Computers and biomedical research, an international journal最新文献

{"title":"Evaluation of Measures of Technical Image Quality for Intracranial Magnetic Resonance Angiography","authors":"Brian E Chapman ,&nbsp;Craig K Goodrich ,&nbsp;Andrew L Alexander ,&nbsp;Duane D Blatter ,&nbsp;Dennis L Parker","doi":"10.1006/cbmr.1999.1526","DOIUrl":"10.1006/cbmr.1999.1526","url":null,"abstract":"<div><p>We evaluate three measures of technical image quality for intracranial magnetic resonance angiography (MRA): (1) a two-alternative forced choice (2AFC) evaluation of vessel visibility, (2) vessel-to-background signal-difference-to-noise ratio (SDNR), and (3) observer ranking of the fidelity of vessel morphology compared to that in a gold standard image. The gold standard used for both the 2AFC and ranking measures is intraarterial catheter angiography. These measures are applied to healthy arterial segments. The 2AFC and SDNR measures directly evaluate the visibility of artery segments for which the existence is known from the gold standard images. We argue that (1) 2AFC evaluates the carrier signals on which any vascular disease process is modulated and provides an upper bound on the detectibility of vascular lesions, (2) SDNR is a predictor of 2AFC, and (3) ranking may be used to predict the relative performance of techniques in the detection of vascular lesions.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21445201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Author Index for Volume 32","authors":"","doi":"10.1006/cbmr.1999.1532","DOIUrl":"https://doi.org/10.1006/cbmr.1999.1532","url":null,"abstract":"","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92343398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fuzzy Logic Approach to Identifying Brain Structures in MRI Using Expert Anatomic Knowledge","authors":"Gilbert R Hillman ,&nbsp;Chih-Wei Chang ,&nbsp;HaoYing Ying ,&nbsp;John Yen ,&nbsp;Leena Ketonen ,&nbsp;Thomas A Kent","doi":"10.1006/cbmr.1999.1516","DOIUrl":"10.1006/cbmr.1999.1516","url":null,"abstract":"<div><p>We report a novel computer method for automatic labeling of structures in 3D MRI data sets using expert anatomical knowledge that is coded in fuzzy sets and fuzzy rules. The method first identifies major structures and then uses spatial relationships to these landmarks to recognize other structures. This labeling process simulates the iterative process that we ourselves use to locate structures in images. We demonstrate its application in three data sets, labeling brain MRI by locating the longitudinal and lateral fissures and the central sulci and then determining boundaries for the frontal lobes. Our method is adaptable to the identification of other anatomical structures.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21445199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Models for Nonstationary Signal Segmentation","authors":"William D. Penny,&nbsp;Stephen J. Roberts","doi":"10.1006/cbmr.1999.1511","DOIUrl":"10.1006/cbmr.1999.1511","url":null,"abstract":"<div><p>This paper investigates Hidden Markov Models (HMMs) in which the observations are generated from an autoregressive (AR) model. The overall model performs nonstationary spectral analysis and automatically segments a time series into discrete dynamic regimes. Because learning in HMMs is sensitive to initial conditions, we initialize the HMM model with parameters derived from a cluster analysis of Kalman filter coefficients. An important aspect of the Kalman filter implementation is that the state noise is estimated on-line. This allows for an initial estimation of AR parameters for each of the different dynamic regimes. These estimates are then fine-tuned with the HMM model. The method is demonstrated on a number of synthetic problems and on electroencephalogram data.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21445198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Asthmatic Breath Sounds: Preliminary Results of the Classifying Capacity of Human Examiners versus Artificial Neural Networks","authors":"S. Rietveld ,&nbsp;M. Oud ,&nbsp;E.H. Dooijes","doi":"10.1006/cbmr.1999.1522","DOIUrl":"10.1006/cbmr.1999.1522","url":null,"abstract":"<div><p>For continuous monitoring of the respiratory condition of patients, e.g., at the intensive care unit, computer assistance is required. Existing mechanical devices, such as the peak expiratory flow meter, provide only with incidental measurements. Moreover, such methods require cooperation of the patient, which at, e.g., the ICU is usually not possible. The evaluation of complicated phenomena such as asthmatic respiratory sounds may be accomplished by use of artificial neural networks. To investigate the merit of artificial neural networks, the capacities of neural networks and human examiners to classify breath sounds were compared in this study. Breath sounds were <em>in vivo</em> recorded from 50 school-age children with asthma and from 10 controls. Sound intervals with a duration of 20 seconds were randomly sampled from asthmatics during exacerbation, asthmatics in remission, and controls. The samples were digitized and related to peak expiratory flow. From each interval, two full breath cycles were selected. Of each selected breath cycle, a Fourier power spectrum was calculated. The so-obtained set of spectral vectors was classified by means of artificial neural networks. Humans evaluated graphic displays of the spectra. Human examiners could not clearly discriminate between the three groups by inspecting the spectrograms. Classification by self-classifying neural networks confirmed the existence of at least three classes; however, discrimination of 11 classes seemed more appropriate. Good results were obtained with supervised networks: as much as 95% of the training vectors could be classified correctly, and 43% of the test vectors. The three patient groups, as discriminated in advance, do not correspond with three sharply separated sets of spectrograms. More than three classes seem to be present. Humans cannot take up the spectral complexity and showed negative classification results. Artificial neural networks, however, are able to handle classification tasks and show positive results.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21390285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison of Logistic Regression to Decision Tree Induction in the Diagnosis of Carpal Tunnel Syndrome","authors":"Stephan M. Rudolfer ,&nbsp;Georgios Paliouras ,&nbsp;Ian S. Peers","doi":"10.1006/cbmr.1999.1521","DOIUrl":"10.1006/cbmr.1999.1521","url":null,"abstract":"<div><p>This paper aims to compare and contrast two types of model (logistic regression and decision tree induction) for the diagnosis of carpal tunnel syndrome using four ordered classification categories. Initially, we present the classification performance results based on more than two covariates (multivariate case). Our results suggest that there is no significant difference between the two methods. Further to this investigation, we present a detailed comparison of the structure of bivariate versions of the models. The first surprising result of this analysis is that the classification accuracy of the bivariate models is slightly higher than that of the multivariate ones. In addition, the bivariate models lend themselves to graphical analysis, where the corresponding decision regions can easily be represented in the two-dimensional covariate space. This analysis reveals important structural differences between the two models.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21390872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performances of Hill-Type and Neural Network Muscle Models—Toward a Myosignal-Based Exoskeleton","authors":"Jacob Rosen ,&nbsp;Moshe B. Fuchs ,&nbsp;Mircea Arcan","doi":"10.1006/cbmr.1999.1524","DOIUrl":"10.1006/cbmr.1999.1524","url":null,"abstract":"<div><p>Muscle models are the essential components of any musculoskeletal simulation. In addition, muscle models which are incorporated in neural-based prosthetic and orthotic devices may significantly improve their performance. The aim of the study was to compare the performances of two types of muscle models in terms of predicting the moments developed at the human elbow joint complex based on joint kinematics and neuromuscular activity. The performance evaluation of the muscle models was required to implement them in a powered myosignal-driven exoskeleton (orthotic device). The experimental setup included a passive exoskeleton capable of measuring the joint kinematics and dynamics in addition to the muscle myosignal activity (EMG). Two types of models were developed and analyzed: (i) a Hill-based model and (ii) a neural network. The task, which was selected for evaluating the muscle models performance, was the flexion–extension movement of the forearm with a hand-held weight. For this task the muscle model inputs were the normalized neural activation levels of the four main flexor–extensor muscles of the elbow joint, and the elbow joint angle and angular velocity. Using this inputs, the muscle model predicted the moment applied on the elbow joint during the movement. Results indicated a good performance of the Hill model, although the neural network predictions appeared to be superior. Relative advantages and shortcomings of both approaches were presented and discussed.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21390284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Best Parameters Selection for Wavelet Packet-Based Compression of Magnetic Resonance Images","authors":"A.N. Abu-Rezq ,&nbsp;A.S. Tolba ,&nbsp;G.A. Khuwaja ,&nbsp;S.G. Foda","doi":"10.1006/cbmr.1999.1523","DOIUrl":"10.1006/cbmr.1999.1523","url":null,"abstract":"<div><p>Transmission of compressed medical images is becoming a vital tool in telemedicine. Thus new methods are needed for efficient image compression. This study discovers the best design parameters for a data compression scheme applied to digital magnetic resonance (MR) images. The proposed technique aims at reducing the transmission cost while preserving the diagnostic information. By selecting the wavelet packet's filters, decomposition level, and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimal distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of MR images. In all cases, decomposition at three or four levels with the Coiflet 5 wavelet (Coif 5) results in better compression performance than the other wavelets. Image resolution is found to have a remarkable effect on the compression rate.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21390286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of the Occluded Artery in Patients with Myocardial Ischemia Induced by Prolonged Percutaneous Transluminal Coronary Angioplasty Using Traditional vs Transformed ECG-Based Indexes","authors":"José Garcı́a ,&nbsp;Galen Wagner ,&nbsp;Leif Sömmo ,&nbsp;Paul Lander ,&nbsp;Pablo Laguna","doi":"10.1006/cbmr.1999.1520","DOIUrl":"10.1006/cbmr.1999.1520","url":null,"abstract":"<div><p>We have studied the spatial properties of ischemic changes as induced by prolonged angioplasty and how the changes are related to different ECG indexes. Indexes based on measurements at specific points in time (ST level at <em>J</em> + 60 ms point, maximal T wave amplitude and position, QT interval, and QRS duration) and global indexes (based on the Karhunen–Loève transform and applied to the QRS complex, ST–T complex, ST segment, and T wave), considering both repolarization and depolarization information, were analyzed. The changes during the occlusion period of the different indexes were used as variables in a multivariate discriminant analysis to determine which indexes showed the best discrimination of the three major occlusion sites (corresponding to LAD, RCA, and LCX coronary arteries). Occlusions in LCX artery were the most difficult to classify. With three local indexes (<em>ST</em>60 level measured in lead <em>V</em>3, <em>T</em> wave amplitude in <em>I</em>, and <em>ST</em>60 in <em>III</em>) it was possible to correctly classify 76% of patients by the occlusion site, and with three KLT-derived indexes (first-order KLT index for ST-T complex in <em>I</em> and for QRS in leads <em>V</em>3 and <em>I</em>) 83% of correct classification was obtained. Using six indexes for local and KLT-derived indexes the correct classification was increased to 85 and 90% of patients, respectively. The use of different ECG indexes (from different intervals) on quasiorthogonal leads permitted the identification of the occluded artery in patients undergoing PTCA and may be extended to more general use.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21390287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Logic Alternative for Analysis in the Biomedical Sciences","authors":"I. Virant-Klun ,&nbsp;J. Virant","doi":"10.1006/cbmr.1999.1517","DOIUrl":"10.1006/cbmr.1999.1517","url":null,"abstract":"<div><p>Fuzzy logic brings new possibilities into control, modeling, data analysis, diagnostics, decision making, and other working fields in biomedical sciences. This paper presents how fuzzy logic can be used as an alternative or supplement to statistics in biomedical analysis. It shows an adaptive neuro-fuzzy inference computing in comparison with linear and curvilinear regression. The main goal of this presentation is to involve fuzzy logic in biomedical research. Thus, we carried out a mathematical treatment of the clinical sample, semen of infertile man, with the independent variable Concentration of spermatozoa and the dependent variable Number of spermatozoa by 230 observations.</p></div>","PeriodicalId":75733,"journal":{"name":"Computers and biomedical research, an international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cbmr.1999.1517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21334281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}