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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Automated long-term EEG analysis to localize the epileptogenic zone

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Epilepsia Open 2017年第3期2卷 322-333页

作者： van Mierlo, Pieter Strobbe, Gregor Keereman, Vincent Birot, Gwénael Gadeyne, Stefanie Gschwind, Markus Carrette, Evelien Meurs, Alfred Van Roost, Dirk Vonck, Kristl Seeck, Margitta Vulliémoz, Serge Boon, Paul Medical Image and Signal Processing Group Department of Electronics and Information Systems Ghent University–iMinds Medical IT Department Ghent Belgium Functional Brain Mapping Laboratory Department of Fundamental Neurosciences University of Geneva Geneva Switzerland Laboratory for Clinical and Experimental Neurophysiology Neurobiology and Neuropsychology Department of Neurology Ghent University Hospital Ghent Belgium Epilepsy and EEG Unit University Hospital of Geneva Geneva Switzerland Department of Neurosurgery Ghent University Hospital Ghent Belgium

Objective: We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring. Methods: In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified. Results: In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%. Significance: We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus. © 2017 The Authors. Epilepsia Open published by Wiley Periodicals Inc. on behalf of International League Against Epilepsy.

关键词： Automated spike detection Automated spike localization EEG source imaging Patient-specific head model

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

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arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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Semi-automated extraction of retinal blood vessel network with Bifurcation and crossover points 12th

Semi-automated extraction of retinal blood vessel network wi...

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12th International Symposium on Visual Computing, ISVC 2016

作者： Nougrara, Z. Kihal, N. Meunier, J. Faculty of Mathematics and Computer Science Department of Mathematics University of Sciences and Technology of Oran Oran Algeria Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB Algiers Algeria Department of Computer Science and Operations Research University of Montreal Montreal Canada

ISBN: (纸本)9783319508313

Among different retinal analysis tasks, blood vessel extraction plays an important role as it is often the first essential step before any measurement can be made for various applications such as biometric authentication or diagnosis of retinal vascular diseases. In this paper, we present a new method for extraction of blood vessel network with its nodes (bifurcation and crossover points) from retinal images. The first step is to identify pixels with homogeneous vessel elements with a set of four directional filters. Then another step is applied to extract local linear components assuming that a vessel is a set of short linear segments. Through an optimization process this information is combined to extract the vessel network and its nodes. The proposed algorithm was tested on the publicly available DRIVE retinal fundus image database. The experimental results show good precision, recal. The proposed algorithm was tested on the publicly available DRIVE retinal fundus image database. The experimental results show good precision, recall and F-measure compared to ground truth and a state-of-the-art algorithm for the same dataset. © Springer International Publishing AG 2016.

关键词： Blood vessels

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ONLINE PERSONALIZED QOS PREDICTION APPROACH FOR CLOUD SERVICES

ONLINE PERSONALIZED QOS PREDICTION APPROACH FOR CLOUD SERVIC...

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2016 4th IEEE International Conference on Cloud Computing and Intelligence Systems（IEEE CCIS2016）

作者： Jianlong Xu Zibin Zheng Zhun Fan Wenhua Liu Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Department of Electronics EngineeringShantou University College of Science Shantou University School of Data and Computer Science Sun Yat-sen University

ISBN: (纸本)9781509012572

Personalized Quality-of-Service(Qo S) prediction is an indispensable technique to select suitable services for service-based cloud applications. Considering the dynamic nature of services, efficiently and accurately predicting Qo S value becomes an urgent and crucial research issue. In this paper, we propose an online personalized Qo S prediction approach for cloud service, namely online learning based matrix factorization(OLMF). We build the objective function of online matrix factorization and use stochastic gradient descent algorithm to solve the function. Extensive experiments are conducted on real world public datasets, which verify the effectiveness and efficiency of our proposed approach.

关键词： Cloud service Online learning QoS prediction Matrix factorization Cloud computing

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Multimodal biometric system based on palmprint using progressive image compression

Multimodal biometric system based on palmprint using progres...

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International Conference on Information Technology for Organizations Development (IT4OD)

作者： Djamel Samai Abdallah Meraoumia Salim Chitroub Noureddine Doghmane Abdelmalik Taleb-Ahmed Faculté des Nouvelles Technologies de l'Information et de la Communication Laboratoire de Génie Électrique Ouargla Algérie Electronics and Computer Science Faculty Signal and Image Processing Laboratory Algiers Algeria Department of Electronics Badji Mokhtar University Annaba Algeria UMR University of Valenciennes France

In this paper, we propose a multimodal palmprint verification system based on two bands (gray scale and near infrared) using a progressive image compression through the famous Set Partitioning In Hierarchical Trees (SPIHT) coder. Each palmprint image is compressed an decompressed at 0.5 bit per pixel (bpp). By exploiting the progressiveness of the SPIHT algorithm, we decompressed the palmprint images in two bitrates. We obtained three images, an image at 0.25 bpp which represents the image approximation, the other at 0.25 bpp which represents the edges or details, and the third is the global of the both preceding. After analyzing the extracted features from different bands, we propose a score-level fusion scheme to integrate the multimodal information. The palmprint verification experiments demonstrated the superiority of multibands fusion to each single band, which results in higher verification accuracy.

关键词： Biometrics (access control) Feature extraction image coding Databases image resolution Authentication Bit rate

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Corneal topography: An emerging biometric system for person authentication 14

Corneal topography: An emerging biometric system for person ...

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14th International Conference of the Biometrics Special Interest Group, BIOSIG 2015

作者： Kihal, Nassima Polette, Arnaud Chitroub, Salim Brunette, Isabelle Meunier, Jean Signal and Image Processing Laboratory Electronics Computer Science Faculty USTHB Algiers Algeria University of Montreal Canada Aix-Marseille University CNRS LSIS UMR 7296 France Maisonneuve-Rosemont Hospital Department of Ophthalmology University of Montreal Canada

ISBN: (纸本)9783885796398

Corneal topography is a non-invasive medical imaging technique to assess the shape of the cornea in ophthalmology. In this paper we demonstrate that in addition to its health care use, corneal topography could provide valuable biometric measurements for person authentication. To extract a feature vector from these images (topographies), we propose to fit the geometry of the corneal surface with Zernike polynomials, followed by a linear discriminant analysis (LDA) of the Zernike coefficients to select the most discriminating features. The results show that the proposed method reduced the typical d-dimensional Zernike feature vector (d=36) into a much lower r-dimensional feature vector (r=3), and improved the Equal Error Rate from 2.88% to 0.96%, with the added benefit of faster computation time. © 2015 Gesellschaft für Informatik e.V.

关键词： Topography

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Robust Multimodal Biometric Identification System Using Finger-Knuckle-Print features

Robust Multimodal Biometric Identification System Using Fing...

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International Conference on Control, Engineering & Information Technology

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Univ Ouargla Fac. des nouvelles technologies de l'information et de la communication Lab. de Genie Electrique Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB School of Computing Engineering and Information Sciences Northumbria University

ISBN: (纸本)9781479982134

Automatic personal identification is becoming an increasingly important requirement in a variety of applications like access control, surveillance systems and physical buildings. In recent years, biometric systems are used in these fields, offering greater convenience and several advantages over traditional security. In this paper, we propose an efficient online personal identification system based on Finger-Knuckle-Print (FKP) images. In this study, the finger image is characterized by the two dimensional Bloc based Discrete Cosine Transform (2D-BDCT) coefficients. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same finger is transformed by the two dimensional Discrete Fourier Transform (2D-DFT). The response of this transformation is directly used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. The results on a medium-size database, 150 users, show good identification performance based on individual modalities as well as after fusing multiple finger types.

关键词： Biometrics Identification FKP 2D-BDCT 2D-DFT HMM Data fusion

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Fusion of iris and palmprint for multimodal biometric authentication

Fusion of iris and palmprint for multimodal biometric authen...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Nassima Kihal Salim Chitroub Jean Meunier Department of Computer Science and Operations Research (DIRO) University of Montreal Montreal QC Canada Electronics and Computer Science Faculty Signal and Image Processing Laboratory Algiers Algeria

This paper presents a multimodal biometric system for authentication, based on the fusion of iris and palmprint. We propose an approach for feature extraction of each modality by using wavelet packet decomposition at four levels. This gives 256 packets which can generate a compact binary code. It is obtained from the first three highest energy peaks to compute an adapted threshold that enable to affect 0 or 1 to each wavelet packet. Different fusion strategies were tested at different levels: feature level, score level and error level. The first fusion is a simple concatenation of iris and palmprint codes. The second employs a weighted sum rule to matching scores. The third applies the Hamacher t-norm to the errors. The proposed approach and each fusion strategy were tested for their accuracy on the Casia iris database fused with the Casia palmprint database, and then with the PolyU database. The proposed approach for multimodal biometric system achieves a recognition improvement with each fusion method.

关键词： Iris recognition Wavelet packets Databases Feature extraction Iris Vectors

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Robust human identity identification system by using hand biometric traits 26

Robust human identity identification system by using hand bi...

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2014 26th International Conference on Microelectronics, ICM 2014

作者： Meraoumia, Abdallah Chitroub, Salim Bouridane, Ahmed Univ Ouargla Fac. des Nouvelles Technologies de L'information et de la Communication Lab. de Génie Électrique Ouargla30 000 Algeria Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB P.O. box 32 El Alia Bab Ezzouar Algiers16111 Algeria Department of Computer Science and Digital Technologies Northumbria University Newcastle Pandon Building Newcastle-upon-TyneNE2 1XE United Kingdom

ISBN: (纸本)9781479981533

Automatic personal identification has become an important issue in several applications, such as physical buildings and information systems. Nowadays, biometric techniques are an important and effective solution for automatic personal identification. One of the most popular biometric systems is based on the hand due to its ease of use. Hand has several modalities to be extracted, among them, Finger-Knuckle-Print (FKP) and PaLMprint (PLM), has attracted an increasing amount of attention. This paper investigates these modalities for elaborating an efficient multimodal biometric identification system. For that, the texture information of FKP and PLM is characterized by the Local Binary Pattern (LBP). During the matching phase, an Euclidian distance score is employed to measure the similarity between templates. The proposed system is tested and evaluated using a database of 165 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate. © 2014 IEEE.

关键词： Biometrics

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