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Brain Tumor Segmentation From Multimodal MRI Data Based on GLCM and SVM Classifier

Brain Tumor Segmentation From Multimodal MRI Data Based on GLCM and SVM Classifier

Na Li, Zheng Yang
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 18
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa15
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MLA

Li, Na, and Zheng Yang. "Brain Tumor Segmentation From Multimodal MRI Data Based on GLCM and SVM Classifier." IJCINI vol.15, no.4 2021: pp.1-18. http://doi.org/10.4018/IJCINI.20211001.oa15

APA

Li, N. & Yang, Z. (2021). Brain Tumor Segmentation From Multimodal MRI Data Based on GLCM and SVM Classifier. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-18. http://doi.org/10.4018/IJCINI.20211001.oa15

Chicago

Li, Na, and Zheng Yang. "Brain Tumor Segmentation From Multimodal MRI Data Based on GLCM and SVM Classifier," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-18. http://doi.org/10.4018/IJCINI.20211001.oa15

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Abstract

The segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure.This manuscript per the authors focus on the effects of GLCM texture on brain tumor segmentation. The result is different from the application of the GLCM texture in other types of image processing.The experimental material was a dataset called BraTs2015. The segmented five different labels are normal brain, necrosis, edema, non-enhancing tumor, and enhancing tumor. The proposed model was verified with the Dice coefficient. The result demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.