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Fully Automatic Segmentation of MRI Brain Images Using Probabilistic Anisotropic Diffusion and Multi-scale Watersheds [chapter]

Carl Undeman, Tony Lindeberg
2003 Lecture Notes in Computer Science  
(iii) A multi-scale watershed segmentation step creates a slightly oversegmented image, where the brain contour constitutes a subset of the watershed boundaries.  ...  This article presents a fully automatic method for segmenting the brain from other tissue in a 3-D MR image of the human head.  ...  Acknowledgements We gratefully acknowledge the support from the EU project NeuroGenerator, the Swedish Research Council for Engineering Sciences, the Royal Swedish Academy of Sciences and the Knut and  ... 
doi:10.1007/3-540-44935-3_45 fatcat:rrhcwljnz5eqxo23a4wqp4xmpa

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  
MRIbased brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images.  ...  Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed.  ...  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

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

P. Kalavathi, V. B. Surya Prasath
2015 Journal of digital imaging  
The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques.  ...  The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography  ...  A method by SFU (Simon Fraser University) is a fully automatic MRI brain segmentation algorithm developed by Atkins and Mackiewich [86] .  ... 
doi:10.1007/s10278-015-9847-8 pmid:26628083 pmcid:PMC4879034 fatcat:w4u22v5auffohkiayu5isyjbaq

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification  ...  In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

Brain tumor detection in magnetic resonance images

2017 International Journal of Latest Trends in Engineering and Technology  
They also discussed the preprocessing methods like Anisotropic diffusion filtering method, Bias correction and Sharpening of image to detect brain tumor from MRI images.  ...  Bhima and Jagan [11] discussed the Watershed algorithm for gray scale MRI images based on region direction and mathematically morphologically.  ... 
doi:10.21172/1.91.15 fatcat:ikjdiwfpl5bwrprsia66rv2jai

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Elsa Angelini, Olivier Clatz, Emmanuel Mandonnet, Ender Konukoglu, Laurent Capelle, Hugues Duffau
2007 Current Medical Imaging Reviews  
atlases and patient brain MRI data.  ...  In this paper, we provide an extensive review of existing algorithms for the three computational tasks involved in patient-specific tumor modeling: image segmentation, image registration, and in silico  ...  at the nanoscopic scale, as a first step toward a multi-scale approach of grade II gliomas.  ... 
doi:10.2174/157340507782446241 fatcat:yhgc5aownnbpld2xqxlilvmf3u

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Hafiz Zia Ur Rehman, Hyunho Hwang, Sungon Lee
2020 Applied Sciences  
The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols.  ...  Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the  ...  For skull stripping in brain MRIs, manual brain and non-brain segmentation methods are considered more robust and accurate than semi or fully automatic methods.  ... 
doi:10.3390/app10051773 fatcat:nwp2z2y4jzgoxinv7sfppbfrfa

On the Methods for Detecting Brain Tumor from MRI images

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning.  ...  Brain tumor detection from MRI images is a challenging process due to high diversity in the tumor pixels of different peoples.  ...  So automatic detection became important and this study explores many methods used for detection and segmentation of tumor from MRI images.  ... 
doi:10.35940/ijitee.i1007.0799s20 fatcat:mpo2gpnwwvhv3fj74idihf4uom

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

Gengyan Zhao, Fang Liu, Jonathan A. Oler, Mary E. Meyerand, Ned H. Kalin, Rasmus M. Birn
2018 NeuroImage  
Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures.  ...  Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume.  ...  Brain Imaging and Behavior, and the Wisconsin National Primate Research Center.  ... 
doi:10.1016/j.neuroimage.2018.03.065 pmid:29604454 pmcid:PMC6095475 fatcat:jmf4m5sadbaelk4dyvwrnclgmm

A review on brain structures segmentation in magnetic resonance imaging

Sandra González-Villà, Arnau Oliver, Sergi Valverde, Liping Wang, Reyer Zwiggelaar, Xavier Lladó
2016 Artificial Intelligence in Medicine  
Future trends should combine multi-atlas with learning-based or deformable approaches Abstract Background and objectives: Automatic brain structures segmentation in magnetic resonance images has been widely  ...  Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure specific segmentation methods to whole brain parcellation approaches.  ...  González-Villà holds a UdG-BRGR2015 grant from the University of Girona.  ... 
doi:10.1016/j.artmed.2016.09.001 pmid:27926381 fatcat:morzi7uy6zch7p73l7t7mrq2ti

Computer-Assisted Analysis of Biomedical Images [article]

Leonardo Rundo
2021 arXiv   pre-print
In conclusion, the ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies, leading towards biomedical data integration  ...  Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications.  ...  [396] presented and evaluated two fully automated brain MRI tumor segmentation approaches: supervised k-Nearest Neighbors (kNN) and automatic Knowledge-Guided (KG) methods.  ... 
arXiv:2106.04381v1 fatcat:osqiyd3sbja3zgrby7bf4eljfm

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Adrien Depeursinge, Antonio Foncubierta-Rodriguez, Dimitri Van De Ville, Henning Müller
2014 Medical Image Analysis  
It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions.  ...  Three-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data.  ...  of the Khresmoi project (FP7-257528).  ... 
doi:10.1016/j.media.2013.10.005 pmid:24231667 fatcat:6luivlhrtrdv5dlbh626ohkps4

Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond

Thomas Schultz, Marc Herbstritt
2011 Dagstuhl Publications  
By measuring the anisotropic self-diffusion rates of water, Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) provides a unique noninvasive probe of fibrous tissue.  ...  In particular, it has been explored widely for imaging nerve fiber tracts in the human brain. Geometric features provide a quick visual overview of the complex datasets that arise from DW-MRI.  ...  Acknowledgement I would like to thank Gordon Kindlmann for helpful discussions about this paper, and Alfred Anwander (MPI CBS, Leipzig, Germany) for providing the DW-MRI dataset that was used to create  ... 
doi:10.4230/dfu.vol2.sciviz.2011.322 dblp:conf/dagstuhl/Schultz11 fatcat:4ovqks4gcbf5fadsjj3ts7b2nq

Brain Tumour Segmentation in FLAIR MRI Using Sliding Window Texture Feature Extraction Followed by Fuzzy C-Means Clustering

Sanjay Saxena, Nitu Kumari, Swati Pattnaik
2021 International Journal of Healthcare Information Systems and Informatics  
The first phase is used for detecting the tumorous brain MR scans by implementing pre-processing techniques followed by texture features extraction and classification.  ...  The second phase consists of the localization of the tumorous region using sliding window mechanism, in which a sized window sweeps through the whole tumorous MR scan and the window is classified as tumorous  ...  Brain tumor image data used in this article were obtained from the MICCAI 2017 Challenges on Multimodal Brain Tumor Segmentation.  ... 
doi:10.4018/ijhisi.20210701.oa1 fatcat:gehmbge6dfdldjipe5qndbn7oe

Contrast-Based Fully Automatic Segmentation of White Matter Hyperintensities: Method and Validation

Thomas Samaille, Ludovic Fillon, Rémi Cuingnet, Eric Jouvent, Hugues Chabriat, Didier Dormont, Olivier Colliot, Marie Chupin, Karl Herholz
2012 PLoS ONE  
Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images  ...  This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm), a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies.  ...  Discussion We have presented WHASA, a new method for automatically segmenting white matter hyperintensities from FLAIR and T1 images in multi centre studies.  ... 
doi:10.1371/journal.pone.0048953 pmid:23152828 pmcid:PMC3495958 fatcat:d4odvpsiifgkzjcebarjj3qeje
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