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Fully Automatic Segmentation of MRI Brain Images Using Probabilistic Anisotropic Diffusion and Multi-scale Watersheds
[chapter]
2003
Lecture Notes in Computer Science
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(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
2014
Tsinghua Science and Technology
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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
2015
Journal of digital imaging
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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
2020
Brain Sciences
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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
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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
2007
Current Medical Imaging Reviews
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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
2020
Applied Sciences
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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
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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
2018
NeuroImage
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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
2016
Artificial Intelligence in Medicine
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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]
2021
arXiv
pre-print
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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
2014
Medical Image Analysis
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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
2011
Dagstuhl Publications
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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
2021
International Journal of Healthcare Information Systems and Informatics
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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
2012
PLoS ONE
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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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