|
Brain tumor segmentation using hierarchical combination of fuzzy logic and cellular automata
Roqaie Kalantari1, Roqaie Moqadam2, Nazila Loghmani3, Armin Allahverdy4, Mohammad Bagher Shiran1, Arash Zare-Sadeghi1
1 Finetech in Medicine Research Center, School of Medicine, Iran University of Medical Sciences (IUMS); Department of Medical Physics, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
2 Neuroimaging and Analysis group, Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute; Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
3 Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
4 Department of Radiology, Sari School of Allied Medical Sciences, Mazandaran University of Medical Sciences, Sari, Iran
Correspondence Address:
Arash Zare-Sadeghi
Department of Medical Physics, Iran University of Medical Sciences, Tehran
Iran
Armin Allahverdy
Department of Radiology, Sari School of Allied Medical Sciences, Mazandaran University of Medical Sciences, Sari
Iran
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/jmss.jmss_128_21
|
|
Background: Magnetic resonance (MR) image is one of the most important diagnostic tools for brain tumor detection. Segmentation of glioma tumor region in brain MR images is challenging in medical image processing problems. Precise and reliable segmentation algorithms can be significantly helpful in the diagnosis and treatment planning. Methods: In this article, a novel brain tumor segmentation method is introduced as a postsegmentation module, which uses the primary segmentation method's output as input and makes the segmentation performance values better. This approach is a combination of fuzzy logic and cellular automata (CA). Results: The BraTS online dataset has been used for implementing the proposed method. In the first step, the intensity of each pixel is fed to a fuzzy system to label each pixel, and at the second step, the label of each pixel is fed to a fuzzy CA to make the performance of segmentation better. This step repeated while the performance saturated. The accuracy of the first step was 85.8%, but the accuracy of segmentation after using fuzzy CA was obtained to 99.8%. Conclusion: The practical results have shown that our proposed method could improve the brain tumor segmentation in MR images significantly in comparison with other approaches.
|
Next article
