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Assessment of depth of anesthesia using principal component analysis

Journal of Biomedical Science and engineering 2009年第1期2卷 9-15页

作者： Mina Taheri Behzad Ahmadi Rassoul Amirfattahi Mojtaba Mansouri Digital Signal Processing Research Lab Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156-83111 Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran. Correspondence should be addressed to Mina Taheri

A new approach to estimating level of uncon-sciousness based on Principal Component Analysis (PCA) is proposed. The Electroen-cephalogram (EEG) data was captured in both Intensive Care Unit (ICU) and operating room, using different anesthetic drugs. Assuming the central nervous system as a 20-tuple source, window length of 20 seconds is applied to EEG. The mentioned window is considered as 20 nonoverlapping mixed-signals (epoch). PCA algorithm is applied to these epochs, and larg-est remaining eigenvalue (LRE) and smallest remaining eigenvalue (SRE) were extracted. Correlation between extracted parameters (LRE and SRE) and depth of anesthesia (DOA) was measured using Prediction probability (PK). The results show the superiority of SRE than LRE in predicting DOA in the case of ICU and isoflurane, and the slight superiority of LRE than SRE in propofol induction. Finally, a mixture model containing both LRE and SRE could predict DOA as well as Relative Beta Ratio (RBR), which expresses the high capability of the proposed PCA based method in estimating DOA.

关键词： Bispectral Index Depth of Anesthesia Eignevalue Decomposition Principal Component Analysis

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Development and Evaluation of Text Localization Techniques Based on Structural Texture Features and Neural Classifiers

Development and Evaluation of Text Localization Techniques B...

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International Conference on Document Analysis and Recognition

作者： Christos Emmanouilidis Costas Batsalas Nikos Papamarkos Computational Systems Unit CETI/R. C. ATHENA Xanthi Greece Image Processing and Multimedia Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Xanthi Greece Democritus University of Thrace Komotini GR

ISBN: (纸本)9781424445004

This paper presents a text localization approach for binarized printed document images. Emphasis is given to the feature extraction and feature selection stages. In the former, several document structure elements and spatial features, likely to convey useful information, are extracted. In the latter, evolutionary multi-objective feature selection is employed to identify combinations of features with simultaneous good performance in terms of text localization sensitivity and specificity. The selected features are applied to a range of classifiers. Performance results over document image sets from known databases are presented, employing the classifiers with or without feature selection. The results suggest that the hybrid techniques, which utilize the classifiers in combination with the customized pre-processing, feature extraction and feature selection stages, exhibit promising performance on a range of document images.

关键词： Feature extraction Data mining image segmentation Text analysis image analysis image texture analysis Sensitivity and specificity Support vector machines Support vector machine classification image recognition

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Multi-area integrated E/MEG and fMRI modeling

Multi-area integrated E/MEG and fMRI modeling

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Babajani-Feremi, Abbas Bowyer, Susan Moran, John Elisevich, Kost Podell, Kenneth Soltanian-Zadeh, Hamid Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit MI 48202 United States Neuromagnetism Lab. Neurology Department Henry Ford Hospital Detroit MI 48202 United States Department of Neurology Henry Ford Hospital Detroit MI 48202 United States Division of Neuropsychology and Sports Concussion Safety Program Henry Ford Hospital Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran

ISBN: (纸本)9780819475138

Functional magnetic resonance imaging (fMRI) has complementary spatiotemporal resolution compared to Electroencephalography (EEG) as well as Magnetoencephalography (MEG). Thus, their integrated analysis should improve the overall resolution. To integrate analysis of E/MEG and fMRI, we extend our previously proposed integrated E/MEG and fMRI neural mass model to a multi-area model by defining two types of connections: the Short-Range Connections (SRCs) between minicolumns within the areas and Long-Range Connections (LRCs) between inter-areas minicolumns. The nonlinear input/output relationship in the proposed model is derived from the state space representation of the multi-area model. The E/MEG signals are originated from the overall synaptic activities of the pyramidal cells of all minicolumns and can be calculated using the lead field matrix (i.e., forward electromagnetic model). The fMRI signal is extracted from the proposed integrated model by calculating the overall neural activities in the areas and using it as the input of the extended balloon model (EBM). Using the simulation results, the capabilities of the proposed model to generate E/MEG and fMRI signals is shown. In addition, changes in the dynamics of the model to variations of its parameters were evaluated and lead us to the appropriate ranges for the parameters. In conclusion, this work proposes an effective method to integrate E/MEG and fMRI for the more effective use of these techniques in functional neuroimaging. © 2009 SPIE.

关键词： Electroencephalography

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Estimation of proper parameter values for document binarization

Estimation of proper parameter values for document binarizat...

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10th IASTED International Conference on computer Graphics and Imaging, CGIM 2008

作者： Badekas, E. Papamarkos, N. Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece

ISBN: (纸本)9780889867192

Most of the existing document-binarization techniques deal with many parameters that require a priori setting of their values. Due to the unknown of the ground-truth images, the evaluation of document binarization techniques is subjective and employs human observers for the estimation of the appropriate parameter values. The selection of the appropriate values for these parameters is crucial and influences to the final binarization. However, there is no predetermined set of parameters that guarantees optimal binarization for all document images. This paper proposes a new technique that allows the estimation of proper parameters values for each one of the document binarization techniques. The proposed approach is based on a statistical performance analysis of a set of binarization results, which are obtained by applying various binarization techniques with different parameter values. The proposed statistical performance analysis can also depicts the best document binarization result obtained by a set of document binarization techniques.

关键词： Parameter estimation

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Efficient geometric matching with higher-order features

Efficient geometric matching with higher-order features

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作者： Paramanand, C. Rajagopalan, A.N. Image Processing and Computer Vision Lab. Department of Electrical Engineering IIT Madras India

ISBN: (纸本)9781424421756

We propose a new technique in which line segments and elliptical arcs are used as features for recognizing image patterns. By using this approach, the process of locating a model in a given image is efficient since the number of features to be compared is few. We propose distance measures to evaluate the similarity between the features of the model and that of the image. The model transformation parameters are found by searching the transformation space using cell decomposition. © 2008 IEEE.

关键词： Pattern recognition

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DEVELOPING document image retrieval system

DEVELOPING document image retrieval system

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computer Graphics and Visualization 2008 and Gaming 2008: Design for Engaging Experience and Social Interaction 2008, MCCSIS'08 - IADIS Multi Conference on computer Science and Information Systems

作者： Zagoris, Konstantinos Ergina, Kavallieratou Papamarkos, Nikos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece Department of Information and Communication Systems Engineering University of the Aegean Samos 83100 Greece

ISBN: (纸本)9789728924638

A system was developed able to retrieve specific documents from a document collection. In this system the query is given in text by the user and then transformed into image. Appropriate features were in order to capture the general shape of the query, and ignore details due to noise or different fonts. In order to demonstrate the effectiveness of our system, we used a collection of noisy documents and we compared our results with those of a commercial OCR package. © 2008 IADIS.

关键词： Search engines

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An evaluation survey of binarization algorithms on historical documents

An evaluation survey of binarization algorithms on historica...

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作者： Stathis, Pavlos Kavallieratou, Ergina Papamarkos, Nikos Department of Electrical and Computer Engineering Image Processing and Multimedia Laboratory Democritus University of Thrace 67100 Xanthi Greece Dept. of Information and Communication Systems Engineering University of the Aegean 83200 Karlovassi Samos Greece

ISBN: (纸本)9781424421756

Document binarization is an active research area for many years. There are many difficulties associated with satisfactory binarization of document images and especially in cases of degraded historical documents. In this paper, we try to answer the question "how well an existing binarization algorithm can binarize a degraded document image?" We propose a new technique for the validation of document binarization algorithms. Our method is simple in its implementation and can be performed on any binarization algorithm since it doesn't require anything more than the binarization stage. Then we apply the proposed technique to 30 existing binarization algorithms. Experimental results and conclusions are presented. © 2008 IEEE.

关键词： History

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Dialog Act classification using acoustic and discourse information of MapTask Data

Dialog Act classification using acoustic and discourse infor...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fatema N. Julia Khan M. Iftekharuddin Intelligent System and Image Processing Lab Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

In this work, we analyze both acoustic and discourse information for Dialog Act (DA) classification of HCRC MapTask dataset. We extract several different acoustic features and exploit these features in a Hidden Markov Model (HMM) to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features manyfold. To classify discourse information, we exploit two classifiers such as a HMM and a Support Vector Machine (SVM) respectively. We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify twelve different DAs in HCRC MapTask data. We obtain accuracy of rate 65.2% (58.06% with cross validation) and 55.4% (51.08% with cross validation) DA classification using acoustic and discourse information respectively. Furthermore, we obtain combined accuracy of 68.6% (61.02% with cross validation) for DA classification. These accuracy rates of DA classification are comparable to previously reported results for the same HCRC MapTask dataset. In terms of average Precision and Recall, we obtain accuracy of 74.89% and 69.83% (without cross validation) respectively. Therefore, we obtain much better precision and recall rate for most of the classified DAs when compared to existing works on the same dataset.

关键词： Hidden Markov models Acoustics Feature extraction Support vector machines Classification algorithms Accuracy Tagging

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A biologically-inspired computational model for transformation invariant target recognition

A biologically-inspired computational model for transformati...

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International Joint Conference on Neural Networks (IJCNN)

作者： Khan M. Iftekharuddin Yaqin Li Intelligence System and Image Processing Lab Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. One of the primary challenges is detection and recognition of objects in the presence of transformations such as resolution, rotation, translation, scale and occlusion. In this work, we investigate a biologically-inspired computational modeling approach that exploits reinforcement learning (RL) for transformation-invariant image recognition. The RL is implemented in an adaptive critic design (ACD) framework to approximate the neuro-dynamic programming. Two ACD algorithms such as heuristic dynamic programming (HDP) and dual heuristic dynamic programming (DHP) are investigated and compared for transformation invariant recognition. The two learning algorithms are evaluated statistically using simulated transformations in 2-D images as well as with a large-scale UMIST 2-D face database with pose variations. Our simulations show promising results for both HDP and DHP for transformation-invariant image recognition as well as face authentication. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. On the other hand, HDP is more robust than DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational complexity involved in HDP is much less.

关键词： Artificial neural networks Visualization Biology Biological system modeling image recognition Dynamic programming image resolution

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Class specific gene expression estimation and classification in microarray data

Class specific gene expression estimation and classification...

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International Joint Conference on Neural Networks (IJCNN)

作者： Atiq Islam Khan M. Iftekharuddin E. Olusegun. George Intelligence System and Image Processing Lab Department of Electrical and Computer Engineering University of Memphis Memphis TN USA Department of Mathematics University of Memphis Memphis TN USA

In this work, we characterize genes using an oligonucleotide affymetrix gene expression dataset and propose a novel gene selection method based on samples from the posterior distributions of class-specific gene expression measures. We construct a hierarchical Bayesian framework for a random effect ANOVA model that allows us to obtain the posterior distributions of the class-specific gene expressions. We also formalize a novel class prediction scheme based on the samples from new posterior distributions of group specific gene expressions. Our experimental results show the class-discriminating power of the selected genes. Furthermore, we demonstrate that our prediction scheme classifies tissue samples into appropriate treatment groups with high accuracy. The computations are implemented by using Gibbs sampling. We compare the efficacy of our proposed gene selection and prediction methods with that of Pomeroy et. al (Nature, 2002) on the same CNS tumor sample dataset.

关键词： Gene expression Arrays Bayesian methods Prediction algorithms Tumors Computational modeling Biological system modeling

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