Automated brain tumor segmentation and classification for MRI analysis system

Mohd Saad, Norhashimah and Mohd Saad, Wira Hidayat and Zainal, Nur Azmina and Abdullah, Abdul Rahim and Mohd Noor, Nor Shahirah and Yaakub, Muhamad Faizal (2019) Automated brain tumor segmentation and classification for MRI analysis system. Indonesian Journal Of Electrical Engineering And Computer Science, 15 (3). pp. 1337-1344. ISSN 2502-4752

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Abstract

This paper proposed a new analysis technique of brain tumor segmentation and classification for Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI). 25 FLAIR MRI images were collected from online database of Multimodal Brain Tumor Segmentation Challenge 2015 (BRaTS’15). The analysis comprised four stages which are preprocessing, segmentation, feature extraction and classification. Fuzzy CMeans (FCM) was proposed for brain tumor segmentation. Mean, median, mode, standard deviation, area and perimeter were calculated and utilized as the features to be fed into a rule-based classifier. The segmentation performances were assessed based on Jaccard, Dice, False Positive and False Negative Rates (FPR and FNR). The results indicate that FCM offered high similarity indices which were 0.74 and 0.83 for Jaccard and Dice indices, respectively. The technique can possibly provide high accuracy and has the potential to detect and classify brain tumor from FLAIR MRI database

Item Type:	Article
Uncontrolled Keywords:	Classification, Fuzzy C-Means, MRI, Segmentation, Tumor
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	30 Mar 2022 16:31
Last Modified:	30 Mar 2022 16:31
URI:	http://eprints.utem.edu.my/id/eprint/24376
Statistic Details:	View Download Statistic

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