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Workshop on Mathematical Methods in Biomedical Image Analysis

作者： Xuejun Sun Wei Qian Dansheng Song A.C. Robert Department of Interdisciplinary Oncology H. Lee Mojfitt Cancer Center and Research Institute University of South Florida USA Department of Interdisciplinary Oncology H. Lee Moffitt Cancer Center and Research Institute at the University of South Florida

ISBN: (纸本)0769513360

An ipsilateral multi-view computer-aided diagnosis (CAD) scheme is presented for the earlier mass detection in digital mammograms. Tree structured nonlinear filtering (TSF) is used in image noise suppression. Two wavelet-based methods, directional wavelet transform (DWT) and tree structured wavelet transform (TSWT) are employed for image enhancement. Adaptive fuzzy-C means (FCM) algorithm is conducted for segmentation. Concurrent analysis is employed for iterative analysis of ipsilateral multi-view mammograms to raise detection sensitivity and specificity, and a supervised three-layer artificial neural network (ANN) in which the backpropagation (BP) algorithm combined with Kalman filtering is used as training algorithm is developed as a classifier, which has been trained using the training database with biopsy proven truth files. The application of such CAD system in digital mammography is reported in this article. The test database consists of 200 cases in which the distribution of normal, abnormal cases balanced, and free-response receiver operating characteristic (FROC) analysis method is used to test the performance of the developed unilateral CAD system. The performance comparison has been conducted between the final ipsilateral mufti-view CAD system and current single-view CAD system. The study results have shown that the advantages of ipsilateral mufti-view CAD method over current single-view CAD system express the feasibility of ipsilateral multi-view CAD system combined with concurrent analysis method described in this paper for the improvement of overall performance of CAD system in the early stage mass detection.

关键词： Iterative algorithms backpropagation algorithms Wavelet transforms Algorithm design and analysis Artificial neural networks Filtering algorithms Databases System testing Performance analysis Computer aided diagnosis

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Efficient training algorithm for Takagi-Sugeno type neuro-fuzzy network

Efficient training algorithm for Takagi-Sugeno type neuro-fu...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： A.K. Palit R. Babuska Repas-AEG Information Technologies GmbH Bremen Germany ITS/Control Engineering Laboratory Delft University of Technnology Delft Netherlands ITS / Control Engineering Laboratory Delft University of Technnology Delft Netherlands

The paper describes an algorithm that can be used to train the Takagi-Sugeno (TS) type neuro-fuzzy network very efficiently. The training algorithm is very efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than that the classical backpropagation algorithm (BPA). The proposed training algorithm is based on a slight modification of the Levenberg-Marquardt training algorithm (LMA) which takes into account the modified error index extension of sum squared error as the new performance index of the network. The Levenberg-Marquardt algorithm uses the Jacobian matrix in order to approximate the Hessian matrix and that is the most important and difficult step in implementing this LMA. Therefore, a simple technique has been described to compute first the transpose of Jacobian matrix by comparing two equations and thereafter by further transposing the former one the actual Jacobian matrix is computed that is found to be robust against the modified error index extension. Furthermore, care has been taken to suppress or control the oscillation magnitude during the training of neuro-fuzzy network. Finally, the above training algorithm is tested on neuro-fuzzy modeling and prediction applications of time series and nonlinear plant.

关键词： Fuzzy neural networks backpropagation algorithms Fuzzy logic Predictive models Jacobian matrices Laboratories Takagi-Sugeno model Control engineering Neural networks Information technology

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A hybrid model approach to generalization in sequence learning

A hybrid model approach to generalization in sequence learni...

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International Joint Conference on Neural Networks (IJCNN)

作者： R. Spiegel I.P.L. McLaren Department of Experimental Psychology University of Cambridge Cambridge UK

ISBN: (纸本)0780370449

Both recurrent neural networks and humans are able to learn sequential information and generalize to sequences they have not experienced in training. However, they sometimes seem to differ in the way they perform generalization. A new hybrid model is introduced that relies on both a recurrent neural network and rules typically applied by human subjects.

关键词： Humans Recurrent neural networks Predictive models Context modeling Intelligent networks Psychology Neural networks Artificial intelligence backpropagation algorithms Testing

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Grouping connected components using minimal path techniques. Application to reconstruction of vessels in 2D and 3D images

Grouping connected components using minimal path techniques....

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： L.D. Cohen T. Deschamps CEREMADE UMR CNRS 7534 Université de Paris IX Dauphine Paris France Medical Imaging Systems Group Philips Research France Suresnes France

ISBN: (纸本)0769512720

We address the problem of finding a set of contour curves in a 2D or 3D image. We consider the problem of perceptual grouping and contour completion, where the data is an unstructured set of regions in the image. A new method to find complete curves from a set of edge points is presented. Contours, are found as minimal paths between connected components, using the fast marching algorithm. We find the minimal paths between each of these components, until the complete set of these "regions" is connected. The paths are obtained using backpropagation from the saddle points to both components. We then extend this technique to 3D. The data is a set of connected components in a 3D image. We find 3D minimal paths that link together these components. Using a potential based on vessel detection, we illustrate the capability of our approach to reconstruct tree structures in a 3D medical image dataset.

关键词： Image reconstruction Object detection Biomedical imaging Active contours backpropagation algorithms Tree data structures Level set Topology Shape Humans

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Statistical analysis of neural network modeling and identification of nonlinear systems with memory

Statistical analysis of neural network modeling and identifi...

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International Symposium on Signal Processing and Its Applications (ISSPA)

作者： M. Ibnkahla Electrical and computer engineering department Queen's university Kingston Ontario Canada

The paper presents a statistical analysis of neural network modeling and identification of nonlinear systems with memory. The nonlinear system model is comprised of a discrete-time linear filter H followed by a zero-memory nonlinear function g(.). The system is corrupted by input and output independent Gaussian noise. The neural network is used to identify and model the unknown linear filter H and the unknown nonlinearity g(.). The network architecture is composed of a linear adaptive filter and a two-layer nonlinear neural network (with an arbitrary number of neurons). The network is trained using the backpropagation algorithm. The paper studies the MSE surface and the stationary points of the adaptive system. Recursions are derived for the mean transient behavior of the adaptive filter coefficients and the neural network weights for slow learning. It is shown that the adaptive filter converges to a scaled version of the unknown filter H, and that the nonlinear neural network converges to an approximation of the unknown nonlinearity. Computer simulations show good agreement between theory and experimental results.

关键词： Statistical analysis Neural networks Adaptive filters Nonlinear filters Nonlinear systems Gaussian noise Neurons backpropagation algorithms Adaptive systems Computer simulation

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Adaptation of neural network and application of digital ultrasonic image processing for the pattern recognition of defects in semiconductor

Adaptation of neural network and application of digital ultr...

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International Symposium on Electronic Materials and Packaging

作者： Jae-Yeol Kim Hyun-Jo Jeong Hun-Cho Kim Chang-Hyun Kim The Division of Mechanical Engineering Chosun University Kwangju South Korea The Division of Mechanical Engineering Wonkwang University South Korea The Korea Chamber of Commerce and Industry Kwangju South Korea The depature of Precission Mechanical Engineering Chosun Graduate School South Korea

ISBN: (纸本)0780371577

In this study, the classification of artificial defects in semiconductor devices are performed by using pattern recognition technology. For this target, a pattern recognition algorithm including user made software was developed and the total procedure including image processing and self-organizing map was treated by a backpropagation neural network, where image processing was composed of ultrasonic image acquisition, equalization filtering, binary processing and edge detection. Image processing and self-organizing map were compared as preprocessing methods for the reduction of dimensionality as input data into multi-layer perceptron or backpropagation neural networks. Also, the pattern recognition technique has been applied to classify two kinds of semiconductor defects: cracks and delamination. According to these results, it was found that the self-organizing map provided recognition rates of 83.4% and 75.7% for delamination and cracks, respectively, while BP provided 100% recognition rates for the results.

关键词： Neural networks Pattern recognition Image processing Artificial neural networks Delamination Application software Semiconductor devices Software algorithms backpropagation algorithms Filtering algorithms

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Isolated speech recognition using artificial neural networks

Isolated speech recognition using artificial neural networks

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： P.D. Polur Ruobing Zhou Jun Yang F. Adnani R.S. Hobson Department of Biomedical Engineering Virginia Commonwealth University Virginia USA Department of Electrical Engineering Virginia Commonwealth University Virginia USA

In this project artificial neural networks are used as research tool to accomplish automated speech recognition of normal speech. A small size vocabulary containing the words YES and NO is chosen. Spectral features using cepstral analysis are extracted per frame and imported to a feed-forward neural network which uses a backpropagation with momentum training algorithm. The network is trained to recognize and classify the incoming words into the respective categories. The output from the neural network is loaded into a pattern search function, which matches the input sequence with a set of target word patterns. The level of variability in input speech patterns limits the vocabulary and affects the reliability of the network. The results from the first stage of this. work are satisfactory and thus the application of artificial neural networks in conjunction with cepstral analysis in isolated word recognition holds promise.

关键词： Speech recognition Artificial neural networks Vocabulary Cepstral analysis Neural networks Feedforward systems Feedforward neural networks backpropagation algorithms Pattern matching Impedance matching

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Edge representation and recognition using neural networks

Edge representation and recognition using neural networks

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ohjae Kwon Chulhee Lee Department Electrical and Electronic Engineering Yonsei University Seoul South Korea

ISBN: (纸本)0780370902

In this paper, we propose a new approach to represent and recognize edges of objects using multilayer feedforward neural networks. First, we show how then edge of an object can be represented by neural networks. This is accomplished by generating two classes consisting of samples that lie on each side of the edge and then by training a neural network to classify the two classes. If the training is successfully accomplished, the resulting neural network will have a decision boundary that matches the edge we want to represent. Second, we will propose a matching algorithm that identifies an arbitrarily rotated and shifted edge. The matching algorithm uses a gradient descent algorithm. The proposed algorithm can be used in the area of object representation and recognition. In addition, we investigate the relationship between the number of hidden neurons and complexity of edges.

关键词： Neural networks Neurons Multi-layer neural network Feedforward neural networks Fingerprint recognition Face recognition Shape Information security backpropagation algorithms

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Artificial neural network implementation on a single FPGA of a pipelined on-line backpropagation 13

Artificial neural network implementation on a single FPGA of...

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13th International Symposium on System Synthesis (ISSS 2000)

作者： Gadea, R Cerdá, J Ballester, F Mocholí, A Univ Politecn Valencia Dept Elect Engn Valencia 46022 Spain

ISBN: (纸本)0769507662

The paper describes the implementation of a systolic array for a multilayer perceptron on a Virtex XCV400 FPGA with a hardware-friendly learning algorithm. A pipelined adaptation of the on-line backpropagation algorithm is shown. Parallelism is better exploited because both forward and backward phases can be performed simultaneously. We can implement very large interconnection layers by using large Xilinx devices with embedded memories alongside the projection used in the systolic architecture. These physical and architectural features - together with the combination of FPGA reconfiguration properties with a design flow based on generic VHDL - create an easy, flexible, and fast method of designing a complete ANN on a single FPGA. The result offers a high degree of parallelism and fast performance.

关键词： Arithmetic Artificial neural networks backpropagation algorithms Field programmable gate arrays Logic arrays Logic devices Neural networks Neurons Signal processing algorithms Systolic arrays

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A novel fuzzy neural network based distance relaying scheme

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IEEE TRANSACTIONS ON POWER DELIVERY 2000年第3期15卷 902-907页

作者： Dash, PK Pradhan, AK Panda, G Reg Engn Coll Dept Elect Engn Rourkela India

This paper presents a nem approach to distance relaying using fuzzy neural network (FNN). The FNN can be viewed either as a fuzzy system, a neural network or fuzzy neural network. The structure is seen as a neural network for training and a fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The number of rules is determined by the data itself and therefore smaller number of rules is produced. The network is trained with back propagation algorithm. A pruning strategy is applied to eliminate the redundant rules and fuzzification neurons. consequently a compact structure is achieved. The classification and location tasks are accomplished by using different FNN's, Once the fault type is identified by the FNN classifier the selected fault locating FNN estimates the location of the fault accurately, Normalized peaks of fundamental voltage and current waveforms are considered as inputs to all the networks and an additional input derived from df component is fed to fault locating networks. The peaks and de component are extracted from sampled signals by the EKF, Test results show that the nem approach provides robust and accurate classification/location of faults for a variety of power system operating conditions even with resistance in the fault path.

关键词： backpropagation fault location fuzzy neural nets power system analysis computing power system faults power system protection relay protection DC component EKF backpropagation algorithm current waveform distance relaying scheme fault path resistance fault Fault location Power system analysis computing Power system faults backpropagation algorithms back propagation Fuzzy neural networks Relay protection Power system protection DC component

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