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Invariant 3D spharm features for characterizing fMRI activations in ROIs while minimizing effects of intersubject anatomical variability

Invariant 3D spharm features for characterizing fMRI activat...

引用

IEEE International Symposium on Biomedical Imaging

作者： Ashish Uthama Rafeef Abugharbieh Samantha J. Palmer Anthony Traboulsee Martin J. McKeown Department of Electrical and Computer Engineering Biomedical Signal and Image Computing Laboratory Department of Neurology University of British Columbia Vancouver Canada

Spatial patterns of activation statistics within anatomically-defined regions of interest (ROIs) in functional magnetic resonance imaging (fMRI) data were recently shown to be sensitive markers of brain activation changes. Most current methods that analyze fMRI activation statistics largely ignore this. The accuracy and validity of the prevalent approach of spatial normalization of functional data is also being debated. In this paper we present a novel spherical harmonics based rotational, translation and scale invariant feature representation of fMRI data which allows for direct quantification of activation patterns within ROIs without any need for spatial normalization. We also present a novel parallel technique for quantifying anatomical properties of the ROIs where we employ a principal component based approach to reduce the effects of anatomical variability in the ROI on functional pattern analysis. We validate our proposed method and demonstrate its improved sensitivity over conventional methods using real and simulated fMRI data.

关键词： Magnetic analysis Shape Statistics Magnetic resonance imaging Statistical analysis Magnetic resonance Scanning probe microscopy Biomedical computing Laboratories Nervous system

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Foveated Object Recognition Using Corners

Foveated Object Recognition Using Corners

引用

IEEE Southwest Symposium on image analysis and Interpretation

作者： Thomas Arnow Alan C. Bovik Laboratory for Image and Video Enginrring (LIVE) Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA

ISBN: (纸本)9781424422968;1424422965

We present a gray scale object recognition system that is based on foveated corner finding and that uses elements of Lowe's SIFT algorithm. The principles behind the algorithm are the use of high-information gray-scale corners as features, and an efficient corner- finding strategy to find them. The system is tested on a set of tool and airplane images and shown to perform well.

关键词： Object recognition

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Distributed Data Association and Filtering for Multiple Target Tracking

Distributed Data Association and Filtering for Multiple Targ...

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26th IEEE Conference on computer vision and Pattern Recognition (CVPR 2008), vol.6

作者： Ting Yu Ying Wu Nils O. Krahnstoever Peter H. Tu GE Global Research Visualization and Computer Vision Laboratory Niskayuna NY USA Department of Electrical Engineering and Computer Sciences Northwestern University Evanston IL USA

This paper presents a novel distributed framework for multi-target tracking with an efficient data association computation. A decentralized representation of trackers' motion and association variables is adopted. Considering the interleaved nature of data association and tracker filtering, the multi-target tracking is formulated as a missing data problem, and the solution is found by the proposed variational EM algorithm. We analytically show that 1) the posteriori distributions of trackers' motions (the real interests in terms of tracking applications) can be effectively computed in the E-step of the EM iterations, and 2) the solution of trackers' association variables can be pursued under a derived graph-based discrete optimization formulation, thus efficiently estimated in the M-step by the recently emerging graph optimization algorithms. The proposed approach is very general such that sophisticated data association priori and likelihood function can be easily incorporated. This general framework is tested with both simulation data and real world surveillance video. The reported qualitative and quantitative studies verify the effectiveness and low computational cost of the algorithm.

关键词： Target tracking Distributed computing Filtering algorithms Algorithm design and analysis Motion analysis Motion estimation Testing Computational modeling Surveillance Computational efficiency

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Digital mammography: Towards pectoral muscle removal via Independent Component analysis

Digital mammography: Towards pectoral muscle removal via Ind...

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4th IET International Conference on Advances in Medical, Signal and Information Processing - MEDSIP 2008

作者： N. Nicolaou S. Petroudi J. Georgiou M. Polycarpou M. Brady Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Wolfson Medical Vision Laboratory Oxford University Oxford UK

The extraction of features for automated assessment for breast cancer detection and diagnosis requires identification of the breast tissue. The pectoral muscle in medio-lateral oblique (MLO) mammogram images is one of the few landmarks in the breast. Yet, it can bias and affect the results of any mammogram processing method. To avoid such effects it is often necessary to automatically identify and segment the pectoral muscle prior to breast tissue image analysis. We propose the use of Independent Component analysis (ICA) for identification and subsequent removal of the pectoral muscle. The identification is posed as classification of image subsections corresponding to pectoral muscle and breast tissue as represented by a set of ICA basis functions. Average classification rates 97.3% and 83.3% for pectoral muscle and breast tissue respectively have been obtained.

关键词： Independent Component analysis Digital Mammography Pectoral Muscle Identification Digital Mammography Independent component analysis Pectoralis Muscles Breast tissue component analysis

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Color Effect on the Face Recognition with Spatial Resolution Constraints

Color Effect on the Face Recognition with Spatial Resolution...

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IEEE International Symposium on Multimedia

作者： Jae Young Choi Seungji Yang Yong Man Ro Konstantinos N. Plataniotis Image and Video System Laboratory Information and Communications University South Korea Department of Electrical and Computer Engineering University of Toronto Canada

In the practical face recognition (FR) applications, low-resolution faces (20 times 20 pixels or less) are commonly encountered and negatively impact on reliable performance. To overcome low-resolution face problem, we show that face color can significantly improve the performance compared to intensity-based features. The contribution of this paper is twofold. First, a new metric called dasiavariation ratio gainpsila (VRG) is proposed to theoretically prove the significance of color effect on low-resolution faces. Second, we conduct extensive performance comparison studies. In particular, 3,192 color facial images corresponding to 341 subjects, collected from three standard CMU PIE, FERET, and XM2VTSDB face databases, were used to perform comparative studies of color effect on various face resolutions. Experimental results verified that face color feature improves the degraded recognition rate due to low-resolution faces by at least an order of magnitude over intensity-based features.

关键词： Face recognition Spatial resolution Face detection image color analysis Gray-scale Reluctance generators Robustness Degradation Principal component analysis Linear discriminant analysis

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Principal curves to extract vessels in 3D angiograms

Principal curves to extract vessels in 3D angiograms

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Wilbur C. K. Wong Albert C. S. Chung Lo Kwee-Seong Medical Image Analysis Laboratory Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Segmentation of blood vessels and extraction of their centerlines in 3D angiography are essential to diagnosis and prognosis of vascular diseases, and advanced image processing and analysis. In this paper, we propose a semi-automatic method to perform those two tasks simultaneously. A user supplies two end points to the algorithm and a vessel centerline between the two given points is extracted automatically. Local vessel widths are estimated as byproducts. Additional anchor points can be added in between to handle difficult situation. Our method is based upon a polygonal line algorithm. This algorithm is used to find principal curves, nonlinear generalization of principal components, from point clouds. We discuss an application of principal curve to vessel extraction from a theoretical view point. A novel algorithm is then proposed for the application. No data interpolation is needed in the algorithm and centerlines extracted are adaptive to the vasculature complexity on account of their nonparametric representation. We have tested the method on two synthetic data sets and two clinical data sets. Results show that it has high robustness to variation in image resolution, voxel anisotropy and noise. Moreover, centerlines obtained are in subvoxel precision and local widths estimated are accurate under limit of image resolution.

关键词： Data mining image resolution image segmentation Blood vessels Biomedical imaging Angiography Diseases image processing image analysis Clouds

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A systematic topological method for fingerprint singular point detection

A systematic topological method for fingerprint singular poi...

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International Conference on Control, Automation, Robotics and vision (ICARCV)

作者： H.K. Lam Z. Hou W.Y. Yau T.P. Chen J. Li Computer Vision and Image Understanding Department Institute for Infocomm Research-Agency for Science Technology and Research Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Singular point detection is an important issue in fingerprint image analysis. General methods like Poincare index method can detect singular points in non-arch type fingerprints but fail on arch-type fingerprints. Some more sophisticated methods like complex filter method also face the same problem. In this paper, we propose a systematic method for detecting singular points in fingerprint images which utilizes the most fundamental topological feature of acquired fingerprints as the basis for singular point identification. The method differentiates the input fingerprint between arch type and non-arch type. For non-arch type fingerprints singular points are detected as intersection points in a c(>2) level segmentation map. As for arch type fingerprints, singular points are identified from the symmetric line of the fingerprint structure. The method is evaluated using the NIST DB4 database and compared with the complex filter method. The proposed method attains near 90% success rate in detecting singular points and the displacement from ground truth is comparable to that of the complex filter method. Moreover our method is more than once faster in CPU time.

关键词： Fingerprint recognition image matching Filters Robotics and automation computer vision image segmentation Automatic control Robot control Control systems Robot vision systems

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Automatic segmentation of brain structures from MRI integrating atlas-based labeling and level set method

Automatic segmentation of brain structures from MRI integrat...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： Matineh Shaker Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

The purpose of this study is to segment hippocampus, amygdala and entorhinal cortex in magnetic resonance images (MRI) of temporal lobe epilepsy (TLE) patients. The proposed method consists of two separate parts. First, we use an atlas-based segmentation method to obtain initial segmentation results for desired structures. Using additional preprocessing steps for image registration and gray matter (GM) segmentation is the specification of this stage of the work. Then, all of the GM voxels are labeled using an anatomical atlas. In the next stage, variational level set formulation without re-initialization is applied on the images. We use the boundaries obtained by atlas-based segmentation as the contour for initialization of level set function. Automatic generation of initial contour makes the final segmentation results operator-independent. The proposed approaches are evaluated by comparing automatic and expertpsilas segmentation results and confirming their similarity.

关键词： Brain Magnetic resonance imaging Labeling Level set image segmentation Hippocampus Magnetic resonance Temporal lobe Epilepsy image registration

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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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Automated facial feature detection and face recognition using Gabor features on range and portrait images

Automated facial feature detection and face recognition usin...

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IEEE International Conference on image Processing

作者： Sina Jahanbin Hyohoon Choi Rana Jahanbin Alan C. Bovik Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA Computer Vision Research Laboratory Sealed Air Corporation San Jose CA USA Department of Biomedical Engineering University of Texas Austin Austin TX USA

In this paper, we present a novel identity verification system based on Gabor features extracted from range (3D) representations of faces. Multiple landmarks (fiducials) on a face are automatically detected using these Gabor features. Once the landmarks are identified, the Gabor features on all fiducials of a face are concatenated to form a feature vector for that particular face. Linear discriminant analysis (LDA) is used to reduce the dimensionality of the feature vector while maximizing the discrimination power. These novel features were tested on 1196 range images. The same features were also extracted from portrait images, and the accuracies of both modalities were compared. A superior verification accuracy was obtained using the range data, and a highly competitive accuracy to that of other techniques in the literature was also obtained for the portrait data.

关键词： Facial features Face detection Face recognition Biometrics Fingerprint recognition Iris Feature extraction Vectors Linear discriminant analysis Testing

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