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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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Combining global and local features for food identification in dietary assessment

Combining global and local features for food identification ...

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IEEE International Conference on image processing

作者： Marc Bosch Fengqing Zhu Nitin Khanna Carol J. Boushey Edward J. Delp School of Electrical and Computer Engineering Video and Image Processing Laboratory USA Department of Foods and Nutrition Purdue University West Lafayette IN USA

Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we describe a new approach to food identification using several features based on local and global measures and a “voting” based late decision fusion classifier to identify the food items. Experimental results on a wide variety of food items are presented.

关键词： image color analysis Visualization Feature extraction image segmentation Color Conferences Vectors

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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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A comparative assessment of several processing chains for hyperspectral image classification: What features to use?

A comparative assessment of several processing chains for hy...

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Workshop on Hyperspectral image and Signal processing: Evolution in remote sensing, WHISPERS

作者： Inmaculada Dópido Alberto Villa Antonio Plaza Paolo Gamba Hyperspectral Computing Laboratory University of Extremadura Caceres Spain GIPSA-Laboratory Signal & Image Department Grenoble Institute of Technology (INPG) France Faculty of Electrical and Computer Engineering University of Iceland Iceland Telecommunications and Remote Sensing Laboratory University of Pavia Pavia Italy

Hyperspectral image classification is a very active research area. Over the last years, several advanced feature extraction techniques have been integrated in processing chains intended for this purpose. In the context of supervised classification, the good generalization capability of machine learning techniques such as the support vector machine (SVM) can still be enhanced by an adequate selection of the number of features to be used for classification purposes. This number depends on the size of the available training set, which opens the way for the incorporation of supervised techniques for feature extraction in addition to more classic unsupervised ones. In this paper, we particularly investigate the issue of how many (and what type of) features can be used effectively for SVM-based classification. For this purpose, we consider different types of feature extraction strategies - unsupervised and supervised - in the context of different types of processing chains (all based on the SVM as the baseline classifier). We also explore the role of different dimensionality estimation techniques. Our study, conducted using a variety of hyperspectral scenes collected by different instruments, provides practical observations regarding the utility and number of features needed for different analysis scenarios.

关键词： Feature extraction Training Hyperspectral imaging Estimation Support vector machines

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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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Effect of Different Diffusion Maps on Registration Results

Effect of Different Diffusion Maps on Registration Results

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Iranian Machine Vision and image processing (MVIP)

作者： Maryam Afzali Emad Fatemizadeh Hamid Soltanian-Zadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper, we compare registration results obtained using different diffusion maps extracted from diffusion tensor imaging (DTI). Fractional Anisotropy (FA) and Ellipsoidal Area Ratio (EAR) are two diffusion maps (indices) that may be used for image registration. First, we use FA maps to find deformation matrix and register diffusion weighted images. Then, we use EAR maps and finally we use both of FA and EAR maps to register diffusion weighted images. The difference between FA values before deformation and after registration using the FA alone or EAR alone has a median of 0.57 and using both of them has a median of 0.29. Therefore, the results of registration using both of the FA and EAR indices are superior to those obtained using only one of them alone.

关键词： Ear Diffusion tensor imaging Tensile stress Registers Eigenvalues and eigenfunctions Anisotropic magnetoresistance image registration

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Dual H-and E-field tunable multiferroic bandpass filters with yttrium iron garnet film

Dual H-and E-field tunable multiferroic bandpass filters wit...

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Microwave, MTT-S International Symposium

作者： Guo-Min Yang Jing Lou Jing Wu Ming Liu Geyi Wen Yaqiu Jin Nian X. Sun Key Laboratory of Wave Scattering and Remote Sensing Information Department of Communication Science and Engineering Fudan University Shanghai China Department of Electrical and Computer Engineering Northeastern University Boston MA USA

A new type of dual magnetically (H-field) and electrically (E-field) tunable bandpass filters (BPF) with an yttrium iron garnet (YIG) film and with a YIG/ PZN-PT (lead zinc niobate-lead titanate) multiferroic heterostructure have been designed, fabricated and characterized. Single-crystal YIG films at their ferromagnetic resonance were adopted as the resonator which was coupled to a T-shaped microstrip coupling structure to form a new bandpass filter design. The operating frequency of the BPF can be tuned via a bias magnetic field, and a large tunability of the operation frequency of 190 MHz ~ 840 MHz with relatively small bias field of 50 ~ 250 Oe, together with a low insertion loss of

关键词： Band pass filters Magnetic resonance Microwave filters Magnetic separation Films Microwave circuits

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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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The texture classification using the fusion of decisions from different texture classifiers

The texture classification using the fusion of decisions fro...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： S. Pharsook T. Kasetkasem P. Larmsrichan S. Siddhichai T. Chanwimaluang T. Isshiki Department of Electrical Engineering Kasetsart University Thailand Image Processing Laboratory National Electronics and Computer Technology Center Thailand Department of Communication and Integrated Systems Tokyo Institue of Technology Japan

The improved of texture classification accuracy by using the probability weighted combination method of three texture features extraction consist of thE0020 Gray-Level Co-occurrence Matrix (GLCM), Semivariogram Function and Gaussian Markov Random Fields (GMRFs). Five different textures images are used in the experiment. The classifier that use for classify the extracted features in this research is Support Vector Machines (SVMs). The experimental result shows that the average accuracy of the combination method with probability weight up to 95.71%, which is better than the simple combination method about 2%.

关键词： Markov processes Tutorials Entropy Arrays Sensors

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Integrated database system for mobile dietary assessment and analysis

Integrated database system for mobile dietary assessment and...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Marc Bosch TusaRebecca Schap Fengqing Zhu Nitin Khanna Carol J. Boushey Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Foods and Nutrition Purdue University West Lafayette IN USA

Of the 10 leading causes of death in the US, 6 are related to diet. Unfortunately, methods for real-time assessment and proactive health management of diet do not currently exist. There are only minimally successful tools for historical analysis of diet and food consumption available. In this paper, we present an integrated database system that provides a unique perspective on how dietary assessment can be accomplished. We have designed three interconnected databases: an image database that contains data generated by food images, an experiments database that contains data related to nutritional studies and results from the image analysis, and finally an enhanced version of a nutritional database by including both nutritional and visual descriptions of each food. We believe that these databases provide tools to the healthcare community and can be used for data mining to extract diet patterns of individuals and/or entire social groups.

关键词： image analysis image databases Visualization Servers Visual databases

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