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A Coupled Minimization Problem for Medical Image Segmentation with Priors

Yunmei Chen, Feng Huang, Hemant D. Tagare, Murali Rao
2006 International Journal of Computer Vision  
We present a coupled minimization problem for image segmentation using prior shape and intensity profile.  ...  By this coupling the segmentation arrives at higher image gradient, forms a shape similar to the prior, and captures the prior intensity profile.  ...  Edward A. Geiser, Division of Cardiology, Department of Medicine, University of Florida for providing the  ... 
doi:10.1007/s11263-006-8524-2 fatcat:3y47cqeykzdy7bzhbklernr6ie

Using prior shape and intensity profile in medical image segmentation

Yunmei Chen, Feng Huang, Tagare, Murali Rao, Wilson, Geiser
2003 Proceedings Ninth IEEE International Conference on Computer Vision  
In this note we present a coupled optimization model for boundary determination. One part of the model incorporates a prior shape into a geometric active contour model with a fixed parameter.  ...  The existence of a solution to the proposed minimization problem is also discussed.  ...  The solution of the first problem minimizes an energy functional, that consists of two terms depending on image gradient and a prior shape with respectively with a parameter balancing these two terms.  ... 
doi:10.1109/iccv.2003.1238474 dblp:conf/iccv/ChenHTRWG03 fatcat:upc23ozo6vdlhiltghluejcasu

(Hyper)-graphical models in biomedical image analysis

Nikos Paragios, Enzo Ferrante, Ben Glocker, Nikos Komodakis, Sarah Parisot, Evangelia I. Zacharaki
2016 Medical Image Analysis  
a variety of problems in biomedical image analysis.  ...  Hyper-Graph representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem.  ...  Pair-Wise Graphical Models in Biomedical Imaging Knowledge-based image segmentation is a fundamental problem in medical imaging.  ... 
doi:10.1016/j.media.2016.06.028 pmid:27377331 fatcat:f2jy24hynva2fgjfyvrcmfs6e4

Multi-object segmentation using coupled nonparametric shape and relative pose priors

Mustafa Gökhan Uzunbas, Octavian Soldea, Müjdat Çetin, Gözde Ünal, Aytül Erçil, Devrim Unay, Ahmet Ekin, Zeynep Firat, Charles A. Bouman, Eric L. Miller, Ilya Pollak
2009 Computational Imaging VII  
In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem.  ...  We present a new method for multi-object segmentation in a maximum a posteriori estimation framework.  ...  The authors formulate the segmentation problem as a MAP estimation problem, where they use a nonparametric shape prior.  ... 
doi:10.1117/12.815215 dblp:conf/cimaging/UzunbasSCUEUEF09 fatcat:ylrcpgoevrfprjr254z7772bba

Coupled Nonparametric Shape and Moment-Based Intershape Pose Priors for Multiple Basal Ganglia Structure Segmentation

Mustafa Gökhan Uzunbas, O Soldea, Devrim Ünay, Müjdat Çetin, Gözde Ünal, Aytül Erçil, A Ekin
2010 IEEE Transactions on Medical Imaging  
We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm.  ...  Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative  ...  Given these learned prior densities, we pose the segmentation problem as a maximum a posteriori estimation problem combining the prior densities with data.  ... 
doi:10.1109/tmi.2010.2053554 pmid:21118755 fatcat:ytmq7ad4qzel7epws2bggyjrim

Incorporating prior knowledge in medical image segmentation: a survey [article]

Masoud S. Nosrati, Ghassan Hamarneh
2016 arXiv   pre-print
Medical image segmentation, the task of partitioning an image into meaningful parts, is an important step toward automating medical image analysis and is at the crux of a variety of medical imaging applications  ...  We conclude the survey by discussing different aspects of designing an energy functional for image segmentation, open problems, and future perspectives.  ...  Nambakhsh et al. (2013) proposed an efficient method for left ventricle (LV) segmentation that iteratively minimizes a convex upper bound energy functional for a coupled surface.  ... 
arXiv:1607.01092v1 fatcat:cq6ihf475ngtnbrebnr73qsa2m

Coupled Shape Distribution-Based Segmentation of Multiple Objects [chapter]

Andrew Litvin, William C. Karl
2005 Lecture Notes in Computer Science  
We apply this methodology to problems in medical image segmentation.  ...  In this paper we develop a multi-object prior shape model for use in curve evolution-based image segmentation.  ...  Finally, we suggest the promise of this prior for challenging medical image segmentation tasks through an example.  ... 
doi:10.1007/11505730_29 fatcat:j2wqqzsaqjdsleatsauqzy5dbu

Volumetric segmentation of multiple basal ganglia structures using nonparametric coupled shape and inter-shape pose priors

Mustafa Gokhan Uzunbas, Octavian Soldea, Mujdat Cetin, Gozde Unal, Aytul Ercil, Devrim Unay, Ahmet Ekin, Zeynep Firat
2009 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm.  ...  Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative  ...  Given these learned prior densities, we pose the segmentation problem as a maximum a posteriori estimation problem combining the prior densities with data.  ... 
doi:10.1109/isbi.2009.5192975 dblp:conf/isbi/UzunbasSCUEUEF09 fatcat:uq3dhdwazngxjdjdoidm44e7my

Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models [chapter]

Mustafa Gökhan Uzunbaş, Chao Chen, Shaoting Zhang, Kilian M. Pohl, Kang Li, Dimitris Metaxas
2013 Lecture Notes in Computer Science  
It brings global and local deformation constraints into a unified framework for simultaneous segmentation of multiple objects in an image.  ...  Organ segmentation is a challenging problem on which significant progress has been made.  ...  Introduction Segmenting anatomical regions from medical images has been studied extensively and it is a critical process in many medical applications.  ... 
doi:10.1007/978-3-642-40763-5_20 fatcat:gzw5harb3nbndot5l3a2dt2mxq

Coupled nonparametric shape priors for segmentation of multiple basal ganglia structures

Gokhan Uzunbas, Mujdat Cetin, Gozde Unal, Aytul Ercil
2008 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel  ...  Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially aid in segmentation if properly  ...  Kutlay Karaman, radiologist at Anadolu Medical Centre, for very helpful conversations and for providing the MR data.  ... 
doi:10.1109/isbi.2008.4540971 dblp:conf/isbi/UzunbasCUE08 fatcat:4tw6cinwdrec5l7lgjqg2zo6ya

Automatic Multiorgan Segmentation Using Hierarchically Registered Probabilistic Atlases [chapter]

Razmig Kéchichian, Sébastien Valette, Michel Desvignes
2017 Cloud-Based Benchmarking of Medical Image Analysis  
We propose a generic method for the automatic multiple-organ segmentation of 3D images based on a multilabel graph cut optimization approach which uses location likelihood of organs and prior information  ...  Prior and likelihood models are then introduced in a joint centroidal Voronoi image clustering and graph cut multiobject segmentation framework.  ...  Multiorgan Image Segmentation We formulate image segmentation as a labelling problem, defined as the assignment of a label from a set of labels L representing the structures to be segmented to each of  ... 
doi:10.1007/978-3-319-49644-3_11 fatcat:f2xvnojjzvatpm4lwlfmab5774

Supervised Variational Model With Statistical Inference and Its Application in Medical Image Segmentation

Changyang Li, Xiuying Wang, Stefan Eberl, Michael Fulham, Yong Yin, David Dagan Feng
2015 IEEE Transactions on Biomedical Engineering  
Conventional region-based level-set algorithms often assume piecewise constant (PC) or piecewise smooth (PS) for segments, which are implausible for general medical image segmentation.  ...  Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation.  ...  Fig. 2 illustrates the schematic flowchart of our model for segmenting a liver with multiple liver tumors on a high-contrast CT image.  ... 
doi:10.1109/tbme.2014.2344660 pmid:25099393 fatcat:4wxtq6mxjzdz5k67ladoull2du

Discrete Visual Perception

Nikos Paragios, Nikos Komodakis
2014 2014 22nd International Conference on Pattern Recognition  
Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem.  ...  image analysis.  ...  Given such a prior model, then inference was expressed through a coupled formulation seeking the optimal class for the prior model and assigning labels to the image [29] according to it (given the class  ... 
doi:10.1109/icpr.2014.13 dblp:conf/icpr/ParagiosK14 fatcat:dxkgxm4zonh2tlk5xa6dlsku6u

Cortex segmentation: a fast variational geometric approach

R. Goldenberg, R. Kimmel, E. Rivlin, M. Rudzsky
2002 IEEE Transactions on Medical Imaging  
An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain images [magnetic resonance (MR) or computed tomography] is a well known problem in medical image processing.  ...  In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces.  ...  Lohmann of Max Planck Institute of Cognitive Neuroscience who kindly provided them with the brain MR images.  ... 
doi:10.1109/tmi.2002.806594 pmid:12588038 fatcat:vjmagel3vzdohfe4yxxabjftha

COMBINING ATLAS AND ACTIVE CONTOUR FOR AUTOMATIC 3D MEDICAL IMAGE SEGMENTATION

Yi Gao, Allen Tannenbaum
2011 IEEE International Symposium on Biomedical Imaging  
Atlas based methods and active contours are two families of techniques widely used for the task of 3D medical image segmentation.  ...  Finally, the method is tested on various 3D medical images to demonstrate its robustness as well as accuracy.  ...  This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.  ... 
pmid:23685464 pmcid:PMC3655328 fatcat:37wxl46isvbahpamhfyew7anmm
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