Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks. (21st October 2018)

• Record Type: Journal Article
• Authors: Arunkumar, N.; Mohammed, Mazin Abed; Mostafa, Salama A.; Ibrahim, Dheyaa Ahmed; Rodrigues, Joel J.P.C.; de Albuquerque, Victor Hugo C.
• Other Names: Sangaiah Arun Kumar guestEditor.; Pham Hoang guestEditor.; Qiu Tie guestEditor.; Muhammad Khan guestEditor.; Awan Irfan guestEditor.; Younas Muhammad guestEditor.; Hussain Farookh guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-21
• Subjects: artificial neural network -- brain classification -- brain tumor -- magnetic resonance imaging -- trainable segmentation; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4962
• Languages: English
• ISSNs: 1532-0626

Abstract

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 using MR images. The method includes three computer vision fiction strategies which are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOG features. 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. A total 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.