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A knowledge-based system for brain tumor segmentation using only 3D FLAIR images

  • Yalda Amirmoezzi
  • Sina Salehi
  • Hossein ParsaeiEmail author
  • Kamran Kazemi
  • Amin Torabi Jahromi
Scientific Paper
  • 16 Downloads

Abstract

This study aims to develop a semi-automatic system for brain tumor segmentation in 3D MR images. For a given image, noise was corrected using SUSAN algorithm first. A specific region of interest (ROI) that contains tumor was identified and then the intensity non-uniformity in ROI was corrected via the histogram normalization and intensity scaling. Each voxel in ROI was presented using 22 features and then was categorized as tumor or non-tumor by a multiple-classifier system. T1- and T2-weighted images and fluid-attenuated inversion recovery (FLAIR) were examined. The system performance in terms of Dice index (DI), sensitivity (SE) and specificity (SP) was evaluated using 150 simulated and 30 real images from the BraTS 2012 database. The results showed that the presented system with an average DI > 0.85, SE > 0.90, and SP > 0.98 for simulated data and DI > 0.80, SE > 0.84, and SP > 0.98 for real data might be used for accurate extraction of the brain tumors. Moreover, this system is 6 times faster than a similar system that processes the whole image. In comparison with two state-of-the-art tumor segmentation methods, our system improved DI (e.g., by 0.31 for low-grade tumors) and outperformed these algorithms. Considering the costs of imaging procedures, tumor identification accuracy and computation times, the proposed system that augmented general pathological information about tumors and used only 4 features of FLAIR images can be suggested as a brain tumor segmentation system for clinical applications.

Keywords

Classification Feature extraction 3D MR image segmentation Tumor segmentation Multiple-classifier system 

Notes

Acknowledgements

The present article was extracted from parts of the M.Sc. thesis written by Yalda Amirmoezzi and was financially supported by Shiraz University of Medical Sciences (Grant No. 95-01-01-11982). The authors wish to thank Mr. H. Argasi at the Research Consultation Center (RCC) of Shiraz University of Medical Sciences for his invaluable assistance in editing this manuscript.

Funding

This study was funded by Shiraz University of Medical Sciences (Grant No. 95-01-01-11982)

Compliance with ethical standards

Conflict of interest

All authors declared that they have no conflict of interest.

Informed consent

Since we used publicly available data (BraTS 2012 database) in this study, human participants or animals were not involved in this research.

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.Department of Medical Physics and EngineeringShiraz University of Medical SciencesShirazIran
  2. 2.Shiraz Neuroscience Research Center, Shiraz University of Medical SciencesShirazIran
  3. 3.Department of Electrical and Electronics EngineeringShiraz University of TechnologyShirazIran
  4. 4.Electrical and Electronic Engineering Group, Engineering CollegePersian Gulf UniversityBushehrIran

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