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PubMed2002

Category regions as new geometrical concepts in Fuzzy-ART and Fuzzy-ARTMAP.

In this paper we introduce novel geometric concepts, namely category regions, in the original framework of Fuzzy-ART (FA) and Fuzzy-ARTMAP (FAM). The definitions of these regions are based on geometric interpretations of the vigilance test and the F2 layer competition of committed nodes with uncommitted ones, that we call commitment test. It turns out that not only these regions have the same geometrical shape (polytope structure), but they also share a lot of common and interesting properties that are demonstrated in this paper. One of these properties is the shrinking of the volume that each one of these polytope structures occupies, as training progresses, which alludes to the stability of learning in FA and FAM, a well-known result. Furthermore, properties of learning of FA and FAM are also proven utilizing the geometrical structure and properties that these regions possess; some of these properties were proven before using counterintuitive, algebraic manipulations and are now demonstrated again via intuitive geometrical arguments. One of the results that is worth mentioning as having practical ramifications is the one which states that for certain areas of the vigilance-choice parameter space (rho,a), the training and performance (testing) phases of FA and FAM do not depend on the particular choices of the vigilance parameter. Finally, it is worth noting that, although the idea of the category regions has been developed under the premises of FA and FAM, category regions are also meaningful for later developed ART neural network structures, such as ARTEMAP, ARTMAP-IC, Boosted ARTMAP, Micro-ARTMAP, Ellipsoid-ART/ARTMAP, among others.

Fuzzy Logic↗
PubMed2002

An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

Algorithms↗
PubMed2003

Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems.

An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt. A qualitative analysis of the distributed learning properties of dARTMAP is made, focusing on the new elements introduced to endow Fuzzy ARTMAP with distributed learning. In addition, a quantitative study on a selected set of classification problems points out that problems have to present certain features in their output classes in order to noticeably reduce the number of recruited categories and achieve an acceptable classification accuracy. As part of this analysis, distributed learning was successfully adapted to a member of the Fuzzy ARTMAP family, FasArt, and similar procedures can be used to extend distributed learning capabilities to other Fuzzy ARTMAP based systems.

Fuzzy Logic↗
PubMedDate not supplied

Fuzzy neural identification and forecasting techniques to process experimental urban air pollution data.

This paper focuses on the processing of experimentally measured pollution data. Measuring locally both air quality parameters and atmospheric data can show how complex can be their interrelations and how they change spatially. Furthermore, apart from physical and biochemical dependencies, two important aspects need to be incorporated in the model, traffic data and topographic information, like presence and configuration of buildings and roads. Since estimating the evolution of pollutant in the urban air can have significant economic impact already on a short term basis as well as relevant consequences on public health on a medium-long term scale, various interdisciplinary researches are under way on this subject. In this work, we pursue two goals. The first one is to derive a representative model of the multivariate relationships that should be able to reproduce local interactions; the second goal of the paper is to predict, when possible, the short term evolution of pollutants in order to prevent the onset of above threshold levels of pollutants that can be dangerous to humans. The threshold levels of interest are fixed by both EU recommendations and regional regulations. As a by-product of the research, we could derive some directives to be supplied to local authorities to properly organize car traffic in advance based on the estimated parameters. The case study here proposed is that of Villa San Giovanni, a small town at the tip of Italy, located just in front of Sicily, on the Messina Strait. This is a significant case, since the city is affected by the heavy traffic directed (and coming from) Sicily. The main results here reported include the short time prediction of the concentration of hydrocarbons (HC) in the local air, the comparison between different methods based on fuzzy neural systems, and the proposal of local models of non-linear interactions among traffic, atmospheric and pollution data. Additionally, comments on a longer horizon forecast are given.

Air Pollution↗
PubMed2003

Adaptive categorization of ART networks in robot behavior learning using game-theoretic formulation.

Adaptive Resonance Theory (ART) networks are employed in robot behavior learning. Two of the difficulties in online robot behavior learning, namely, (1) exponential memory increases with time, (2) difficulty for operators to specify learning tasks accuracy and control learning attention before learning. In order to remedy the aforementioned difficulties, an adaptive categorization mechanism is introduced in ART networks for perceptual and action patterns categorization in this paper. A game-theoretic formulation of adaptive categorization for ART networks is proposed for vigilance parameter adaptation for category size control on the categories formed. The proposed vigilance parameter update rule can help improving categorization performance in the aspect of category number stability and solve the problem of selecting initial vigilance parameter prior to pattern categorization in traditional ART networks. Behavior learning using physical robot is conducted to demonstrate the effectiveness of the proposed adaptive categorization mechanism in ART networks.

Algorithms↗
PubMedDate not supplied

Fuzzy least squares support vector machines for multiclass problems.

In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems. In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable regions for multiclass problems. We define a membership function in the direction perpendicular to the optimal separating hyperplane that separates a pair of classes. Using the minimum or average operation for these membership functions, we define a membership function for each class. Using some benchmark data sets, we show that recognition performance of fuzzy LS-SVMs with the minimum operator is comparable to that of fuzzy SVMs, but fuzzy LS-SVMs with the average operator showed inferior performance.

Fuzzy Logic↗
PubMed2003

A functions localized neural network with branch gates.

In this paper, a functions localized network with branch gates (FLN-bg) is studied, which consists of a basic network and a branch gate network. The branch gate network is used to determine which intermediate nodes of the basic network should be connected to the output node with a gate coefficient ranging from 0 to 1. This determination will adjust the outputs of the intermediate nodes of the basic network depending on the values of the inputs of the network in order to realize a functions localized network. FLN-bg is applied to function approximation problems and a two-spiral problem. The simulation results show that FLN-bg exhibits better performance than conventional neural networks with comparable complexity.

Animals↗
PubMedDate not supplied

A self-learning segmentation framework--the Taguchi approach.

The detection of object boundary is an interesting and challenging task in computer vision and medical image processing. The active contour model (snake model) has attracted much attention for object boundary detection in the past decade. However, due to the lack of understanding on the effect of different energy terms to the behavior of related objective functions for an image, the assignment of weights for different energy terms in this model is usually fulfilled empirically. Few discussions have been brought out specifically for assigning these weights automatically. In this paper, a novel self-learning segmentation framework, based on the snake model is proposed and applied to the detection of cardiac boundaries from ultrasonic images. The framework consists of a learning section and a detection section, and provides a training mechanism to obtain the weights from a desired object contour given manually. This mechanism first employs Taguchi's method to determine the weight ratios among distinct energy terms, followed by a weight refinement step with a genetic algorithm. The refined weights can be treated as the a priori knowledge embedded in the manually defined contour and be used for subsequent contour detection. Experiments with both synthetic and real echocardiac images were conducted with satisfactory outcomes. Results also show that the present method can be used to analyze successive images of the same object with only one training contour. Finally, the validity of the weight determining process was verified by the analysis of variance method (ANOVA).

Algorithms↗
PubMedDate not supplied

Spatial registration of digital brain atlases based on fuzzy set theory.

We present a semiautomatic method based on fuzzy set theory for adjusting a computerized brain atlas to magnetic resonance images (MRIs) of the human cerebral cortex. The atlas was registered to three-dimensional MRI data sets of 10 healthy volunteers. After a global matching using the external contour of the brain, several local procedures were performed regarding selected primary furrows and cytoarchitectonic areas. The final transformation matrix was calculated with respect to these anatomical structures and to their local matrices. Evaluation revealed an increase in accuracy as expressed by a reduction of the visible mismatch with respect to the registration of cortical and subcortical brain structures.

Atlases as Topic↗
PubMedDate not supplied

Myocardium extraction in positron emission tomography based on soft computing.

This paper presents an efficient and accurate approach to myocardium extraction in Positron Emission Tomography (PET) images based on a careful application of soft computing techniques. PET images present a noisy background, making the automatic myocardium extraction and uptake quantification a difficult task. In this work a Self Organized Radial Basis Function Network (SRBFN) is designed to focus on the myocardium in an iterative process until the total extraction of the myocardium from the noisy background is achieved. Fuzzy sets and fuzziness measures are used to compute the error of the network. The method was tested on a set of nine images of different patients and its effectiveness is illustrated in two patients showing tracer uptake defects.

Fluorodeoxyglucose F18↗
PubMedDate not supplied

3DVIEWNIX-AVS: a software package for the separate visualization of arteries and veins in CE-MRA images.

Our earlier study developed a computerized method, based on fuzzy connected object delineation principles and algorithms, for artery and vein separation in contrast enhanced Magnetic Resonance Angiography (CE-MRA) images. This paper reports its current development-a software package-for routine clinical use. The software package, termed 3DVIEWNIX-AVS, consists of the following major operational parts: (1) converting data from DICOM3 to 3DVIEWNIX format, (2) previewing slices and creating VOI and MIP Shell, (3) segmenting vessel, (4) separating artery and vein, (5) shell rendering vascular structures and creating animations. This package has been applied to EPIX Medical Inc's CE-MRA data (AngioMark MS-325). One hundred and thirty-five original CE-MRA data sets (of 52 patients) from 6 hospitals have been processed. In all case studies, unified parameter settings produce correct artery-vein separation. The current package is running on a Pentium PC under Linux and the total computation time per study is about 3 min. The strengths of this software package are (1) minimal user interaction, (2) minimal anatomic knowledge requirements on human vascular system, (3) clinically required speed, (4) free entry to any operational stages, (5) reproducible, reliable, high quality of results, and (6) cost effective computer implementation. To date, it seems to be the only software package (using an image processing approach) available for artery and vein separation of the human vascular system for routine use in a clinical setting.

Algorithms↗
PubMedDate not supplied

Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia.

Fuzzy C-means clustering and principal components analysis were used to analyze a temporal series of near-IR images taken of a human forearm during periods of venous outflow restriction and complete forearm ischemia. The principal component eigen-time course analysis provided no useful information and the principal component eigen-image analysis gave results that correlated poorly with anatomical features. The fuzzy C-means clustering analysis, on the other hand, showed distinct regional differences in the hemodynamic response and scattering properties of the tissue, which correlated well with the anatomical features of the forearm.

Cluster Analysis↗
PubMedDate not supplied

Surface matching of multimodality image volumes by a fuzzy elastic registration technique.

Multimodality image registration is useful in diagnostic imaging and treatment planning for radiation therapy. In this paper, we present a technique which registers the surfaces of two volumes acquired by different medical imaging modalities. We represent the image volumes in terms of their surface elements known as tiles. We identify the fuzzy variables, assign fuzzy membership functions to them and generate a fuzzy rule database. The fuzzy algorithm reduces the discrepancy between the two set of tiles until the surfaces are matched. In order to study the efficacy of our approach, we severely warp a simulated image and register it with its original. We register CT and MR volumes of humanoid phantom images. Finally, we present the results at the end of the article.

Algorithms↗
PubMedDate not supplied

Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images.

We present a knowledge-based approach to segmentation and analysis of the lung boundaries in chest X-rays. Image edges are matched to an anatomical model of the lung boundary using parametric features. A modular system architecture was developed which incorporates the model, image processing routines, an inference engine and a blackboard. Edges associated with the lung boundary are automatically identified and abnormal features are reported. In preliminary testing on 14 images for a set of 18 detectable abnormalities, the system showed a sensitivity of 88% and a specificity of 95% when compared with assessment by an experienced radiologist.

Algorithms↗
PubMed2000

Cooperation of fuzzy segmentation operators for correction aliasing phenomenon in 3D color Doppler imaging.

The study described in this paper concerns natural object modeling in the context of uncertain, imprecise and inconsistent representation. We propose a fuzzy system which offers a global modeling of object properties such as color, shape, velocity, etc. This modeling makes a transition from a low level reasoning (pixel level), which implies a local precise but uncertain representation, to a high level reasoning (region level), inducing a certain assignment. So, we use fuzzy structured partitions characterizing these properties. At this level. each property will have its own global modeling. Then, these different models are merged for decision making. Our approach was tested with several applications. In particular, we show here its performance in the area of blood flow analysis from 3D color Doppler images in order to quantify and study the development of this flow. We present methods that detect and correct aliasing phenomenon, i.e. inconsistent information. At first, the flow space is partitioned into fuzzy sectors where each sector is defined by a center, an angle and a direction. In parallel, the velocity information carried by the pixels is classified into fuzzy classes. Then, by combining these two partitions, we obtain the velocity distribution into sectors. Moreover, for each found path (from the first sector to the last one), we locate and correct inconsistent velocities by applying global rules. After extracting some meaningful sector features, the fuzzy modeling, applied to the aliasing correction, makes it possible to simplify and synthesize the blood flow direction.

Blood Flow Velocity↗
PubMed2000

A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders.

Neural networks have been widely used as tools for prediction in medicine. We expect to see even more applications of neural networks for medical diagnosis as recently developed neural network rule extraction algorithms make it possible for the decision process of a trained network to be expressed as classification rules. These rules are more comprehensible to a human user than the classification process of the networks which involves complex nonlinear mapping of the input data. This paper reports the results from two neural network rule extraction techniques, NeuroLinear and NeuroRule applied to the diagnosis of hepatobiliary disorders. The dataset consists of nine measurements collected from patients in a Japanese hospital and these measurements have continuous values. NeuroLinear generates piece-wise linear discriminant functions for this dataset. The continuous measurements have previously been discretized by domain experts. NeuroRule is applied to the discretized dataset to generate symbolic classification rules. We compare the rules generated by the two techniques and find that the rules generated by NeuroLinear from the original continuously valued dataset to be slightly more accurate and more concise than the rules generated by NeuroRule from the discretized dataset.

Algorithms↗
PubMedDate not supplied

Automatic segmentation of non-enhancing brain tumors in magnetic resonance images.

Tumor segmentation from magnetic resonance (MR) images may aid in tumor treatment by tracking the progress of tumor growth and/or shrinkage. In this paper we present the first automatic segmentation method which separates non-enhancing brain tumors from healthy tissues in MR images to aid in the task of tracking tumor size over time. The MR feature images used for the segmentation consist of three weighted images (T1, T2 and proton density (PD)) for each axial slice through the head. An initial segmentation is computed using an unsupervised fuzzy clustering algorithm. Then, integrated domain knowledge and image processing techniques contribute to the final tumor segmentation. They are applied under the control of a knowledge-based system. The system knowledge was acquired by training on two patient volumes (14 images). Testing has shown successful tumor segmentations on four patient volumes (31 images). Our results show that we detected all six non-enhancing brain tumors, located tumor tissue in 35 of the 36 ground truth (radiologist labeled) slices containing tumor and successfully separated tumor regions from physically connected CSF regions in all the nine slices. Quantitative measurements are promising as correspondence ratios between ground truth and segmented tumor regions ranged between 0.368 and 0.871 per volume, with percent match ranging between 0.530 and 0.909 per volume.

Astrocytoma↗