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PubMed2002

Improved optimization for the robust and accurate linear registration and motion correction of brain images.

Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.

Acoustic Stimulation↗

PubMed2003

Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images.

A local image model is proposed to eliminate the adverse impact of both artificial and inherent intensity inhomogeneities in magnetic resonance imaging on intensity-based image segmentation methods. The estimation and correction procedures for intensity inhomogeneities are no longer indispensable because the highly convoluted spatial distribution of different tissues in the brain is taken into consideration. On the basis of the local image model, multicontext fuzzy clustering (MCFC) is proposed for classifying 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid automatically. In MCFC, multiple clustering contexts are generated for each pixel, and fuzzy clustering is independently performed in each context to calculate the degree of membership of a pixel to each tissue class. To maintain the statistical reliability and spatial continuity of membership distributions, a fusion strategy is adopted to integrate the clustering outcomes from different contexts. The fusion result is taken as the final membership value of the pixel. Experimental results on both real MR images and simulated volumetric MR data show that MCFC outperforms the classic fuzzy c-means (FCM) as well as other segmentation methods that deal with intensity inhomogeneities.

Artifacts↗

PubMed2003

The human action recognition system and its relationship to Broca's area: an fMRI study.

Primate studies have identified populations of neurons that are capable of action recognition. These "mirror neurons" show spiking activity both when the monkey executes or observes a grasping movement. These neurons are located in the ventral premotor cortex, possibly the homologue of "Broca's area" in human. This led to the speculation that action recognition and language production share a common system [Trends Neurosci. 21 (1998), 188]. To test this hypothesis, we combined an action recognition with a language production (VERB) and a grasping movement task (MOVE) by using functional magnetic resonance imaging. Action recognition-related activation was observed in the left inferior frontal gyrus and on the border between the inferior frontal gyrus and precentral gyrus (defined as IFG/PG), the ventral occipitotemporal junction, the superior and inferior parietal cortex, and in the intraparietal sulcus in the left hemisphere. An overlap of activations due to the language production, movement execution, and action recognition was found in the parietal cortex, the left inferior frontal gyrus, and the IFG-PG border (IFG/PG). The activation peaks of action recognition and verb generation were always different in single subjects, but no consistent spatial relationship was detected, in accord with the hypothesis that action recognition and language production share a common functional architecture.

Algorithms↗

PubMed2003

Human motor cortex activity during mental rotation.

The functional role of human premotor and primary motor cortex during mental rotation has been studied using functional MRI at 3 T. Fourteen young, male subjects performed a mental rotation task in which they had to decide whether two visually presented cubes could be identical. Exploratory Fuzzy Cluster Analysis was applied to identify brain regions with stimulus-related time courses. This revealed one dominant cluster which included the parietal cortex, premotor cortex, and dorsolateral prefrontal cortex that showed signal enhancement during the whole stimulus presentation period, reflecting cognitive processing. A second cluster, encompassing the contralateral primary motor cortex, showed activation exclusively after the button press response. This clear separation was possible in 3 subjects only, however. Based on these exploratory results, the hypothesis that primary motor cortex activity was related to button pressing only was tested using a parametric approach via a random-effects group analysis over all 14 subjects in SPM99. The results confirmed that the stimulus response via button pressing causes activation in the primary motor cortex and supplementary motor area while parietal cortex and mesial regions rostral to the supplementary motor area are recruited for the actual mental rotation process.

Adult↗

PubMed2003

Region growing method for the analysis of functional MRI data.

Existing analytical techniques for functional magnetic resonance imaging (fMRI) data always need some specific assumptions on the time series. In this article, we present a new approach for fMRI activation detection, which can be implemented without any assumptions on the time series. Our method is based on a region growing method, which is very popular for image segmentation. A comparison of performance on fMRI activation detection is made between the proposed method and the deconvolution method and the fuzzy clustering method with receiver operating characteristic (ROC) methodology. In addition, we examine the effectiveness and usefulness of our method on real experimental data. Experimental results show that our method outperforms over the deconvolution method and the fuzzy clustering method on a number of aspects. These results suggest that our region growing method can serve as a reliable analysis of fMRI data.

Acoustic Stimulation↗

PubMed2003

System for upper airway segmentation and measurement with MR imaging and fuzzy connectedness.

RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate whether a computerized system developed to help delineate the upper airway and surrounding structures with magnetic resonance (MR) imaging was effective for aiding in the diagnosis of upper airway disorders in children. MATERIALS AND METHODS: The authors performed axial T2-weighted MR imaging to gather information about different aspects of the airway and its surrounding soft-tissue structures, including the adenoid and palatine tonsils, tongue, and soft palate. Images were processed and segmented to compute the architectural parameters of the airway (eg, surface description, volume, central [medial] line, and cross-sectional areas at planes perpendicular to the central line). The authors built a software package for the visualization, segmentation, registration, prefiltering, interpolation, standardization, and quantitative analysis of the airway and tonsils. RESULTS: The system was tested with 40 patient studies. For every study, the system segmented and displayed a smooth three-dimensional rendition of the airway and its central line and a plot of the cross-sectional area of the airway orthogonal to the central line as a function of the distance from one end of the central line. The precision and accuracy for segmentation was 97%. The mean time taken per study was about 4 minutes and included the operator interaction time and processing time. CONCLUSION: This method provides a robust and fast means of assessing the airway size, shape, and level of restriction, as well as a structural data set suitable for use in modeling studies of airflow and mechanics.

Algorithms↗

PubMed1999

Digital mammography: comparison of adaptive and nonadaptive CAD methods for mass detection.

RATIONALE AND OBJECTIVES: The authors compared the performance of adaptive and nonadaptive computer-aided diagnostic (CAD) methods for breast mass detection with digital mammography. MATERIALS AND METHODS: Both adaptive and nonadaptive modular CAD methods employed recent advances in multiresolution and mutiorientation wavelet transforms for improved feature extraction. The nonadaptive method uses fixed parameters for the image preprocessing modules. The adaptive method, a new class of algorithms, adapts to image content by selecting parameters for the image preprocessing modules within a parameter range. Comparison of the two methods was performed for each individual CAD module with a region-of-interest (ROI) database containing all mass types and normal tissue. RESULTS: Receiver operating characteristic (ROC) analysis clearly demonstrated an improvement in performance for the three adaptive modules and a significant overall difference between the two methods. The average ROC area index (Az) values were 0.86 and 0.95 for the nonadaptive and adaptive methods, respectively. The corresponding P value is .0145. For a previously reported database of full mammographic images containing 50 abnormal cases with all mass types and 50 normal images, the adaptive CAD method had a sensitivity of 96% (1.71 false-positive results per image) compared with 89% (1.91 false-positive results per image) for the nonadaptive CAD method. CONCLUSION: The adaptive CAD method demonstrated better performance. A study is in progress to determine the generalizability of the adaptive CAD method by applying it to larger retrospective image databases with different film digitizers.

Algorithms↗

PubMed2000

Ventilators and weaning modes.

Although new ventilator modes have become available to facilitate weaning, there is little evidence that these have improved weaning outcomes. Knowledge based computer weaning systems have also been described, but these are in their infancy, and their role is unclear. Prospective, randomized clinical studies are required to examine whether such modalities are superior to existing approaches.

Fuzzy Logic↗

PubMed2000

On automatic identification of upper-limb movements using small-sized training sets of EMG signals.

We evaluate the performance of a variety of neural and fuzzy networks for discrimination among three planar arm-pointing movements by means of electromyographic (EMG) signals, when learning is based on small-sized training sets. The aim of this work is to underline the importance that the sparse data problem has in designing pattern classifiers with good generalisation properties. The results indicate that one of the proposed fuzzy networks is more robust than the other classifiers when working with small training sets.

Arm↗

PubMed2001

Fuzzy clustering of gait patterns of patients after ankle arthrodesis based on kinematic parameters.

Kinematic parameters for 10 normal subjects and 10 patients with ankle arthrodesis are grouped using the fuzzy cluster paradigm. The features chosen for clustering are Euler angles of the sagittal plane in the hindfoot, the forefoot and combined hindfoot and forefoot joints. Gait patterns are identified using information provided by cluster validity techniques, giving three, three and two clusters for the hindfoot, forefoot and combined hindfoot and forefoot joints, respectively. The cluster centers represent distinct walking strategies adopted by normal subjects and patients after ankle arthrodesis. Utilizing angle values normalized by gait cycle, it is possible to classify any subject and to generate an individual's membership value for each of the clusters. The clinical utility of the fuzzy clustering approach is demonstrated with data for subjects with ankle arthrodesis, where changes in membership of the clusters provide an objective technique for measuring changes of gait pattern after ankle arthrodesis. This approach can be adopted to study other clinical entities where different cluster centers would be established using the algorithm provided in this study.

Adult↗

PubMedDate not supplied

Automated recognition of spontaneous versus voluntary cough.

Cough or cough epochs may be an important and persistent symptom in many respiratory diseases requiring both a continuous and objective observation. The research presented in this paper is aimed at assessing a blind data-based classification between 'spontaneous' and 'voluntary' human cough on individual sound samples. Cough sounds were registered in the free acoustic field on 3 pathological and 9 healthy non-smoking subjects, all aged between 20 and 30. Each sound is represented by the normalized power spectral density (PSD). Different transformations of the cough PSD-vector are chosen as input features to the classification algorithm. An experimental error rate comparison between different neural and fuzzy classification networks is performed. All evaluated algorithms used the Euclidean metric. This resulted in a correct class-discrimination between 'spontaneous' and 'voluntary' cough for 96% of the cough database.

Acoustics↗

PubMed1999

Automatic synthesis of synergies for control of reaching--hierarchical clustering.

In this paper we describe a novel method for determining synergies between joint motions in reaching movements by hierarchical clustering. A set of recorded elbow and shoulder trajectories is used in a learning algorithm to determine the relationships between angular velocities at elbow and shoulder joints. The learning algorithm is based on optimal criteria for obtaining the hierarchy of descriptions of movement trajectories. We show that this method finds complex synergism between optimal joint trajectories for a given set of data and angular velocities at the shoulder and elbow joints. Three other machine learning techniques (ML) are used for comparison with our method of hierarchical clustering of trajectories. These MLs are: (1) radial basis functions (RBF), (2) inductive learning (IL), and (3) adaptive-network-based fuzzy inference system (ANFIS). Better error characteristics were obtained using the method of hierarchical clustering in comparison with the other techniques. The advantage of the method of hierarchical clustering with respect to the other MLs is in integrating the spatial and temporal elements of reaching movements. Determination and analysis of spatio-temporal events of movement trajectories is a useful tool in designing control systems for functional electrical stimulation (FES) assisted manipulation.

Algorithms↗

PubMed2000

Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account.

Segmentation of the skull in medical imagery is an important stage in applications that require the construction of realistic models of the head. Such models are used, for example, to simulate the behavior of electro-magnetic fields in the head and to model the electrical activity of the cortex in EEG and MEG data. In this paper, we present a new approach for segmenting regions of bone in MRI volumes using deformable models. Our method takes into account the partial volume effects that occur with MRI data, thus permitting a precise segmentation of these bone regions. At each iteration of the propagation of the model, partial volume is estimated in a narrow band around the deformable model. Our segmentation method begins with a pre-segmentation stage, in which a preliminary segmentation of the skull is constructed using a region-growing method. The surface that bounds the pre-segmented skull region offers an automatic 3D initialization of the deformable model. This surface is then propagated (in 3D) in the direction of its normal. This propagation is achieved using level set method, thus permitting changes to occur in the topology of the surface as it evolves, an essential capability for our problem. The speed at which the surface evolves is a function of the estimated partial volume. This provides a sub-voxel accuracy in the resulting segmentation.

Computer Simulation↗

PubMed2001

On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series.

The aim of this paper is to present an exploratory data-driven strategy based on Unsupervised Fuzzy Clustering Analysis (UFCA) and its potential for fMRI data analysis in the temporal domain. The a priori definition of the number of clusters is addressed and solved using heuristics. An original validity criterion is proposed taking into account data geometry and the partition Membership Functions (MFs). From our simulations, this criterion is shown to outperform other indices used in the literature. The influence of the fuzziness index was studied using simulated activation combined with real life noise data acquired from subjects under a resting state. Receiver Operating Characteristics (ROC) methodology is implemented to assess the performance of the proposed UFCA with respect to the fuzziness index. An interval of choice around 2, a value widely used in FCA, is shown to yield the best performance.

Algorithms↗

PubMed2001

Hybrid expert system for decision supporting in the medical area: complexity and cognitive computing.

This paper proposes a hybrid expert system (HES) to minimise some complexity problems pervasive to the artificial intelligence such as: the knowledge elicitation process, known as the bottleneck of expert systems; the model choice for knowledge representation to code human reasoning; the number of neurons in the hidden layer and the topology used in the connectionist approach; the difficulty to obtain the explanation on how the network arrived to a conclusion. Two algorithms applied to developing of HES are also suggested. One of them is used to train the fuzzy neural network and the other to obtain explanations on how the fuzzy neural network attained a conclusion. To overcome these difficulties the cognitive computing was integrated to the developed system. A case study is presented (e.g. epileptic crisis) with the problem definition and simulations. Results are also discussed.

Algorithms↗

PubMed2001

Generation of an intelligent medical system, using a real database, to diagnose bacterial infection in hospitalized patients.

The initial diagnosis of bacterial infections in the absence of laboratory microbiological data requires physicians to use clinical algorithms based on symptoms, patient history and infection site. Optimization of such algorithms would be achieved by including as many variables associated with bacterial infection as possible. Demographic data are easily available and frequently used to sub-group human populations. A prospective investigation was, therefore, undertaken to examine the influence of demographic variables on bacterial infection rates, using data obtained from 173 patients presenting to Albert Einstein Medical Center. Data was randomly selected from 149 of these patients and used to generate fuzzy rules to model an intelligent medical system. To test the accuracy of this system at determining bacterial infection, based solely on demographic data, the program was given the remaining 24 patients' information. All 18 patients with either streptococcal, staphylococcal or Escherichia coli infections were correctly diagnosed. Non-E.coli GNR were misdiagnosed as E. coli infections in two patients resulting in an overall prediction rate for the 24 patients of 91.66%. This study suggests that the direct correlation of demographic variables with a predisposition to bacterial infection allow the design of an intelligent medical system, which shows great future potential as a diagnostic tool for all physicians.

Adult↗

PubMed2001

Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach.

In this article, we revise and try to resolve some of the problems inherent in questionnaire screening of sleep apnea cases and apnea diagnosis based on attributes which are relevant and reliable. We present a way of learning information about the relevance of the data, comparing this with the definition of the information by the medical expert. We generate a predictive data model using a data aggregation operator which takes relevance and reliability information about the data into account to produce a diagnosis for each case. We also introduce a grade of membership for each question response which allows the patient to indicate a level of confidence or doubt in their own judgement. The method is tested with data collected from patients in a Sleep Clinic using questionnaires specially designed for the study. Other artificial intelligence predictive modeling algorithms are also tested on the same data and their predictive accuracy compared to that of the aggregation operator.

Adult↗

PubMed2001

A fuzzy sets theory application in determining the severity of respiratory failure.

This paper proposes to apply the theory of fuzzy sets in determining the severity of respiratory distress of a patient in an intensive care unit. The problem is modelled as a fuzzy multicriteria decision-making one. Theoretical approaches to the situation are considered. The proposed fuzzy selection method, based on the usage of the fuzzy intervals, is described. An example of determining the severity of respiratory distress is used to illustrate the presentation. Computer implementation of the model is considered, and the directions for possible further work are pointed out.

Decision Support Techniques↗