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IEEE International Conference on image processing

作者： Ahmed Gawish Paul Fieguth Vision and Image Processing (VIP) Lab Department of Systems Design Engineering University of Waterloo

ISBN: (纸本)9781479983407

A limitation of active contours models (both parametric and geometric) is their sensitivity to noise. Many solutions to noise sensitivity have been proposed in the literature, with the current state-of-the-art based on image blurring and multiresolution processing. However a significant drawback of both approaches is the side effect of edge delocalization. In this paper, gradient information extracted from all resolutions of the undecimated wavelet transform is used to build the external force map for the active contour. The new map accurately drives the active contour and improves edge localization. The proposed method builds on both Gradient Vector Flow and Vector Field Convolution active contours. Comparisons to classical and state-of-the-art methods show a dramatic improvement in active contour convergence for all levels of noise.

关键词： Active contours Undecimated wavelet transform Active contours contour image blur Noise sensitivity Side Effect wavelet transforms Gradient vector Noise Parametric

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IMPROVED FINE STRUCTURE MODELING VIA GUIDED STOCHASTIC CLIQUE FORMATION IN FULLY CONNECTED CONDITIONAL RANDOM FIELDS

IMPROVED FINE STRUCTURE MODELING VIA GUIDED STOCHASTIC CLIQU...

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IEEE International Conference on image processing

作者： M. J. Shafiee A. G. Chung A. Wong P. Fieguth Vision & Image Processing Lab Systems Design Engineering Dept. University of Waterloo

ISBN: (纸本)9781479983407

Markov random fields (MRFs) and conditional random fields (CRFs) are influential tools in image modeling, particularly for applications such as image segmentation. Local MRFs and CRFs utilize local nodal interactions when modeling, leading to excessive smoothness on boundaries (i.e., the short-boundary bias problem). Recently, the concept of fully connected conditional random fields with stochastic cliques (SFCRF) was proposed to enable long-range nodal interactions while addressing the computational complexity associated with fully connected random fields. While SFCRF was shown to provide significant improvements in segmentation accuracy, there were still limitations with the preservation of fine structure boundaries. To address these limitations, we propose a new approach to stochastic clique formation for fully connected random fields (G-SFCRF) that is guided by the structural characteristics of different nodes within the random field. In particular, fine structures surrounding a node are modeled statistically by probability distributions, and stochastic cliques are formed by considering the statistical similarities between nodes within the random fields. Experimental results show that G-SFCRF outperforms existing fully connected CRF frameworks, SFCRF, and the principled deep random field framework for image segmentation.

关键词： Stochastic Cliques SFCRF Guided Stochastic Cliques CRF Segmentation Fully connected Corticotropin-Releasing Hormone segmentation Segmentation image segmentation fine structure Modeling Random Markov random fields all-pass Ganglia Probability distribution Structural properties complexity classes

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HIGH DYNAMIC RANGE MAP ESTIMATION VIA FULLY CONNECTED RANDOM FIELDS WITH STOCHASTIC CLIQUES

HIGH DYNAMIC RANGE MAP ESTIMATION VIA FULLY CONNECTED RANDOM...

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IEEE International Conference on image processing

作者： F. Y. Li M. J. Shafiee A. G. Chung B. Chwyl F. Kazemzadeh A. Wong J. Zelek Vision & Image Processing Lab Systems Design Engineering Dept. University of Waterloo

ISBN: (纸本)9781479983407

The reconstruction of high dynamic range (HDR) images via conventional camera systems and low dynamic range (LDR) images is a growing field of research in image acquisition. The radiance map associated with the HDR image of a scene is typically computed using multiple images of the same scene captured at different exposures (i.e., bracketed LDR imzages). This approach, though inexpensive, is sensitive to noise under high camera ISO. Each bracketed image is associated with a different level of noise due to the change in exposure time, and the noise is further amplified when tone-mapping the HDR image for display. A new framework is proposed to address the associated noise in the context of random fields. The estimation of the HDR image from a set of LDR images is formulated as a stochastically fully connected conditional random field where the spatial information is incorporated to compute the HDR value in combination with the LDR image values. Experimental results show that the proposed framework compensated the non-stationary ISO noise while preserving the boundaries in the estimated HDR images.

关键词： High Dynamic Range Imaging Conditional Random Fields image Denoising HDR Reconstruction SFCRF image denoising hot dry rocks GATA3 gene LAND DISPOSAL RESTRICTIONS Noise images HIGH DYNAMIC RANGE Markov random fields field surveys Noise levels Brackets Cameras

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TIGER: A TEXTURE-ILLUMINATION GUIDED ENERGY RESPONSE MODEL FOR ILLUMINATION ROBUST LOCAL SALIENCY

TIGER: A TEXTURE-ILLUMINATION GUIDED ENERGY RESPONSE MODEL F...

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IEEE International Conference on image processing

作者： B. Chwyl A. G. Chung F. Y. Li A. Wong D. A. Clausi Vision & Image Processing Lab Systems Design Engineering Dept. University of Waterloo

ISBN: (纸本)9781479983407

Local saliency models are a cornerstone in image processing and computer vision, used in a wide variety of applications ranging from keypoint detection and feature extraction, to image matching and image representation. However, current models exhibit difficulties in achieving consistent results under varying, non-ideal illumination conditions. In this paper, a novel texture-illumination guided energy response (TIGER) model for illumination robust local saliency is proposed. In the TIGER model, local saliency is quantified by a modified Hessian energy response guided by a weighted aggregate of texture and illumination aspects from the image. A stochastic Bayesian disassociation approach via Monte Carlo sampling is employed to decompose the image into its texture and illumination aspects for the saliency computation. Experimental results demonstrate that higher correlation between local saliency maps constructed from the same scene under different illumination conditions can be achieved using the TIGER model when compared to common local saliency approach, i.e., Laplacian of Gaussian, Difference of Gaussians, and Hessian saliency models.

关键词： Local saliency model Illumination robust Local features Bayesian estimation Stochastic Texture Illumination illumination Lighting Stochastic Bayes methods textures local image feature texture images Gauss Energy responses image representation image matching

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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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Mobile's millimeter-wave makeover: Wireless engineers long considered high frequencies worthless for cellular systems. They couldn't be more wrong

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IEEE Spectrum 2014年第9期51卷 34-58页

作者： Rappaport, Theodore Roh, Wonil Cheun, Kyungwhoon Vision and Image Processing Research Group Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

People in real estate joke that there are only three things that matter in the business of buying and selling property: location, location, location. The same could be said for radio spectrum. The frequencies used for cellular communications have acquired the status of waterfront lots-highly coveted and woefully scarce. And like beach-home buyers in a bidding war, mobile operators must constantly jockey for these prime parcels, sometimes shelling out as much as tens of billions of dollars for just a small sliver of the electromagnetic pie. © 1964-2012 IEEE.

关键词： Location

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Body appreciation around the world: Measurement invariance of the Body Appreciation Scale-2 (BAS-2) across 65 nations, 40 languages, gender identities, and age

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Body image 2023年 46卷 449-466页

作者： Viren Swami Ulrich S Tran Stefan Stieger Toivo Aavik Hamed Abdollahpour Ranjbar Sulaiman Olanrewaju Adebayo Reza Afhami Oli Ahmed Annie Aimé Marwan Akel Hussam Al Halbusi George Alexias Khawla F Ali Nursel Alp-Dal Anas B Alsalhani Sara Álvares-Solas Ana Carolina Soares Amaral Sonny Andrianto Trefor Aspden Marios Argyrides John Jamir Benzon R Aruta Stephen Atkin Olusola Ayandele Migle Baceviciene Radvan Bahbouh Andrea Ballesio David Barron Ashleigh Bellard Sóley Sesselja Bender Kerime Derya Beydağ Gorana Birovljević Marie-Ève Blackburn Teresita Borja-Alvarez Joanna Borowiec Miroslava Bozogáňová Solfrid Bratland-Sanda Matthew H E M Browning Anna Brytek-Matera Marina Burakova Yeliz Çakır-Koçak Pablo Camacho Vittorio Emanuele Camilleri Valentina Cazzato Silvia Cerea Apitchaya Chaiwutikornwanich Trawin Chaleeraktrakoon Tim Chambers Qing-Wei Chen Xin Chen Chin-Lung Chien Phatthanakit Chobthamkit Bovornpot Choompunuch Emilio J Compte Jennifer Corrigan Getrude Cosmas Richard G Cowden Kamila Czepczor-Bernat Marcin Czub Wanderson Roberto da Silva Mahboubeh Dadfar Simon E Dalley Lionel Dany Jesus Alfonso D Datu Pedro Henrique Berbert de Carvalho Gabriel Lins de Holanda Coelho Avila Odia S De Jesus Sonia Harzallah Debbabi Sandesh Dhakal Francesca Di Bernardo Donka D Dimitrova Jacinthe Dion Barnaby Dixson Stacey M Donofrio Marius Drysch Hongfei Du Angel M Dzhambov Claire El-Jor Violeta Enea Mehmet Eskin Farinaz Farbod Lorleen Farrugia Leonie Fian Maryanne L Fisher Michał Folwarczny David A Frederick Matthew Fuller-Tyszkiewicz Adrian Furnham Antonio Alías García Shulamit Geller Marta Ghisi Alireza Ghorbani Maria Angeles Gomez Martinez Sarah Gradidge Sylvie Graf Caterina Grano Gyöngyvér Gyene Souheil Hallit Motasem Hamdan Jonathan E Handelzalts Paul H P Hanel Steven R Hawks Issa Hekmati Mai Helmy Tetiana Hill Farah Hina Geraldine Holenweger Martina Hřebíčková Olasupo Augustine Ijabadeniyi Asma Imam Başak İnce Natalia Irrazabal Rasa Jankauskiene Ding-Yu Jiang Micaela Jiménez-Borja Verónica Jiménez-Borja Evan M Johnson Veljko Jov School of Psychology and Sport Science Anglia Ruskin University Cambridge United Kingdom Centre for Psychological Medicine Perdana University Kuala Lumpur Malaysia. Electronic address: viren.swami@aru.ac.uk. Department of Cognition Emotion and Methods in Psychology Faculty of Psychology University of Vienna Vienna Austria. Department of Psychology and Psychodynamics Karl Landsteiner University of Health Sciences Krems an der Donau Austria. Institute of Psychology University of Tartu Tartu Estonia. Department of Psychology Koç University Istanbul Turkiye. Department of Psychology and Behavioural Studies Ekiti State University Ado-Ekiti Nigeria. Department of Art Studies Tarbiat Modares University Tehran Iran. Department of Psychology University of Chittagong Chattogram Bangladesh. Department of Psychoeducation and Psychology Université du Québec en Outaouais Saint-Jérôme Canada. INSPECT-LB: National Institute of Public Health Clinical Epidemiology and Toxicology Beirut Lebanon School of Pharmacy Lebanese International University Beirut Lebanon. Department of Management Ahmed Bin Mohammad Military College Doha Qatar. Faculty of Psychology Panteion University of Social and Political Sciences Athens Greece. Royal College of Surgeons Ireland-Bahrain Adliya Bahrain. Department of Midwifery Faculty of Health Sciences Munzur University Tunceli Turkiya. Department of Oral Medicine and Diagnostic Sciences Vision College of Dentistry and Nursing Vision Colleges Riyadh Saudi Arabia. Facultad de Ciencias de la Vida Universidad Regional Amazónica Ikiam Muyuna Ecuador. Federal Institute of Education Science and Technology of Southeast Minas Gerais Barbacena Brazil. Department of Psychology Universitas Islam Indonesia Yogyakarta Indonesia. Department of Psychology Aberystwyth University Ceredigion United Kingdom. Department of Psychology Neapolis University Pafos Paphos Cyprus. Department of Psychology De La Salle University Phillippines. Department of Gen

The Body Appreciation Scale-2 (BAS-2) is a widely used measure of a core facet of the positive body image construct. However, extant research concerning measurement invariance of the BAS-2 across a large number of nations remains limited. Here, we utilised the Body image in Nature (BINS) dataset - with data collected between 2020 and 2022 - to assess measurement invariance of the BAS-2 across 65 nations, 40 languages, gender identities, and age groups. Multi-group confirmatory factor analysis indicated that full scalar invariance was upheld across all nations, languages, gender identities, and age groups, suggesting that the unidimensional BAS-2 model has widespread applicability. There were large differences across nations and languages in latent body appreciation, while differences across gender identities and age groups were negligible-to-small. Additionally, greater body appreciation was significantly associated with higher life satisfaction, being single (versus being married or in a committed relationship), and greater rurality (versus urbanicity). Across a subset of nations where nation-level data were available, greater body appreciation was also significantly associated with greater cultural distance from the United States and greater relative income inequality. These findings suggest that the BAS-2 likely captures a near-universal conceptualisation of the body appreciation construct, which should facilitate further cross-cultural research.

关键词： Body appreciation Body appreciation Scale-2 (BAS-2) Cross-cultural Measurement invariance Multi-group confirmatory factor analysis (MG-CFA) Psychometrics Structural analysis

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Efficient Bayesian inference using fully connected conditional random fields with stochastic cliques

Efficient Bayesian inference using fully connected condition...

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作者： Shafiee, M.J. Wong, A. Siva, P. Fieguth, P. Vision and Image Processing Lab System Design Engineering Dept. University of Waterloo Canada

ISBN: (纸本)9781479957514

Conditional random fields (CRFs) are one of the most powerful frameworks in image modeling. However practical CRFs typically have edges only between nearby nodes;using more interactions and expressive relations among nodes make these methods impractical for large-scale applications, due to the high computational complexity. Recent work has shown that fully connected CRFs can be tractable by defining specific potential functions. In this paper, we present a novel framework to tackle the computational complexity of a fully connected graph without requiring specific potential functions. Instead, inspired by random graph theory and sampling methods, we propose a new clique structure called stochastic cliques. The stochastically fully connected CRF (SFCRF) is a marriage between random graphs and random fields, benefiting from the advantages of fully connected graphs while maintaining computational tractability. The effectiveness of SFCRF was examined by binary image labeling of highly noisy images. The results show that the proposed framework outperforms an adjacency CRF and a CRF with a large neighborhood size. © 2014 IEEE.

关键词： Computational complexity

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Semi-automatic Fisher-Tippett Guided Active Contour for Lumbar Multifidus Muscle Segmentation

Semi-automatic Fisher-Tippett Guided Active Contour for Lumb...

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

作者： Dorothy Lui Christian Scharfenberger Diana E. De Carvalho Jack P. Callaghan Alexander Wong Faculty of Systems Design Engineering Vision and Image Processing Lab University of Waterloo Waterloo Canada The Department of Kinesiology University of Waterloo Waterloo Canada

Rehabilitative Ultrasound Imaging or diagnostic ultrasound is used to measure geometric properties of the lumbar multifidus muscle to infer muscle strength or degeneration for back pain therapy. For this purpose, a novel semi-automatic approach (FTS: Fisher-Tippett Segmentation) based upon the Decoupled Active Contour is proposed to reliably and quickly segment the lumbar multifidus muscle in diagnostic ultrasound. To overcome speckle or hardly visible region boundaries in ultrasound images, we first propose a novel external energy functional to explicitly consider the underlying Fisher-Tippett distribution of ultrasound data. We then introduce a user-guided Hidden Markov Model trellis formation for improved segmentation of weakly-defined regions. Extensive experiments have shown that our approach not only improves the segmentation performance when compared to existing methods, but also does not rely on sub-specialized knowledge for segmentation.

关键词： Muscles Ultrasonic imaging image segmentation Hidden Markov models Back Medical treatment Active contours

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UNDECIMATED HIERARCHICAL ACTIVE CONTOURS FOR OCT image SEGMENTATION

UNDECIMATED HIERARCHICAL ACTIVE CONTOURS FOR OCT IMAGE SEGME...

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IEEE International Conference on image processing

作者： Ahmed Gawish Paul Fieguth Sebastian Marschall Kostadinka Bizheva Vision and Image Processing (VIP) Lab Dept. of Systems Design Engineering University of Waterloo Dept. of Physics and Astronomy University of Waterloo

ISBN: (纸本)9781479957521

A limitation of Optical Coherence Tomography (OCT) image segmentation is the poor signal-to-noise ratio of the imaging process, particularly because images are sampled quickly, at high resolutions, and in-vivo. Furthermore, speckle noise is generated by the reflections of the OCT LASER. Because OCT is widely used in imaging the cornea, retina, and skin, OCT layer segmentation is of key interest in all applications. In this paper, a multi-resolution parametric active contour is used for OCT segmentation. The proposed method uses an undecimated wavelet transform to obtain scale-dependent noise reduction, while the active contour is initialized with a generalized Hough transform. Experimental results show that the proposed method outperforms classical as well as state-of-the-art methods and segments OCT images with high level of accuracy.

关键词： Undecimated wavelet transform Optical coherence tomography Active contours Hough transform

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