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IEEE International Conference on image processing

作者： Melih S. Aslan Eslam Mostafa Hossam Abdelmunim Ahmed Shalaby Aly A. Farag Ben Arnold Computer Vision and Image Processing Lab. University of Louisville Louisville KY USA Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Computer and Systems Engineering Department Ain Shams University Cairo Egypt Image Analysis Inc. Columbia KY USA

We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a probabilistic model. The shape model is embedded into the image domain taking in consideration the evolution of a contour represented by a level set function. The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images. Our results shows that the technique is accurate and robust compared with the other alternative in the literature.

关键词： Shape image segmentation Level set Computed tomography Accuracy Probabilistic logic

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Single channel speech separation with a frame-based pitch range estimation method in modulation frequency

2010 5th International Symposium on Telecommunications, IST ...

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2010 5th International Symposium on Telecommunications, IST 2010 2010年 609-613页

作者： Mahmoodzadeh, A. Abutalebi, H.R. Soltanian-Zadeh, H. Sheikhzadeh, H. Speech Proc. Research Lab. ECE Dept. Yazd University Iran Control and Intelligent Processing Center of Excellence University of Tehran Iran EE Dept. Amirkabir University of Technology Tehran Iran Idiap Research Institute Martigny Switzerland Image Analysis Lab. Henry Ford Health System Detroit United States

ISBN: (纸本)9781424481835

Computational Auditory Scene analysis (CASA) has attracted a lot of interest in segregating speech from monaural mixtures. In this paper, we propose a new method for single channel speech separation with frame-based pitch range estimation in modulation frequency domain. This range is estimated in each frame of modulation spectrum of speech by analyzing onsets and offsets. In the proposed method, target speaker is separated from interfering speaker by filtering the mixture signal with a mask extracted from the modulation spectrogram of mixture signal. Systematic evaluation shows an acceptable level of separation comparing with classic methods. © 2010 IEEE.

关键词： Frequency estimation

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Determination of pitch range based on onset and offset analysis in modulation frequency domain

2010 5th International Symposium on Telecommunications, IST ...

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2010 5th International Symposium on Telecommunications, IST 2010 2010年 604-608页

作者： Mahmoodzadeh, A. Abutalebi, H.R. Soltanian-Zadeh, H. Sheikhzadeh, H. Speech Proc. Research Lab. ECE Dept. Yazd University Yazd Iran Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran EE Dept. Amirkabir University of Technology Tehran Iran Idiap Research Institute Martigny Switzerland Image Analysis Lab. Henry Ford Health System Detroit United States

ISBN: (纸本)9781424481835

Auditory scene in a natural environment contains multiple sources. Auditory scene analysis (ASA) is the process in which the auditory system segregates a scene into streams corresponding to different sources. The determination of range of pitch frequency is necessary for segmentation. We propose a system to determine the range of pitch frequency by analyzing onsets and offsets in modulation frequency domain. In the proposed system, first the modulation spectrum of speech is calculated and then, in each subband onsets and offsets will be detected. Thereafter, the segments are generated by matching corresponding onset and offset front. Finally, by choosing the desired segments, the rage of pitch frequency is determined. Systematic evaluation shows that the range of pitch frequency is estimated with good accuracy. © 2010 IEEE.

关键词： Frequency modulation

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Evaluating deformation patterns of the thoracic aorta in gated CTA sequences

Evaluating deformation patterns of the thoracic aorta in gat...

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IEEE International Symposium on Biomedical Imaging

作者： Ernst Schwartz Roman Gottardi Johannes Holfeld Christian Loewe Martin Czerny Georg Langs Computational Image Analysis and Radiology Lab (CIR) Medical University of Vienna Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Division of Cardiothoracic Surgery Department of Surgery Medical University of Vienna Austria Division of Interventional Radiology Department of Radiology Medical University of Vienna Austria Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA

ISBN: (纸本)9781424441259;9781424441266

Cardiovascular interventions in the region of the aortic isthmus such as stent-grafting and vessel transposition introduce substantial changes in the deformation properties of the affected vessels. The changes play a fundamental role in the long-term prognosis for any such treatment, but are only poorly understood to date. We explore a fully automated method to quantify the deformation patterns of the thoracic aorta in gated computed tomography sequences. The aorta is segmented by a level set approach that accurately identifies the vessel lumen in each frame of the sequence. Consequently, landmarks on the vessel wall in each frame are registered using a probabilistic method. This allows for the measurement of global and local deformation properties. We evaluate our method on synthetic datasets and report first results of its application on real world data.

关键词： Biomedical imaging image segmentation Radiology Cardiology Surgery Computed tomography Level set Electrocardiography Pathology Deformable models

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Combinational method for focal brain activation detection using MEG signal

Combinational method for focal brain activation detection us...

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

作者： Rajabioun, Mehdi Babajani-Feremi, Abbas Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit MI 48202 United States

ISBN: (纸本)9780819475138

Magnetoencephalography (MEG) is a neuroimaging technique for brain activation detection. This technique does not provide a unique solution due to ill-posedness of its inverse solution. Several methods are proposed to improve the MEG inverse solution. Minimum Norm (MN) is a simple method whose solution is distributed and biased toward the superficial sources. In addition, its solution is sensitive to the noise. Several methods are proposed to improve performance of the MN method. In this paper, we propose a method whose solution is less sensitive to the noise and spatially unbiased toward the superficial sources. Control of focal solution properties is achieved by specifying a parameter in the proposed method. Performance of the proposed method is compared to others using simulation studies consisting of single and multiple dipole sources as well as an extended source model. Proposed method has superior performance compared to non-iterative methods. Its performance is similar to the iterative methods but its computational load is lower. © 2009 SPIE.

关键词： Inverse problems

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Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model

Variational Bayesian framework for estimating parameters of ...

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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 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 Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran

ISBN: (纸本)9780819475138

The integrated analysis of the Electroencephalography (EEG), Magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) are instrumental for functional neuroimaging of the brain. A bottom-up integrated E/MEG and fMRI model based on physiology as well as a method for estimating its parameters are keys to the integrated analysis. We propose the variational Bayesian expectation maximization (VBEM) method to estimate parameters of our proposed integrated model. VBEM method iteratively optimizes a lower bound on the marginal likelihood. An iteration of the VBEM consists of two steps: a variational Bayesian expectation step implemented using the extended Kalman smoother (EKS) and the posterior probability of the parameters in the previous step, and a variational Bayesian maximization step to estimate the posterior distributions of the parameters. For a given external stimulus, a variety of multi-area models can be considered in which the number of areas and the configuration and strength of connections between the areas are different. The proposed VBEM method can be used to select an optimal model as well as estimate its parameters. The efficiency of the proposed VBEM method is illustrated using simulation and real datasets. The proposed VBEM method can be used to estimate parameters of other non- linear dynamical systems. This study proposes an effective method to integrate E/MEG and fMRI and plans to use these techniques in functional neuroimaging. © 2009 SPIE.

关键词： Electroencephalography

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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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Directed epileptic network from scalp and intracranial EEG of epileptic patients

Directed epileptic network from scalp and intracranial EEG o...

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IEEE Workshop on Machine Learning for Signal processing

作者： L. Amini C. Jutten S. Achard O. David H. Soltanian-Zadeh G.A. Hossein-Zadeh P. Kahane L. Minotti L. Vercueil Grenoble INP GIPSA Laboratory Grenoble France Grenoble INP GIPSA-LAB Grenoble Cedex France Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Tehran Iran CHUG Neurology Department GIN U836 INSERM Grenoble France Neurology Department CHUG Grenoble France Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA

We proposed recently the computation of epileptic connectivity graphs based on wavelet correlation coefficients between EEG signals. The suspected epileptiform electrodes are recognized using the clustering of the topological properties of the graph that can be useful for pre-surgical studies. Here, we present a method for comparing epileptic networks estimated from scalp and intracranial EEG (IEEG) in partial epilepsy patients. The results are presented for a patient with left temporal epilepsy. Good spatial correspondence between the IEEG and the scalp EEG epileptic graphs is obtained. These results are consistent with the patient's clinical diagnosis.

关键词： Epilepsy Scalp Electroencephalography Electrodes Surgery Time frequency analysis Indium phosphide Radio control Process control Control systems

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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11th International Conference on Information Fusion, FUSION 2008

作者： Tajbakhsh, Nima Araabi, Babak N. Soltanianzadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Eng. University of Tehran Tehran Iran School of Cognitive Sciences IPM Tehran Iran Image Analysis Lab. Radiology Dept. Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)9783000248832

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Biometrics

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An intelligent decision combiner applied to noncooperative iris recognition

An intelligent decision combiner applied to noncooperative i...

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11th International Conference on Information Fusion, FUSION 2008

ISBN: (纸本)9783000248832

In despite of successful implementation of iris recognition systems, noncooperative recognition is still remained as an unsolved problem. Unexpected behavior of the subjects and uncontrolled lighting conditions as the main aspects of noncooperative iris recognition result in blurred and noisy captured images. This issue can degrade the performance of iris recognition system. In this paper, to address the aforementioned challenges, an intelligent decision combiner is proposed in which prior to perform decision fusion;an automatic image quality inspection is carried out. The goal is to determine whether captured decisions based on visible light (VL) and near infrared (NIR) images have enough reliability to incorporate into final decision making. Experimental results on the UTIRIS confirm the superior performance of the proposed combiner in comparison with other common nontrainable decision combiners whereas in all cases, the effectiveness of fusion approach makes it a reliable solution to noncooperative subjects' behavior and uncontrolled lighting conditions.

关键词： Decision making

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