Literature DB >> 26628083

Methods on Skull Stripping of MRI Head Scan Images-a Review.

P Kalavathi1, V B Surya Prasath2.   

Abstract

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 (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

Keywords:  Brain extraction; Brain segmentation; Brain structure segmentation; MRI brain; Skull stripping

Mesh:

Year:  2016        PMID: 26628083      PMCID: PMC4879034          DOI: 10.1007/s10278-015-9847-8

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  75 in total

1.  Magnetic resonance imaging deformation-based segmentation of the hippocampus in patients with mesial temporal sclerosis and temporal lobe epilepsy.

Authors:  R E Hogan; K E Mark; I Choudhuri; L Wang; S Joshi; M I Miller; R D Bucholz
Journal:  J Digit Imaging       Date:  2000-05       Impact factor: 4.056

2.  Segmentation of brain 3D MR images using level sets and dense registration.

Authors:  C Baillard; P Hellier; C Barillot
Journal:  Med Image Anal       Date:  2001-09       Impact factor: 8.545

3.  Learning-based meta-algorithm for MRI brain extraction.

Authors:  Feng Shi; Li Wang; John H Gilmore; Weili Lin; Dinggang Shen
Journal:  Med Image Comput Comput Assist Interv       Date:  2011

4.  Transcranial ultrasonography system for visualizing skull and brain surface aided by fuzzy expert system.

Authors:  Yutaka Hata; Syoji Kobashi; Katsuya Kondo; Yuri T Kitamura; Toshio Yanagida
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2005-12

5.  Automatic detection of brain contours in MRI data sets.

Authors:  M E Brummer; R M Mersereau; R L Eisner; R J Lewine
Journal:  IEEE Trans Med Imaging       Date:  1993       Impact factor: 10.048

Review 6.  An artificial immune-activated neural network applied to brain 3D MRI segmentation.

Authors:  Akmal Younis; Mohamed Ibrahim; Mansur Kabuka; Nigel John
Journal:  J Digit Imaging       Date:  2007-12-11       Impact factor: 4.056

7.  Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm.

Authors:  Artem Mikheev; Gregory Nevsky; Siddharth Govindan; Robert Grossman; Henry Rusinek
Journal:  J Magn Reson Imaging       Date:  2008-06       Impact factor: 4.813

8.  A meta-algorithm for brain extraction in MRI.

Authors:  David E Rex; David W Shattuck; Roger P Woods; Katherine L Narr; Eileen Luders; Kelly Rehm; Sarah E Stoltzner; Sarah E Stolzner; David A Rottenberg; Arthur W Toga
Journal:  Neuroimage       Date:  2004-10       Impact factor: 6.556

9.  Automated medical image segmentation techniques.

Authors:  Neeraj Sharma; Lalit M Aggarwal
Journal:  J Med Phys       Date:  2010-01

10.  Gender differences in the left inferior frontal gyrus in normal children.

Authors:  Rebecca E Blanton; Jennifer G Levitt; Jeffrey R Peterson; David Fadale; Mike L Sporty; Mimi Lee; Dennis To; Elizabeth C Mormino; Paul M Thompson; James T McCracken; Arthur W Toga
Journal:  Neuroimage       Date:  2004-06       Impact factor: 6.556

View more
  16 in total

1.  Deep Learning-Based Detection of Intracranial Aneurysms in 3D TOF-MRA.

Authors:  T Sichtermann; A Faron; R Sijben; N Teichert; J Freiherr; M Wiesmann
Journal:  AJNR Am J Neuroradiol       Date:  2018-12-20       Impact factor: 3.825

2.  State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms.

Authors:  Anam Fatima; Ahmad Raza Shahid; Basit Raza; Tahir Mustafa Madni; Uzair Iqbal Janjua
Journal:  J Digit Imaging       Date:  2020-12       Impact factor: 4.056

3.  Automated 2D Slice-Based Skull Stripping Multi-View Ensemble Model on NFBS and IBSR Datasets.

Authors:  Anam Fatima; Tahir Mustafa Madni; Fozia Anwar; Uzair Iqbal Janjua; Nasira Sultana
Journal:  J Digit Imaging       Date:  2022-01-26       Impact factor: 4.056

4.  A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network.

Authors:  Linmin Pei; Murat Ak; Nourel Hoda M Tahon; Serafettin Zenkin; Safa Alkarawi; Abdallah Kamal; Mahir Yilmaz; Lingling Chen; Mehmet Er; Nursima Ak; Rivka Colen
Journal:  Sci Rep       Date:  2022-06-27       Impact factor: 4.996

5.  Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI).

Authors:  Ruhul Amin Hazarika; Arnab Kumar Maji; Raplang Syiem; Samarendra Nath Sur; Debdatta Kandar
Journal:  J Digit Imaging       Date:  2022-03-18       Impact factor: 4.903

Review 6.  Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology.

Authors:  M Ak; S A Toll; K Z Hein; R R Colen; S Khatua
Journal:  AJNR Am J Neuroradiol       Date:  2021-10-14       Impact factor: 4.966

7.  Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.

Authors:  Manohar Latha; Ganesan Kavitha
Journal:  MAGMA       Date:  2018-02-03       Impact factor: 2.310

8.  Automated segmentation of cerebral deep gray matter from MRI scans: effect of field strength on sensitivity and reliability.

Authors:  Renxin Chu; Shelley Hurwitz; Shahamat Tauhid; Rohit Bakshi
Journal:  BMC Neurol       Date:  2017-09-05       Impact factor: 2.474

9.  Automatic and efficient MRI-US segmentations for improving intraoperative image fusion in image-guided neurosurgery.

Authors:  J Nitsch; J Klein; P Dammann; K Wrede; O Gembruch; J H Moltz; H Meine; U Sure; R Kikinis; D Miller
Journal:  Neuroimage Clin       Date:  2019-03-12       Impact factor: 4.881

10.  MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain.

Authors:  Bin He; Zhengyi Yang; Lingzhong Fan; Bin Gao; Hai Li; Chuyang Ye; Bo You; Tianzi Jiang
Journal:  Front Neuroinform       Date:  2020-04-28       Impact factor: 4.081

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.