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MRI-based brain tumor segmentation using Gaussian mixture model with reversible jump Markov chain Monte Carlo algorithm

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AIP Conference Proceedings 2019年第1期2194卷

作者： Anindya Apriliyanti Pravitasari Yusuf Puji Hermanto Nur Iriawan Irhamah Kartika Fithriasari Santi Wulan Purnami Widiana Ferriastuti 1Department of Statistics Faculty of Mathematics and Natural Sciences Universitas Padjajaran 45363 Bandung Indonesia 2Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember 60111 Surabaya Indonesia 3Department of Radiology Faculty of MedicineUniversitas Airlangga 60115 Surabaya Indonesia

A brain tumor is the 15th deadly disease in Indonesia according to the WHO in 2018. In medical treatment, brain tumors can be detected through Magnetic Resonance Imaging (MRI). The main problem is how to separate the brain tumor area as the Region of interest (ROI) with the other healthy part (Non-ROI) in the MRI. In the computational statistics, a method used in image segmentation is cluster analysis. Model-Based Clustering with Gaussian Mixture Model (GMM) is often used to find the cluster where the tumor is placed. The EM Algorithm and Bayesian coupled with Markov chain Monte Carlo (MCMC) could be used to optimize the GMM. However, both EM and Bayesian MCMC are assumed that the number of clusters is fixed. Therefore, to select the optimum number of clusters, we have to use certain cluster selection criteria. This process makes the segmentation quite complicated and is not automatic. This study tries to employ the GMM using Reversible Jump Markov Chain Monte Carlo Algorithm (GMM-RJMCMC) to segment the MRI-based brain tumor and compare it with the GMM-MCMC. The use of RJMCMC is expected to accelerate the calculation process, which can provide the number of optimum clusters automatically; moreover, the MRI image segmentation could become more adaptive. The result shows that from the Correct Classification Ratio (CCR), the GMM-RJMCMC could provide an equal segmentation results compared to the GMM-MCMC, however, GMM-RJMCMC has the advantage, that is faster in executing the algorithm, this makes GMM-RJMCMC more efficient in finding the optimum number of clusters.

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Mixture model for image segmentation using Gaussian, Student’s t, and Laplacian distribution with spatial dependence

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AIP Conference Proceedings 2019年第1期2194卷

作者： Nur Iriawan Anindya Apriliyanti Pravitasari Irhamah Kartika Fithriasari Santi Wulan Purnami Widiana Ferriastuti 1Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember 60111 Surabaya Indonesia 2Department of Statistics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran 45363 Bandung Indonesia 3Department of Radiology Faculty of Medicine Universitas Airlangga 60115 Surabaya Indonesia

MRI grayscale image data pattern, in reality, is not always symmetrical. Sometimes, it has a skewed pattern, leptokurtic, mesokurtic, platykurtic and even fat-tail in distribution. The Gaussian approach is not always able to explain this kind of data pattern. Therefore, other approaches to employ other distributions are used to overcome this problem. This study tries to compare the Gaussian, Student’s t, and Laplacian mixture model in the modeling of MRI brain tumor image segmentation. In addition, we used the Markov Random Field as the prior to the spatial dependence. The cluster validation is done by calculating the Silhouette Index (SI) and Misclassification Ratio (MCR). The results demonstrate that for the skewed and leptokurtic data pattern, the Laplacian mixture model shows the best representation, while the Student’s t mixture model has a great performance in fat-tail data pattern and also more robust against noise.

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Bayesian spatially constrained Fernandez-Steel Skew Normal Mixture model for MRI-based brain tumor segmentation

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AIP Conference Proceedings 2019年第1期2194卷

作者： Anindya Apriliyanti Pravitasari Nur Indah Nirmalasari Nur Iriawan Irhamah Kartika Fithriasari Santi Wulan Purnami Widiana Ferriastuti 1Department of Statistics Faculty of Mathematics and Natural Sciences Universitas Padjajaran 45363 Bandung Indonesia 2Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember 60111 Surabaya Indonesia 3Department of Radiology Faculty of Medicine Universitas Airlangga 60115 Surabaya Indonesia

Brain scanning using Magnetic Resonance Imaging (MRI) can be used to detect the brain tumor. MRI could detect the soft tissue abnormalities better than the other radiological devices. However, the noise in the image of the MRI sometimes appears randomly, so that it is difficult to detect the tumor more precisely. The image segmentation, therefore, is needed to be able to diagnose the location of the brain tumor by separating the tumor as the Region of Interest (ROI) from other regions. Gaussian Mixture Model (GMM) is commonly used for image segmentation. This method, however, frequently provides a poor result since it is less able to explain the skew pattern of MRI data. Moreover, the GMM is not considering the spatial dependencies between pixel, therefore it is less capable of handling noise. This study tries to employ the Fernandez Steel Skew Normal (FSSN) distribution as the replacement of the Gaussian in the GMM. The FSSN distribution could accommodate symmetrical and even asymmetrical pattern of the MRI data adaptively. In order to increase the noise robustness, the spatial dependencies with Markov Random Field (MRF) are used as a prior in Bayesian Markov chain Monte Carlo. The proposed model is called as the Spatially Constrained FSSN mixture model (Sc-FSSNMM). The results show that by applying the Sc- FSSNMM, the segmentation result is better than the GMM. In additions, the Sc-FSSNMM is more robust to noise while also more parsimony.

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Fractional kalman filter to estimate the concentration of air pollution

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Journal of Physics: Conference Series 2018年第1期1008卷

作者： Yessy Vita Oktaviana Erna Apriliani Didik Khusnul Arif Department of Mathematics Faculty of MathematicsComputing and Data Science Sepuluh Nopember Institute of Technology Indonesia

Air pollution problem gives important effect in quality environment and quality of human's life. Air pollution can be caused by nature sources or human activities. Pollutant for example Ozone, a harmful gas formed by NOx and volatile organic compounds (VOCs) emitted from various sources. The air pollution problem can be modeled by TAPM-CTM (The Air Pollution Model with Chemical Transport Model). The model shows concentration of pollutant in the air. Therefore, it is important to estimate concentration of air pollutant. Estimation method can be used for forecast pollutant concentration in future and keep stability of air quality. In this research, an algorithm is developed, based on Fractional Kalman Filter to solve the model of air pollution's problem. The model will be discretized first and then it will be estimated by the method. The result shows that estimation of Fractional Kalman Filter has better accuracy than estimation of Kalman Filter. The accuracy was tested by applying RMSE (Root Mean Square Error).

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

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International Conference on Applied Mathematics & Computer science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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GSTARIX Model for Forecasting Spatio-Temporal data with Trend, Seasonal and Intervention

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Journal of Physics: Conference Series 2018年第1期1097卷

作者： M A Novianto Suhartono D D Prastyo A Suharsono Setiawan Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Generalized Space-Time Autoregressive (GSTAR) is a statistics model that usually applied for forecasting data that have both spatial and temporal dependency. The monthly tourist arrival data in some locations are example of spatio-temporal data. Most of previous researches in GSTAR model only focused on stationary data. Otherwise, tourist arrival data in Indonesia mostly contain trend, seasonal, and some extreme values caused by interventions or outliers. The objective of this study is to apply and develop GSTAR model for forecasting spatio-temporal data with trend, seasonal, and interventions or outliers. This model is then known as GSTAR with exogeneous variables or GSTARIX model. Then, the forecast accuracy of GSTARIX model are compared to VAR with exogenous variables or VARIX model. Monthly data about number of tourist arrivals to Jakarta, Surakarta, Surabaya, and Denpasar are used as case study. Moreover, two methods are used for parameter estimation, i.e. Ordinary Least Square (OLS) and Generalized Least Square (GLS). The criteria for selecting the best model is Root Mean Square Error (RMSE). The results showed that the best model for forecasting tourist arrivals in each location are different. The best model for forecasting number of tourist arrivals to Jakarta and Surabaya is GSTARIX with OLS method or GSTARIX-OLS. Whereas, the best model for Denpasar and Surakarta data are VARIX and GSTARIX-GLS, respectively.

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An Energy-Efficient VM Consolidation Algorithm for Cloud data Centers

An Energy-Efficient VM Consolidation Algorithm for Cloud Dat...

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ISCC 2017-the 5th International Conference on Information science and Cloud computing

作者： Xiaodong Wu Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing

Cloud data centers consume vast amounts of electrical energy all over the world. Energy conservation in data centers has become a severe problem. This paper presents an energyefficient virtual machine（VM） consolidation algorithm for cloud data centers. The proposed policy exploits the average utilization of each VM to estimate the expected utilization of physical hosts. Then, the expected utilization, the actual utilization and the static threshold are combined together to determine whether a host is at the status of overloading or not. Some VM（s） on the overloading hosts are then selected for migration to avoid Service Level Agreements（SLA） violations. The results of the experiment show that the proposed algorithm can effectively reduce energy consumption while guarantee SLAs.

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Searching for GRBs at VHE with MAGIC: The status before CTA

arXiv

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arXiv 2019年

作者： Berti, Alessio Antonelli, Lucio Angelo Bošnjak, Zeljka Cortina, Juan Covino, Stefano D’Elia, Valerio do Souto Espiñeira, Elia Fukami, Satoshi Inoue, Susumu Longo, Francesco Miceli, Davide Moretti, Elena Nava, Lara Noda, Koji Nozaki, Seya Peresano, Michele Suda, Yusuke Will, Martin University of Torino INFN Torino Torino Italy INAF National Institute for Astrophysics Rome Italy Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia CIEMAT Madrid Spain INAF Osservatorio Astronomico di Brera Merate Italy Space Science Data Center - Agenzia Spaziale Italiana Rome Italy Spain ICRR University of Tokyo Kashiwa Chiba Japan RIKEN Wako Saitama Japan University of Trieste INFN Trieste Trieste Italy University of Udine INFN Trieste Udine Italy Department of Physics Kyoto University Kyoto Japan Max-Planck-Institut für Physik Munich Germany

Gamma-Ray Bursts (GRBs) are one of the main targets for current and next generation Imaging Atmospheric Cherenkov Telescopes (IACTs). Given their transient behavior, especially in the case of their prompt emission phase, performing fast follow-up observations is challenging for IACTs, which have a narrow field of view and limited duty cycle. Despite this, MAGIC plays a major role in the search for Very High Energy (VHE, E>100 GeV) gamma-ray emission from GRBs: this is possible thanks to its fast repositioning speed, low energy threshold and high sensitivity at the lowest energies. In 2013 the MAGIC GRB automatic procedure was upgraded, increasing the number of GRBs followed in the prompt and early afterglow phases and decreasing dramatically hardware failures during fast repositioning. Currently, only GRB 190114C was firmly detected in the VHE band, while for other GRBs no significant detection was achieved. In such a case, upper limits (ULs) can give insight into the physics driving such eluding sources, especially on their emission mechanisms. In this contribution we report on the status of the GRB follow-up with MAGIC and focus on the ULs and results obtained from a sample of GRBs observed between 2013 and 2018. This GRB catalog is the result of the MAGIC well-designed and tested follow-up procedure, and it serves as a precursor of GRBs observation with the next generation IACT system, the Cherenkov Telescope Array (CTA). Copyright © 2019, The Authors. All rights reserved.

关键词： Gamma rays

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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