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检索条件"机构=Computer Vision and Image Processing Laboratory Electrical and Computer Engineering"

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Assessment of Preprocessing on Classifiers Used in the P300 Speller Paradigm

Assessment of Preprocessing on Classifiers Used in the P300 ...

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

作者： H. Mirghasemi M. B. Shamsollahi R. Fazel-Rezai BDP laboratory Department of Electrical Engineering Sharif University of Technology Tehran Iran BDP laboratory Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical and Computer Engineering Brain Image and Signal Processing Laboratory University of Manitoba Winnipeg MAN Canada

Artifact removal is an essential part in electroencephalogram (EEG) recording and the raw EEG signals require preprocessing before feature extraction. In this work, we implemented three filtering methods and demonstrated their effects on the performance of different classifiers. Bandpass digital filtering, median filtering and facet method are three preprocessing approaches investigated in this paper. We used data set lib from the BCI competition 2003 for training and testing phase. Our accuracy varied between 80% and 96%. In our work, we demonstrated that the problems of choosing the classifier and preprocessing methods are not independent of each other. Two of our approaches could achieve the 96% accuracy i.e. 31 of 32 characters were predicted correctly. These two approaches have different classifier and different preprocessing method. It means that the performance of each classifier can be enhanced with a specific preprocessing method. In our approach, we used only three electrodes of 64 applied electrodes. Therefore it can noticeably reduce the time and cost of EEG measurement

关键词： Electroencephalography Band pass filters Filtering Electrodes Testing Noise reduction Feature extraction Principal component analysis Digital filters Cities and towns

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An Over-Complete Independent Component Analysis (ICA) Approach to Magnetic Resonance image Analysis

An Over-Complete Independent Component Analysis (ICA) Approa...

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

作者： Jing Wang C.-I. Chang Hsiang Ming Chen C.C.-C. Chen Jyh Wen Chai Yen-Chieh Ouyang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan

This paper presents a new application of independent component analysis (ICA) in magnetic resonance (MR) image analysis. One of most successful applications for ICA-based approaches in MR imaging is functional MRI (fMRI) which basically deals with one-dimensional temporal signals. The ICA approach proposed in this paper is rather different and considers a set of MR images acquired by different pulse sequences as a 3-dimensional image cube and performs image analysis rather than signal analysis. One major difference between the fMRI-based ICA approaches and our proposed ICA-based image analysis is that the ICA used in the former is undercomplete as opposed to the latter which uses over-complete ICA. Such a fundamental difference results in completely different applications

关键词： Independent component analysis Magnetic resonance image analysis image sequence analysis Magnetic resonance imaging Signal processing image processing Principal component analysis Pixel Remote sensing

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Robust Segmentation and Volumetric Registration in a Multi-view 3D Freehand Ultrasound Reconstruction System

Robust Segmentation and Volumetric Registration in a Multi-v...

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Asilomar Conference on Signals, Systems & computers

作者： Honggang Yu Marios S. Pattichis M. Beth Goens Department of Electrical and Computer Engineering image and video Processing and Communications Laboratory University of New Mexico Albuquerque NM USA Pediatric Cardiology Clinic of the Children's Hospital Heart Center University of New Mexico Albuquerque NM USA

In this paper, we present a new reconstruction method with volumetric registration and semi-automatic segmentation in a multi-view 3D freehand ultrasound imaging system. The new volumetric registration approach is performed on binarized walls using non-linear least squares. It can provide accurate multi-view reconstructions despite significant rigid target motion between different acoustic window acquisitions. A hybrid adaptive gradient vector flow (GVF) geometric active contour (GAC) model is used for image sequence segmentation. It allows for relatively simple initialization of the deformable model, while avoiding edge leaking at poor edges and small boundary gaps. The algorithms were validated on four ultrasound phantom data sets (eight sequences of a total of 336 images) and two echocardiography data sets (four sequences for a total of 75 images). Quantitative evaluation shows that automatic segmentation is comparable with manual segmentation. Using breath-holding and cardiac gating, volume estimates from multi- view reconstructions of the left-ventricle were found to be in better agreement with clinical estimates than volumes estimated from single view reconstructions.

关键词： Robustness Ultrasonic imaging image segmentation image reconstruction Reconstruction algorithms Acoustic imaging Least squares methods Active contours Solid modeling image sequences

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A Key Frame Selection-Based Facial Expression Recognition System

A Key Frame Selection-Based Facial Expression Recognition Sy...

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International Conference on Innovative Computing, Information and Control (ICICIC)

作者： S.M. Guo Y.A. Pan Y.C. Liao C.Y. Hsu J.S.H. Tsai C.I. Chang Department of Computer Science and Information Engineering National Engineering Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan Republic of China Department of Computer Science and Electrical Engineering University of Maryland Baltimore County (UMBC) Baltimore MD U.S.A. National Cheng Kung University Tainan TW Department of Electrical Engineering National Engineering Kung University Tainan Taiwan Department of Electrical Engineering National Cheng-Kung University Tainan Taiwan Republic of China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore Baltimore MD USA

Automatic recognition of facial expressions has received considerable interest in the computer vision. This paper presents a key frame selection-based system for recognition of four basic facial expressions: happiness, anger, sadness and surprise, in image sequences. The proposed new approach is designed for recognition of facial expressions via a key frame selection of the facial motion from an image sequence where an optical flow is used to determine changes in a facial expression, experimental results demonstrate that the proposed automatic recognition process significantly improves the computational complexity encountered in facial expression recognition while enhancing the ability of recognition

关键词： Face recognition Face detection image recognition image sequences Feature extraction Facial features computer science Remote sensing Signal processing image processing

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Rule-based decision-making framework for knowledge-based anatomical landmark localization (K-BALL)

Rule-based decision-making framework for knowledge-based ana...

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IEEE International Symposium on Biomedical Imaging

作者： M.-R. Siadat H. Soltanian-Zadeh K. Elisevich Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Neurosurgery Henry Ford Health System Detroit MI USA

K-BALL is a general method for localization of anatomical phenomena of the same origin with natural discrepancies distributed over a reference space, e.g., human brain anatomical structures. In this paper, we focus on information analysis step (2nd step) of K-BALL during which landmarks extracted in its first step are evaluated. We provide a framework in which rules are automatically generated based on estimated and derived models. We show that the rules based on the derived models can improve the overall success rate of K-BALL. Each rule evaluates the extracted points by producing an intermediate confidence factor (ICNF). A total confidence factor is calculated using ICNF's to facilitate the acceptance or rejection of a set of points as landmarks of interest. Using the rules merely based on the estimated models, simulation study produced an overall success rate of 91.8%. Using the rules based on both of the estimated and derived models, this rate increased to 92.5%

关键词： Decision making Hippocampus Information analysis Data mining Humans Radiology image analysis Radio control Process control Control systems

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Determination of the Grey Level Ranks for the Segmentation of Textured images

Determination of the Grey Level Ranks for the Segmentation o...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Z. Ameur A. Adane S. Ameur Faculty of Electrical Engineering and Computer Science Department of Electronics Laboratory of Random Phenomena Analysis and Modeling LAMPA Mouloud Mammeri University Tizi Ouzou Algeria Faculty of Electronics and Computer Science Department of Telecommunications Laboratory of Image Processing and Radiation University of Sciences and Technology of Algiers U. S. T. H. B. Bab-Ezzouar Algeria

In many cases, an image is made of various types of texture. Such diversity can be usefully exploited to get a suitable decomposition of the image into typical classes and then, identify its constitutive elements. Among the approaches used to segment textured images, those based on the statistical analysis of the neighborhood of each pixel seem to be most efficient. In this paper, this kind of method is implemented, which consists in coding the pixels surrounding each point of the image by taking into account the path traveling through them and their grey levels. The rank vectors namely the codes obtained for all the possible paths, are then classified using the K-means algorithm. Considering the 24- rank vectors, this method is tested on different images of the Brodatz album and compared with Laws filters and GLCM. It yields a satisfactory reproduction of the image contours and a classification ratio exceeding 98 %. The 24 rank based-method is also applied to meteorological images collected by Meteosat over Europe and North Africa during December 1994. It is found that these images can be segmented into eight typical classes assignable to the soils, the seas and the clouds observed in the regions under study.

关键词： image segmentation Pixel Statistical analysis image coding Testing Filters Meteorology Europe Africa Soil

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3D ROC Analysis for Medical Imaging Diagnosis

3D ROC Analysis for Medical Imaging Diagnosis

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

作者： Su Wang C.-I. Chang Sheng-Chih Yang Giu-Cheng Hsu Hsian-He Hsu Pau-Choo Chung Shu-Mei Guo San-Kan Lee Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA Department of Electrical Engineering National Cheng Kung University Taiwan Department of Radiology Tri-Service General Hospital and National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Administrative Office Suao Veterans Hospital Yilan Taiwan

Receiver operating characteristics (ROC) has been widely used as a performance evaluation tool to measure effectiveness of medical modalities. It is derived from a standard detection theory with false alarm and detection power interpreted as false positive (FP) and true positive (TP) respectively in terms of medical diagnosis. The ROC curve is plotted based on TP versus FP via hard decisions. This paper presents a three dimensional (3D) ROC analysis which extends the traditional two-dimensional (2D) ROC analysis by including a threshold parameter in a third dimension resulting from soft decisions, (SD). As a result, a 3D ROC curve can be plotted based on three parameters, TP, FP and SD. By virtue of such a 3D ROC curve three two-dimensional (2D) ROC curves can be derived, one of which is the traditional 2D ROC curve of TP versus FP with SD reduced to hard decision. In order to illustrate its utility in medical diagnosis, its application to magnetic resonance (MR) image classification is demonstrated

关键词： image analysis Biomedical imaging Radar detection Medical diagnosis Detectors computer science Hospitals Medical diagnostic imaging Magnetic analysis Performance analysis

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Medical Data Mining using Particle Swarm Optimization for Temporal Lobe Epilepsy

Medical Data Mining using Particle Swarm Optimization for Te...

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Congress on Evolutionary Computation

作者： M. Ghannad-Rezaie H. Soltanain-Zadeh M.-R. Siadat K.V. Elisevich Henry Ford Health System and Wayne State University Detroit MI USA Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Neurosurgery Department Henry Ford Health System Detroit MI USA

In clinical problems, numerous factors are usually involved in a medical syndrome. New advances in medicine provide a broad range of diagnosis methods to cover all aspects of a disease. However, huge amounts of raw information may confuse clinicians and decrease decision accuracy. computerized knowledge extraction is an active area of research in medical informatics. This paper suggests a new medical data mining approach using an advanced swarm intelligence data mining algorithm. Considering medical knowledge discovery difficulties, this approach addresses common issues such as missing value management and interactive rule extraction. Here, surgery candidate selection in temporal lobe epilepsy is the main target application. However, the general idea can be applied to other medical knowledge discovery problems. Experimental results show noticeable performance improvement in the final rule-set quality while the method is flexible and fast.

关键词： Data mining Particle swarm optimization Temporal lobe Epilepsy Medical diagnostic imaging Biomedical imaging Coronary arteriosclerosis Diseases Surgery Medical services

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Brain MR image segmentation using tabu search and hidden markov random field model

Brain MR image segmentation using tabu search and hidden mar...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Nanda, P.K. Patra, D. Pradhan, A. Department of Electrical Engineering Image Processing and Computer Vision Laboratory National Institute of Technology Rourkela Orissa 769008 India

ISBN: (纸本)0972741216

In this paper, we propose a hybrid Tabu Expectation Maximization (TEM) Algorithm for segmentation of Brain Magnetic Resonance (MR) images in both supervised and unsupervised framewrok. Gaussian Hidden Markov Random Field (GHMRF) is used to model the available degraded image. In supervised framework, the apriori image MRF model parameters as well as the GHMRF model parameters are assumed to be known. The class labels are estimated using the Maximum a Posteriori (MAP) estimation criterion. In unsupervised framework, the problem of model parameter estimation and label estimation is formulated in Expectation Maximization (EM) framework. The labels are estimated using the proposed Tabu Search algorithm while the model parameters are the maximum likelihood estimates. Our proposed algorithm yields results with arbitrary initial paramater set and thus overcomes the problem of proper choice of initial parameters. The results obtained are comparable with the results obtained by using the algorithm proposed by Zhang ***. [15] , where the Iterated Conditional Mode (ICJM) algorithm is used for computing the MAP estimates. Copyright © IICAI 2005.

关键词： Tabu search

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Color reduction by using a new self-growing and self-organized neural network

Color reduction by using a new self-growing and self-organiz...

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2nd International Conference on Video, vision and Graphics, VVG 2005

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

ISBN: (纸本)9783905673579

A new method for the reduction of the number of colors in a digital image is proposed. The new method is based on the developed of a new neural network classifier that combines the advantages of the Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (SOFM) neural networks. We call the new neural network: Self-Growing and Self-Organized Neural Gas (SGONG). Its main advantage is that it defines the number of the created neurons and their topology in an automatic way. As a consecutive, isolated color classes, which may correspond to significant image details, can be obtained. The SGONG is fed by the color components and additional spatial features. To speed up the entire algorithm and to reduce memory requirements, a fractal scanning sub-sampling technique is used. The method is applicable to any type of color images and it can accommodate any type of color space. © The Eurographics Association 2005.

关键词： Color

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