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2015第七届国际心脏病大会

作者： Xinjian Chen Medical Image Processing Analysis & Visualization Lab School of Electrical and Information Engineering Soochow University

Cardiac CT(CCT) is widely available and has been validated for the detection of focal myocardial scar using a delayed enhancement technique in this ***,however,has not been previously evaluated for quantification of diffuse myocardial *** our investigation,we sought to evaluate the potential of low-dose CCT for the measurement of myocardial whole heart extracellular volume(ECV) *** is altered under conditions of increased myocardial fibrosis.A framework consisting of three main steps was proposed for CCT whole heart ECV ***,a shape-constrained graph cut(GC) method was proposed for myocardium and blood pool segmentation on postcontrast ***,the symmetric demons deformable registration method was applied to register precontrast to postcontrast *** the correspondences between the voxels from precontrast to postcontrast images were ***,the whole heart ECV value was *** proposed method was tested on 20 clinical low-dose CCT datasets with precontrast and postcontrast *** preliminary results demonstrated the feasibility and efficiency of the proposed method.

关键词： A Framework of Whole Heart Extracellular Volume Fraction Estimation for Low-Dose Cardiac CT images CCT Framework

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Ensemble-based Learning Using Few Training Samples for Video Surveillance Scenarios

Ensemble-based Learning Using Few Training Samples for Video...

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International Conference on image processing Theory, Tools and Applications

作者： C. A. Mitrea S. Carata B. Ionescu T. Piatrik M. Ghenescu Softrust Vision Analytics UTI Grup Image Processing and Analysis Lab UPB Multimedia and Vision Group QMUL

ISBN: (纸本)9781479986385

The article targets the task of content-based multiple-instance people retrieval from video surveillance footage. This task is particularly challenging when applied on such datasets as the available samples to train the decisioning system and formulate the query are insufficient (one image, few frames, or seconds of video recording). To cope with these challenges we investigate three established ensemble-based learning techniques, e.g., boosting, bagging and blending (stacking). Such methods are based on a set of procedures employed to train multiple learning algorithms and combine their outputs, while functioning together as a unified system of decision making. The approach was evaluated on two standard datasets (accounting for 16 people searching scenario on ca. 53000 labeled frames). Performance in terms of F2-Score attained promising results while dealing with our current task.

关键词： Reduced training samples Ensemble learning Classification Multiple instance retrieval Video Surveillance video monitoring current task Video Recording Dataset Blending

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image Matching Using Mutual k-Nearest Neighbor Graph

Image Matching Using Mutual k-Nearest Neighbor Graph

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Ting-ting Li Bo Jiang Zheng-zheng Tu Bin Luo Jin Tang School of Computer Science and Technology Anhui University Key Lab of Industrial Image Processing and Analysis of Anhui Province

Though weighted voting matching is one of most successful image matching methods,each candidate correspondence receives voting score from all other candidates,which can not apparently distinguish correct matches and incorrect matches using voting *** this paper,a new image matching method based on mutual k-nearest neighbor(k-nn) graph is ***,the mutual k-nn graph is constructed according to similarity between candidate ***,each candidate only receives voting score from its mutual k nearest ***,based on voting scores,the matching correspondences are computed by a greedy ranking *** results demonstrate the effectiveness of the proposed method.

关键词： image matching mutual k-nn graph spectral technique

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Retrieval of distinctive regions of interest from video surveillance footage: a real use case study

Retrieval of distinctive regions of interest from video surv...

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6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15)

作者： C.A. Mitrea T. Piatrik B. Ionescu M. Neville Image Processing and Analysis Lab UPB Romania Queen Mary University of London London London GB Central Forensic Image Team MET UK

ISBN: (纸本)9781510814240

The article addresses the issue of retrieving distinctive regions of interest or patterns (DROP) in video surveillance datasets. DROP may include logos, tattoos, color regions or any other distinctive features that appear recorded on video. These data come in particular with specific difficulties such as low image quality, multiple image perspectives, variable lighting conditions and lack of enough training samples. This task is a real need functionality as the challenges are derived from practice of police forces. We present our preliminary results on tackling such scenario from Scotland Yard, dealing with the constraints of a real world use case. The proposed method is based on two approaches: employment of a dense SIFT-based descriptor (Pyramidal Histogram of Visual Words), and use of image segmentation (Mean-Shift) with feature extraction on each segment computed. Tested on real data we achieve very promising results that we believe will contribute further to the ground development of advanced methods to be applied and tested in real forensics investigations.

关键词： Distinctive regions of interest Content-based retrieval Video surveillance Real-world data

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SCOUTER: Content-based multiple-instance object retrieval system

SCOUTER: Content-based multiple-instance object retrieval sy...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： C.A. Mitrea S. Carata M. Ghenescu Multimedia and Vision Group QMUL UK UPB Image Processing and Analysis Lab Romania UTI GRUP Bucharest Romania

The article presents the main functionalities and outcomes of the SCOUTER project, which is developed by UTI GRUP in partnership with the “Politehnica” University of Bucharest and co-funded by the Romanian R&D National Framework. SCOUTER is built as an offline processing application module for coping with multiple instance objects retrieval task from video surveillance footage (e.g., searching of a general class, a particular object, retrieval of distinctive regions of interest or patterns). Such functionalities are user-driven as the challenges are derived from the practice of police forensics teams on dealing with analysis of large video datasets. Findings and outputs of the presented project are contributed to the understanding of the constraints and issues that are particular to real-world video surveillance datasets and processing systems and further to alleviate analysis efforts of police investigations.

关键词： Video surveillance Search problems Feature extraction image color analysis Conferences Algorithm design and analysis Shape

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On the performance of focus measures in infrared and near-infrared imagery

On the performance of focus measures in infrared and near-in...

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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Constantin Vertan Corneliu Florea Laura Florea Silviu Bădoiu Image Processing and Analysis Lab (LAPI) Politehnica University of Bucharest Romania “Carol Davila” University of Medicine and Pharmacy Bucharest Romania

The infrared imaging (comprising both the thermal infrared and the near infrared bands) became an imaging modality at hand, with the potential of providing insights in the unseen components of various physical and biological processes. Various experiments have shown that the focusing of infrared images is significant different from the focus of visible band images, posing significant problems in autofocus. This contribution investigates the performance of several classical and recent focus measures in the infrared image domain.

关键词： Focusing Cameras Noise measurement Pollution measurement Noise Biomedical imaging

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Binary pattern flavored feature extractors for Facial Expression Recognition: An overview

Binary pattern flavored feature extractors for Facial Expres...

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Proceedings of the International Convention MIPRO

作者： Rasmus Lyngby Kristensen Zheng-Hua Tan Zhanyu Ma Jun Guo Section of Image Analysis and Computer Graphics Technical University of Denmark Kgs. Lyngby Denmark Signal and Information Processing section (SIP) Aalborg University Aalborg Denmark Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781479981748

This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression Recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution is to provide a solid overview for future research.

关键词： Feature extraction Face Face recognition Databases Three-dimensional displays Accuracy Gold

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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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Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR images of Epileptic Patients

Statistical Validation of Automatic Methods for Hippocampus ...

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

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Dario Pompili Hamid Soltanian-Zadeh Department of Electrical and Computer Engineering Rutgers University Medical Image Analysis Lab Radiology Department Henry Ford Health System Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the bench-mark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.

关键词： Segmentation Hippocampus Magnetic Resonance Imaging (MRI) Comparison Epilepsy

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Support Vector Machine with Nonlinear-Kernel Optimization for Lateralization of Epileptogenic Hippocampus in MR images

Support Vector Machine with Nonlinear-Kernel Optimization fo...

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

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Fariborz Mahmoudi Hao Ying Hamid Soltanian-Zadeh Medical Image Analysis Lab. Radiology Department Henry Ford Health System Department of Electrical and Computer Engineering Wayne State University Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods;then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.

关键词： Magnetic Resonance Imaging (MRI) Hippocampus Epilepsy Lateralization Support Vector Machine (SVM)

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