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At least 703 records · Page 39Linked to original sources
PubMed1996

Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network.

This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for finding weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the 'within-class scatter matrix' principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.

Algorithms↗
PubMed1994

Biomolecular computing: from the brain-machine disanalogy to the brain-machine analogy.

The analogy between the main information features of the brain and molecular non-discrete information processing devices based on non-linear dynamic mechanisms is considered. These information processing mechanisms predetermine the character of basic primitive operations of these devices, which seem to be capable of solving problems of a rather high computational complexity. Non-linear dynamic processing mechanisms open the way to elaboration of devices embodying, in a natural way, the fuzziness of information features that is typical of information processing inherent in soft 'humanistic' systems.

Brain↗
PubMed1995

Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme.

The application of a raw data-based, operator-independent MR segmentation technique to differentiate boundaries of tumor from edema or hemorrhage is demonstrated. A case of a glioblastoma multiforme with gross and histopathologic correlation is presented. The MR image data set was segmented into tissue classes based on three different MR weighted image parameters (T1-, proton density-, and T2-weighted) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition. A radiological examination of the MR images and correlation with fuzzy clustering segmentations was performed. Results were confirmed by gross and histopathology which, to the best of our knowledge, reports the first application of this demanding approach. Based on the results of neuropathologic correlation, the application of FCM MR image segmentation to several MR images of a glioblastoma multiforme represents a viable technique for displaying diagnostically relevant tissue contrast information used in 3D volume reconstruction. With this technique, it is possible to generate segmentation images that display clinically important neuroanatomic and neuropathologic tissue contrast information from raw MR image data.

Adult↗
PubMedDate not supplied

Left ventricular boundary detection from spatio-temporal volumetric computed tomography images.

This paper presents a new automatic technique for left ventricle boundary detection from a set of three-dimensional (3D) computed tomography (CT) volumetric cardiac images. The goals of this paper are to incorporate the temporal information into LV boundary detection, to link the shape modeling and LV boundary detection together, and to provide a compact representation of recovered LV boundaries to cardiac imaging. The proposed technique introduces spatio-temporal boundary detection and iterative model-based boundary refinement to left ventricular boundary extraction. The proposed technique has been applied to two sets of four-dimensional (4D) computed tomography images. Experimental results are compared with the manually edited images.

Algorithms↗
PubMedDate not supplied

Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images.

An algorithm and set of procedures for measuring volumes of cerebrospinal fluid (CSF), white matter, and gray matter from transaxial magnetic resonance images (MRI) of the brain are described. The algorithm is a variant of the fuzzy c-means clustering method for texture identification. This technique is used mainly to solve the problem of volume averaging of tissue compartments, but also has other advantages. It is fast, accurate, and relatively operator independent. Furthermore, it does not depend on statistical assumptions such as data normality, nor does it require any a priori heuristics. The procedure was tested successfully on imaged phantoms of known volume composition and compared with results achieved using a standard morphometric measurement approach. The procedure was also applied to brain MRIs of three clinically normal children and three age-matched children with hydrocephalus using both proton density and T2-weighted images. The algorithm was able to detect the expected increased amounts of CSF and decreased amounts of white matter characteristic of the hydrocephalic brain.

Algorithms↗
PubMedDate not supplied

Computer-assisted bone age assessment based on features automatically extracted from a hand radiograph.

This paper presents a computer-aided classification algorithm to assist the radiologist in the bone age assessment of pediatric patients. The classification is based on features automatically extracted from two regions of Computed Radiography (CR) left hand wrist images: phalangeal region of interest (PROI) and carpal bone region of interest (CROI). Due to imprecise nature of the bone age assessment problem, a fuzzy classifier for both regions has been developed. After defining a membership function for each region, features are processed yielding a matrix which maps the set of features to a year of age within the predefined range. The grades of membership are described as membership function values in the interval [0, 1]. A classification rule based on a max-sum operator, processes the matrix assessing the bone age. Since both regions are analyzed independently, two bone age assessments are obtained. They reflect the phalangeal and carpal bones maturity individually. In pathological cases the discrepancy between both assessments may reach as much as 2 yr.

Age Determination by Skeleton↗
PubMedDate not supplied

Segmentation of multivariate medical images via unsupervised clustering with "adaptive resolution".

The need for quantitative information is becoming increasingly important in the clinical field. In this paper we present an interactive X11 based system, devoted to segmentation of multivariate medical images, including an unsupervised neural network approach to clustering. The following steps are considered in the analysis sequence: feature extraction, reduction of dimensionality, unsupervised data clustering, voxel classification, interactive post-processing refinement. The environment turns out to be extremely interactive, thus making the user able to display and modify data during processing, to set parameters, to choose different methods and different tools for each step, and to define online the whole analysis sequence.

Algorithms↗
PubMedDate not supplied

Delineation and quantitation of brain lesions by fuzzy clustering in positron emission tomography.

In this study, we investigate the application of the fuzzy clustering to the anatomical localization and quantitation of brain lesions in Positron Emission Tomography (PET) images. The method is based on the Fuzzy C-Means (FCM) algorithm. The algorithm segments the PET image data points into a given number of clusters. Each cluster is an homogeneous region of the brain (e.g. tumor). A feature vector is assigned to a cluster which has the highest membership degree. Having the label affected by the FCM algorithm to a cluster, one may easily compute the corresponding spatial localization, area and perimeter. Studies concerning the evolution of a tumor after different treatments in two patients are presented.

Algorithms↗
PubMed1995

Neural network classification of infrared spectra of control and Alzheimer's diseased tissue.

Artificial neural network classification methods were applied to infrared spectra of histopathologically confirmed Alzheimer's diseased and control brain tissue. Principal component analysis was used as a preprocessing technique for some of these artificial neural networks while others were trained using the original spectra. The leave-one-out method was used for cross-validation and linear discriminant analysis was used as a performance benchmark. In the cases where principal components were used, the artificial neural networks consistently outperformed their linear discriminant counterparts; 100% versus 98% correct classifications, respectively, for the two class problem, and 90% versus 81% for a more complex five class problem. Using the original spectra, only one of the three selected artificial neural network architectures (a variation of the back-propagation algorithm using fuzzy encoding) produced results comparable to the best corresponding principal component cases: 98% and 85% correct classifications for the two and five class problems, respectively.

Alzheimer Disease↗
PubMed1995

Adaptive controllers for intelligent monitoring.

The project we describe here is aimed at assisting out-patients affected by Insulin Dependent Diabetes Mellitus. Our approach exploits the usual scheme of diabetic patients management, based on (i) a periodic evaluation of patients' metabolic control performed by the physician, and (ii) patient-tailored tables for self-adjustments of insulin dosages. Following this scheme we have defined a system built on a two-level architecture. The High Level Module exploits both medical knowledge and clinical information in order to assess an insulin protocol, defined in terms of insulin timing, type, and total amount. The High Level Module exchanges information with the Low Level Module in order to define the control actions to be taken at the low level, as well as to periodically evaluate protocol adequacy on the basis of patient data. The goal of the Low Level Module, whose characteristics can be adaptively modified by the High Level Module, is to suggest the next insulin dosage, depending on the actual blood glucose measurement and a certain pre-defined insulin delivery protocol. The Level Control Module is based on an adaptive controller, consisting of a Fuzzy Set Controller and an ARX (Autoregressive eXogenous input) Model. The scheme here presented may be conveniently viewed in a telemedicine context, in which the low level controller is implemented on a portable device communicating to the high level controller, implemented on a remote computer. A preliminary assessment has been performed, analyzing a data set of 60 patients provided by the American Association of Artificial Intelligence, Artificial Intelligence in Medicine Subgroup, and the implementation of the system is currently in progress.

Computer Simulation↗
PubMed1996

Synthesis of an anaesthetic agent administration system using fuzzy inductive reasoning.

Control of the depth of anaesthesia is a difficult undertaking. Progress has been made during recent years by use of different methodologies and monitoring systems that suggest the safe amount of an anaesthetic drug, considering the condition of an individual patient. Despite these improvements, anaesthetists still rely heavily on personal experience when suggesting the anaesthetic dosage during surgical operations. The purposes of this paper are twofold. One is a description of the design of an anaesthetic agent control system using a qualitative modelling and simulation methodology called Fuzzy Inductive Reasoning (FIR). A comparison with a system developed for the same application using a neural network approach is also presented. The second purpose is a discussion of the problem of separating system-generic from patient-specific behaviour in the context of inductive modeling using the FIR methodology. In order to be useful, the model generated by FIR should reflect upon system-generic behavioural characteristics exclusively, while suppressing patient-specific behavioural patterns. A technique based on combining knowledge obtained from different patients is designed that makes it possible to derive a single model characterizing a specific class of similar patients undergoing similar operations, preserving the common characteristics of all these patients while filtering out the specific behavioural patterns of any one of the individual patients from whom the data were obtained.

Anesthetics↗
PubMed1996

The interpretation of time-varying data with DIAMON-1.

Applying the methods of Artificial Intelligence to clinical monitoring requires some kind of signal-to-symbol conversion as a prior step. Subsequent processing of the derived symbolic information must also be sensitive to history and development, as the failure to address temporal relationships between findings invariably leads to inferior results. DIAMON-1, a framework for the design of diagnostic monitors, provides two methods for the interpretation of time-varying data: one for the detection of trends based on classes of courses, and one for the tracking of disease histories modelled through deterministic automata. Both methods make use of fuzzy set theory taking account of the elasticity of medical categories and allowing discrete disease models to mirror the patient's continuous progression through the stages of illness.

Artificial Intelligence↗
PubMed1996

Fuzzy gating and the problem of screening.

The problem of population screening is very important for medical statistics. It allows one to analyze the expression of certain parameters in the population of healthy persons, in order to compare it to the expression of these parameters in the persons with a specific disease. If a parameter is expressed differently in ill and healthy persons, then this parameter may serve as a pointer to the disease, especially in its earlier stages. While the analysis of given parameters in people with the established diagnosis does not represent many difficulties, the analysis of the general population is not easily carried out. The problem is that the general population contains both ill and healthy people. The population of healthy people is said to be 'contaminated' by the noise-subpopulation of ill people. The resulting statistical parameters are, therefore, biased and in order to find their correct values one needs to cancel out the input of the noise. In this paper we propose a new method to cancel out the noise, based on the theory of fuzzy sets. We assume that an auxiliary parameter is measured simultaneously and it is used to separate the subpopulations. If two subpopulations (the data and the noise) form clearly distinguished clusters in respect to this auxiliary parameter, one creates a gate and throws out the events outside the gate assuming that they are noise. However, when the clusters overlap, this procedure is no longer useful, and it is this particular situation for which we have developed fuzzy gating. In addition to the fact that the gate is fuzzified, a specifically designed algorithm is applied to compute the probability density functions for both subpopulations. Our algorithm gives a very high precision and is very robust as to the level of noise and the type of distributions.

Algorithms↗
PubMed1996

Sleep classification in infants by decision tree-based neural networks.

This paper presents an AI-based approach to automatic sleep stage scoring. The system TBNN (Tree-Based Neural Network) uses a decision-tree generator to provide knowledge that defines the architecture of a backpropagation neural network, including feature selection and initialisation of the weights. The case study reports a successful application to the data from polygraphic all-night sleep of 8 babies aged 6 months. The teaching input was provided by a medical expert in accordance with the rules of Guilleminault and Souquet. The performance of TBNN is compared with 5 other methods and the results are discussed.

Artificial Intelligence↗
PubMed1996

Application of the fuzzy ARTMAP neural network model to medical pattern classification tasks.

This paper presents research into the application of the fuzzy ARTMAP neural network model to medical pattern classification tasks. A number of domains, both diagnostic and prognostic, are considered. Each such domain highlights a particularly useful aspect of the model. The first coronary care patient prognosis, demonstrates the ARTMAP voting strategy involving 'pooled' decision-making using a number of networks, each of which has learned a slightly different mapping of input features to pattern classes. The second domain, breast cancer diagnosis, demonstrates the model's symbolic rule extraction capabilities which support the validation and explanation of a network's predictions. The final domain, diagnosis of acute myocardial infarction, demonstrates a novel category pruning technique allowing the performance of a trained network to be altered so as to favour predictions of one class over another (e.g. trading sensitivity for specificity or vice versa). It also introduces a 'cascaded' variant of the voting strategy intended to allow identification of a subset of cases which the network has a very high certainty of classifying correctly.

Artificial Intelligence↗
PubMed2005

Intraoperative tumor segmentation and volume measurement in MRI-guided glioma surgery for tumor resection rate control.

RATIONALE AND OBJECTIVES: Gross-total surgery under intraoperative magnetic resonance imaging (MRI) is a promising method of glioma removal. The purpose of this article is intraoperative measurement of resected tumor volume in MRI-guided glioma surgery using semiautomatic image segmentation to unbiased resection rate control. MATERIALS AND METHODS: A newly developed software program based on a fuzzy connectedness (FC) segmentation algorithm was used to achieve fast and semiautomatic tumor segmentation and tumor volume measurement. The program was validated by retrospective study of eight glioma cases and then applied to seven glioma cases. All clinical cases underwent actual MRI-guided surgery using 0.3-T open magnets. RESULTS: The volume of the tumor before resection ranged from 10.1 to 206.7 mL. A comparison of the results of manual segmentation with those of the semiautomatic FC-based segmentation gave an average dice similarity coefficient of 0.80 and an average match of 76%. Volume measurement combined with a developed software program enabled quantitative monitoring of tumor removal, which was critical in the near-total resection of glioma in MRI-guided surgery. CONCLUSION: The FC-based tumor segmentation method can be used for intraoperative tumor segmentation and volume measurement in MRI-guided glioma surgery using 0.3-T open magnets. This method is useful for objective resection rate monitoring, which may ultimately minimize the amount of residual tumor in glioma surgery.

Adolescent↗
PubMed2006

A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images.

RATIONALE AND OBJECTIVES: Accurate quantification of the shape and extent of breast tumors has a vital role in nearly all applications of breast magnetic resonance (MR) imaging (MRI). Specifically, tumor segmentation is a key component in the computerized assessment of likelihood of malignancy. However, manual delineation of lesions in four-dimensional MR images is labor intensive and subject to interobserver and intraobserver variations. We developed a computerized lesion segmentation method that has the advantage of being automatic, efficient, and objective. MATERIALS AND METHODS: We present a fuzzy c-means (FCM) clustering-based method for the segmentation of breast lesions in three dimensions from contrast-enhanced MR images. The proposed lesion segmentation algorithm consists of six consecutive stages: region of interest (ROI) selection by a human operator, lesion enhancement within the selected ROI, application of FCM on the enhanced ROI, binarization of the lesion membership map, connected-component labeling and object selection, and hole-filling on the selected object. We applied the algorithm to a clinical MR database consisting of 121 primary mass lesions. Manual segmentation of the lesions by an expert MR radiologist served as a reference in the evaluation of the computerized segmentation method. We also compared the proposed algorithm with a previously developed volume-growing (VG) method. RESULTS: For the 121 mass lesions in our database, 97% of lesions were segmented correctly by means of the proposed FCM-based method at an overlap threshold of 0.4, whereas 84% of lesions were correctly segmented by means of the VG method. CONCLUSION: Our proposed algorithm for breast-lesion segmentation in dynamic contrast-enhanced MRI was shown to be effective and efficient.

Algorithms↗
PubMed2004

A novel kernelized fuzzy C-means algorithm with application in medical image segmentation.

Image segmentation plays a crucial role in many medical imaging applications. In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data. The algorithm is realized by modifying the objective function in the conventional fuzzy C-means (FCM) algorithm using a kernel-induced distance metric and a spatial penalty on the membership functions. Firstly, the original Euclidean distance in the FCM is replaced by a kernel-induced distance, and thus the corresponding algorithm is derived and called as the kernelized fuzzy C-means (KFCM) algorithm, which is shown to be more robust than FCM. Then a spatial penalty is added to the objective function in KFCM to compensate for the intensity inhomogeneities of MR image and to allow the labeling of a pixel to be influenced by its neighbors in the image. The penalty term acts as a regularizer and has a coefficient ranging from zero to one. Experimental results on both synthetic and real MR images show that the proposed algorithms have better performance when noise and other artifacts are present than the standard algorithms.

Algorithms↗