Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks
The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues usingMR images. The method includes three...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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John Wiley and Sons
2018
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| Online Access: | http://eprints.uthm.edu.my/4964/ http://eprints.uthm.edu.my/4964/1/AJ%202020%20%2845%29.pdf |
| _version_ | 1848888427418746880 |
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| author | Arunkumar, N. AbedMohammed, Mazin A. Mostafa, Salama Ahmed Ibrahim, Dheyaa Rodrigues, Joel J.P.C. Albuquerque, Victor Hugo C. de |
| author_facet | Arunkumar, N. AbedMohammed, Mazin A. Mostafa, Salama Ahmed Ibrahim, Dheyaa Rodrigues, Joel J.P.C. Albuquerque, Victor Hugo C. de |
| author_sort | Arunkumar, N. |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues usingMR images. The method includes three computer vision fiction strategieswhich are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOGfeatures. A fully automatic model-based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI.However, identification the tumor kind utilizing the texture features.Atotal of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model-based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%,with 89 sensitivity and 94 specificity. |
| first_indexed | 2025-11-15T20:10:07Z |
| format | Article |
| id | uthm-4964 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:10:07Z |
| publishDate | 2018 |
| publisher | John Wiley and Sons |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-49642022-01-03T01:50:28Z http://eprints.uthm.edu.my/4964/ Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks Arunkumar, N. AbedMohammed, Mazin A. Mostafa, Salama Ahmed Ibrahim, Dheyaa Rodrigues, Joel J.P.C. Albuquerque, Victor Hugo C. de QA75 Electronic computers. Computer science T Technology (General) TK7800-8360 Electronics The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues usingMR images. The method includes three computer vision fiction strategieswhich are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOGfeatures. A fully automatic model-based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI.However, identification the tumor kind utilizing the texture features.Atotal of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model-based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%,with 89 sensitivity and 94 specificity. John Wiley and Sons 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4964/1/AJ%202020%20%2845%29.pdf Arunkumar, N. and AbedMohammed, Mazin and A. Mostafa, Salama and Ahmed Ibrahim, Dheyaa and Rodrigues, Joel J.P.C. and Albuquerque, Victor Hugo C. de (2018) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurrency and Computation: Practice and Experience, 32 (1). pp. 1-9. ISSN 1532-0626 https://doi.org/10.1002/cpe.4962 |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) TK7800-8360 Electronics Arunkumar, N. AbedMohammed, Mazin A. Mostafa, Salama Ahmed Ibrahim, Dheyaa Rodrigues, Joel J.P.C. Albuquerque, Victor Hugo C. de Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks |
| title | Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks |
| title_full | Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks |
| title_fullStr | Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks |
| title_full_unstemmed | Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks |
| title_short | Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks |
| title_sort | fully automatic model-based segmentation and classification approach for mri brain tumor using artificial neural networks |
| topic | QA75 Electronic computers. Computer science T Technology (General) TK7800-8360 Electronics |
| url | http://eprints.uthm.edu.my/4964/ http://eprints.uthm.edu.my/4964/ http://eprints.uthm.edu.my/4964/1/AJ%202020%20%2845%29.pdf |