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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Vladimir Frants Viacheslav Voronin Alexander Zelenskiy Sos Agaian Metrological lab. “Small GIC” Moscow State University of Technology “STANKIN” Moscow Russian Federation Pro-rector for Research Work and R&D Politics Moscow State University of Technology “STANKIN” Moscow Russian Federation Lab. “Mathematical methods of image processing and intelligent computer vision systems.” Don State Technical University Rostov-on-Don Russian Federation Dept. of Computer Science CUNY The College of Staten Island Staten Island New York United States

In this paper, we present a new video quality metric targeted for use within cloud-based video storage systems. Because of the limited capacity of storage solutions currently in use, it is common for stored videos to have low perceived video quality. Ability to predict the quality of coded video sequence in fully automatic fashion is crucial for optimal coding parameters selection. We have developed general quality estimation approach applicable for different kinds of video content and useful for online correction of perceived quality of stored video sequence. Proposed objective video quality metric is designed to have a high level of correlation with human-based quality assessment results. A high level of conformity of predicted video quality to perceived quality level is achieved by using a convolutional neural network trained on a large volume of video data in the framework of generative adversarial learning and combination of carefully selected regularization techniques. Evaluation of developed VQA metric on commonly used datasets shows equal or better correlation with MOS than the current state of the art approaches.

关键词： Quality assessment Measurement Video recording Video sequences Streaming media Training Visualization

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Analytic Bilinear Appearance Subspace Construction for Modeling image Irradiance Under Natural Illumination and Non-Lambertian Reflectance

Analytic Bilinear Appearance Subspace Construction for Model...

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IEEE Conference on computer vision and Pattern Recognition

作者： Shireen Y. Elhabian Aly A. Farag Computer Vision and Image Processing Laboratory ECE Dept. University of Louisville

ISBN: (纸本)9781467364102

Conventional subspace construction approaches suffer from the need of "large-enough" image ensemble rendering numerical methods intractable. In this paper, we propose an analytic formulation for low-dimensional subspace construction in which shading cues lie while preserving the natural structure of an image sample. Using the frequency-space representation of the image irradiance equation, the process of finding such subspace is cast as establishing a relation between its principal components and that of a deterministic set of basis functions, termed as irradiance harmonics. Representing images as matrices further lessen the number of parameters to be estimated to define a bilinear projection which maps the image sample to a lower-dimensional bilinear subspace. Results show significant impact on dimensionality reduction with minimal loss of information as well as robustness against noise.

关键词： Lighting Harmonic analysis Principal component analysis image reconstruction Materials Gold Surface reconstruction

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A 3D RECONSTRUCTION OF THE HUMAN JAW FROM A SINGLE image

A 3D RECONSTRUCTION OF THE HUMAN JAW FROM A SINGLE IMAGE

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IEEE International Conference on image processing

作者： Aly Abdelrahim Ahmed Shalaby Shireen Elhabian James Graham Aly Farag Computer Vision and Image Processing Laboratory ECE Dept. University of Louisville Louisville KY 40292 USA. Faculty of Computers and Information Cairo Univeristy Egypt ECE Dept. University of Louisville Louisville KY 40292 USA.

ISBN: (纸本)9781479923427

Accurate 3D modeling of the human teeth/jaw helps patients avoid the discomfort of the mold process, and improves the data accuracy for oral orthodontists and dental care personnel. Since the surface of the human tooth is almost textureless, Shape from Shading (SFS) has been successfully adopted in solving this problem. In this paper, we attempt to improve the limitations of previous 3D tooth reconstruction algorithms by developing a new approach for shape reconstruction from single image shading with Two-Dimensional Principle Component Analysis (2D-PCA) shape priors. The surface reflectance is modeled using Oren-Nayar-Wolff model which accounts for the retro-reflection case and we experimentally prove that the teeth surface follows the microfacet theory. Our formulation exploits the shape priors as extracted from a set of training CT scans of real human jaws. Our experiments provide promising quantitative metric results for the proposed approach. This work is fundamental for establishing an optical-based approach for reconstructing the human jaw that is inexpensive and does not use ionizing radiation.

关键词： jaw modeling Oren-Nayar-Wolff reflectance model 2D-PCA SFS shape priors Jaw three-dimensional displays surface reflectance Data Accuracy images shape reconfiguration Tooth tooth flank HUMAN TOOTH retro-reflection artificial shade IONIZING RADIATIONS

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NOVEL VARIATIONAL APPROACH FOR THE PERSPECTIVE SHAPE FROM SHADING PROBLEM USING CALIBRATED imageS

NOVEL VARIATIONAL APPROACH FOR THE PERSPECTIVE SHAPE FROM SH...

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IEEE International Conference on image processing

作者： Aly Abdelrahim Hossam Abdelmunim James Graham Aly Farag Computer Vision and Image Processing Laboratory ECE Dept. University of Louisville Louisville KY 40292 USA. Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo 11517 Egypt. ECE Dept. University of Louisville Louisville KY 40292 USA.

ISBN: (纸本)9781479923427

In this paper, We propose a new approach for solving the Lamber-tian shape from shading (SFS) problem. The proposed technique employs image information as well as camera calibration parameters. We claim that the incorporation of camera parameters results in a better surface reconstruction. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. We propose a new variational formulation that relates an evolving surface model with image information, taking into consideration that the image is taken by a perspective camera with known parameters. A new energy functional is formulated to incorporate brightness, smoothness, and integrability constraints. All of these terms assume a hyper surface that evolves in time to meet their criteria. Gradient descent optimization with Euler-Lagrange is used for optimization. We constructed a database of real surfaces as a ground-truth for the experiments. The proposed approach shows superior performance when compared with other approaches in the literature.

关键词： camera calibration perspective SFS variational approach camera calibration artificial shade images Perspective image information ground truth surface modeling Surface reconstruction

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Mass detection in mammograms using ga based PCA and Haralick features selection

Mass detection in mammograms using ga based PCA and Haralick...

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作者： Amroabadi, Sayedmasoud Hashemi Ahmadzadeh, Mohammad Reza Hekmatnia, Ali Electrical and Computer Eng. Dept. University of Toronto Toronto Canada Electrical and Computer Eng. Dept. Isfahan University of Tech. Isfahan Iran Medical Image and Signal Processing Center Isfahan University of Medical Science Isfahan Iran

ISBN: (纸本)9789644634284

Many existing researches utilized different types of feature extraction techniques to detect masses in ROI images. Based on our observations, inclusion of additional features beyond a certain point worsens the performance rather than enhancing it. This paper describes a hybrid method of mammogram recognition which is based on principle component analysis, Haralick features and Genetic algorithm to select the best features. © 2011 AmirKabir Univ of tech.

关键词： Genetic algorithms

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A comparision of Model-based methods for knee cartilage segmentation

A comparision of Model-based methods for knee cartilage segm...

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2nd International Conference on computer vision Theory and Applications, VISAPP 2007

作者： Cheong, J. Faggian, N. Langs, G. Suter, D. Cicuttini, F. Dept. of Electrical Dept. of Computer Systems Engineering Monash University Australia Clayton School of Information Technology Monash University Australia Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria Dept. of Epidemiology Dept. of Preventive Medicine Monash University Australia

Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient's knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current "gold standard" (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.

关键词： Medical imaging

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A probabilistic framework for content-based diagnosis of retinal disease

A probabilistic framework for content-based diagnosis of ret...

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29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07

作者： Tobin, Kenneth W. Abdelrahman, Mohamed Chaum, Edward Govindasamy, V. Priya Karnowski, Thomas P. IEEE Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN 37831-6075 United States Electrical and Computer Engineering Dept. Tennessee Tech. University Cookeville TN 38505-0001 United States Department of Retinal Diseases Hamilton Eye Institute University of Tennessee Memphis TN 38163 United States

ISBN: (纸本)1424407885

Diabetic retinopathy is the leading cause of blindness in the working age population in the industrialized world. computer assisted analysis has the potential to assist in the early detection of diabetes by regular screening of large populations. The widespread availability of digital fundus cameras today is leading to the accumulation of large image archives of diagnosed patient data that captures historical knowledge of retinal pathology. Through this research we are developing a content-based image retrieval method to verify our hypothesis that retinal pathology can be identified and quantified from visually similar retinal images in an image archive. We will present diagnostic results for specificity and sensitivity on a population of 395 fundus images representing the normal fundus and 14 stratified disease states. © 2007 IEEE.

关键词： Diseases

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A Probabilistic Framework for Content-Based Diagnosis of Retinal Disease

A Probabilistic Framework for Content-Based Diagnosis of Ret...

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

作者： Kenneth W. Tobin Mohamed Abdelrahman Edward Chaum V. Priya Govindasamy Thomas P. Karnowski Image Science and Machine Vision Group at the Oak Ridge National Laboratory Oak Ridge Tennessee USA Electrical and Computer Engineering Dept. Tennessee Tech University Cookeville Tennessee USA Retinal Diseases at the University of Tennessee Health Science Center Hamilton Eye Institute Memphis TN USA

关键词： Retina image retrieval Degenerative diseases Laboratories image analysis Pathology Content based retrieval Cameras Blindness Machine vision

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Features extraction based on fuzzy clustering and segmentation onto the motion region for medium field surveillance application

Features extraction based on fuzzy clustering and segmentati...

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International Conference on computer Graphics, Imaging and Visualization, CGIV

作者： M.N. Maliki S.A.R.A.B. Al-Attas Computer Vision Video and Image Processing (CVVIP) Research Group Department of Microelectronic and Computer Engineering (MiCE) Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Computer Vision Universiti Teknologi Malaysia Skudai Johor Malaysia Universiti Teknologi Malaysia Skudai Johor MY Dept. of Microelectron. & Comput. Eng. Univ. Teknologi Malaysia Johor Malaysia

Fuzzy-based clustering algorithm is becoming popular in image processing, especially in image segmentation. Based on the degree of the membership function, each pixel can be grouped into "natural group" of pixels according to the spatial coordinate using their values. In this work we present features extraction based on fuzzy clustering and segmentation onto the motion region for medium field surveillance application. Instead of using blob analysis, we believe that fuzzy based clustering algorithm, can also be used to generate different clusters. Incorporating the motion-based segmentation the complexity of the fuzzy clustering is also reduced because only the motion region is processed to the clustering algorithm.

关键词： Feature extraction Surveillance Clustering algorithms image segmentation Equations Prototypes Application software computer vision image processing Data mining

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