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

作者： Y. Chen C.-I. Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

This work presents new texture shape feature coding (TSFC)-based computer aided diagnostic (CAD) classification methods for classification of masses on mammograms. It introduces a new concept of 1.5-order 3-neighbor 3/spl times/3 connectivity to extract texture shape features that can describe multiples of 22.5/spl deg/. In order to effectively utilize these shape features, two new methods of implementing TFSC are further proposed to convert these features to texture feature numbers (TFNs), TFNs in quaternary (TFNq) which expresses a TFN in quaternary expansion and TFNs in product (TFNx ) that represents a TFN in terms of a product. Both TFNq and TFNx can then produce texture shape histograms in the same way that a gray-level histogram is generated for an image. Such a texture shape histogram is further used to generate various shape features of masses on mammograms for classification. In order to demonstrate the promise of our TSFC-based CAD methods, the MiniMammographic database provided by the Mammographic image Analysis Society (MIAS) is used for experiments.

关键词： Shape Feature extraction Histograms image coding Character recognition Remote sensing Signal processing image processing Laboratories computer science

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Real-time vision-based approach for probe tracking in operating room

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International Congress Series 2004年 1268卷 467-472页

作者： Aly A. Farag M.Sabry Hassouna Ayman El-Baz S. Hushek Computer Vision and Image Processing Laboratory Electrical and Computer Engineering University of Louisville 403 Lutz Hall Bldg. Louisville KY 40292 USA Department of Neurological Surgery University of Louisville USA

A hand-held surgical probe is an essential component of any image-guided neurosurgery (IGNS). During the surgical procedure, the IGNS tracks the probe position and displays the anatomy beneath it as three orthogonal image slices on a workstation-based 3D imaging system. Existing IGNS systems use different tracking techniques including mechanical, optical, ultrasonic, and electromagnetic systems. In this paper, we present a new computational vision-based probe tracking technique, which provides both position and orientation. We also propose another new computational vision-based technique to track the patient head and thus compensate for its movement during probing procedure. The proposed system is completely passive, its stereo cameras detect the presence of the probe using available visible light, and reports on their exact location and orientation in patient physical space in real time. We have validated the system using a skull phantom and a hand made probe.

关键词： image-guided neurosurgery Head motion tracking Probe tracking Vision tracking

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Enhancement of stents using interference of low-frequency ultrasonic echo signals

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Journal of Medical Ultrasonics 2004年第2期31卷 67-73页

作者： Ohashi, Gosuke Ohya, Akihisa Tobise, Kazimobu Akiyama, Iwaki Faculty of Engineering Shizuoka University Hamamatsu Japan Institute of Information Sciences and Electronics University of Tsukuba Tsukuba Japan Shoryo Electronic Corporation Kamakura Japan Faculty of Engineering Shonan Institute of Technology Fujisawa Japan Image Processing and Vision Research Laboratory Department of Electrical and Computer Engineering University of California at Santa Barbara Santa Barbara CA 93106-9560 United States

Purpose. We set out to enhance the imaging of stents used for coronary coarctation surgery without imaging the reticulation of the stent. Because the stent is a reticulated structure, there is a relation between ultrasound wavelength and the interstice reticulation that produces the echo. Methods. First we performed computer simulation by ultrasound frequency of transmitted signals to analyze this relation. Then, instead of controlling the transmitted wave, we processed the returned signals through a band-pass filter because of the difficulty of controlling the transmitted waves of diagnostic ultrasound equipment. Following this, we conducted experiments on an agar gel phantom containing a stent to verify the effectiveness of this method. Results. The simulation confirmed that the stent was apparent when the ultrasonic wavelength equaled the product of an integer and twice the interval of the reticulation of the stent. Application of a band-pass filter of 0.8 to 1.5MHz to the waveform returned from the agar gel phantom containing the stent. Conclusions. The effectiveness of this method in depicting the stent was confirmed. © The Japan Society of Ultrasonics in Medicine 2004.

关键词： Interference Received waveform Signal processing Stent Ultrasonic echography

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A uniform projection-based unsupervised classification for hyperspectral imagery

A uniform projection-based unsupervised classification for h...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jing Wang Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

ISBN: (纸本)0780387422

Orthogonal subspace projection (OSP) has received considerable interest in hyperspectral image classification. It performs well when the complete knowledge of image endmembers is available. In reality, obtaining such prior knowledge is very difficult, if not impossible. This work investigates a variant of OSP, called uniform projection (UP) and presents a UP-based unsupervised algorithm for hyperspectral image classification. It first implements a uniform target detector (UTD) to find a set of interfering signatures, I, then another uniform I-annihilated detector (UIAD) to find desired target signatures. The signatures in I are combined with the UIAD found desired signatures to form a target signatures matrix M which can be used for supervised image classification. In this case, OSP can be used for this purpose. In order to demonstrate the utility of the proposed unsupervised classification technique for hyperspectral imagery, a series of computer simulations and experiments are conducted for analysis.

关键词： Hyperspectral imaging Data mining Hyperspectral sensors image classification Detectors Histograms Remote sensing Signal processing image processing Laboratories

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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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Discrimination and identification for subpixel targets in hyperspectral imagery

Discrimination and identification for subpixel targets in hy...

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IEEE International Conference on image processing

作者： Chein-I Chang Weimin Liu Chein-Chi Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Civil and Environmental Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780385543

Spectral measures have been used in material identification and discrimination. They are effective if the spectral signatures are calibrated and not contaminated. However, it may not be true in many real applications, specifically, for mixed pixels and subpixel targets. This paper investigates the issue of discrimination and identification for subpixel targets and further develops sample spectral covariance/correlation matrix-based hyperspectral measures to account for spectral variability within subpixel targets. Two types of measures are of interest and studied, Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures. In order to substantiate the proposed measures, a real data-based comparative analysis is conducted and compared to two spectral similarity measures, spectral angle mapper (SAM) and spectral information divergence (SID) for performance evaluation. The experiments show that both Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures work very effectively and outperformed the SAM and the SID in discrimination and identification for subpixel targets.

关键词： Hyperspectral imaging Pollution measurement Hyperspectral sensors Covariance matrix Matched filters Performance analysis Performance evaluation Remote sensing Signal processing image processing

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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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Multiresolution automatic segmentation of T1-weighted brain MR images

Multiresolution automatic segmentation of T1-weighted brain ...

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IEEE International Symposium on Biomedical Imaging

作者： M. Zeydabadi R.A. Zoroofi H. Soltanian-Zadeh 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

Automatic segmentation of brain tissues is crucial to many medical imaging applications. We use a multi-resolution analysis and a power transform to extend the well-known Gaussian mixture model expectation maximization based algorithm for segmentation of white matter, gray matter, and cerebrospinal fluid from T1-weighted magnetic resonance images (MRI) of the brain. Experimental results with near 4000 synthetic and real images are included. The results illustrate that the proposed method outperforms six existing methods.

关键词： image resolution image segmentation Biomedical imaging Multiresolution analysis image analysis Magnetic analysis Algorithm design and analysis Brain modeling Magnetic liquids Magnetic resonance

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MRSI brain tumor characterization using wavelet and wavelet packets feature spaces and artificial neural networks

MRSI brain tumor characterization using wavelet and wavelet ...

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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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Full-field mammogram analysis based on the identification of normal regions

Full-field mammogram analysis based on the identification of...

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IEEE International Symposium on Biomedical Imaging

作者： Y. Sun C. Babbs E.J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Basic Medical Sciences School of Veterinary Medicine Purdue University West Lafayette IN USA

We present a new method for full-field mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present methods for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique reclassification method to boost the classification performance. We have tested this technique on a set of ground-truth full-field mammograms.

关键词： Feature extraction Breast cancer Cancer detection Mammography Biomedical imaging image analysis Delta-sigma modulation Spatial databases image databases Optical scattering

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