Arunkumar, N. and Mohammed, Mazin Abed and A. Mostafa, Salama and Ahmed Ibrahim, Dheyaa and Rodrigues, Joel J. P. C. and C. de Albuquerque, Victor Hugo (2018) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurrency Computat Pract Exper. pp. 1-9.
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Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Artificial neural network; brain classification; brain tumor; magnetic resonance imaging; trainable segmentation. |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television |
Depositing User: | Mr. Abdul Rahim Mat Radzuan |
Date Deposited: | 17 Oct 2021 07:07 |
Last Modified: | 17 Oct 2021 07:07 |
URI: | http://eprints.uthm.edu.my/id/eprint/876 |
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