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PubMed2000

Mapping and fuzzy classification of macromolecular images using self-organizing neural networks.

In this work the effectiveness of the fuzzy kohonen clustering network (FKCN) in the unsupervised classification of electron microscopic images of biological macromolecules is studied. The algorithm combines Kohonen's self-organizing feature maps (SOFM) and Fuzzy c-means (FCM) in order to obtain a powerful clustering technique with the best properties inherited from both. Exploratory data analysis using SOFM is also presented as a step previous to final clustering. Two different data sets obtained from the G40P helicase from B. Subtilis bacteriophage SPP1 have been used for testing the proposed method, one composed of 2458 rotational power spectra of individual images and the other composed by 338 images from the same macromolecule. Results of FKCN are compared with self-organizing feature maps (SOFM) and manual classification. Experimental results prove that this new technique is suitable for working with large, high-dimensional and noisy data sets and, thus, it is proposed to be used as a classification tool in electron microscopy.

Algorithms↗
PubMed1998

A symmetry perceiving adaptive neural network and facial image recognition.

The paper deals with the forensic problem of comparing nearly from view and facial images for personal identification. The human recognition process for such problems, is primarily based on both holistic as well as feature-wise symmetry perception aided by subjective analysis for detecting ill-defined features. It has been attempted to approach the modelling of such a process by designing a robust symmetry perceiving adaptive neural network. The pair of images to be compared should be presented to the proposed neural network (NN) as source (input) and target images. The NN learns about the symmetry between the pair of images by analysing examples of associated feature pairs belonging to the source and the target images. In order to prepare a paired example of associated features for training purpose, when we select one particular feature on the source image as a unique pixel, we must associate it with the corresponding feature on the target image also. But, in practice, it is not always possible to fix the latter feature also as a unique pixel due to pictorial ambiguity. The robust or fault tolerant NN takes care of such a situation and allows fixing the associated target feature as a rectangular array of pixels, rather than fixing it as a unique pixel, which is pretty difficult to be done with certainty. From such a pair of sets of associated features, the NN searches out proper locations of the target features from the sets of ambiguous target features by a fuzzy analysis during its learning. If any of target features, searched out by the NN, lies outside the prespecified zone, the training of the NN is unsuccessful. This amounts to non-existence of symmetry between the pair of images and confirms non-identity. In case of a successful training, the NN gets adapted with appropriate symmetry relation between the pair of images and when the source image is input to the trained NN, it responds by outputting a processed source image which is superimposable over the target images and identity may subsequently be established by examining detailed matching in machine-made superimposed/composite images which are also suitable for presentation before the court. The performance of the proposed NN has been tested with various cases including simulated ones and it is hoped to serve as a working tool of forensic anthropologists.

Face↗
PubMed2002

Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms.

This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FCM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFCM), was used for MRI segmentation in Ophthalmology. These unsupervised segmentation algorithms can help Ophthalmologists to reduce the medical imaging noise effects originating from low resolution sensors and/or the structures that move during the data acquisition. They may be particularly helpful in the clinical oncological field as an aid to the diagnosis of Retinoblastoma, an inborn oncological disease in which symptoms usually show in early childhood. For the purpose of early treatment with radiotherapy and surgery, the newly proposed AFCM is preferred to provide more information for medical images used by Ophthalmologists. Comparisons between FCM and AFCM segmentations are made. Both fuzzy clustering segmentation techniques provide useful information and good results. However, the AFCM method has better detection of abnormal tissues than FCM according to a window selection. Overall, the newly proposed AFCM segmentation technique is recommended in MRI segmentation.

Algorithms↗
PubMed2003

Generalized Kohonen's competitive learning algorithms for ophthalmological MR image segmentation.

Kohonen's self-organizing map is a two-layer feedforward competitive learning network. It has been used as a competitive learning clustering algorithm. In this paper, we generalize Kohonen's competitive learning (KCL) algorithm with fuzzy and fuzzy-soft types called fuzzy KCL (FKCL) and fuzzy-soft KCL (FSKCL). These generalized KCL algorithms fuse the competitive learning with soft competition and fuzzy c-means (FCM) membership functions. We then apply these generalized KCLs to MRI and MRA ophthalmological segmentations. These KCL-based MRI segmentation techniques are useful in reducing medical image noise effects using a learning mechanism. They may be particularly helpful in clinical diagnosis. Two real cases with MR image data recommended by an ophthalmologist are examined. First case is a patient with Retinoblastoma in her left eye, an inborn malignant neoplasm of the retina frequently metastasis beyond the lacrimal cribrosa. The second case is a patient with complete left side oculomotor palsy immediately after a motor vehicle accident. Her brain MRI with MRA, skull routine, orbital CT, and cerebral angiography did not reveal brainstem lesions, skull fractures, or vascular anomalies. These generalized KCL algorithms were used in segmenting the ophthalmological MRIs. KCL, FKCL and FSKCL comparisons are made. Overall, the FSKCL algorithm is recommended for use in MR image segmentation as an aid to small lesion diagnosis.

Algorithms↗
PubMed1997

Normal brain volume measurements using multispectral MRI segmentation.

The performance of a supervised k-nearest neighbor (kNN) classifier and a semisupervised fuzzy c-means (SFCM) clustering segmentation method are evaluated for reproducible measurement of the volumes of normal brain tissues and cerebrospinal fluid. The stability of the two segmentation methods is evaluated for (a) operator selection of training data, (b) reproducibility during repeat imaging sessions to determine any variations in the sensor performance over time, (c) variations in the measured volumes between different subjects, and (d) variability with different imaging parameters. The variations were found to be dependent on the type of measured tissue and the operator performing the segmentations. The variability during repeat imaging sessions for the SFCM method was < 3%. The absolute volumes of the brain matter and cerebrospinal fluid between subjects varied quite large, ranging from 9% to 13%. The intraobserver and interobserver reproducibility for SFCM were < 4% for the soft tissues and 6% for cerebrospinal fluid. The corresponding results for the kNN segmentation method were higher compared to the SFCM method.

Adult↗
PubMed1997

Monitoring brain tumor response to therapy using MRI segmentation.

The performance evaluation of a semi-supervised fuzzy c-means (SFCM) clustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented. The tumor volume determined using the SFCM method was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level thresholding and seed growing (ISG-SG) method and c) a manual pixel labeling (GT) method for ground truth estimation. The SFCM and kNN methods are applied to the multispectral, contrast enhanced T1, proton density, and T2 weighted, magnetic resonance images (MRI) whereas the ISG-SG and GT methods are applied only to the contrast enhanced T1 weighted image. Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during treatment. The tumor cases studied include one meningioma, two brain metastases and five gliomas. Comparisons with manually labeled ground truth estimations showed that there is a limited agreement between the segmentation methods for absolute tumor volume measurements when using images of patients after treatment. The average intraobserver reproducibility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively. The average of the interobserver reproducibility of these methods was found to be 5.5%, 6.5% and 11.4%, respectively. For the measurement of relative change of tumor volume as required for the response assessment, the multi-spectral methods kNN and SFCM are therefore preferred over the seedgrowing method.

Adult↗
PubMed1998

Quantification in functional magnetic resonance imaging: fuzzy clustering vs. correlation analysis.

The potential of functional MRI (fMRI) data analysis using the paradigm independent fuzzy cluster analysis (FCA) applied in the time domain compared to frequently used paradigm based correlation analysis (CA) was studied with simulated and in vivo fMRI data. The performance of FCA and CA was investigated in a typical contrast-to-noise range for fMRI, ranging from 1.33 to 3.33. Using simulated fMRI data the methods were quantitatively compared in terms of generation of true positives, false positives, and the corresponding signal enhancement. Even without prior knowledge about the stimulation paradigm and the actual hemodynamic response function the performance of FCA was comparable to that of CA where extensive prior knowledge has to be added. Furthermore, discrimination of nonanticipated hemodynamic responses by FCA, such as different levels of activation and delayed response, are demonstrated in simulated and in vivo fMRI data. We demonstrate that using CA one cannot differentiate between these responses at least without extensive prior knowledge, i.e., FCA yields a more particular description of fMRI data. This may be worthwhile for analysis and optimization of data quality in fMRI as well as in the final data analysis.

Adult↗
PubMed1999

Three-dimensional magnetic resonance imaging of the interosseous membrane of forearm: a new method using fuzzy reasoning.

We now report newly developed three-dimensional magnetic resonance imaging (3D-MRI) system which is based on semiautomatic tissue extraction from the axial MR images utilizing the fuzzy reasoning calculation method and 3D-image reconstruction with surface rendering. We also studied normal in vivo dynamic changes of the interosseous membrane (IOM) of forearm during rotation using this 3D-MRI. Serial axial MRI of right forearms of five healthy volunteers was obtained in five rotational positions, and extraction and 3D-reconstruction of the radius, ulna, and IOM was made using the system. Extraction results were well with the fuzzy reasoning method. 3D-MRI of the radius and ulna, IOM were reconstructed from these images respectively, and their 3D-shapes were almost identical to the anatomic shape. 3D-MRI showed there were wavy deformities on the IOM in pronation position in the all five subjects and dorsiflexion on the most dorsal portion of the IOM at maximum supination in three forearms. In neutral position, the IOM of all five volunteers was almost flat. From anatomic orientation, these dynamic changes of the IOM mainly occurred at the membranous portion, which is soft, thin, and elastic. Otherwise, the tendinous portion which is a thick and strong complex of 5 to 10 bundles run from proximal one third of the radius to distal one fourth of the ulna, demonstrated minimal dynamic changes on the 3D-MRI. Therefore, the tendinous portion is considered to be taut during rotation to provide stability between the radius and the ulna, while the membranous portion is easy to deform and allowing smooth rotation. Furthermore, because of wide-use, our 3D-MRI system is useful for in vivo analysis of soft tissue kinesiology in normal and abnormal musculoskeletal systems.

Adult↗
PubMed1999

A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images.

Methods for brain tissue classification or segmentation of structural magnetic resonance imaging (MRI) data should ideally be independent of human operators for reasons of reliability and tractability. An algorithm is described for fully automated segmentation of dual echo, fast spin-echo MRI data. The method is used to assign fuzzy-membership values for each of four tissue classes (gray matter, white matter, cerebrospinal fluid and dura) to each voxel based on partition of a two dimensional feature space. Fuzzy clustering is modified for this application in two ways. First, a two component normal mixture model is initially fitted to the thresholded feature space to identify exemplary gray and white matter voxels. These exemplary data protect subsequently estimated cluster means against the tendency of unmodified fuzzy clustering to equalize the number of voxels in each class. Second, fuzzy clustering is implemented in a moving window scheme that accommodates reduced image contrast at the axial extremes of the transmitting/receiving coil. MRI data acquired from 5 normal volunteers were used to identify stable values for three arbitrary parameters of the algorithm: feature space threshold, relative weight of exemplary gray and white matter voxels, and moving window size. The modified algorithm incorporating these parameter values was then used to classify data from simulated images of the brain, validating the use of fuzzy-membership values as estimates of partial volume. Gray:white matter ratios were estimated from 20 twenty normal volunteers (mean age 32.8 years). Processing time for each three-dimensional image was approximately 30 min on a 170 MHz workstation. Mean cerebral gray and white matter volumes estimated from these automatically segmented images were very similar to comparable results previously obtained by operator dependent methods, but without their inherent unreliability.

Adult↗
PubMed2000

An inference modeling of human visual judgment of sagittal jaw-base relationships based on cephalometry: part II.

In a clinical situation, visual judgment of sagittal jaw-base relationships is described and handled as fuzzy terms, such as "slight" skeletal 3 tendency or "relatively severe" skeletal 2. The purpose of this study was to develop an inference system in order to be able to describe the degree of certainty for sagittal skeletal discrepancy automatically and in a mathematical way. Orthodontic records of 137 adult female patients were judged by 3 orthodontists. Based on these data and the multiple regression model described in Part I of this study, we developed an inference system that consisted of 2 membership functions and 6 incorporating rules. The performance reliability of the system was tested for 175 female adult cases by 7 orthodontic experts. In 97% (170 of 175) of the cases, the orthodontic experts agreed with advice proposed by the inference system. We conclude that the current model is an effective means of deriving the opinion of an experienced clinician from a cephalometric tracing and provides an interesting insight into how orthodontists are influenced by the face when attempting to judge the sagittal relationship clinically. In addition, the current system would be an initial stage in the development of the decision-making system for the orthodontic diagnosis and treatment planning.

Adult↗
PubMed2000

Fuzzy lattice neurocomputing (FLN) models.

In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the capacity to deal rigorously with: disparate types of data such as numeric and linguistic data, intervals of values, 'missing' and 'don't care' data. A novel notation for the FL-framework is introduced here in order to simplify mathematical expressions without losing content. Two concrete FLN models are presented, namely 'sigma-FLN' for competitive clustering, and 'FLN with tightest fits (FLNtf)' for supervised clustering. Learning by the sigma-FLN, is rapid as it requires a single pass through the data, whereas learning by the FLNtf, is incremental, data order independent, polynomial theta(n3), and it guarantees maximization of the degree of inclusion of an input in a learned class as explained in the text. Convenient geometric interpretations are provided. The sigma-FLN is presented here as fuzzy-ART's extension in the FL-framework such that sigma-FLN widens fuzzy-ART's domain of application to (mathematical) lattices by augmenting the scope of both of fuzzy-ART's choice (Weber) and match functions, and by enhancing fuzzy-ART's complement coding technique. The FLNtf neural model is applied to four benchmark data sets of various sizes for pattern recognition and rule extraction. The benchmark data sets in question involve jointly numeric and nominal data with 'missing' and/or 'don't care' attribute values, whereas the lattices involved include the unit-hypercube, a probability space, and a Boolean algebra. The potential of the FL-framework in computing is also delineated.

Fuzzy Logic↗
PubMed2001

A real-coded genetic algorithm for training recurrent neural networks.

The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference.

Algorithms↗
PubMed2001

Quick fuzzy backpropagation algorithm.

A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.

Algorithms↗
PubMed2001

Pseudo-outer product based fuzzy neural network fingerprint verification system.

Fingerprint identification has been used in law enforcement applications over the last century, and has become the de facto international standard for positive identification. With the emergence of automated fingerprint identification technologies, it has assisted in making the once labour-intensive process of classifying, searching and matching a thing of the past. As a biometrics proof of identification, not many have ventured into the world of fingerprint identification using fuzzy neural networks. In this paper, a database of fingerprint images is constructed and a fuzzy neural network called the pseudo outer product fuzzy neural network (POPFNN) [Zhou, R.W. & Quek, C. (1996). A pseudo outer-product based fuzzy neural network. Neural Networks, 9(9), 1569-1581] is trained to detect similarity between two fingerprints and decide whether they belong to the same person. The fundamental idea is that, given a person's fingerprints taken under different conditions, the POPFNN based fingerprint verification system should be sufficiently robust to distinguish the difference. The people providing the fingerprint samples are subjected to different 'adverse' conditions; from wetness to chemical treatments. Fingerprint images are taken after conditions such as: after a shower, holding pineapples (mild acid from fruit), after washing one's hands, etc. The characteristics of POPFNN, such as the learning, generalisation, and high computational abilities, make fingerprint verification particularly powerful when verifying authentic fingerprints subjected to external conditions and recognising spurious ones. In order to demonstrate the efficacy of POPFNN and its application in the fingerprint verification system (FVS), several types of experiments have been designed and implemented in this work. The experimental results and analysis are presented at the end of the paper for discussion.

Algorithms↗
PubMed2001

Cross-validation in fuzzy ARTMAP for large databases.

In this paper we are examining the issue of overtraining in Fuzzy ARTMAP. Over-training in Fuzzy ARTMAP manifests itself in two different ways: (a) it degrades the generalization performance of Fuzzy ARTMAP as training progresses; and (b) it creates unnecessarily large Fuzzy ARTMAP neural network architectures. In this work, we are demonstrating that overtraining happens in Fuzzy ARTMAP and we propose an old remedy for its cure: cross-validation. In our experiments, we compare the performance of Fuzzy ARTMAP that is trained (i) until the completion of training, (ii) for one epoch, and (iii) until its performance on a validation set is maximized. The experiments were performed on artificial and real databases. The conclusion derived from those experiments is that cross-validation is a useful procedure in Fuzzy ARTMAP, because it produces smaller Fuzzy ARTMAP architectures with improved generalization performance. The trade-off is that cross-validation introduces additional computational complexity in the training phase of Fuzzy ARTMAP.

Algorithms↗
PubMed2001

Connectionist inference models.

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modelling.

Animals↗
PubMed2001

The POP learning algorithms: reducing work in identifying fuzzy rules.

A novel fuzzy neural network, the Pseudo Outer-Product based Fuzzy Neural Network (POPFNN), and its two fuzzy-rule-identification algorithms are proposed in this paper. They are the Pseudo Outer-Product (POP) learning and the Lazy Pseudo Outer-Product (LazyPOP) leaning algorithms. These two learning algorithms are used in POPFNN to identify relevant fuzzy rules. In contrast with other rule-learning algorithms, the proposed algorithms have many advantages, such as being fast, reliable, efficient, and easy to understand. POP learning is a simple one-pass learning algorithm. It essentially performs rule-selection. Hence, it suffers from the shortcoming of having to consider all the possible rules. The second algorithm, the LazyPOP learning algorithm, truly identifies the fuzzy rules which are relevant and does not use a rule-selection method whereby irrelevant fuzzy rules are eliminated from an initial rule set. In addition, it is able to adjust the structure of the fuzzy neural network. The proposed LazyPOP learning algorithm is able to delete invalid feature inputs according to the fuzzy rules that have been identified. Extensive experimental results and discussions are presented for a detailed analysis of the proposed algorithms.

Algorithms↗
PubMed2002

A neuro-fuzzy framework for inferencing.

Earlier we proposed a connectionist implementation of compositional rule of inference (COI) for rules with antecedents having a single clause. We first review this net, then generalize it so that it can deal with rules with antecedent having multiple clauses. We call it COIN, the compositional rule of inferencing network. Given a relational representation of a set of rules, the proposed architecture can realize the COI. The outcome of COI depends on the choice of both the implication function and the inferencing scheme. The problem of choosing an appropriate implication function is avoided through neural learning. COIN can automatically find a 'good' relation to represent a set of fuzzy rules. We model the connection weights so as to ensure learned weights lie in [0,1]. We demonstrate through extensive numerical examples that the proposed neural realization can find a much better representation of the rules than that by usual implication and hence results in much better conclusions than the usual COI.

Fuzzy Logic↗