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Atlas based segmentation of white matter fiber bundles in DTMRI using fractional anisotropy and principal eigen vectors

Atlas based segmentation of white matter fiber bundles in DT...

引用

IEEE International Symposium on Biomedical Imaging

作者： Esmaeil Davoodi-Bojd Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineeringr University of Tehran Tehran Iran Radiology Department Henry Ford Hospital Image Analysis and Communications Laboratory Detroit MI USA

In this work, we develop an atlas based method for automatic segmentation of white matter fiber bundles. To this end, we propose a new method for registration of diffusion tensor (DT) images using DTI information which is also used in the fiber tracking process, and we also propose a strategy for segmenting the fiber bundles using the new registration method and a probabilistic white matter atlas. We apply the registration method to 13 real DTI data sets and evaluate the results by comparing the level of alignment of all fibers. Then, we use the proposed strategy to segment 10 major fiber bundles in one of the subjects. One of the advantages of such a method is the robustness of the results thanks to using prior knowledge. The segmented results can be used for comparing and evaluating other fiber bundle segmentation methods.

关键词： Anisotropic magnetoresistance image segmentation image registration Tensile stress Diffusion tensor imaging Cost function Robustness Automatic control Radio control Process control

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4D wavelet noise suppression of MR diffusion tensor data

4D wavelet noise suppression of MR diffusion tensor data

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hesamoddin Jahanian Azadeh Yazdan-Shahmorad Hamid Soltanian-Zadeh Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Department of Biomedical Engineering University of Michigan Ann Arbor MI USA Controll and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Diffusion tensor imaging (DTI) is known to be promising for providing anatomical information about white-matter fiber bundles that cannot be obtained by other non-invasive in vivo imaging methods. However, its application is limited because of its low signal-to-noise ratio and significant imaging artifacts. To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging 4D wavelet denoising technique is used to improve the signal to noise ratio (SNR) of diffusion tensor images. To evaluate the proposed method, a high SNR data set is built by repeating and averaging the data acquisition several times and is compared to the denoised data. Our results revealed that wavelets would effectively reduce the noise in DTI data with less blurring of tissue types, especially in the white matter. It would suggest that by using the 4D wavelet noise suppression, one could decrease the acquisition time and still have an acceptable SNR.

关键词： Tensile stress Diffusion tensor imaging Signal to noise ratio Noise reduction Magnetic noise Anisotropic magnetoresistance Eigenvalues and eigenfunctions In vivo Magnetic resonance imaging Wavelet analysis

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MR artifact reduction in the simultaneous acquisition of EEG and FMRI of epileptic patients

MR artifact reduction in the simultaneous acquisition of EEG...

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European Signal Processing Conference (EUSIPCO)

作者： L. Amini R. Sameni C. Jutten G.A. Hossein-Zadeh H. Soltanian-Zadeh GIPSA-LAB INPG Grenoble France Electrical and Computer Engineering Department University of Tehran Tehran Iran School of Electrical Engineering Sharif University of Technology Tehran Iran Radiology Image Analysis-Laboratory Henry Ford Hospital Detroit MI USA

Integrating high spatial resolution of functional magnetic resonance imaging (fMRI) and high temporal resolution of electroencephalogram (EEG) is promising in simultaneous EEG and fMRI analysis, especially for epileptic patients. The EEG recorded inside an MR scanner is interfered with MR artifacts. In this article, we propose new artifact reduction approaches and compare them with the conventional artifact reduction methods. Our proposed approaches are based on generalized eigenvalue decomposition (GEVD) and median filtering. The proposed methods are applied on experimental simultaneous EEG and fMRI recordings of an epileptic patient. The results show significant improvement over conventional MR artifact reduction methods.

关键词： Electroencephalography Magnetic resonance imaging Pollution measurement Eigenvalues and eigenfunctions Signal processing Covariance matrices Brain modeling

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Towards an inclusive computational model of visual cortex

Towards an inclusive computational model of visual cortex

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： M. Gheiratmand H. Soltanian-Zadeh H. Khaloozadeh Electrical Engineering Department K.N. Toosi University of Technology Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA School of Cognitive Sciences Institute for Studies Theoretical Physics and Mathematics (IPM) Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

Understanding the primatespsila visual system has been one of the challenging problems of different groups of scientists for years. Though many studies, from physiology and neuroscience to computer vision, are done on different aspects of visual processing in the cortex, a comprehensive computational model of visual cortex is still missing. We have implemented a computational model of object recognition in ventral visual pathway in our previous work. This hierarchical model covers visual areas V1/V2, V4/PIT, and AIT sending inputs to the Prefrontal Cortex (PFC) for categorization. To extend our model, in this work, we have added a simple model of motion detection in neurons of areas V1 and MT of the dorsal stream to our previous model. This has enabled the model to perform another principal function of the visual cortex, i.e., motion perception.

关键词： Brain modeling Computational modeling Neurons Biological system modeling Object recognition Motion analysis Solid modeling Visual system computer vision Motion detection

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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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International Conference on Information Fusion

作者： Nima Tajbakhsh Babak N. Araabi Hamid Soltanianzadeh Control & Intelligent Processing Center of Excellence School of Electrical & Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

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： Iris Feature extraction Discrete cosine transforms image recognition Databases Training Humans

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Constrained optimization of nonparametric entropy-based segmentation of brain structures

Constrained optimization of nonparametric entropy-based segm...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent deformations). On the other hand, to allow variations among coupled structures, it registers each structure separately when building the shape models. It defines an entropy-based energy function, which is minimized using quasi-Newton algorithm. To this end, probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.

关键词： Constraint optimization Brain Iterative algorithms image segmentation Shape Couplings Magnetic resonance Magnetic resonance imaging Principal component analysis Buildings

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FAST MOTION IMAGING USING REDUCED FIELD OF VIEW PARTIAL FOURIER MRI

FAST MOTION IMAGING USING REDUCED FIELD OF VIEW PARTIAL FOUR...

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IEEE International Symposium on Biomedical Imaging

作者： Harsh K. Agarwal Khaled Z. Abd-elmoniem Jerry L. Prince Image Analysis and Communication Laboratory Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

Fast MR imaging techniques often exploit the redundancy present in an underlying MR image time series to compensate for k-space undersampling. When imaging motion using techniques like HARP, DENSE, and phase contrast (PC), it is the phase in static regions that is constant and therefore redundant. In this paper, we present a technique that first estimates the phase in the static portion of an undersampled MR image time series and then uses a partial Fourier reconstruction technique to combine phase in the static portion and undersampled data to reconstruct the full image time series. The technique is illustrated using a computational phantom undergoing simulated cardiac motion and imaged using the HARP protocol. Results demonstrate a gradual degradation of accuracy with loss of data due to undersampling, indicating that a 25% speedup in imaging time is possible for an image time series in which 50% of the pixels correspond to the object that do not move over time.

关键词： Magnetic resonance imaging image reconstruction Phase estimation Protocols Frequency estimation Motion analysis image motion analysis Laboratories Imaging phantoms Computational modeling

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RECOVERY LIMITATIONS OF MEG SOURCE LOCALIZATION MODEL FOR EPILEPSY

RECOVERY LIMITATIONS OF MEG SOURCE LOCALIZATION MODEL FOR EP...

引用

IEEE International Symposium on Biomedical Imaging

作者： Mostafa Ghannad-Rezaie Kourosh Jafari-Khouzani Hamid Soltanian-Zadeh Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Department of Electrical and Computer Engineering Wayne State University Detroit MI USA

ISBN: (纸本)9781424406715;1424406714

Source localization in magnetoencephalography (MEG) has received a lot of attention in the recent years. MEG inverse problem is an ill-posed problem in from of B ntimesl =G ntimesm J mtimesl , where B is the vector of magnetic fields measured by the magnetic sensors, G is the lead field matrix, and J is the vector of source current in the brain. In practice, nLtm and J is usually a sparse vector. In this paper, we employ a complete reconstruction criterion to estimate the maximum number of active sources in the model or equivalently the volume of the active areas that guarantees a stable solution to the MEG inverse problem. Based on some prior information about the volume of the active areas, we then evaluate the stability of the solution with the employed model. Our approach guarantees an error upper bound on the solution with a certain number of active points. In the experiments, we have demonstrated the technique using some simulations.

关键词： Epilepsy Inverse problems Magnetic field measurement Magnetic sensors Spatial resolution Brain modeling Current measurement Magnetoencephalography Sparse matrices Cost function

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THREE-POINT METHOD FOR FAST AND ROBUST FIELD MAPPING FOR EPI GEOMETRIC DISTORTION CORRECTION

THREE-POINT METHOD FOR FAST AND ROBUST FIELD MAPPING FOR EPI...

引用

IEEE International Symposium on Biomedical Imaging

作者： Pelin Aksit John A. Derbyshire Jerry L. Prince Global Applied Science Laboratory GE Healthcare Johns Hopkins University USA Laboratory of Cardiac Energetics NHLBI National Institutes of Health Johns Hopkins University USA Image Analysis and Communications Laboratory Electrical and Computer Engineering Johns Hopkins University USA

ISBN: (纸本)9781424406715

Because of its superb temporal resolution, echo planar imaging (EPI) is widely used for functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and monitoring dynamic processes. EPI suffers from geometric distortions caused by magnetic field inhomogeneities, and although these distortions can be corrected by field mapping, current methods require lengthy auxiliary image acquisitions and/or phase unwrapping. We present a 3-point method for field mapping that circumvents these problems and is robust to additive noise. Results demonstrate that the performance of this approach is markedly better than the standard two-point method and is comparable to methods requiring much longer auxiliary image acquisitions

关键词： Magnetic resonance imaging Phase distortion Laboratories Diffusion tensor imaging Nonuniform electric fields Radio frequency Medical services Noise robustness Phase measurement Distortion measurement

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A Hierarchical Clustering Based on Mutual Information Maximization

A Hierarchical Clustering Based on Mutual Information Maximi...

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IEEE International Conference on image Processing

作者： M. Aghagolzadeh H. Soltanian-Zadeh B. Araabi A. Aghagolzadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

关键词： Mutual information Clustering algorithms Clustering methods Data mining Data structures Density functional theory Computational complexity Entropy Higher order statistics Probability density function

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