Quantitative Comparison of Four Brain MRI Segmentation Techniques. | ||||
| MEJ- Mansoura Engineering Journal | ||||
| Article 6, Volume 34, Issue 1, March 2009, Page 1-13 PDF (556.5 K) | ||||
| Document Type: Research Studies | ||||
| DOI: 10.21608/bfemu.2020.125382 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
A. M. Riad  1; Hamdy K. Elminir 2; R. R. Mostaffa3
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| 1Head of information System Department | ||||
| 2Kafr El-Sheikh University., Faculty of Engineering, Department of Electrical Engineering., Kafr El-Sheikh., Egypt. | ||||
| 3Faculty of Computer and Information Sciences. Mansoura University, Mansoura, Egypt | ||||
| Abstract | ||||
| Magnetic resonance jmaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. The goal of brain magnetic resonance image segmentation is to accurately identify the principal tissue structures in these image volumes. There are many methods that exist to segment the brain. One of these, conventional methods that use pure image processing techniques are not preferred because they need human interaction for accurate and reliable segmentation. Unsupervised methods, on the other hand, do not require any human interference and can segment the brain with high precision, in the light to this fact, we in this paper compare the performance of our image segmentation techniques in the subject of brain MR image. Results show that fuzzy Kohonen's Competitive Learning Algorithms performs better in terms of segmentation accuracy, while FCM performs better in terms of speed of computation | ||||
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