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Resilient subclass discriminant analysis with application to prelens tear film interferometry

Resilient subclass discriminant analysis with application to...

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Lecture Notes in computer Science

作者： Boyer, Kim L. Wu, Dijia Signal Analysis and Machine Perception Laboratory Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY United States Siemens Corporate Research Princeton NJ United States

ISBN: (纸本)9783642215865

The study of tear film thickness and breakup has important implications for understanding tear physiology and dynamics. We have developed a complete end-to-end automated system for robust and accurate measurements of the tear film thickness from interferometric video as a function of position and time (following a blink). This paper will primarily address the problem of identifying dry regions on the surface of the contact lens, which is one of the four major components of the system. (The others are motion stabilization, image normalization, and phase demodulation to infer absolute thickness and map the surface. To address the challenging wet/dry segmentation problem, we propose a new Gaussian clustering method for feature extraction in high dimensional spaces. Each class is modeled as a mixture of Gaussians, clustered using Expectation-Maximization in the lower-dimensional Fisher's discriminant space. We show that this approach adapts to a wide range of distributions and is insensitive to training sample size. We present experimental results on the real-world problem of identifying regions of breakup (drying) of the prelens tear film from narrowband interferometry for contact lens wearers in vivo. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Feature extraction

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Structure and motion estimation from rolling shutter video

Structure and motion estimation from rolling shutter video

引用

International Conference on computer vision Workshops (ICCV Workshops)

作者： Johan Hedborg Erik Ringaby Per-Erik Forssén Michael Felsberg Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

The majority of consumer quality cameras sold today have CMOS sensors with rolling shutters. In a rolling-shutter camera, images are read out row by row, and thus each row is exposed during a different time interval. A rolling-shutter exposure causes geometric image distortions when either the camera or the scene is moving, and this causes state-of-the-art structure and motion algorithms to fail. We demonstrate a novel method for solving the structure and motion problem for rolling-shutter video. The method relies on exploiting the continuity of the camera motion, both between frames, and across a frame. We demonstrate the effectiveness of our method by controlled experiments on real video sequences. We show, both visually and quantitatively, that our method outperforms standard structure and motion, and is more accurate and efficient than a two-step approach, doing image rectification and structure and motion.

关键词： Cameras Three dimensional displays Lenses Sensors Optimization image reconstruction Robustness

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Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest

Evaluation of geometric feature descriptors for detection an...

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IEEE International Symposium on Biomedical Imaging

作者： Amal Farag Asem Ali James Graham Aly Farag Salwa Elshazly Robert Falk Computer Vision and Image Processing Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Medical Imaging Division Jewish Hospital Louisville KY USA

This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured by the normalized cross-correlation (NCC). Geometric feature descriptors (e.g., SIFT, LBP and SURF) are applied to the output of the detection step, in order to extract features from the nodule candidates, for further enhancement of output and possible reduction of false positives. Results on the clinical ELCAP database showed that the descriptors provide 2% enhancements in the specificity of the detected nodule above the NCC results when used in a k-NN classifier. Thus quantitative measures of enhancements of the performance of CAD models based on LDCT are now possible and are entirely model-based. Most importantly, our approach is applicable for classification of nodules into categories and pathologies.

关键词： Feature extraction Lungs Training Databases Solid modeling Principal component analysis Computed tomography

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Assessing lung volumetric variation to detect and stage COPD

Assessing lung volumetric variation to detect and stage COPD

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2011 1st Middle East Conference on Biomedical engineering, MECBME 2011

作者： Hosseini, M. Parsa Soltanian-Zadeh, Hamid Akhlaghpoor, Shahram Department of Electrical Engineering Islamic Azad University Science and Research Branch of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States Department of Radiology Tehran University of Medical Sciences Sina Hospital Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424470006

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs). © 2011 IEEE.

关键词： computerized tomography

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A novel method for identification of COPD in inspiratory and expiratory states of CT images

A novel method for identification of COPD in inspiratory and...

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2011 1st Middle East Conference on Biomedical engineering, MECBME 2011

ISBN: (纸本)9781424470006

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful for evaluation of treatment effects. This paper presents a new method for identifying COPD from three-dimensional (3-D) pulmonary X-ray CT images. The method has five main steps. First, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Then, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations in each lung are found as a relationship between inspiratory and expiratory states. Finally, a classifier is used to decide about the disease and its severity. A t-test is done to evaluate the results. Twelve patients with variable severity of COPD and twelve normal adults were included in this study. The proposed method may assist radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. © 2011 IEEE.

关键词： Pulmonary diseases

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Development of a variational Bayesian expectation maximization (VBEM) method for model inversion of multi-area E/MEG model

Development of a variational Bayesian expectation maximizati...

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IEEE International Symposium on Biomedical Imaging

作者： Abbas Babajani-Feremi Kourosh Jafari-Khouzani Hamid Soltanian-Zadeh Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA CIPCE Electrical and Computer Engineering Department University of Tehran Tehran Iran

ISBN: (纸本)9781424441273

We develop and evaluate a variational Bayesian expectation maximization (VBEM) method for model inversion of our multi-area extended neural mass model (MEN) using EEG/MEG data. Parameters of MEN have suitable prior distributions that enable us to use properties of a conjugate-exponential model in implementing VBEM. Consequently, VBEM leads to analytically tractable forms that starts with initialization and consists of repeated iterations of a variational Bayesian expectation step (VB E-step) and a variational Bayesian maximization step (VB M-step). Posterior distributions of model parameters are updated in the VB M-step. Distribution of the hidden state is updated in the VB E-step using variational extended Kalman smoother. We evaluate and validate performance of VBEM method for model inversion of MEN using simulation studies in various signal-to-noise ratios. The proposed approach provides a useful technique for analyzing effective connectivity using non-invasive EEG and MEG methods.

关键词： Brain modeling Signal to noise ratio Data models Bayesian methods Computational modeling Mathematical model Analytical models

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Static hand gesture recognition of Persian sign numbers using thinning method

Static hand gesture recognition of Persian sign numbers usin...

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International Conference on Multimedia Technology

作者： Alaa Barkoky Nasrollah M. Charkari Image processing Laboratory Department of electrical engineering & computer science Tarbiat Moalem University Tehran Iran

ISBN: (纸本)9781612847719

Sign language is the most natural way of communication for the people with hearing problems. One of its most appealing applications is developing interfacing of human machine interaction with more effective. A hand gesture recognition system can provide an opportunity for deaf persons to communicate with normal people without the need of an interpreter or Intermediate. In this article, we propose a method to recognize the image-based numbers of Persian sign language (PSL) using thinning method on segmented image. In this approach, after cleaning thinned image, the real endpoints have been used for recognition. The method is qualified to provide real time recognition and is not affected by hand rotation and scaling. Experimental results on 300 images show that our approach recognition rate is 96.6% as average.

关键词： Gesture recognition image segmentation image color analysis Handicapped aids image recognition Skin Humans

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Voxel based treatment prediction using diffusion anisotropy indices and spatial information in Glioblastoma Multiform tumor

Voxel based treatment prediction using diffusion anisotropy ...

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2011 18th Iranian Conference of Biomedical engineering, ICBME 2011

作者： Sabahi, Hadi Soltanian-Zadeh, Hamid Scarpace, Lisa Mikkelsen, Tom Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI 48202 United States Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)9781467310055

In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures. © 2011 IEEE.

关键词： Tumors

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Model-Based Video Coding Using Colour and Depth Cameras

Model-Based Video Coding Using Colour and Depth Cameras

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Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： David Sandberg Per-Erik Forssen Jens Ogniewski Computer Vision Laboratory Department of Electrical Engineering Linköping University Linkoping Sweden Information Coding Department of Electrical Engineering Linköping University Linkoping Sweden

In this paper, we present a model-based video coding method that uses input from colour and depth cameras, such as the Microsoft Kinect. The model-based approach uses a 3D representation of the scene, enabling several other applications besides video playback. Some of these applications are stereoscopic viewing, object insertion for augmented reality and free viewpoint viewing. The video encoding step uses computer vision to estimate the camera motion. The scene geometry is represented by key frames, which are encoded as 3D quads using a quad tree, allowing good compression rates. Camera motion in-between key frames is approximated to be linear. The relative camera positions at key frames and the scene geometry are then compressed and transmitted to the decoder. Our experiments demonstrate that the model-based approach delivers a high level of detail at competitively low bit rates.

关键词： Cameras Three dimensional displays image color analysis Solid modeling PSNR Bit rate Encoding

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Effective Supervised Classification of fMRI Activation Maps between Populations by Spatial Descriptors

Effective Supervised Classification of fMRI Activation Maps ...

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Iranian Machine vision and image Processing (MVIP)

作者： Shaghayegh Eshaghian Gholam-Ali Hossein-Zadeh Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI USA

The major obstacle in discrimination between different groups of subjects in a common cognitive state, by functional Magnetic Resonance Imaging (fMRI), has been the high inter- subject functional and anatomical variability in the spatial patterns of brain activity. To overcome this, we have used two types of spatial descriptors that characterize the brain regions of interest (ROIs) involved in the cognitive tasks. They include, firstly three-dimensional invariant moment descriptors (3-DMIs), and secondly k-dimensional feature vectors based on concentric spheres. Both types of descriptors are applied to analyze the spatial patterns of cognitive activity of a challenging task and then to classify them across two different subject groups. SVM classifiers along with sequential floating forward feature selection technique are applied to the extracted descriptors of each ROI across the subjects. Our method is applied to experimental fMRI data with the aim of discriminating mental status of heroin IV (Intravenous) abusers and from of those in control subjects in a visual cue task which can induce drug craving. Our results demonstrate that 3-D texture of activation maps provide a good discrimination (with high accuracy) between healthy and addict group.

关键词： Feature extraction Accuracy Drugs Brain Support vector machine classification Visualization Humans

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