Literature DB >> 31975661

Review on 2D and 3D MRI Image Segmentation Techniques.

S Shirly1, K Ramesh1.   

Abstract

BACKGROUND: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. DISCUSSION: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation.
CONCLUSION: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  2-dimensional; 3-zzm321990dimensional image segmentation; Magnetic resonance imaging; image processing; image segmentation

Year:  2019        PMID: 31975661     DOI: 10.2174/1573405613666171123160609

Source DB:  PubMed          Journal:  Curr Med Imaging Rev        ISSN: 1573-4056


  2 in total

1.  Age-dependent decrease in dental pulp cavity volume as a feature for age assessment: a comparative in vitro study using 9.4-T UTE-MRI and CBCT 3D imaging.

Authors:  Maximilian Timme; Jens Borkert; Nina Nagelmann; Adam Streeter; André Karch; Andreas Schmeling
Journal:  Int J Legal Med       Date:  2021-04-26       Impact factor: 2.686

2.  A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation.

Authors:  Xiyu Liu; Lin Wang; Jianhua Qu; Ning Wang
Journal:  Comput Intell Neurosci       Date:  2020-08-07
  2 in total

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