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A knowledge-guided active model method of skull segmentation on T1-weighted MR images

Zuyao Y. Shan, Chia-Ho Hua, Qing Ji, Carlos Parra, Xiaofei Ying, Matthew J. Krasin, Thomas E. Merchant, Larry E. Kun, Wilburn E. Reddick, Josien P. W. Pluim, Joseph M. Reinhardt
2007 Medical Imaging 2007: Image Processing  
This study utilized a knowledge-guided active model (KAM) method to segmented skull on MR images in order to enable radiation therapy planning with MR images as the primary planning dataset.  ...  Skull is the anatomic landmark for patient set up of head radiation therapy. Skull is generally segmented from CT images because CT provides better definition of skull than MR imaging.  ...  In this paper, we described a knowledge-guided active mesh model method to segment skull on T1-weighted MR images.  ... 
doi:10.1117/12.709801 dblp:conf/miip/ShanHJPYKMKR07 fatcat:fwhhqctuo5hptotd6gpgp2sqf4

Methods on Skull Stripping of MRI Head Scan Images—a Review

P. Kalavathi, V. B. Surya Prasath
2015 Journal of digital imaging  
This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.  ...  The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques.  ...  Park and Lee [39] developed a skull stripping method for T1-weighted MR brain images based on 2D region growing method.  ... 
doi:10.1007/s10278-015-9847-8 pmid:26628083 pmcid:PMC4879034 fatcat:w4u22v5auffohkiayu5isyjbaq

Novel Quantitative Techniques in Hybrid (PET-MR) Imaging of Brain Tumors

Srinivasan Senthamizhchelvan, Habib Zaidi
2013 PET Clinics  
The success of image-guided radiotherapy is directly related to the accuracy of imaging methods in distinguishing tumors from surrounding normal tissues, which makes PET-MR an essential imaging modality  ...  In radiation oncology, image-guided patient-specific treatment planning has become a standard practice, making use of high-precision dose-delivery techniques.  ...  The procedure was further refined by automating the segmentation of the skull procedure of T1-weighted MR image using a sequence of mathematical morphologic operations. 29 A proof of principle of the  ... 
doi:10.1016/j.cpet.2012.09.007 pmid:27157949 fatcat:bihzfoad3zfbjorgozh4mvb7ei

Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

Amir Alansary, Marwa Ismail, Ahmed Soliman, Fahmi Khalifa, Matthew Nitzken, Ahmed Elnakib, Mahmoud Mostapha, Austin Black, Katie Stinebruner, Manuel F. Casanova, Jacek M. Zurada, Ayman El-Baz
2016 IEEE journal of biomedical and health informatics  
In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images.  ...  The proposed framework consists of three main steps: (i) Following bias correction of the brain, a new 3D MGRF having a 26pairwise interaction model is applied to enhance the homogeneity of MR images and  ...  [12] developed a contour-based method to segment the brain from T1-, T2-, and proton density weighted MRI of human head scans in 2 phases.  ... 
doi:10.1109/jbhi.2015.2415477 pmid:25823048 fatcat:khjujvbcvfegxdrzdqk45q6day

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Imene Mecheter, Lejla Alic, Maysam Abbod, Abbes Amira, Jim Ji
2020 Journal of digital imaging  
MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners.  ...  The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging.  ...  [63] developed a multiscale segmentation approach using radon transform of T1-weighted MR images to segment the head image into skull, scalp, and brain tissue.  ... 
doi:10.1007/s10278-020-00361-x pmid:32607906 fatcat:4ncpszzr6jhsxkyfrci2lbuhau

Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue

U. V., S. S., U. V.
2017 International Journal of Computer Applications  
Accuracy of segmentation methods is of great importance in brain image analysis.  ...  This paper reviews the performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI have been developed.  ...  MAHAMUNI and J.R KAWALE for the paper that encouraged, as well as for the lots of discussions carried on long-distance via communication.  ... 
doi:10.5120/ijca2017912784 fatcat:kcaaelrd2jgcrbn7gthrwp2uui


Minu George, Gopika S.
2016 International Journal of Advanced Research  
Reddick et al(1997) presented a new method for the automatic segmentation of brain MR-Images using neural network.  ...  Manual segmentation of brain MR images is a time consuming process.  ... 
doi:10.21474/ijar01/1063 fatcat:pdwohgc74rc2fezjsqjhonfj7u

Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI

Abolfazl Mehranian, Hossein Arabi, Habib Zaidi
2016 NeuroImage  
A robust atlas-registration based AC method was developed for pseudo-CT generation using local weighted fusion of atlases based on their morphological similarity to target MR images.  ...  To generate 3-class attenuation maps, T1-weighted MRI images were segmented into background air, fat and soft-tissue classes followed by assignment of constant linear attenuation coefficients of 0, 0.0864  ...  The unknown tissue class was therefore obtained by segmentation of superimposed T1-and T2-weighted images (which in fact resemble proton-density weighted MR images) using a heuristically-adjusted thresholding  ... 
doi:10.1016/j.neuroimage.2016.01.060 pmid:26853602 fatcat:e7bb4xtnq5gxtghv4rhlsv2ela

Construction and validation of a database of head models for functional imaging of the neonatal brain

Liam H. Collins‐Jones, Tomoki Arichi, Tanya Poppe, Addison Billing, Jiaxin Xiao, Lorenzo Fabrizi, Sabrina Brigadoi, Jeremy C. Hebden, Clare E. Elwell, Robert J. Cooper
2020 Human Brain Mapping  
We have validated a method to segment the extra-cerebral tissue against manual segmentation.  ...  The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age-specific head models.  ...  (86% of 366) was that the extra-cerebral tissue extended out of the field of view in the T1-weighted MR images.  ... 
doi:10.1002/hbm.25242 pmid:33068482 pmcid:PMC7814762 fatcat:hsrrpz7kljgzjg2ioi6jnccanq

A survey of MRI-based brain tumor segmentation methods

Jin Liu, Min Li, Jianxin Wang, Fangxiang Wu, Tianming Liu, Yi Pan
2014 Tsinghua Science and Technology  
The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods.  ...  Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given.  ...  Acknowledgements This work was supported in part by the National Natural Science Foundation of China (Nos. 61232001 and 61379108).  ... 
doi:10.1109/tst.2014.6961028 fatcat:qsb42j4k5rgvlaf56icxqbumpq

ISLES Challenge 2015: Automated Model-Based Segmentation of Ischemic Stroke in MR Images [chapter]

Tom Haeck, Frederik Maes, Paul Suetens
2016 Lecture Notes in Computer Science  
We present a novel fully-automated generative ischemic stroke lesion segmentation method that can be applied to individual patient images without need for a training data set.  ...  The segmentation is represented by a level-set that is iteratively updated to label voxels as either normal or pathological, based on which intensity model explains the voxels' intensity the best.  ...  For segmentation of the SPES data, we use the T2-weighted and TTP-weighted MR images and for SISS the diffusion weighted and FLAIR-weighted MR images.  ... 
doi:10.1007/978-3-319-30858-6_21 fatcat:frfomcsq5ndqdgztf4ks4hgwgy

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Javier Juan-Albarracín, Elies Fuster-Garcia, José V. Manjón, Montserrat Robles, F. Aparici, L. Martí-Bonmatí, Juan M. García-Gómez, Jesus Malo
2015 PLoS ONE  
In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images.  ...  Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes.  ...  Acknowledgments The authors would like to thank Jose Enrique Romero Gómez (IBIME, ITACA, UPV) for his support in some of the pre-processing techniques used in this study. Author Contributions  ... 
doi:10.1371/journal.pone.0125143 pmid:25978453 pmcid:PMC4433123 fatcat:47ipdmdvrbh7jav7dbvyfuq6nq

Computer Aided Diagnostic System For Detection And Classification Of A Brain Tumor Through Mri Using Level Set Based Segmentation Technique And Ann Classifier

Atanu K Samanta, Asim Ali Khan
2017 Zenodo  
The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level  ...  Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable  ...  Active contour based models are one of the most powerful methods for image segmentation.  ... 
doi:10.5281/zenodo.1130734 fatcat:t2vhjhc3ljgy7fcj7vy3diml4m

Non-model segmentation of brain glioma tissues with the combination of DWI and fMRI signals

Min Lu, Xiaojie Zhang, Mingyu Zhang, Hongyan Chen, Weibei Dou, Shaowu Li, Jianping Dai, Feng Liu, Dong-Hoon Lee, Ricardo Lagoa, Sandeep Kumar
2015 Bio-medical materials and engineering  
This is a non-model segmentation scheme of brain glioma tissues in a particular perspective of combining multi-parameters of DWI and BOLD contrast functional Magnetic Resonance Imaging (fMRI).  ...  Most of the morphological analyses are based on T1-weighted and T2-weighted signals, called traditional MRI. But more detailed information about tumorous tissues could not be explained.  ...  Acknowledgment We thank Beijing Tiantan Hospital, Capital Medical University for providing the clinical data, for guiding this study and for suggestions in manual segmentation as "ground truth".  ... 
doi:10.3233/bme-151429 pmid:26405892 fatcat:kj4yzfjcv5ceromvtmynztfwya

A finite-element reciprocity solution for EEG forward modeling with realistic individual head models

Ziegler Erik, Chellappa Sarah, Gaggioni Giulia, Ly Julien, Vandewalle Gilles, André Elodie, Geuzaine Christophe, Phillips Christophe
2014 Frontiers in Human Neuroscience  
By calculating conductivity tensors from diffusion-weighted MR images, we also demonstrate one of the main benefits of FEM: the ability to include anisotropic conductivities within the head model.  ...  We applied the method to real human brain MRI data and created a model with five tissue types: white matter, grey matter, cerebrospinal fluid, skull, and scalp.  ...  Acknowledgments The authors would like to acknowledge the assistance of Axel Thielscher and André Antunes for the design of the software and Johannes Vorwerk for his guidance with SimBio.  ... 
doi:10.3389/conf.fnhum.2014.214.00072 fatcat:fpdv7ieeebgh3ouihd3f72em7i
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