model using three well-known methodological approaches: (a) object-oriented system analysis; (b) task analysis; and, (c) prototyping, in a triangulation work. This approach seems to be a good solution for the requirements engineering process in this particular case of the health care domain, since the inherent weaknesses of individual methods are reduced, and emergent requirements are easier to elicit. Moreover, the requirements triangulation matrix gives the opportunity to look across the results of all used methods and decide what requirements are critical for the system success.

CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms

Daren Lee, Ivo Dinov, Bin Dong, Boris Gutman, Igor Yanovsky, Arthur W. Toga — 2012-06-01

Abstract: As neuroimaging algorithms and technology continue to grow faster than CPU performance in complexity and image resolution, data-parallel computing methods will be increasingly important. The high performance, data-parallel architecture of modern graphical processing units (GPUs) can reduce computational times by orders of magnitude. However, its massively threaded architecture introduces challenges when GPU resources are exceeded. This paper presents optimization strategies for compute- and memory-bound algorithms for the CUDA architecture. For compute-bound algorithms, the registers are reduced through variable reuse via shared memory and the data throughput is increased through heavier thread workloads and maximizing the thread configuration for a single thread block per multiprocessor. For memory-bound algorithms, fitting the data into the fast but limited GPU resources is achieved through reorganizing the data into self-contained structures and employing a multi-pass approach. Memory latencies are reduced by selecting memory resources whose cache performance are optimized for the algorithm's access patterns. We demonstrate the strategies on two computationally expensive algorithms and achieve optimized GPU implementations that perform up to 6× faster than unoptimized ones. Compared to CPU implementations, we achieve peak GPU speedups of 129× for the 3D unbiased nonlinear image registration technique and 93× for the non-local means surface denoising algorithm.

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Hrvoje Kalinić, Sven Lončarić, Maja Čikeš, Davor Miličić, Bart Bijnens — 2012-06-01

Abstract: Cardiovascular disease is the leading cause of death worldwide and for this reason computer-based diagnosis of cardiac diseases is a very important task. In this article, a method for segmentation of aortic outflow velocity profiles from cardiac Doppler ultrasound images is presented. The proposed method is based on the statistical image atlas derived from ultrasound images of healthy volunteers. The ultrasound image segmentation is done by registration of the input image to the atlas, followed by a propagation of the segmentation result from the atlas onto the input image. In the registration process, the normalized mutual information is used as an image similarity measure, while optimization is performed using a multiresolution gradient ascent method. The registration method is evaluated using an in-silico phantom, real data from 30 volunteers, and an inverse consistency test. The segmentation method is evaluated using 59 images from healthy volunteers and 89 images from patients, and using cardiac parameters extracted from the segmented image. Experimental validation is conducted using a set of healthy volunteers and patients and has shown excellent results. Cardiac parameter segmentation evaluation showed that the variability of the automated segmentation relative to the manual is comparable to the intra-observer variability. The proposed method is useful for computer aided diagnosis and extraction of cardiac parameters.

Supervised restoration of degraded medical images using multiple-point geostatistics

Tuan D. Pham — 2012-06-01

Abstract: Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing.

Effect of missing RR-interval data on nonlinear heart rate variability analysis

Ko Keun Kim, Hyun Jae Baek, Yong Gyu Lim, Kwang Suk Park — 2012-06-01

Abstract: The effects of missing RR-interval data on nonlinear heart rate variability (HRV) analysis were investigated using simulated missing data in actual RR-interval tachograms and actual missing RR-interval data. For the simulation study, randomly selected data (ranging from 0 to 100s) were removed from actual data in the MIT-BIH normal sinus rhythm RR-interval database. The selected data are considered as a simulated artefact section. In all, 7182 tachograms of 5-min duration were used for this analysis. For each missing interval, the analysis was performed by 100 Monte Carlo runs. Poincaré plot, detrended fluctuation, and entropy analysis were executed for the nonlinear HRV parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters, were calculated. In this process, the usefulness of reconstruction was considered, for which bootstrapping and several interpolation methods (nearest neighbour, linear, cubic spline, and piecewise cubic Hermite) were used. The rules for the reconstruction, derived from the results of these simulations, were evaluated with actual missing RR-interval data obtained from a capacitive-coupled ECG during sleep. In conclusion, nonlinear parameters, excepting Poincaré-plot-analysis parameters, may not be appropriate for the accurate HRV analysis with missing data, since these parameters have relatively larger error values than time- or frequency-domain HRV parameters. However, the analysis of the long-term variation for nonlinear HRV values can be available through applying the rules for the reconstruction obtained in this study.

SOA-based digital library services and composition in biomedical applications

Xia Zhao, Enjie Liu, Gordon J. Clapworthy, Marco Viceconti, Debora Testi — 2012-06-01

Abstract: Carefully collected, high-quality data are crucial in biomedical visualization, and it is important that the user community has ready access to both this data and the high-performance computing resources needed by the complex, computational algorithms that will process it. Biological researchers generally require data, tools and algorithms from multiple providers to achieve their goals. This paper illustrates our response to the problems that result from this. The Living Human Digital Library (LHDL) project presented in this paper has taken advantage of Web Services to build a biomedical digital library infrastructure that allows clinicians and researchers not only to preserve, trace and share data resources, but also to collaborate at the data-processing level.

Mammogram retrieval on similar mass lesions

Chia-Hung Wei, Sherry Y. Chen, Xiaohui Liu — 2012-06-01

Abstract: Enormous numbers of digital mammograms have been produced in hospitals and breast screening centers. To exploit those valuable resources in aiding diagnoses and research, content-based mammogram retrieval systems are required to effectively access the mammogram databases. This paper presents a content-based mammogram retrieval system, which allows medical professionals to seek mass lesions that are pathologically similar to a given example. In this retrieval system, shape and margin features of mass lesions are extracted to represent the characteristics of mammographic lesions. To compare the similarity between the query example and any lesion within the databases, this study proposes a similarity measure scheme which involves the hierarchical arrangement of mammographic features and a weighting distance measure. This makes similarity measure of the retrieval system consistent with the way radiologists observe mass lesions. This study used the DDSM dataset to evaluate the effectiveness of the extracted shape feature and margin feature, respectively. Experimental results demonstrate that, when Zernike moments are used, round-shape masses are the most discriminative among four types of shape; the circumscribed-margin masses can be effectively discriminated among the four types of margins. Moreover, the result also shows that, when retrieving round-shape and circumscribed margin masses, this retrieval system can achieve the highest precision among all mass lesion types.

A real time online assessment system with modelized architecture on clinical infometrics for patient reported outcomes of prostate cancer

2012-06-01

Abstract: Objective: The aim of this study was to establish a real time online health and decision support system with the novel information technology integrating modelized architecture and Web services for clinical infometrics on patient reported outcome (PRO) and quality of life (QOL) for prostate cancer patients.Methods: The patient-oriented interface was practiced with object relation mapping (ORM) and clinical data warehouse to collaborate QOL measurement and medical informatics through internet by incorporating a variety of hospital information systems. The conceptual infrastructure was designed by five primary layers to organize the data flow of online assessment and clinical data for real-time decision support.Results: A preliminary knowledge bank was formed by feedback of expert opinions to provide online guidance for decision references. Observation and assessment of prostate cancer patients’ QOL and clinical markers were immediately tracked with automatic computation algorithm to improve health care quality in the treatment cycle.Conclusions: The established Web-based system can help clinicians concurrently collect and analyze real-time PROs and QOL for enhancing communication with patients and improving the quality of care.

Availability of a newly devised ambulatory urodynamics monitoring system based on personal device assistance in patients with spinal cord injury

Keo Sik Kim, Chul Gyu Song — 2012-06-01

Abstract: Conventional urodynamics systems have been widely used for the assessment of bladder functions. However, they have some drawbacks due to the unfamiliar circumstances for the patient, restrictive position during the test, expense and immovability of the instrument as well as the unphysiological filling of the bladder. To mitigate these problems, we developed a fully ambulatory urodynamics monitoring system, which enables the abdominal pressure to be measured in a non-invasive manner, as well as the manual recording of various events such as the bladder sensations or leakage of urine. Conventional (CMG) and furosemide-stimulated filling cystometry (FCMG) were performed for 28 patients with neurogenic bladders caused by spinal cord injury (24 males and 4 females, age: 49.4±13.9 years, BMI: 23.5±2.4). There were high correlation coefficients (r=0.97±0.02) between the clinical parameters measured by the conventional rectal catheter and those measured by our non-invasive algorithm in the FCMG studies. Also, 10 of the patients (36%) were diagnosed as having different reflexibility of the bladder between conventional CMG and FCMG (p<0.05). In the patients with detrusor overactivity, the average volume and detrusor pressure at bladder sensation in FCMG were lower than those in CMG, while the average compliance was higher (p<0.05). In the patients with areflexic bladders, the number of patients with detrusor overactivity was higher in FCMG and leakage was observed more frequently. These results showed that our system could be a useful additional tool in the clinical assessment of patients in which conventional cystometry failed to explain their symptoms.

A bootstrap approach for lower injury levels of the risk curves

Yuan-Chiao Lu, Costin D. Untaroiu — 2012-06-01

Abstract: Survival analysis is widely applied to develop injury risk curves from biomechanical data. To obtain more accurate estimation of confidence intervals of parameters, bootstrap method was evaluated by a designed simulation process. Four censoring schemes and various sample sizes were considered to investigate failure time parameters corresponding to low-level injury probabilities. In the numerical simulations, the confidence interval ranges developed by bootstrapping were about two-third of the corresponding ranges calculated by asymptotical normal approximation and showed highest reduction for censored datasets with smaller sample size (≤40). In analysis of two experimental datasets with reduced sample sizes and mixed censored data, it was shown that the bootstrapping reduce significantly the confidence intervals as well. The results presented in this study recommend using bootstrapping in development of more accurate confidence intervals for risk curves in injury biomechanics, which consequently will lead to better regulations and safer vehicle designs.

An associative memory approach to medical decision support systems

2012-06-01

Abstract: Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets.

Medical imaging correction: A comparative study of five contrast and brightness matching methods

G.K. Matsopoulos — 2012-06-01

Abstract: Contrast and brightness matching are often required in many medical imaging applications, especially when comparing medical data acquired over different time periods, due to dissimilarities in the acquisition process. Numerous methods have been proposed in this field, ranging from simple correction filters to more complicated recursive techniques. This paper presents a comprehensive comparison of five methods for matching the contrast and brightness of medical image pairs, namely, Contrast Stretching, Ruttimann's Robust Film Correction, Boxcar Filtering, Least-Squares Approximation and Histogram Registration. The five methods were applied to a total of 100 image pairs, divided into five sets, in order to evaluate the performance of the compared methods on images with different levels of contrast, brightness and combinational contrast and brightness variations. Qualitative evaluation was performed by means of visual assessment on the corrected images as well as on digitally subtracted images, in order to estimate the deviations relative to the reference data. Quantitative evaluation was performed by pair-wise statistical evaluation on all image pairs in terms of specific features of merit based on widely used metrics. Following qualitative and quantitative analysis, it was deduced that the Histogram Registration method systematically outperformed the other four methods in comparison in most cases on average.

Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging

2012-06-01

Abstract: In this work we describe an integrated and automated workflow for a comprehensive and robust analysis of multimodal MR images from a cohort of more than hundred subjects. Image examinations are done three years apart and consist of 3D high-resolution anatomical images, low resolution tensor-valued DTI recordings and 4D resting state fMRI time series. The integrated analysis of the data requires robust tools for segmentation, registration and fiber tracking, which we combine in an automated manner. Our automated workflow is strongly desired due to the large number of subjects. Especially, we introduce the use of histogram segmentation to processed fMRI data to obtain functionally important seed and target regions for fiber tracking between them. This enables analysis of individually important resting state networks. We also discuss various approaches for the assessment of white matter integrity parameters along tracts, and in particular we introduce the use of functional data analysis (FDA) for this task.