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

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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

Multi-scale application of camera calibration in tourism service platforms

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Journal of Information Hiding and Multimedia Signal processing 2017年第4期8卷 929-936页

作者： Ding, Xintao Luo, Yonglong Cai, Guorong Zhang, Xiaobo School of Territorial Resources and Tourism Anhui Normal University No. 819 South Jiuhua Road Wuhu241002 China School of Mathematics and Computer Science Anhui Normal University No. 819 South Jiuhua Road Wuhu241002 China Computer Engineering College Jimei University No. 183 Yinjiang Road Xiamen China Institute of Graphics and Image Processing Xianyang Normal University No. 43 Wenlin Road Xianyang China

Full view panoramas are able to simulate tele-presence or virtual reality experience, thus panoramic technology is an important implementation approach for the visual expression of tourism service platforms (TSP). On one hand, camera calibration, which is generally implemented under a certain resolution, may be a necessary preprocessing stage for panoramic technology. On the other hand, multi-scale applications are potentially involved in TSP for visualization expression. Therefore, calibration parameters resulting in one scale should be extended to multi-scale applications, but the extension research on this topic is insufficient. In this paper, the relations of intrinsic and distortion calibration parameters under different scales were both presented. Then, based on these two claims, a multi-scale correction algorithm is proposed. After demonstration experiments, with the help of the algorithm, different scale panoramas were employed for visualization expression in TSP. The multi-scale application of camera calibration in TSP is beneficial to bring intuition feelings to tourists. © 2017, Ubiquitous International. All rights reserved.

关键词： Calibration

A novel attack model for collusion secure fingerprinting codes

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A novel attack model for collusion secure fingerprinting cod...

Media Watermarking, Security, and Forensics 2016

作者： Schäfer, Marcel Berchtold, Waldemar Stark, Teetje Reimers, Nils Steinebach, Martin Fraunhofer-Institute SIT Darmstadt Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Ubiquitous Knowledge Processing Lab - Department of Computer Science Technische Universitat Darmstadt Darmstadt Germany

Probabilistic or bias-based fingerprinting codes to counter collusion attacks are applied to enhance the security of transaction watermarking applications. Into every media copy to be distributed is embedded a unique fingerprint via watermarking techniques. With it a distributor is able to trace back unauthorized redistributed versions to its source with a high probability even if the media was subject to a collusion attack. The seminal Tardos fingerprinting codes and all its derivatives rely on position independent fingerprints. Hence in most work it is assumed that the attackers also rely on the position independence when creating the media forgery containing a manipulated fingerprint. However, they need not follow this assumption. In this work we present a novel iterative attack model that does not rely on the position independence but could be applied in practice. The corresponding attacks iteratively adapt the manipulated fingerprint with the intention to maximally reduce their accusation scores in order to escape the accusation. For practical collusion sizes, the attacks show better performance than other attacks typically discussed in literature. In other words, the attacks result in manipulated fingerprints that lead to higher error rates of the fingerprinting scheme, compared to the attacks discussed in literature. © 2016 Society for Imaging Science and Technology.

关键词： Iterative methods

Personalized pancreatic tumor growth prediction via group learning

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arXiv 2017年

作者： Zhang, Ling Lu, Le Summers, Ronald M. Kebebew, Electron Yao, Jianhua Imaging Biomarkers and Computer-Aided Diagnosis Laboratory Clinical Image Processing Service Radiology and Imaging Sciences Department National Institutes of Health Clinical Center BethesdaMD20892 United States Endocrine Oncology Branch National Cancer Institute National Institutes of Health BethesdaMD20892 United States

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% ± 3.6% and RVD of 7.9% ± 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4%±4.0% and RVD 13.9%±9.8% obtained by a previous state-of-the-art model-based method. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

Two-dimensional picture arrays and Parikh q—matrices

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Journal of Physics: Conference Series 2018年第1期1132卷

作者： Somnath Bera Kalpana Mahalingam Linqiang Pan K. G. Subramanian Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Department of Mathematics Indian Institute of Technology Madras Chennai 600036 India School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou 450002 Henan China Department of Mathematics Madras Christian college Tambaram Chennai 600017 India

Based on the idea of 'q—count' of certain subwords of a word and generalizing the notion of Parikh matrix of a word, the notion of Parikh q—matrix of a word over an ordered alphabet was introduced. On the other hand, with a two-dimensional picture array of symbols arranged in rows and columns, two kinds of upper triangular matrices, known as row and column Parikh matrices have also been introduced and investigated. Here combining these two kinds of matrices of a picture array, we introduce row/column Parikh q—matrix of an array, leading to the concept of q—ambiguity of a picture array. Results relating to q—ambiguity of picture arrays are derived in the context of these Parikh q—matrices of arrays.

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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A primal-dual method for optimal control and trajectory generation in high-dimensional systems

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arXiv 2017年

作者： Kirchner, Matthew R. Hewer, Gary Darbon, Jérôme Osher, Stanley Image and Signal Processing Branch Research Directorate Code 4F0000D Naval Air Warfare Center Weapons Division China LakeCA93555 United States Electrical and Computer Engineering Department University of California Santa BarbaraCA93106 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Department of Mathematics University of California Los AngelesCA90095 United States

Presented is a method for efficient computation of the Hamilton-Jacobi (HJ) equation for time-optimal control problems using the generalized Hopf formula. Typically, numerical methods to solve the HJ equation rely on a discrete grid of the solution space and exhibit exponential scaling with dimension. The generalized Hopf formula avoids the use of grids and numerical gradients by formulating an unconstrained convex optimization problem. The solution at each point is completely independent, and allows a massively parallel implementation if solutions at multiple points are desired. This work presents a primal-dual method for efficient numeric solution and presents how the resulting optimal trajectory can be generated directly from the solution of the Hopf formula, without further optimization. Examples presented have execution times on the order of milliseconds and experiments show computation scales approximately polynomial in dimension with very small high-order coefficients. Copyright © 2017, The Authors. All rights reserved.

关键词： Numerical methods

Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

Pushing for weighted tree automata

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arXiv 2017年

作者： Hanneforth, Thomas Maletti, Andreas Quernheim, Daniel Universität Potsdam Human Sciences Faculty Department Linguistik Karl-Liebknecht-Str. 24–25 Potsdam14476 Germany Universität Leipzig Faculty of Mathematics and Computer Science Institute of Computer Science PO box 100 920 Leipzig04009 Germany Universität Stuttgart Institute for Natural Language Processing Pfaffenwaldring 5b Stuttgart70569 Germany

A weight normalization procedure, commonly called pushing, is introduced for weighted tree automata (wta) over commutative semifields. The normalization preserves the recognized weighted tree language even for nondeterministic wta, but it is most useful for bottom-up deterministic wta, where it can be used for minimization and equivalence testing. In both applications a careful selection of the weights to be redistributed followed by normalization allows a reduction of the general problem to the corresponding problem for bottom-up deterministic unweighted tree automata. This approach was already successfully used by Mohri and Eisner for the minimization of deterministic weighted string automata. Moreover, the new equivalence test for two wta M and M′ runs in time O((|M| + |M′|) log (|Q| + |Q′|)), where Q and Q′ are the states of M and M′, respectively, which improves the previously best run-time O(|M| · |M′|).MSC Codes 68Q45, 68Q25 © 2017, CC BY.

关键词： Automata theory