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Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

BIOINFORMATICS 2017年第7期33卷 1064-1072页

作者： Teodoro, George Kurc, Tahsin M. Taveira, Luis F. R. Melo, Alba C. M. A. Gao, Yi Kong, Jun Saltz, Joel H. Univ Brasilia Dept Comp Sci BR-70910900 Brasilia DF Brazil Emory Univ Biomed Informat Dept Atlanta GA 30322 USA SUNY Stony Brook Biomed Informat Dept Stony Brook NY 11794 USA Oak Ridge Natl Lab Sci Data Grp Oak Ridge TN USA

Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential;(ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42 x compared to the results from the default parameters;(iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines.

关键词： SEGMENTATION (Image processing) SENSITIVITY analysis SCALABILITY (systems engineering) FEASIBILITY studies IMAGE analysis algorithms

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Parallel processing over Spatial-Temporal Datasets from Geo, Bio, Climate and Social Science Communities: A Research Roadmap 6

Parallel Processing over Spatial-Temporal Datasets from Geo,...

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IEEE 6th International Congress on Big Data (BigData Congress)

作者： Prasad, Sushil K. Aghajarian, Danial McDermott, Michael Shah, Dhara Mokbel, Mohamed Puri, Satish Rey, Sergio J. Shekhar, Shashi Xe, Yiqun Vatsavai, Ranga Raju Wang, Fusheng Liang, Yanhui Vo, Hoang Wang, Shaowen Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Minnesota Comp Sci & Engn Minneapolis MN 55455 USA Marquette Univ Math Stat & Comp Sci Milwaukee WI 53233 USA Univ Calif Riverside Ctr Spatial Sci Riverside CA 92521 USA Univ Minnesota Dept Comp Sci Minneapolis MN 55455 USA North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA SUNY Stony Brook Biomed Informat Dept Stony Brook NY 11794 USA Univ Illinois Geog & Geog Informat Sci Urbana IL USA

ISBN: (纸本)9781538619964

This vision paper reviews the current state-of art and lays out emerging research challenges in parallel processing of spatial-temporal large datasets relevant to a variety of scientific communities. The spatio-temporal data, whether captured through remote sensors (global earth observations), ground and ocean sensors (e.g., soil moisture sensors, buoys), social media and hand-held, traffic-related sensors and cameras, medical imaging (e.g., MRI), or large scale simulations (e.g., climate) have always been "big." A common thread among all these big collections of datasets is that they are spatial and temporal. processing and analyzing these datasets requires high-performance computing (HPC) infrastructures. Various agencies, scientific communities and increasingly the society at large rely on spatial data management, analysis, and spatial data mining to gain insights and produce actionable plans. Therefore, an ecosystem of integrated and reliable software infrastructure is required for spatial temporal big data management and analysis that will serve as crucial tools for solving a wide set of research problems from different scientific and engineering areas and to empower users with next-generation tools. This vision requires a multidisciplinary effort to significantly advance domain research and have a broad impact on the society. The areas of research discussed in this paper include (i) spatial data mining, (ii) data analytics over remote sensing data, (iii) processing medical images, (iv) spatial econometrics analyses, (v) Map-Reduce based systems for spatial computation and visualization, (vi) CyberGIS systems, and (vii) foundational parallel algorithms and data structures for polygonal datasets, and why HPC infrastructures, including harnessing graphics accelerators, are needed for time-critical applications.

关键词： High performance computing Spatial data mining Remote sensing data Medical images Spatial econometrics Map-reduce systems CyberGIS Parallel algorithms and data structures

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Black Phosphorus Avalanche Photodetector 75

Black Phosphorus Avalanche Photodetector

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75th Annual Device Research Conference (DRC)

作者： Atalla, Mahmoud R. M. Koester, Steven J. Univ Minnesota Twin Cities ECE Dept 200 Union St SE Minneapolis MN 55455 USA

ISBN: (纸本)9781509063284

The strong light-matter interaction of atomically-thin layered black phosphorus (BP) has attracted great attention recently. Exfoliated few-layer and monolayer BP flakes have a layer-tunable direct bandgap that varies from 2.0 eV for monolayer to 0.3 eV for bulk, which makes BP a very promising optoelectronic material in the visible and telecom wavelengths [1-3]. The high mobility of BP devices suggested its utilization as an active material of an avalanche photodetector (APD). Recently, APDs utilizing two-dimensional (2D) materials such as transition-metal dichalcogenides (e.g. MoS₂) and layered iii-VI semiconductors (e.g. InSe) have been demonstrated [4,5]. Both of these 2D materials possess wide bandgaps that discourage their usage in the infrared [6,7]. The successful demonstration of waveguide-integrated BP photodetector working at 1.55 μm wavelength [1] has stimulated the pursuit of high-performance BP APDs. In this work, we report on the first 2D material-based BP APD featuring high external quantum efficiency (EQE) and avalanche gain. These results provide strong evidence that BP is a promising material for high-performance optical communication and imaging systems.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Hand detection using facial information

Hand detection using facial information

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IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Sangpil Han Seokmok Park Min-jae Kim Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science and Film Chung-Ang University Seoul Korea

This paper presents a robust hand detection algorithm using the facial information. The proposed algorithm consists of four steps: (i) detection of a face, (ii) generation of regions of interest (ROI) to detect hands, (iii) skin color extraction from the face region, and (iv) detection of hands using the face skin color in the ROI. The proposed algorithm can reduce false detection caused by a similar skin color, and provides a successful detection rate up to 92 percent.

关键词： Face Skin Color Detection algorithms Image color analysis Robustness Face detection

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RASTER DATA PARTITIONING FOR SUPPORTING DISTRIBUTED GIS processing

RASTER DATA PARTITIONING FOR SUPPORTING DISTRIBUTED GIS PROC...

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ISPRS Geospatial Week

作者： Binh Nguyen Thai Olasz, Angela Eotvos Lorand Univ Dept Cartog & Geoinformat 1-A Pazmany Peter Setany H-1117 Budapest Hungary Inst Geodesy Cartog & Remote Sensing F OMI Dept Geoinformat H-1149 Budapest Hungary

ISBN: (纸本)9781629935201

In the geospatial sector big data concept also has already impact. Several studies facing originally computer science techniques applied in GIS processing of huge amount of geospatial data. In other research studies geospatial data is considered as it were always been big data (Lee and Kang, 2015). Nevertheless, we can prove data acquisition methods have been improved substantially not only the amount, but the resolution of raw data in spectral, spatial and temporal aspects as well. A significant portion of big data is geospatial data, and the size of such data is growing rapidly at least by 20% every year (Dasgupta, 2013). The produced increasing volume of raw data, in different format, representation and purpose the wealth of information derived from this data sets represents only valuable results. However, the computing capability and processing speed rather tackle with limitations, even if semi-automatic or automatic procedures are aimed on complex geospatial data (Kristof et al., 2014). In late times, distributed computing has reached many interdisciplinary areas of computer science inclusive of remote sensing and geographic information processing approaches. Cloud computing even more requires appropriate processing algorithms to be distributed and handle geospatial big data. Map-Reduce programming model and distributed file systems have proven their capabilities to process non GIS big data. But sometimes it's inconvenient or inefficient to rewrite existing algorithms to Map-Reduce programming model, also GIS data can not be partitioned as text-based data by line or by bytes. Hence, we would like to find an alternative solution for data partitioning, data distribution and execution of existing algorithms without rewriting or with only minor modifications. This paper focuses on technical overview of currently available distributed computing environments, as well as GIS data (raster data) partitioning, distribution and distributed processing of GIS algorithms. A

关键词： GIS GIS data decompositioning Distributed processing Geospatial data association Linked dataset

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Review Paper: An outlook on the future of seismic imaging, Part iii: Joint Migration Inversion

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GEOPHYSICAL PROSPECTING 2014年第5期62卷 950-971页

作者： Berkhout, A. J. (Guus) Delft Univ Technol CiTG NL-2628 CN Delft Netherlands

The next generation seismic imaging algorithms will consider multiple scattering as indispensable information, referred to as Full Wavefield Migration. In addition, these algorithms will also include autonomous velocity updating in the migration process, referred to as Joint Migration Inversion. Full wavefield migration and joint migration inversion address the industrial needs of improving images of very complex reservoirs as well as the industry ambition of producing these images in a more automatic manner ('automation in seismic processing'). In this vision paper on seismic imaging, full wavefield migration and joint migration inversion are formulated in terms of a closed-loop, estimation algorithm that can be physically explained by an iterative double focusing process (full wavefield Common-Focus-Point technology). A critical module in this formulation is forward modelling, allowing feedback from migrated output to unmigrated input ('closing the loop'). For this purpose a full wavefield modelling module has been developed that utilizes an operator description of complex geology. Full wavefield modelling module is pre-eminently suited to function in the feedback path of a closed-loop migration algorithm. 'The Future of Seismic imaging' is presented as a coherent trilogy, proposing in three consecutive parts the migration framework of the future. In part I it was shown that the proposed full wavefield modelling module algorithm differs fundamentally from finite difference modelling, as velocities and densities need not be provided. Instead, full wavefield modelling module uses an operator description of the subsurface. In Part II it was shown how the theory of Primary Wavefield Migration can be extended to Full Wavefield Migration by correcting for elastic transmission effects and by utilizing multiple scattering. In Part iii it is shown how the full wavefield migration technology can be extended to Joint Migration Inversion, allowing full wavefield migration of

关键词： imaging systems in seismology STRATIGRAPHIC geology FEEDBACK control systems ITERATIVE methods (Mathematics) NUMERICAL analysis

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Accelerating adaptive ultrasound imaging algorithms by means of general-purpose computing on graphics processing units

Accelerating adaptive ultrasound imaging algorithms by means...

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作者： Jon Petter Helgesen Asen Norwegian University of Science and Technology

学位级别：博士

A rapid development in computer game technology and accompanying programming languages have recently provided researchers with small personal supercomputers, comprised in a single graphics processing unit (GPU). This immense rise in computational capabilities and improved programmability are currently changing how ultrasound imaging systems are designed. When researchers are exploring new algorithms for ultrasound imaging, it is therefore important to keep the architecture of parallel accelerators like the GPU in mind. If a new complex algorithm is supposed to run in real time, it needs to fit the programmable and parallel pipeline of modern ultrasound scanners. The aim of this study has been to investigate the possibility of utilizing GPUs for advanced processing in an ultrasound imaging system. Among the investigated problems are both adaptive beamforming, adaptive visualization of ultrasound volumes, and ultrasound simulations. The presented problems have in common that they require parallel programming in order to reach real-time processing. In the first part of the thesis, the Capon adaptive beamformer is investigated and implemented on a GPU for the application of real-time sonar (Paper I) and medical ultrasound imaging (Paper II). Real-time frame rates are achieved for both modalities. Paper II also presents, for the first time, videos where the Capon beamformer has been applied on loops of simulated and in vivo medical ultrasound images. In Paper iii, we show that Capon beamforming does not provide shift- invariant imaging in a real-time imaging setting. A method is then proposed that improves the shift-invariant property. Shift-invariant imaging is essential if the method is ever to be used in practice. In paper Paper IV we propose an adaptive method for visualization of volumetric cardiac ultrasound images. The method is capable of removing noise that by conventional methods would have occluded cardiac tissue. This work also shows that with modern GPUs it

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Target Recognition in Passive Terahertz Image of Human Body 3

Target Recognition in Passive Terahertz Image of Human Body

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Conference on Infrared, Millimeter-Wave, and Terahertz Technologies iii

作者： Zhao Ran Zhao Yuan-meng Deng Chao Zhang Cun-lin Li Yue Capital Normal Univ Beijing Key Lab Terahertz Spect & Imaging Key Lab Terahertz Optoelect Minist Educ Beijing 100048 Peoples R China

ISBN: (纸本)9781628413489

THz radiation can penetrate through many nonpolar dielectric materials and can be used for nondestructive/noninvasive sensing and imaging of targets under nonpolar, nonmetallic covers or containers. Thus using THz systems to "see through" concealing barriers (i.e. packaging, corrugated cardboard, clothing) has been proposed as a new security screening method. Objects that can be detected by THz include concealed weapons, explosives, and chemical agents under clothing. Passive THz imaging system can detect THz wave from human body without transmit any electromagnetic wave, and the suspicious objects will become visible because the THz wave is blocked by this items. We can find out whether or not someone is carrying dangerous objects through this image. In this paper, the THz image enhancement, segmentation and contour extraction algorithms were studied to achieve effective target image detection. First, the terahertz images are enhanced and their grayscales are stretched. Then we apply global threshold segmentation to extract the target, and finally the targets are marked on the image. Experimental results showed that the algorithm proposed in this paper can extract and mark targets effectively, so that people can identify suspicious objects under clothing quickly. The algorithm can significantly improve the usefulness of the terahertz security apparatus.

关键词： passive terahertz imaging image processing image enhancement contour extraction target detection

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Toward a super imaging system [Invited]

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APPLIED OPTICS 2013年第4期52卷 561-566页

作者： Mendlovic, David Tel Aviv Univ Fac Engn IL-69978 Tel Aviv Israel

This paper deals with the fast-developing area of computational photography where a combination of imaging techniques and efficient image processing algorithms is done to generate a super imaging system. In recent years, three main implementations of the computational photography philosophy were intensively investigated and demonstrated: (i) multiple aperture, (ii) light field photography, and (iii) multiexposure. The paper provides a mini-review of these three approaches and shows ways to improve and combine them toward a super imaging system. As a conclusion, the paper provides preliminary guidelines for fusing the three approaches into one integrated super imaging system with optimized performance. (C) 2013 Optical Society of America

关键词： Image processing Image processing algorithms Image quality imaging systems imaging techniques Optical imaging

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