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Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests

Journal of medical engineering 2017年第1期2017卷 4501647页

作者： Benedikt Lorch Ghislain Vaillant Christian Baumgartner Wenjia Bai Daniel Rueckert Andreas Maier Pattern Recognition Lab Friedrich-Alexander University Erlangen-Nürnberg 91058 Erlangen Germany. Biomedical Image Analysis Group Imperial College London London SW7 2AZ UK.

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the -space data according to a motion trajectory, using the three common -space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

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ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

ICDAR2015 competition on recognition of documents with compl...

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International Conference on Document analysis and recognition

作者： A. Antonacopoulos C. Clausner C. Papadopoulos S. Pletschacher Pattern Recognition and Image Analysis (PRImA) Research Lab University of Salford Greater Manchester United Kingdom

ISBN: (纸本)9781479918065

This paper presents an objective comparative evaluation of page segmentation and region classification methods for documents with complex layouts. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2015, presenting the results of the evaluation of eight methods - four submitted, two state-of-the-art systems (one commercial and one open-source) and their two immediately previous versions. Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmentation and region classification (one with emphasis on text and the other focusing only on text). The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the non-text content.

关键词： Optical character recognition software

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The ENP image and ground truth dataset of historical newspapers

The ENP image and ground truth dataset of historical newspap...

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International Conference on Document analysis and recognition

作者： Christian Clausner Christos Papadopoulos Stefan Pletschacher Apostolos Antonacopoulos Pattern Recognition and Image Analysis (PRImA) Research Lab University of Salford Greater Manchester United Kingdom

ISBN: (纸本)9781479918065

This paper presents a research dataset of historical newspapers comprising over 500 page images, uniquely representative of European cultural heritage from the digitization projects of 12 national and major European libraries, created within the scope of the large-scale digitisation Europeana Newspapers Project (ENP). Every image is accompanied by comprehensive ground truth (Unicode encoded full-text, layout information with precise region outlines, type labels, and reading order) in PAGE format and searchable metadata about document characteristics and artefacts. The first part of the paper describes the nature of the dataset, how it was built, and the challenges encountered. In the second part, a baseline for two state-of-the-art OCR systems (ABBYY FineReader Engine 11 and Tesseract 3.03) is given with regard to both text recognition and segmentation/layout analysis performance.

关键词： Optical character recognition software Europe Engines

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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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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Automatic removal of externally attached fiducial markers in cone beam C-arm CT

Automatic removal of externally attached fiducial markers in...

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2014 - Workshop on image Processing for Medicine: Algorithms - Systems - Applications, BVM 2014

作者： Berger, Martin Forman, Christoph Schwemmer, Chris Choi, Jang H. Müller, Kerstin Maier, Andreas Hornegger, Joachim Fahrig, Rebecca Pattern Recognition Lab FAU Erlangen-Nürnberg Germany Research Training Group 1773 Heterogeneous Image Systems Stanford University StanfordCA United States Stanford University StanfordCA United States Department of Radiology Stanford University StanfordCA United States

ISBN: (纸本)9783642541100

In computed tomography fiducial markers are frequently used to obtain accurate point correspondences for further processing. These markers typically cause metal artefacts, decreasing image quality of the subsequent reconstruction and are therefore often removed from the projection data. The placement of such markers is usually done on a surface, separating two materials, e.g. skin and air. Hence, a correct restoration of the occluded area is difficult. In this work six state-of-the-art interpolation techniques for the removal of high-density fiducial markers from cone-beam CT projection data are compared. We conducted a qualitative and quantitative evaluation for the removal of such markers and the ability to reconstruct the adjoining edge. Results indicate that an iterative spectral deconvolution is best suited for this application, showing promising results in terms of edge, as well as noise restoration. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Restoration

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Application of PR techniques to mail sorting in China

Application of PR techniques to mail sorting in China

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2014 7th International C Conference on Computer Science and Software Engineering, C3S2E 2014

作者： Liu, Li Lu, Shujing Lu, Yue Suen, Ching Y. Department of Computer Science and Technology East China Normal University Shanghai 200241 China ECNU-SRI Joint Lab. for Pattern Analysis and Intelligent System Shanghai Research Institute of China Post Group Shanghai 200062 China Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal QC H3G 1M8 Canada

ISBN: (纸本)9781450327121

Mail sorting machines play an important role in postal automation. In this paper, we give a brief overview of mail sorting machines in China Post from a pattern recognition point of view. OCR techniques such as postcode recognition and address recognition are essential for mail sorting machines, which are considered as class imbalance problems in our study. In addition to OCR that mainly focuses on the characters in the image, some image-level features are exploited for near-duplicate envelope image matching, which is helpful in tracking pieces of mail in the network environment. © 2014 ACM.

关键词： image matching

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Novel Global and Local Features for Near-Duplicate Document image Matching

Novel Global and Local Features for Near-Duplicate Document ...

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International Conference on pattern recognition

作者： Li Liu Yue Lu Ching Y. Suen Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal Canada Department of Computer Science and Technology East China Normal University Shanghai China Joint Lab for Pattern Analysis and Intelligent System Shanghai Research Institute of China Post Shanghai

ISBN: (纸本)9781479952106

A new near-duplicate document image matching approach is proposed. Globally, we model the spatial arrangements of objects in an image. Locally, the micro-patterns within each object are captured. To define a micro-pattern, the N-nary center-symmetric gray value differences in an image local neighborhood of a variable radius are exploited. A visual descriptor is proposed to characterize the appearance of the object based on micro-pattern distributions. By combining the global and local features, each document image is represented by a compact signature with a variable length. We employ Earth Mover's Distance for image dissimilarity computation, which stands out for its remarkable ability to tolerate the instability of object segmentation by allowing many-to-many correspondence among objects. Extensive experiments on two data sets demonstrate the effectiveness of the proposed approach.

关键词： image matching Histograms Mercury (metals) Earth Object segmentation Sorting Databases

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ICDAR 2013 Competition on Historical Book recognition (HBR 2013)

ICDAR 2013 Competition on Historical Book Recognition (HBR 2...

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International Conference on Document analysis and recognition

作者： A. Antonacopoulos C. Clausner C. Papadopoulos S. Pletschacher Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Manchester UK

This paper presents an objective comparative evaluation of layout analysis and recognition methods for scanned historical books. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2013 and the 2nd International Workshop on Historical Document Imaging and Processing (HIP2013), presenting the results of the evaluation of five methods - three submitted and two state-of-the-art systems (one commercial and one open-source). Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions, one evaluating segmentation and region classification (with a text extraction goal) and the other the whole pipeline including recognition. The results indicate that there is a convergence to a certain methodology, in terms of layout analysis, with some variations in the approach. However, there is still a considerable need to develop robust methods that deal with the idiosyncrasies of historical books, especially for OCR.

关键词： Layout Optical character recognition software Particle separators Performance evaluation Video recording Text analysis image segmentation

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ICDAR 2013 Competition on Historical Newspaper Layout analysis (HNLA 2013)

ICDAR 2013 Competition on Historical Newspaper Layout Analys...

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International Conference on Document analysis and recognition

This paper presents an objective comparative evaluation of layout analysis methods for scanned historical newspapers. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2013 and the 2nd International Workshop on Historical Document Imaging and Processing (HIP2013), presenting the results of the evaluation of five submitted methods. Two state-of-the-art systems, one commercial and one open-source, are also evaluated for comparison. Two scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and the other evaluating the whole pipeline of segmentation and region classification (with a text extraction goal). The results indicate that there is a convergence to a certain methodology with some variations in the approach. However, there is still a considerable need to develop robust methods that deal with the idiosyncrasies of historical newspapers.

关键词： Layout Particle separators image segmentation Performance evaluation Video recording Text analysis image analysis

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