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

作者： A. Yazdan-Shahmorad H. Soltanian-Zadeh R.A. Zoroofi Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique for assessing biochemical fingerprint of tissue composition. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of MRSI. There are several technical reasons that make the brain easier than other organs to be examined with MRSI. This work presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types (malignant glioma, astrocytoma, and oligodendroglioma). After extracting features from MRSI data using wavelet and wavelet packets, we use artificial neural networks to determine the abnormal spectra and the type of abnormality. We evaluated the proposed methods using clinical and simulated MRSI data and biopsy results. The MRSI analysis results were correct 97% of the time when classifying the spectra of the clinical MRSI data into normal tissue, tumor, and radiation necrosis. They were correct 72% and 83% of the time when determining tumor types using the clinical and simulated MRSI data, respectively.

关键词： Wavelet packets Neoplasms Artificial neural networks Biopsy Magnetic resonance Spectroscopy Magnetic resonance imaging Fingerprint recognition Surgery Cancer

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The characterization of scanning noise and quantization on texture feature analysis

The characterization of scanning noise and quantization on t...

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IEEE Southwest Symposium on image analysis and Interpretation

作者： G. Martin M.S. Pattichis Image and video Processing and Communications Laboratory (ivPCL) Department of Electrical and Computes Engineering University of New Mexico Albuquerque NM USA Department of Computer Science University of Cyprus Cyprus

The study of the effects of scanning on texture features is of great interest to computer-based screening systems. A mathematical model is developed for understanding how the original image gets distorted due to the contrast variability and geometric distortion inherent in the scanning process. Both quantitative and qualitative results (for sixty common texture features) are given.

关键词： Quantization Testing Optical films Optical distortion Mathematical model Charge coupled devices Optical sensors Sensor arrays Quality control Biomedical optical imaging

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Clustering-based framework for comparing fMRI analysis methods

Clustering-based framework for comparing fMRI analysis metho...

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IEEE International Symposium on Biomedical Imaging

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

In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is replaced with a feature space and each method considered as a clustering method in the new space. As a result, different methods can be compared by means of a cluster validity measure. The feature space is computed using a non-parametric method (principal component analysis-PCA). Four subjects have been analyzed with three methods and the proposed cluster-based framework has evaluated performance of the methods. The results are identical to those of the modified receiver operating characteristics (ROC). This validates the proposed approach.

关键词： Clustering algorithms Algorithm design and analysis image analysis Clustering methods Magnetic analysis Testing Accuracy Reproducibility of results Radiology Magnetic resonance

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Activation detection in multi-subject studies of fMRI using GLRT

Activation detection in multi-subject studies of fMRI using ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： S.M. Shams G.A. Hossein-Zadeh H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Institute for Studies in Theoretical Physics and Mathematics University of Tehran School of Cognitive Sciences Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Institute for Studies in Theoretical Physics and Mathematics and Medical Image Analysis Laboratory Henry Ford Health System University of Tehran School of Cognitive Sciences Iran

A new method based on generalized likelihood ratio test (GLRT) for activation detection in multi-subject studies of functional MRI (fMRI) is proposed. In this method, we test the correlation between the fMRI time series of different subjects and the bases of a signal subspace which increases the flexibility of method in detecting different shapes of hemodynamic response. The proposed multivariate method can be applied to group studies where the conventional cross-correlation method cannot be used due to its univariate property. This method is applied to both experimental and simulated fMRI data and the results are compared to those of general linear model (GLM). We show that the proposed method detects more significant activated regions in analyzing experimental data and more true voxels in simulated data

关键词： Testing Hemodynamics Brain modeling Process control Intelligent control Physics computing Mathematics Time series analysis Magnetic resonance imaging Data analysis

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A phantom validation of the fastharp pulse sequence

A phantom validation of the fastharp pulse sequence

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IEEE International Symposium on Biomedical Imaging, ISBI 2002

作者： Sampath, Smita Parthasarathy, Vijay Prince, Jerry L. Image Analysis and Communications Laboratory Electrical and Computer Engineering Department Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)078037584X

The FastHARP magnetic resonance imaging pulse sequence, coupled with fast processing, has the potential to measure and display cardiac function in real-time during clinical dobutamine stress tests. In this paper, we present a preliminary validation of the tracking capability of material points using this imaging sequence. Points lying on a mesh embedded in a moving gel phantom are tracked manually using high-resolution images and are simultaneously tracked using HARP tools. The experiment proves that for this particular type of motion actuation, the points tracked in images using the FastHARP sequence followed by fast and automated HARP tracking tools are comparable to results obtained from images acquired in much longer times with very tedious analysis involving manual intervention. © 2002 IEEE.

关键词： Phantoms

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Analytical solutions for time-resolved fluorescence lifetime imaging in a turbid medium such as tissue: errata

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Journal of the Optical Society of America A 2003年第9期20卷 1833-1833页

作者： David Hattery Victor Chernomordik Murray Loew Israel Gannot Amir Gandjbakhche Laboratory of Integrative and Medical Biophysics National Institute of Child Health and Human Development National Institutes of Health Building 12A Room 2041 Bethesda Maryland 20892 Department of Electrical and Computer Engineering Institute for Medical Imaging and Image Analysis George Washington University Washington D.C. 20052 Biomedical Engineering Department Faculty of Engineering Tel-Aviv University Tel-Aviv 69978 Israel

No abstract available.

关键词： Fluorescence lifetime imaging Tissue

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A phantom validation of the FastHARP pulse sequence

A phantom validation of the FastHARP pulse sequence

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IEEE International Symposium on Biomedical Imaging

作者： S. Sampath V. Parthasarathy J.L. Prince Image Analysis and Communications Laboratory Electrical and Computer Engineering Department Johns Hopkins University Baltimore MD USA

关键词： Imaging phantoms Magnetic resonance imaging Pulse measurements Tracking Couplings Stress measurement Displays Testing Magnetic materials High-resolution imaging

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Constrained optimization in pattern classification using ART

Constrained optimization in pattern classification using ART

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International Joint Conference on Neural Networks (IJCNN)

作者： Shuyu Yang S. Mitra Department of Electrical and Computer Engineering Computer Vision & Image Analysis Laboratory Texas Tech University TX USA

Pattern classification problems involve constrained optimization in the form of minimization of chosen cost functions. Such embedded constraints in integrated neural-fuzzy pattern recognition systems improve the performance of these systems. The comparative performance of an ART-based pattern classifier integrated with fuzzy optimization constraints is demonstrated in designing vector quantizers.

关键词： Constraint optimization Pattern classification Subspace constraints Clustering algorithms Vector quantization Algorithm design and analysis Pattern recognition Neural networks Fuzzy logic Testing

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ARMA modeling for estimation of permeability from perfusion MRI

ARMA modeling for estimation of permeability from perfusion ...

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IEEE International Symposium on Biomedical Imaging

作者： M.M. Khalighi H. Soltanian-Zadeh J.R. Ewing Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Electrical and Computer Engineering Department University of Tehran Tehran Iran Neurology Department Henry Ford Health System Detroit MI USA

We develop noninvasive MRI techniques that quantify the permeability of the Blood-Brain Barrier (BBB). Using such gadolinium compounds as Gd-DTPA and gadomer-17, changes in R1 (R1=1/T1) can be measured and used as estimates of tissue concentration versus time, thus permitting estimates of BBB permeability parameters. Estimating BBB permeability parameters requires deconvolution in a linear system, a task that has been accomplished heretofore by nonlinear least-squares procedures, although these techniques tend to instability in the presence of noise. We introduce a method using Z-transform and Autoregressive Moving Average (ARMA) modeling to perform this deconvolution. This method has the advantage that it linearizes the optimization procedure by which parametric estimates are formed, and it stabilizes the deconvolution. in the presence of noise.

关键词： Magnetic resonance imaging Autoregressive processes Deconvolution Iron Equations Proposals image analysis Nervous system Permeability measurement Time measurement

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Rate distortion in image coding from embedded optimization constraints in vector quantization

Rate distortion in image coding from embedded optimization c...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shuyu Yang S. Mitra Computer Vision & Image Analysis Laboratory Department of Electrical and Computer Engineering Texas Tech University Lubbock TX USA

ISBN: (纸本)0780370449

We compared the performance of two recently developed vector quantization algorithms using different optimization criteria for clustering, namely, the adaptive fuzzy leader clustering, a neuro-fuzzy algorithm, and the deterministic annealing, another unsupervised clustering algorithm based on probabilistic and statistical physics frameworks, with the rate distortion criterion as a performance measure. Such a comparison is useful for evaluating the efficiency of clustering algorithms for the purpose of image vector quantization instead of the conventional misclassification evaluation. This method is extended from the analysis of image coding in a spatial domain to sample vectors in the wavelet domain with predictable distribution. These sample vectors possess a multidimensional generalized Gaussian distribution through the new multi-scale feature extraction method. Our preliminary results show much improvement on the reconstructed image quality over JPEG.

关键词： Rate-distortion image coding Clustering algorithms Vector quantization Annealing Physics Distortion measurement image analysis Wavelet analysis Wavelet domain

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