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 thr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/876/1/DNJ9704_bf8346f763a86328c625a150b872810a.pdf http://eprints.uthm.edu.my/876/ https://doi.org/10.1002/cpe.4962 |
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| Summary: | 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. |
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