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A novel Bayesian approach to adaptive mean shift segmentation of brain images

Qaiser Mahmood, Artur Chodorowski, Andrew Mehnert, Mikael Persson
2012 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)  
We present a novel adaptive mean shift (AMS) algorithm for the segmentation of tissues in magnetic resonance (MR) brain images.  ...  In particular we introduce a novel Bayesian approach for the estimation of the adaptive kernel bandwidth and investigate its impact on segmentation accuracy.  ...  Conclusion We have presented a novel Bayesian approach to adaptive bandwidth estimation in the mean-shift algorithm and evaluated its application to brain MR images.  ... 
doi:10.1109/cbms.2012.6266304 dblp:conf/cbms/MahmoodCMP12 fatcat:yodtwnisd5db5lgimtmdvv446q

A fully automatic unsupervised segmentation framework for the brain tissues in MR images

Qaiser Mahmood, Artur Chodorowski, Babak Ehteshami Bejnordi, Mikael Persson, Robert C. Molthen, John B. Weaver
2014 Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging  
This paper presents a novel fully automatic unsupervised framework for the segmentation of brain tissues in magnetic resonance (MR) images.  ...  The framework is a combination of our proposed Bayesian-based adaptive mean shift (BAMS), a priori spatial tissue probability maps and fuzzy c-means.  ...  Bayesian-based adaptive mean shift (BAMS) In 22 , we proposed an adaptive bandwidth estimator based on the Bayesian approach for the estimation of bandwidth of the kernel.  ... 
doi:10.1117/12.2043646 fatcat:dgavc6bogfcaroh3x5bye3emsu

Automated MRI brain tissue segmentation based on mean shift and fuzzy c -means using a priori tissue probability maps

Q. Mahmood, A. Chodorowski, M. Persson
2015 IRBM  
The framework is a combination of Bayesian-based adaptive mean shift, a priori spatial tissue probability maps and fuzzy c-means.  ...  The performance of the proposed framework is evaluated relative to the three widely used brain segmentation toolboxes: FAST, SPM and PVC, and the adaptive mean shift (AMS) and classical fuzzy c-means methods  ...  Acknowledgements This work has been supported in part by the Chalmers University of Technology, Sweden (Grant # S2412010) and the Higher Education Commission (HEC) of Pakistan (Grant # PD-2007-1).  ... 
doi:10.1016/j.irbm.2015.01.007 fatcat:74ur463yrjgwlnaynatotlffoq

On the fully automatic construction of a realistic head model for EEG source localization

Qaiser Mahmood, Yazdan Shirvany, Andrew Mehnert, Artur Chodorowski, Johanna Gellermann, Fredrik Edelvik, Anders Hedstrom, Mikael Persson
2013 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
ACKNOWLEDGMENT The authors would like to thank Simon Bergstrand for his participation as the healthy subject in this study.  ...  It is a hierarchical segmentation approach incorporating Bayesian-based adaptive mean-shift segmentation.  ...  The method is based on a hierarchical segmentation approach (HSA) incorporating Bayesian-based adaptive mean-shift segmentation (BAMS).  ... 
doi:10.1109/embc.2013.6610254 pmid:24110441 dblp:conf/embc/MahmoodSMCGEHP13 fatcat:4nmvpbeumffrbf7hnxwpfj2rt4

A fast stochastic framework for automatic MR brain images segmentation

Marwa Ismail, Ahmed Soliman, Mohammed Ghazal, Andrew E. Switala, Georgy Gimel'farb, Gregory N. Barnes, Ashraf Khalil, Ayman El-Baz, Dzung Pham
2017 PLoS ONE  
Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.  ...  The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first-and second-order visual appearance  ...  Acknowledgments This work has been supported by the University of Louisville 21st Century University Initiative on Big Data in Medicine.  ... 
doi:10.1371/journal.pone.0187391 pmid:29136034 pmcid:PMC5685492 fatcat:kvx3gpreirbyliuhi74nrcky5a

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Segmentation 249 A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation 264 Esophageal Gross Tumor Volume Segmentation using a 3D Convolutional Neural Network 274 Cardiac MR Segmentation  ...  Tumor-aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation 667 A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models 668 Efficient Groupwise Registration  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Fast 3D Brain Segmentation Using Dual-Front Active Contours with Optional User-Interaction [chapter]

Hua Li, Anthony Yezzi, Laurent D. Cohen
2005 Lecture Notes in Computer Science  
We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours, which minimize image-based energies in a manner that yields more global minimizers compared to standard active  ...  It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result.  ...  Validation on Real MR Brain Data To further evaluate our segmentation method under a wide range of imaging conditions, we also test the proposed algorithm on 20 real MRI brain images and compare the segmentation  ... 
doi:10.1007/11569541_34 fatcat:wryh7i2jgzdppay5iinlcucn4m

A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry [article]

Yuan Gao and Lok Hin Lee and Richard Droste and Rachel Craik and Sridevi Beriwal and Aris Papageorghiou and Alison Noble
2021 arXiv   pre-print
We propose a novel unsupervised domain adaptation approach to train deep models to be invariant to significant image distribution shift between the image types.  ...  This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries.  ...  More recently, [24] directly regresses HC measurements from ultrasound images without segmentation and [13] directly regresses GA from fetal head images using a Bayesian neural network.  ... 
arXiv:2108.12719v1 fatcat:zlu2cwup2reoxjavlfxd3awbni

Front Matter: Volume 10806

Xudong Jiang, Jenq-Neng Hwang
2018 Tenth International Conference on Digital Image Processing (ICDIP 2018)  
A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  The objective of this conference was to provide a platform for the participants to report and exchange innovative ideas, up-to-date progress and developments, and discuss novel approaches to application  ... 
doi:10.1117/12.2510343 fatcat:maohjht2t5apneao4iivotwxey

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 Information Fusion  
They have been applied to solve a variety of real-world problems in science and engineering.  ...  (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring  ...  Acknowledgment This work was partially supported by the Australian Research Council's Discovery Projects funding scheme (project DP190102181) and the Natural Sciences and Engineering Research Council of  ... 
doi:10.1016/j.inffus.2021.05.008 fatcat:yschhguyxbfntftj6jv4dgywxm

Classification of Brain Tumor in MRI Images Based on Artificial Intelligence

Prachi V. Kale Pooja J. Shinde
2022 Zenodo  
Structural Magnetic Resonance Image (MRI) is a useful technique to examine the internal structure of the brain.  ...  Digital image processing methodologies like preprocessing, segmentation, and classification are useful to clinical experts for the proper diagnosis of brain tumor types.  ...  Raju et al. proposed a novel approach for brain tumor classification using Bayesian fuzzy clustering and HSC-based multi SVN [18] .  ... 
doi:10.5281/zenodo.6675933 fatcat:237tn4eqb5f63gnno4kusg3riu

Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces

Sujit Roy, Anirban Chowdhury, Karl McCreadie, Girijesh Prasad
2020 Frontiers in Neuroscience  
using the novel concept of Mega Blocks for adapting the network against inter-subject variabilities.  ...  Inter-subject transfer learning is a long-standing problem in brain-computer interfaces (BCIs) and has not yet been fully realized due to high inter-subject variability in the brain signals related to  ...  Thus the combination of a 2 s time window and 200 ms shift makes 11 segments within the 5 s MI period producing 11 images in a single trial.  ... 
doi:10.3389/fnins.2020.00918 pmid:33100953 pmcid:PMC7554529 fatcat:3odotsmmurbrdjgjzbms6u4k4u

Anatomically guided voxel-based partial volume effect correction in brain PET: Impact of MRI segmentation

Daniel Gutierrez, Marie-Louise Montandon, Frédéric Assal, Mohamed Allaoua, Osman Ratib, Karl-Olof Lövblad, Habib Zaidi
2012 Computerized Medical Imaging and Graphics  
The principle of the different variants of the image segmentation algorithm is to spatially normalize the subject's MR images to a corresponding template.  ...  The PVC approach aims to compensate for signal dilution in non-active tissues such as CSF, which becomes an important issue in the case of tissue atrophy to prevent a misinterpretation of decrease of metabolism  ...  More recently, a novel class of PVC algorithms that do not require segmentation of anatomical images was introduced [10] .  ... 
doi:10.1016/j.compmedimag.2012.09.001 pmid:23046730 fatcat:kgyfirbe2fg53hxt3lu3iz75na

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation [article]

Devavrat Tomar, Behzad Bozorgtabar, Manana Lortkipanidze, Guillaume Vray, Mohammad Saeed Rad, Jean-Philippe Thiran
2021 arXiv   pre-print
Motivated by atlas-based segmentation, we propose a novel volumetric self-supervised learning for data augmentation capable of synthesizing volumetric image-segmentation pairs via learning transformations  ...  Augmented data generated by our method used to train the segmentation network provide significant improvements over state-of-the-art deep one-shot learning methods on the task of brain MRI segmentation  ...  segmentation performance (mean Dice score in %) of MABMIS (2 atlas) [22] , VoxelMorph [3] , Bayesian [13] , DataAug [52] , LT-Net** [45] , and Ours across various brain structures on CANDI and OASIS datasets  ... 
arXiv:2110.02117v1 fatcat:ampolwvdi5hkrcz4goo5lcjlh4

Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation [article]

Sebastian G. Popescu, David J. Sharp, James H. Cole, Konstantinos Kamnitsas, Ben Glocker
2021 arXiv   pre-print
Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection  ...  Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has never been achieved  ...  Brain tissue segmentation on normal MRI scans Baselines: We compare our model with recent Bayesian approaches for enabling task-specific models (such as image segmentation) to perform uncertainty-based  ... 
arXiv:2104.13756v1 fatcat:wbufp6x2nvfvpl7e5td4mtzhx4
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