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Automatic Segmentation of Left Atrial Scar from Delayed-Enhancement Magnetic Resonance Imaging [chapter]

Rashed Karim, Aruna Arujuna, Alex Brazier, Jaswinder Gill, C. Aldo Rinaldi, Mark O'Neill, Reza Razavi, Tobias Schaeffter, Daniel Rueckert, Kawal S. Rhode
2011 Lecture Notes in Computer Science  
This is implemented as a Markov random field-based energy formulation and solved using graph-cuts.  ...  Delayed-enhancement magnetic resonance imaging is an effective technique for imaging left atrial (LA) scars both pre-and post-radio-frequency ablation for the treatment of atrial fibrillation.  ...  In the context of images, certain MRF-based energy functions are efficiently solved using graph-cuts [9] .  ... 
doi:10.1007/978-3-642-21028-0_8 fatcat:svgcpntkurewzazoghmllqvu6a

Content Based Medical Image Retrieval Using Fuzzy Gaussian Mixture Model with Relevance Feedback

G.Asha Sowjanya
2016 International Journal Of Engineering And Computer Science  
Content-based image retrieval with relevance feedback schemes based on Method Fuzzy Logic Gaussian Mixture model didn't require much time when compared to GMM. Also FLGMM gives efficient clustering.  ...  Results show the potential of relevance feedback techniques in medical image retrieval and the superiority of the proposed algorithm over commonly used approaches.  ...  This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the  ... 
doi:10.18535/ijecs/v4i10.37 fatcat:lwyupgroqvdffc22scvxwqm37u

Perfect Snapping: An Accurate and Efficient Interactive Image segmentation Algorithm

Qingsong Zhu, Guanzheng Liu, Zhanyong Mei, Qi Li, Yaoqin Xie, Lei Wang
2013 Applied Mathematics & Information Sciences  
Interactive image segmentation is a process that extracts a foreground object from an image based on limited user input.  ...  Finally, a Monte Carlo based Expectation Maximization (EM) algorithm is used to perform parametric learning of mixture model for priori knowledge.  ...  Acknowledgement This study has been financed partially by the Projects of National Natural Science Foundation of China (  ... 
doi:10.12785/amis/070417 fatcat:5ldgcs3tgndovo2sswa2pbwt24

PGMM—Pre-Trained Gaussian Mixture Model based Convolution Neural Network for Electroencephalography Imagery Analysis

Ming Yu, Guang Zhang, Qinwei Li, Feng Chen
2020 IEEE Access  
In this article, pre-trained Gaussian mixture model (PGMM) is introduced for improving the accuracy of EEG signal imagery analysis.  ...  The proposed model relies on deep learning classifiers for analyzing the imagery using pixel based segmentation through pre-training models.  ...  Conclusion This paper presents pre-trained Gaussian mixture model for improving the analysis accuracy of EEG imagery.  ... 
doi:10.1109/access.2020.3016481 fatcat:xqtgiukmsbeobcjlaoozb77vo4

Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation [article]

Tim Sodergren and Riddhish Bhalodia and Ross Whitaker and Joshua Cates and Nassir Marrouche and Shireen Elhabian
2019 arXiv   pre-print
We formulate the shape prior as a mixture of Gaussians and learn the corresponding parameters in a high-dimensional shape space rather than pre-projecting onto a low-dimensional subspace.  ...  an intensity-based energy minimization that translates the global notion of a nonlinear shape prior into a set of local penalties.  ...  Conclusion This paper proposed a shape/feature-based generative model for left atrium segmentation by modeling non-Gaussian global shape priors as mixture of Gaussians on landmark-based representations  ... 
arXiv:1903.06260v1 fatcat:dgc7ahto5zebbcdthsxdd24xoe

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

Shobha Rani N, Chinmayi S Rao
2019 International Journal of Research in Pharmaceutical Sciences  
In this work, mammogram images are initially subject to pre-processing using Laplacian filter for enhancement of tumour regions, Gaussian mixture model, Gaussian kernel FCM, Otsu global thresholding and  ...  Linear discriminant analysis classifier is used a combination based on which efficiency used for classification of mammograms. Ensemble methods are evaluated.  ...  ACKNOWLEDGEMENT The authors are grateful to Amrita Vishwa Vidyapeetham and Amrita School of Arts and Sciences for providing an opportunity for this research work.  ... 
doi:10.26452/ijrps.v10i3.1423 fatcat:4zlobsfsubbtfcffxnx2efyzxm

Nuclear Segmentation of Glioblastoma Multiforme Cells by Multireference Level Set

Rujuta O, Vyavahare AJ
2017 Journal of Bioengineering and Biomedical Sciences  
In this paper we developed an approach for nuclear segmentation on tumours histological section, which addresses problems of processing tissues at different laboratory under microscope.  ...  Experimental results show performance of proposed method.  ...  Pre-Processing Our method expressed a preprocessing construction representation of nuclear and background of an image based on nuclear response and image denoising using LoG operator nuclear channel.  ... 
doi:10.4172/2155-9538.1000226 fatcat:5f4b4t43f5govpjyyqzba3goj4

Fully automatic brain tumor segmentation using a normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow

Tao Wang, Irene Cheng, Anup Basu
2010 2010 IEEE International Conference on Image Processing  
This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm.  ...  In our method, a Normalized Gaussian Mixture Model (NGMM) is proposed and used to model the healthy brain tissues.  ...  In the first stage, a "Normalized" Gaussian Mixture Model (NGMM) is proposed and estimated by Expectation-Maximization (EM) based on the ICBM452 brain atlas [11] .  ... 
doi:10.1109/icip.2010.5652559 dblp:conf/icip/WangCB10 fatcat:56tevxa2wfg2lf3lvmll3or3me

Practical automatic background substitution for live video

Haozhi Huang, Xiaonan Fang, Yufei Ye, Songhai Zhang, Paul L. Rosin
2017 Computational Visual Media  
In this paper, we use a color line model to improve the Gaussian mixture model in the background cut method to obtain a binary foreground segmentation result that is less sensitive to brightness differences  ...  Based on the high quality binary segmentation results, we can automatically create a reliable trimap for alpha matting to refine the segmentation boundary.  ...  It optimizes a cost function based on Gaussian mixture models and a conditional random field, using graph-cut.  ... 
doi:10.1007/s41095-016-0074-0 fatcat:dix4nqog4vbjnaqyceh5t37iem


Oren Freifeld, Hayit Greenspan, Jacob Goldberger
2007 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
The probablistic model, termed constrained-GMM, is based on a mixture of many spatially-oriented Gaussians per tissue.  ...  This paper focuses on the detection and segmentation of multiple sclerosis (MS) lesions in magnetic resonance images.  ...  In [6] , the intensity of normal brain tissues is modeled by mixture of Gaussian probability distribution functions (GMMs).  ... 
doi:10.1109/isbi.2007.356922 dblp:conf/isbi/FreifeldGG07 fatcat:mrk7bpkmfvctlayl7ddwhem3ya

Automatic detection of microaneurysms using microstructure and wavelet methods

2015 Sadhana (Bangalore)  
First, the green channel of the colour retinal fundus image is extracted and pre-processed using various enhancement techniques such as bottom-hat filtering and gamma correction.  ...  This paper has developed an approach to automate the detection of microaneurysms using wavelet-based Gaussian mixture model and microstructure texture feature extraction.  ...  The green channel of the image is extracted and pre-processed using various contrast enhancement techniques. Profile is constructed using Gaussian mixture in wavelet domain.  ... 
doi:10.1007/s12046-015-0353-y fatcat:qc4iq3n7nbhm3cjrxhxftwnhai

Generalized Probabilistic U-Net for medical image segementation [article]

Ishaan Bhat, Josien P.W. Pluim, Hugo J. Kuijf
2022 arXiv   pre-print
For the LIDC-IDRI dataset, we show that using a mixture of Gaussians results in a statistically significant improvement in the generalized energy distance (GED) metric with respect to the standard Probabilistic  ...  We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset.  ...  by using a mixture of a sufficient number of Gaussians, with appropriate mixture weights [3] .  ... 
arXiv:2207.12872v1 fatcat:2ctyua57srhsnf6lkysxw645k4

Secured Authentication through Integration of Gait and Footprint for Human Identification

C. Murukesh, K. Thanushkodi, Preethi Padmanabhan, Naina Mohamed D. Feroze
2014 Journal of Electrical Engineering and Technology  
For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM).  ...  Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities.  ...  ROI of the footprint image is based on square-based segmentation. The footprint images are oriented and translated before the segmentation process.  ... 
doi:10.5370/jeet.2014.9.6.2118 fatcat:2o5fpwqikrabhin7v46f6papgm

Improved workflow for unguided multiphase image segmentation

Brendan A. West, Taylor S. Hodgdon, Matthew D. Parno, Arnold J. Song
2018 Computers & Geosciences  
Then the transition regions are classified using a distance function, and finally both segmentations are combined into one classified image.  ...  Quantitative image analysis often depends on accurate classification of pixels through a segmentation process.  ...  Several studies have utilized energy based techniques for segmenting images [10, 13] . These methods are based on finding a full segmentation of the image that minimizes an energy function.  ... 
doi:10.1016/j.cageo.2018.05.013 fatcat:bcn5csrk3zgnldu7urxutqayla

Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge

Rashed Karim, R Housden, Mayuragoban Balasubramaniam, Zhong Chen, Daniel Perry, Ayesha Uddin, Yosra Al-Beyatti, Ebrahim Palkhi, Prince Acheampong, Samantha Obom, Anja Hennemuth, YingLi Lu (+13 others)
2013 Journal of Cardiovascular Magnetic Resonance  
A Gaussian mixture was used for this distribution model. The number of mixtures in the model was kept variable (1 to 5) depending on the configuration which best fits the image.  ...  The Gaussians mixture represent scar and healthy tissue. The mean and standard deviation of each Gaussian in the mixture model is determined using the EM-algorithm.  ...  JC and DP performed analysis of the results, authored two algorithms and provided their outputs. ZC, AU, YA, EP, PA, SO performed fibrosis and scar evaluation.  ... 
doi:10.1186/1532-429x-15-105 pmid:24359544 pmcid:PMC3878126 fatcat:o4dtas2g2fbuzeipqhhqofqoxi
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