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

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 robust FCM algorithm for image segmentation based on spatial information and Total Variation

A robust FCM algorithm for image segmentation based on spati...

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Iranian Machine Vision and image processing (MVIP)

作者： Hassan Akbari Hamed Mohebbi Kalkhoran Emad Fatemizadeh Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in performing it on artificial synthesized images, and the results show the superior accuracy compared to some other state of the art FCM-based segmentation methods.

关键词： image segmentation biomedical imaging Magnetic resonance imaging Clustering algorithms Artificial intelligence

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Fetal ECG extraction using πTucker decomposition

Fetal ECG extraction using πTucker decomposition

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International Conference on Systems, signals and image processing, IWSSIP

作者： Hassan Akbari Mohammad B. Shamsollahi Ronald Phlypo Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran Laboratory Grenoble Image Parole Signal Automatique (GIPSA-lab) Grenoble France

In this paper, we introduce a novel approach based on Tucker Decomposition and quasi-periodic nature of ECG signal for fetal ECG extraction from abdominal ECG mixture. We adapt variable periodicity constraint of the ECG components to main objective function of the Tucker Decomposition and shape it to matrix form in order to simply optimize the objective function. We form a 3rd order tensor by stacking the mixed multichannel ECG and reconstructed fetal and maternal subspaces using BSS methods in order to have the benefit of further artificial observations, and apply our proposed penalized decomposition on it. The proposed method is evaluated on synthetic and real datasets using the criteria signal to Interference plus Noise Ratio (SINR) for fetal component considering mother component as interference. Results and evaluations show a superior SINR improvement of 1 to 4 dB compared to other state of the art methods.

关键词： Electrocardiography Tensile stress Matrix decomposition Interference Noise Linear programming

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Comparative analysis of methods for extracting vessel network on breast MRI images

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Journal of Physics: Conference Series 2017年第1期931卷

作者： Bence T. Gaizer Katerina G. Vassiou Eleftherios Lavdas Dimitrios L. Arvanitis Ioannis V. Fezoulidis Dimitris T. Glotsos Department of Mechatronics Optics and Mechanical Engineering Informatics Budapest University of Technology and Economics Műegyetem rakpart 3 Budapest XI 1111 Hungary Department of Radiology Medical School of Thessaly University Hospital of Larissa Biopolis Larissa 41110 Greece Department of Medical Radiologic Technology Technological Educational Institute of Athens Ag. Spyridonos Egaleo Athens 12210 Greece Department of Anatomy School of Health Sciences University of Larissa Biopolis Larissa 41110 Greece Medical Image and Signal Processing Laboratory Department of Biomedical Engineering Technological Educational Institute of Athens Ag. Spyridonos Egaleo Athens 12210 Greece

Digital processing of MRI images aims to provide an automatized diagnostic evaluation of regular health screenings. Cancerous lesions are proven to cause an alteration in the vessel structure of the diseased organ. Currently there are several methods used for extraction of the vessel network in order to quantify its properties. In this work MRI images (Signa HDx 3.0T, GE Healthcare, courtesy of University Hospital of Larissa) of 30 female breasts were subjected to three different vessel extraction algorithms to determine the location of their vascular network. The first method is an experiment to build a graph over known points of the vessel network; the second algorithm aims to determine the direction and diameter of vessels at these points; the third approach is a seed growing algorithm, spreading selection to neighbors of the known vessel pixels. The possibilities shown by the different methods were analyzed, and quantitative measurements were performed. The data provided by these measurements showed no clear correlation with the presence or malignancy of tumors, based on the radiological diagnosis of skilled physicians.

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Improving the detection of mtbi via complexity analysis in resting - state magnetoencephalography

Improving the detection of mtbi via complexity analysis in r...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Marios Antonakakis Stavros I. Dimitriadis Andrew C. Papanicolaou George Zouridakis Michalis Zervakis Digital Image and Signal Processing Laboratory School of Electronic and Computer Engineering Technical University of Crete Chania Greece Neuroinformatics Group Aristotle University Thessaloniki Greece Cardiff University Brain Research Imaging Centre (CUBRIC) School of Psychology Cardiff University Cardiff United Kingdom Departments of Pediatrics and Anatomy and Neurobiology University of Tennessee Health Science Center Memphis TN USA Neuroscience Institute Le Bonheur Children's Hospital Memphis TN USA Departments of Engineering Technology Computer Science Biomedical Engineering and Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (纸本)9781509018185

Diagnosis of mild Traumatic Brain Injury (mTBI) is difficult due to the variability of obvious brain lesions using imaging scans. A promising tool for exploring potential biomarkers for mTBI is magnetoencephalography which has the advantage of high spatial and temporal resolution. By adopting proper analytic tools from the field of symbolic dynamics like Lempel-Ziv complexity, we can objectively characterize neural network alterations compared to healthy control by enumerating the different patterns of a symbolic sequence. This procedure oversimplifies the rich information of brain activity captured via MEG. For that reason, we adopted neural-gas algorithm which can transform a time series into more than two symbols by learning brain dynamics with a small reconstructed error. The proposed analysis was applied to recordings of 30 mTBI patients and 50 normal controls in δ frequency band. Our results demonstrated that mTBI patients could be separated from normal controls with more than 97% classification accuracy based on high complexity regions corresponding to right frontal areas. In addition, a reverse relation between complexity and transition rate was demonstrated for both groups. These findings indicate that symbolic complexity could have a significant predictive value in the development of reliable biomarkers to help with the early detection of mTBI.

关键词： Complexity theory Time series analysis Sensitivity Integrated circuits Indexes Brain Laplace equations

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Interictal EEG Denoising using Independent Component Analysis and Empirical Mode Decomposition

Interictal EEG Denoising using Independent Component Analysi...

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International Conference on Telecommunications and signal processing

作者： Sina Salsabili Sepideh Hajipour Sardoui Mohammad B. Shamsollahi School of Engineering and Science Sharif University of Technology - International Campus Kish Island Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology

ISBN: (纸本)9781479984992

Noise contamination is inevitable in biomedical recordings. In some cases biomedical recordings are highly contaminated with artifacts which make the effective recovering process hard to achieve. Many different methods have been proposed for artifact removal from biomedical signals but introducing an effective method which can present valuable data for medical analysis, is still an ongoing process. In this paper a new method for interictal EEG denoising is presented. Single-channel ICA denoising method based on EMD decomposition is used to improve the multi-channel ICA denoising results. This method is tested on simulated epileptic recordings which are contaminated with real muscle artifact and EEG background activity.

关键词： Single Channel ICA Multi-channel ICA denoising EMD Interictal Epileptic Spikes Muscle artifact EEG background activity

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ECG Denoising Using Mutual Information Based Classification of IMFs and Interval Thresholding

ECG Denoising Using Mutual Information Based Classification ...

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International Conference on Telecommunications and signal processing

作者： Marjaneh Taghavi Mohammad B. Shamsollahi Lotfi Senhadji School of Engineering and Science Sharif University of Technology International Campus Kish Island Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Laboratoire Traitement du Signal et de l'Image (LTSI) Universitede Rennes 1

ISBN: (纸本)9781479984992

The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Therefore, the quality of information extracted from the ECG has a vital role. In real recordings, ECG is corrupted by artifacts such as prolonged repolarization, respiration, changes of electrode position, muscle contraction, and power line interface. In this paper, a denoising technique for ECG signals based on Empirical Mode Decomposition (EMD) is proposed. We use Ensemble Empirical Mode Decomposition (EEMD) to overcome the limitations of EMD. Moreover, to overcome the limitations of thresholding methods we use the combination of mutual information and two EMD based interval thresholding approaches. Our new method is evaluated on ECG signals available in MIT-BIH database. This method is compared with two EEMD based interval thresholding methods. The results show that our proposed method has a better signal to Noise Ratio improvement (SNRimp) and a lower Mean Square Error (MSE) than the other two methods.

关键词： Ensemble Empirical Mode Decomposition Denoising Mutual Information Instantaneous Half Period Interval Thresholding ECG

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Optimizing Dynamical Similarity Index Extraction Window for Seizure Detection

Optimizing Dynamical Similarity Index Extraction Window for ...

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

作者： Leila Azinfar Ahmed Rabbi Mohammdreza Ravanfar Sima Noghanian Reza Fazel-Rezai Biomedical Signal and Image Processing Laboratory Hawaii Center for Advanced Communications University of Hawaii Department of Electrical Engineering University of North Dakota Biomedical Signal and Image Processing Laboratory Electrical Engineering Department University of North Dakota

ISBN: (纸本)9781424479276

This paper addresses an optimization problem in choosing optimum window length for feature extraction in automatic seizure detection. The processing window length plays an important role in reducing the false positive and false negative rates and decreasing required processing time for seizure detection. This study presents an approach for selecting the optimum window length toward the extraction of dynamical similarity index (DSI) feature. Then, the optimal window value in DSI extraction was used to detect seizure onset automatically. The algorithm was applied to electroencephalogram (EEG) signals from European Epilepsy Database. Although the main purpose of this study was not the seizure detection and mainly focuses on proposing an approach for finding an optimum window length for feature extraction towards the early seizure detection, the results showed that the proposed method achieves 83.99% of sensitivity in seizure detection. The low false positive rate per hour (FPR/h) was also significant due to continuous EEG analysis. The method showed fast computation speed which promises a potential for the real time applications. The proposed method for the window optimization in feature extraction of DSI can be implemented for other features to further improve the performance of seizure detection.

关键词： Feature extraction Electroencephalography Indexes Optimization Epilepsy Sensitivity

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Manifold Learning Based Registration Algorithms Applied to Multimodal images

Manifold Learning Based Registration Algorithms Applied to M...

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

作者： Mohammad Farid Azampour Aboozar Ghaffari Azam Hamidinekoo Emad Fatemizadeh Biomedical Signal and Image Processing Laboratory (BiSIPL) Electrical Engineering Department Sharif University of Technology

ISBN: (纸本)9781424479276

Manifold learning algorithms are proposed to be used in image processing based on their ability in preserving data structures while reducing the dimension and the exposure of data structure in lower dimension. Multi-modal images have the same structure and can be registered together as mono-modal images if only structural information is shown. As a result, manifold learning is able to transform multi-modal images to mono-modal ones and subsequently do the registration using mono-modal methods. Based on this application, in this paper novel similarity measures are proposed for multi-modal images in which Laplacian eigenmaps are employed as manifold learning algorithm and are tested against rigid registration of PET/MR images. Results show the feasibility of using manifold learning as a way of calculating the similarity between multi-modal images.

关键词： image Registration Manifold Learning Similarity Measure Laplacian Eigenmaps

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